Business Intelligence (BI) Archives : Planergy Software

Tail Spend Analysis: What Is It, How To Perform It, and the Benefits

Lyle Del Vecchio — Thu, 02 Mar 2023 10:43:29 +0000

Download a free copy of "Indirect Spend Guide", to learn:

Where the best opportunities for savings are in indirect spend.
How to gain visibility and control of your indirect spend.
How to report and analyze indirect spend to identify savings opportunities.
How strategic sourcing, cost management, and cost avoidance strategies can be applied to indirect spend.

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Tail Spend Analysis: What Is It, How To Perform It, and the Benefits

Category

Spend Management

Written by Lyle Del Vecchio
17 min read

Tags

Last edited

July 2, 2024

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Introduction To Tail Spend Analysis
Defining Tail Spend
Benefits of Tail Spend Analysis
Tail Spend Analysis Best Practices
Establishing Tail Spend Analysis Metrics
Developing a Tail Spend Analysis Plan
Analyzing Data and Identifying Trends
Leveraging Tail Spend Analysis To Improve Efficiency
How To Automate Tail Spend Analysis

Tail spend analysis is an important tool for businesses looking to optimize spending and ensure their supply chain runs efficiently.

Tail spend analysis helps companies identify areas of spend that are falling through the cracks, allowing them to make more informed decisions and achieve greater savings.

In this blog post, we’ll cover what tail spend analysis is, its benefits, how to develop a plan for it, how to analyze data and trends, and how to best leverage tail spend analysis to improve efficiency.

Introduction To Tail Spend Analysis

Tail spend analysis examines a company’s spending patterns and identifies opportunities to reduce costs and improve efficiency.

It’s an important part of any organization’s financial management, as it helps identify wasteful spending and uncover potential savings.

Tail spend analysis helps companies identify areas of spend that are falling through the cracks and gain visibility into their overall spending.

By understanding where their money is going, companies can make more informed decisions, reduce waste, and save money.

The process of tail spend analysis involves analyzing data from various sources, including supplier invoices, purchase orders, contracts, and other financial documents.

Having the right information and insights is important to make the most of tail spend analysis.

Defining Tail Spend

Tail spend is a term used to describe the small, often overlooked purchases made by an organization. It’s typically low-value, high-volume purchases made in areas such as office supplies, shipping, and travel.

While these purchases may seem insignificant when taken individually, they can make up a sizable portion of a company’s total spend.

Tail spend follows the Pareto principle – the idea that 80% of a company’s spending can be connected to 20% of the supplier base.

The long-tail is where the majority of purchases occur.

Tail spend should not be confused with maverick spending, which is non-strategic spending that occurs outside of an established internal process.

It’s also not the same as spot buying, which is the unplanned, one-time emergency purchase of a small, inexpensive, infrequently used item.

Tail spend is often unmanaged spend because it’s spread out across multiple departments and suppliers. This can make it difficult to track and manage.

However, this doesn’t mean it needs to be ignored. Tail spend can be an important source of savings and efficiency gains.

Benefits of Tail Spend Analysis

Tail spend analysis offers several benefits for businesses.

By understanding their spending patterns, companies can gain visibility into their overall spending and ensure they get the best value for their money.

Tail spend analysis also helps procurement professionals identify areas of wasteful spending and uncover potential savings.

This can lead to cost reductions and increased efficiency, helping businesses reduce their operational costs and increase their profits, which is good for the bottom line.

Tail spend analysis can also help companies identify opportunities for consolidation and standardization. This can lead to improved relationships with suppliers and greater savings.

Finally, tail spend analysis can help companies better understand their purchasing patterns and identify areas where they can make more informed decisions, leading to better supplier relationships and more efficient purchasing processes.

Tail Spend Analysis Best Practices

There are several best practices to keep in mind when conducting tail spend analysis. Companies should:

Set clear goals and objectives for the initiative
Define the scope of the analysis
Analyze data from multiple sources
Identify trends and opportunities for savings
Establish metrics to track progress
Leverage insights to improve efficiency in the procurement process.

By following these best practices, companies can ensure they get the most out of their tail spend analysis.

When left unmanaged, tail spend can cost an organization millions of dollars. Managing tail spend is a worthy investment of time and resources.

Establishing Tail Spend Analysis Metrics

Companies should establish metrics to measure their progress to ensure they get the most out of their tail spend analysis.

This will help them track their efficiency and identify areas of improvement.

Common metrics for tail spend management include:

Cost savings
Number of suppliers used
Number of contracts renegotiated (contract management)
Number of items consolidated
Number of items standardized

By tracking these metrics, procurement functions can get a clear picture of their progress and identify areas for improvement.

Developing a Tail Spend Analysis Plan

To make the most of tail spend analysis, it’s important to develop a plan.

Companies should identify the goals and objectives of their analysis, the data sources they will use, and the metrics they will measure.

It’s also important to define the scope of the analysis. Companies should decide which categories of spend they want to focus on, such as office supplies, travel, or shipping.

This will help them narrow down the data and focus their efforts.

Once a plan is in place, companies should develop a timeline to ensure they stay on track and meet their goals, to help them stay organized and continue making progress.

Analyzing Data and Identifying Trends

Spend analysis is the art of extracting useful information from your spending data. It can help your company make better business decisions and avoid unnecessary waste.

However, it is also very time-consuming. A proper plan should be implemented to make the process as efficient as possible.

Spend analytics involves classifying your spending data, identifying relevant trends, and implementing solutions that improve your purchasing power.

This can help you to understand your expenditures and budgets and identify cost reduction opportunities.

Spend analytics can be performed by hand or with the help of software. Using data visualization tools, you can explore spend data and discover hidden insights.

Tools often have an intuitive interface that allows non-technical users to build customized dashboards and explore data in real time.

Getting your spend data in order is a key first step in any spend analysis project. Clean spend data will enable you to analyze and improve your spending quickly.

Once a plan is in place, companies should begin analyzing their data. This involves looking at spending patterns and identifying areas of potential savings.

Companies should look for trends in spending, such as overspending on certain items or underutilizing certain suppliers.

Companies should also look for opportunities for consolidation and standardization. This can help them streamline their processes and reduce costs.

It’s important to remember that tail spend analysis is an ongoing process. Companies should regularly review their data and update their plan as needed.

Identify Your Supplier Base
When conducting a tail spend analysis, it’s important to understand your supplier base. Tail spend is often overlooked as a source of significant savings.
But when properly managed, it can save an organization up to 15 percent of its total procurement spending.
It can also help improve the employee experience. If your employees aren’t happy with the quality of products they receive from your suppliers, they’re likely to be less productive.
You’ll also be able to eliminate obstacles like delivery delays.
As many B2B businesses have learned, it pays to consolidate your supply base. Many suppliers are now working as aggregators. This means they’re expanding their business offerings to fulfill customer demands.
The best way to identify your supplier base is to conduct a comprehensive spending analysis. To do this, you’ll need to gather information on all spend data sources. Spend analysis includes grouping spending into standardized categories and cleaning data for errors.
After gathering all your data, you can categorize your expenditures and identify specific small purchases.
Categorizing your spending data by department or type of spend (direct vs. indirect spend) will help you achieve sustainable cost reduction with more strategic purchasing.
Segmenting “Major” Spend from “Tail Spend”
For most procurement teams, this can be a daunting task. However, it’s also an opportunity to create a competitive advantage. Companies often make a multitude of purchases, some of which may not be worth the time or cost.
While not a magic pill, many steps can be taken to improve the process and reduce costs.
Some key points to remember include the importance of data collection and establishing a process to follow. This can include a combination of technology and training.
A good way to start is by examining the various spend categories within your organization. This will give you an idea of where to focus your efforts.
Spend in areas such as direct purchasing and indirect purchasing can be especially beneficial.
Reducing the Number of Suppliers in the Tail-End
Companies can reduce the number of suppliers in the tail-end of their supply chain through digital tools and a well-established procurement framework.
This can help them save time and money while maintaining quality and compliance with business policies.
For instance, one manufacturing company found hundreds of duplicates in its supplier list. The team used an algorithm to identify these duplicates and eliminated them from their RFQ.
They found that their costs could be reduced by about 30%. Another example involves a global chemicals company. By bundling materials with strategic suppliers, they can get better prices, less lead times, and reduced quality issues, making stakeholders happy.
However, switching suppliers can be difficult. Suppliers often need approval from production, R&D, or quality control. When a new supplier is brought in, they must understand the product or service’s value to the company.

Leveraging Tail Spend Analysis To Improve Efficiency

Once companies have identified areas of potential savings through tail spend analysis, they can leverage these insights to improve their efficiency.

This can involve renegotiating contracts with suppliers, consolidating suppliers, standardizing processes, and streamlining operations.

For example, a company may renegotiate contracts with suppliers to get better pricing. Or, they may consolidate suppliers to reduce the number of vendors they manage.

By taking these steps, companies can reduce their costs and improve their efficiency. This can lead to increased profits and a more streamlined supply chain.

How To Automate Tail Spend Analysis

Tail spend analysis can be a time-consuming and labor-intensive process. To make it easier, companies can leverage technology to automate the process of spend analysis.

Automation tools can help companies quickly and easily analyze their data and identify areas of potential savings. They can also help automate supplier contract negotiation and data analysis processes.

Planergy’s procurement system helps you with tail spend analysis with our comprehensive spend management tools and spend analysis software. Our tools make it easy to track spending, analyze data, and identify opportunities for savings.

Our tool can be used to support strategic sourcing efforts and help the procurement department maximize budgets while reducing low-value transactions and one-off purchases.

Tail spend analysis is an important tool for businesses looking to optimize their spending and ensure their supply chain is running efficiently.

It can help companies identify areas of wasteful spending and uncover potential cost savings.

By developing a plan for tail spend analysis, analyzing data, and leveraging insights to improve efficiency, companies can reduce their costs and increase their profits.

Automation tools can also make the process easier and more efficient.

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What’s your goal today?

1. Use Planergy to manage purchasing and accounts payable

We’ve helped save billions of dollars for our clients through better spend management, process automation in purchasing and finance, and reducing financial risks. To discover how we can help grow your business:

Read our case studies, client success stories, and testimonials.
Visit our Spend Analysis Software page to see how Planergy can empower you to get more value from your spend.
Learn about us, and our long history of helping companies just like yours.

2. Download our “Indirect Spend Guide”

Download a free copy of our guide to better manage and make savings on your indirect spend. You’ll also be subscribed to our and notified about new articles or if have something interesting to share.

3. Learn best practices for purchasing, finance, and more

Browse hundreds of articles, containing an amazing number of useful tools, techniques, and best practices. Many readers tell us they would have paid consultants for the advice in these articles.

The post Tail Spend Analysis: What Is It, How To Perform It, and the Benefits appeared first on Planergy Software.

Pareto Analysis in Procurement: How To Use Spend Analysis To Cut Costs

Keith Murphy — Thu, 19 Jan 2023 10:06:50 +0000

Download a free copy of "Indirect Spend Guide", to learn:

Where the best opportunities for savings are in indirect spend.
How to gain visibility and control of your indirect spend.
How to report and analyze indirect spend to identify savings opportunities.
How strategic sourcing, cost management, and cost avoidance strategies can be applied to indirect spend.

Download Free Copy

Pareto Analysis in Procurement: How To Use Spend Analysis To Cut Costs

Category

Spend Management

Written by Keith Murphy
16 min read

Tags

Big Data Business Intelligence (BI)Spend Analysis

Last edited

July 2, 2024

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Pareto Principle vs. Pareto Analysis

The Pareto analysis and Pareto principle are often confused and even used interchangeably, but they are two distinct concepts.

The Pareto principle, also known as the 80/20 Rule, is a heuristic that suggests that 80% of results can be attributed to 20% of causes.

Pareto analysis is a form of data evaluation that looks at how much of an effect individual factors have on a larger system.

This technique prioritizes which factors to address based on how much they affect the entire output or goal, making it easier to target the highest impact areas for improvement.

In short, the Pareto principle expresses the importance of prioritization when making improvements, while Pareto analysis exemplifies this approach through structured evaluation.

Procurement professionals can use pareto analysis to examine various aspects of business and adjust strategy.

What is Pareto Analysis?

Pareto Analysis, also known as ABC analysis, is a data-driven approach for determining which factors are having the greatest impact on a particular outcome.

This concept can be used to identify which products or services are driving the highest costs within an organization’s procurement process.

By analyzing spend data, companies can identify the specific items where it makes sense to focus their efforts for cost savings.

For example, let’s say your company spends $100,000 a year on office supplies like paper and pens.

Upon further analysis, you might find that $70,000 is spent on high-end stationery while only $30,000 is spent on pens and other low-cost items.

This would mean that 70% of your spending is going toward one type of product—in this case, stationery—and 30% is being spent on other types of office supplies.

This is an example of how the Pareto Principle can be applied to spend analysis; by focusing your efforts on reducing costs associated with stationery purchases, you could potentially save more money than if you were trying to reduce all office supply costs equally.

How Pareto Analysis Works?

In a nutshell, Pareto analysis works by sorting data into two categories: “vital few” (which refers to those items or services that produce most of the value) and “useful many” (which refers to those items or services that produce little value).

Using ABC Analysis:

Class A: These purchases account for 80% of your total cost of purchases for 20% of suppliers.
Class B: These purchases account for 15% of the total cost of purchases for 30% of suppliers.
Class C: Tail Spend: These purchases account for 5% of your total cost of the purchases for 50% of your suppliers.

For example, if an organization spends $1 million on supplies each year and finds through Pareto analysis that $800,000 of that amount is being spent on just 20% of suppliers, that 20% of suppliers should get special attention because they are producing the greatest value for money.

Similarly, if an organization finds through its spend analysis that only 10% of its products are generating 90% of its profits, it may make sense to invest more resources into marketing or researching those products while cutting back on other products with lower returns.

The Benefits of Conducting a Pareto Analysis

Conducting a Pareto Analysis in procurement provides numerous benefits for organizations.

Not only does it allow companies to quickly pinpoint areas where they could save money without sacrificing quality or value, it also gives them insights into their customer’s needs and preferences, so they can better tailor their offering accordingly.

Additionally, since it requires minimal effort and resources compared to other cost analysis methods, it has become a popular choice among many businesses looking for ways to optimize their budgeting process.

Pareto Spend Analysis is an incredibly useful tool for optimizing supply chain management processes, reducing costs, and improving overall efficiency.

By leveraging this approach to analyze spending data across various categories, such as supplier type or product category, businesses can quickly identify their highest value suppliers while also uncovering potential cost savings opportunities.

Ultimately this leads to smarter decision making when it comes time to purchase goods or services from vendors – resulting in improved ROI while still maintaining high-quality standards throughout the process.

Procurement professionals should take advantage of Pareto Spend Analysis whenever possible to maximize savings and improve performance in their supply chain management initiatives.

While the main goal is to reduce costs and improve sourcing, it can also help improve supplier relationships, giving your organization a competitive advantage.

How to Conduct Your Own Analysis?

To start your own analysis, choose what you’re going to look at, then collect your data.

For instance, let’s say you want to look at suppliers’ late deliveries. In this case, you’ll want to collect:

All suppliers
All the suppliers’ deliveries over the last year.

Use an Excel spreadsheet to create a chart and help you with the math.

Create a table with:

Supplier Name
Total Deliveries
Late Deliveries
Percent Late
Cumulative Percent Late

Once you’ve collected your data in the chart, sort the table in descending order.

You’ll want to look at the Cumulative percent late column in this case, drawing a line at 80%.

From here, you’ll see the items that add up to 80% are the primary cause of your issues, while those that fall between 80 and 100% are not as important.

Practical Applications of Pareto Analysis

Once you’ve identified which products are driving the highest costs in your supply chain, it’s time to start looking for ways to reduce those costs without sacrificing quality.

Here are some practical applications of pareto analysis in procurement:

Negotiate Prices with Suppliers

Companies can significantly reduce their purchasing costs over time by working directly with suppliers to negotiate lower prices for high-cost items to improve your bottom line.

It’s important to remember that price negotiation isn’t always about getting a lower price; often, suppliers may be willing to offer discounts or free shipping as part of a deal, which can also help reduce expenses in other areas.
Consolidate Suppliers

Supplier consolidation and consolidating orders helps streamline delivery processes and eliminates time wasted coordinating multiple shipments for various items from different suppliers.

A balance needs to be struck between supplier rationalization and ensuring you have flexibility and backup in place for key supplies.

Consolidating orders allows companies to save money by cutting out excess labor costs associated with managing multiple orders at once and reducing inventory overhead expenses since fewer items will be needed in stock at any given time due to fewer orders being placed frequently throughout the year.
Utilize Automation

Automation technologies like robotic process automation (RPA) help streamline cumbersome manual processes related to ordering and invoicing so companies can save time and money while maintaining accuracy in their transactions.

RPA also helps ensure compliance with contractual agreements since it reduces human error and improves accuracy when tracking orders against agreed-upon terms between buyers and sellers.
Leverage Data Analytics

Data analytics tools provide insights into purchasing patterns that allow companies to monitor supply chain performance more closely so they can make informed decisions around pricing strategies or supplier partnerships based on real-time information about what customers are buying when they are buying it, and how much they are paying for it.

Leveraging data analytics tools also help companies identify potential cost-saving opportunities, such as consolidating orders across different departments or negotiating better deals with certain vendors who may have better prices than others for certain goods or services.

Tips for Conducting a Successful Analysis

When conducting a successful Pareto Analysis there are several key tips to keep in mind:

Set Clear Objectives

Before starting your analysis, make sure that you have clearly identified your objectives, including the metrics you’ll use to measure success.

This will help ensure that you stay focused on these objectives throughout the process and don’t get sidetracked by other data points.
Make Use of Data Visualization Tools

Visual aids such as graphs and charts can be extremely helpful when conducting a Pareto Analysis as they make it easier to identify patterns and trends in the data.

Additionally, these visual tools can be helpful when presenting your findings to stakeholders who may not be familiar with complex data sets. And that’s where the Pareto chart comes into play.

A Pareto Chart is a visualization tool professionals employ to assess how much a result or effect comes from a particular cause.

This powerful chart uses relative frequencies on its vertical axis, allowing metrics experts to analyze certain causes more deeply than an ordinary bar chart could.

The horizontal axis underlying the chart ranks data from largest to smallest values in order of importance, providing you with essential information about how particular causes shape an outcome.

When used correctly, this valuable tool can help metrics experts discover deeper trends in data sets and unlock hidden solutions for complex problems.
Test Your Results

Once you have identified potential solutions based on your analysis, it’s important to test them before implementing any changes so that you don’t introduce any unintended consequences into the system.

Testing allows you to validate your assumptions and ensure that any proposed solutions will result in positive outcomes before putting them into practice.

How Pareto Analysis Helps Your Procurement Strategy?

By leveraging Pareto analysis with data analytics tools and automation technologies, businesses can gain valuable insights about their supply chain operations to make smart decisions around cost-cutting initiatives without sacrificing quality or customer service levels.

Through careful monitoring and analysis of spending habits over time using these techniques, businesses can reap significant long-term benefits in terms of cost savings while still providing excellent value for their customers through improved efficiency across their operations.

Download PDF

What’s your goal today?

1. Use Planergy to manage purchasing and accounts payable

Read our case studies, client success stories, and testimonials.
Visit our Procurement Solutions page to see how Planergy can digitize and automate your procurement processes saving you time and money.
Learn about us, and our long history of helping companies just like yours.

2. Download our “Indirect Spend Guide”

3. Learn best practices for purchasing, finance, and more

Browse hundreds of articles, containing an amazing number of useful tools, techniques, and best practices. Many readers tell us they would have paid consultants for the advice in these articles.

The post Pareto Analysis in Procurement: How To Use Spend Analysis To Cut Costs appeared first on Planergy Software.

ESG Analytics: Using Data Analytics To Make Your ESG Strategy A Reality

Lyle Del Vecchio — Thu, 22 Dec 2022 11:45:49 +0000

Download a free copy of "Preparing Your AP Department For The Future", to learn:

How to transition from paper and excel to eInvoicing.
How AP can improve relationships with your key suppliers.
How to capture early payment discounts and avoid late payment penalties.
How better management in AP can give you better flexibility for cash flow management.

Download Free Copy

ESG Analytics: Using Data Analytics To Make Your ESG Strategy A Reality

Category

Business Strategy

Written by Lyle Del Vecchio
16 min read

Tags

Big Data Business Intelligence (BI)Ethical Sustainability & Responsibility

Last edited

July 2, 2024

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KEY TAKEAWAYS

ESG analytics is an emerging data science focused on helping companies see how well they adhere to ESG initiatives.
ESG ratings are not yet universal, so it’s important to look at the methodology to understand why a company received the rating it did
Your procurement department can help keep you in compliance with your ESG strategy.

Environmental, social and governance (ESG) criteria measure an organization or corporation’s sustainability and societal impact.

In recent years, investors have become increasingly interested in putting their money into companies considering ESG in their operations and decision-making.

Environmental: This pillar covers a company’s environmental policies and practices. It includes things like reducing climate risk, emissions reduction, resource conservation, and renewable energy use to help address climate change.
Social: The social pillar covers a company’s policies and practices related to human rights, diversity and inclusion, community engagement, and employee relations.
Governance: The governance pillar covers a company’s leadership, ethical practices, transparency, and accountability.

Taken together, these three factors make up a company’s “triple bottom line.” For many years, businesses have focused primarily on their financial bottom line – making money.

However, there has been a shift towards more sustainable and responsible business practices in recent years.

This is where the triple bottom line comes in. To be successful in the 21st century, businesses need to focus on all three of their bottom lines – not just their financial bottom line.

ESG Strategy and How Procurement Can Keep You Compliant

ESG strategy is all about considering the impact of a company’s activities on the environment, society, and governance. It’s about ensuring a company is sustainable and responsible in its operations.

Many investors are now using ESG criteria to make investment decisions.

They want to invest in companies that are good stewards of the environment and society.

And they want to avoid companies involved in activities that could harm the environment or society.

There are many different ways to implement an ESG strategy. But one common approach is to set targets for improvement in the three areas – environmental, social, and governance.

Then businesses track their progress towards those targets and report their progress publicly.

This helps to create accountability and transparency around ESG issues. It also helps investors see which companies are making progress on ESG issues and which are not.

Another key part of an ESG strategy is engagement with stakeholders. This means talking to and working with groups interested in the company’s activities – such as employees, customers, suppliers, community members, NGOs, etc.

The goal is to get input from these groups on how the company can improve its performance on environmental, social, and governance issues.

There is no one-size-fits-all approach to implementing an ESG strategy. But by taking into account the impact of its activities on the environment, society, and governance – and by engaging with stakeholders – a company can put itself on a path to long-term success.

To comply with an organization’s ESG strategy, procurement must consider the environmental and social impacts of the goods and services being procured. This includes considering the product’s life cycle, from manufacture to disposal.

It also includes assessing the working conditions of those who produce the product and the environmental impact of the production process itself.

Procurement staff can ensure strategy compliance by keeping these things in mind when choosing products and suppliers working with responsible sourcing.

What ESG Analytics are and Why They’re Important

ESG analytics is a tool used by investors to assess a company’s sustainability.

ESG analytics aims to evaluate a company’s impact on the environment and society and its adherence to good governance practices. It involves collecting various data sets and running analysis.

ESG analytics can be used to screen for companies that are likely to experience financial risks due to environmental or social issues or to identify companies that are leaders in sustainable business practices.

In recent years, there has been a growing interest in ESG investing, and ESG analytics plays an important role in this form of investing.

ESG analytics can help investors make more informed investment decisions by providing information about a company’s sustainability performance.

ESG Ratings

ESG ratings, also known as ESG scores, measure a company’s or investment’s impact on environmental and social issues and governance factors such as board diversity.

The ratings are provided by various organizations, including sustainable investing research firms, stock exchanges, and rating agencies.

ESG ratings can be used by investors to screen companies and make more informed investment decisions.

To receive a high ESG rating, a company typically discloses strong policies and practices related to sustainability issues.

However, it is important to note that there is no uniformity in how ESG ratings are calculated, and the criteria used may vary from one organization to another.

As such, it is important to research the methodology behind any ESG rating before making investment decisions.

The Different Types of ESG Data

ESG data encompasses a wide range of information, from a company’s greenhouse gas emissions to its employee satisfaction levels.

This data can be used to assess a company’s impact on the environment, social welfare, and governance.

While all ESG data is important, some types are more commonly used than others.

For example, companies’ carbon footprints are often tracked, as are their water usage levels and waste production.

However, ESG data can also include information on a company’s treatment of employees, its ethical practices, and its charitable giving.

By understanding the different types of ESG data, investors can make more informed decisions about where to put their money.

How to Get Started with ESG Analytics

ESG analytics is a type of data analysis that helps organizations track and assess their impact on key ESG indicators.

While ESG analytics is relatively new, it is already used by many businesses and investors to make more sustainable and responsible decisions.

There are a few different approaches to ESG analytics, but the most common is to use publicly available data to track an organization’s progress on various metrics and key indicators.

This data can then be compared to industry benchmarks or other organizations’ data to identify areas of improvement. Additionally, many companies are now beginning to collect their own internal data on ESG indicators.

The data sources and data quality heavily influence the results, and if you’re using alternative data, you may not get the results you’re looking for. Many of today’s data analytics tools allow for visualization, which makes it easier to get insights from the data your company collects.

This data can be used to create custom benchmarks and track an organization’s progress over time.

Overall, ESG analytics is a powerful tool that can help organizations make more informed and sustainable decisions.

The Benefits of Using ESG Analytics

ESG analytics is a way of measuring the sustainability and social impact of a company or investment.

ESG analytics considers various factors, including a company’s emissions, water use, employee diversity, and human rights record.

Not only can ESG analytics help investors decide if they want to work with a company, but it can also help companies make sure they’re moving in the right direction.

As more and more people become interested in sustainable investing, ESG analytics is likely to become an increasingly important tool for measuring corporate responsibility.

ESG isn’t something companies can afford to ignore anymore, and analytics give them information in real-time, to help ensure they are headed in the right direction.

Case Studies of Companies That Have Successfully Implemented ESG Analytics

Many companies have successfully implemented ESG analytics. One notable example is the French company Veolia.

In 2013, Veolia launched a “Responsible Solutions initiative,” which used ESG data to help the company make better business decisions.

The initiative was a success, and Veolia was able to reduce its environmental impact and improve its financial performance.

Another company that has successfully used ESG analytics is IBM. IBM has long been a leader in corporate sustainability, using ESG data to inform its sustainability strategy.

In 2011, IBM launched an internal carbon pricing program that used ESG data to set prices for carbon emissions. The program was successful, and IBM reduced its emissions by 16 percent in just two years.

These examples show that ESG analytics can be a powerful tool for businesses looking to improve their sustainability performance.

Tips for Making the Most out of your ESG Analytics Strategy

The global pandemic has brought a new level of scrutiny to how corporations are run. In response, many companies are turning to ESG analytics to track their progress on key sustainability indicators.

However, simply collecting data is not enough – it is also important to know how to use it effectively.

Here are four tips for making the most out of your ESG analytics strategy:

Set Clear Goals and Targets

What do you hope to achieve with your ESG data? Do you want to improve your environmental footprint? Boost employee engagement? Or reduce operational risks?

By setting clear goals, you can track your progress and identify areas where you need to improve.
Choose the Right Indicators

Numerous indicators can be used to measure ESG performance. It is important to select indicators and KPIs that are relevant to your company, and that will give you the insights you need to reach your goals.
Integrate ESG Data into Decision-Making

Data is only useful if it is used to guide decision-making. Ensure ESG data is included in all relevant discussions, from strategic planning to everyday operations.

This will help ensure that sustainability remains a top priority for your company.
Communicate Your Results

Sharing your progress on ESG goals can create buy-in from employees, investors, and other stakeholders. It can also help challenge others to do better – ultimately leading to a more sustainable future.

Companies that utilize ESG analytics can make sound, evidence-based decisions about their environmental and societal impact.

This, in turn, allows them to allocate resources effectively and create lasting relationships with key stakeholders. To get started with ESG analytics, consider all the factors involved and set achievable goals.

Procurement Data Plays an Important Role in ESG Analysis

You can invest in ESG reporting and data science tools to help you get ESG insights.

There are a few ESG solutions on the market today to help you get started, but you don’t need to invest in ESG products to do it.

Start by keeping closer track of your procurement data across your supply chain. The data goes a long way in showing you support for various ESG initiatives.

Download PDF

What’s your goal today?

1. Use Planergy to manage purchasing and accounts payable

Read our case studies, client success stories, and testimonials.
Visit our Spend Analysis Software page to see how Planergy can empower you to get more value from your spend.
Learn about us, and our long history of helping companies just like yours.

2. Download our guide “Preparing Your AP Department For The Future”

Download a free copy of our guide to future proofing your accounts payable department. You’ll also be subscribed to our and notified about new articles or if have something interesting to share.

3. Learn best practices for purchasing, finance, and more

Browse hundreds of articles, containing an amazing number of useful tools, techniques, and best practices. Many readers tell us they would have paid consultants for the advice in these articles.

The post ESG Analytics: Using Data Analytics To Make Your ESG Strategy A Reality appeared first on Planergy Software.

Big Data Analytics: What It Is and Why It Is Important

Rob Biedron — Tue, 04 Oct 2022 13:52:37 +0000

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Big Data Analytics: What It Is and Why It Is Important

Category

Technology & IT

Written by Rob Biedron
22 min read

Tags

Big Data Business Intelligence (BI)Spend Analytics

Last edited

July 2, 2024

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What is Big Data Analytics?

Big data analytics is the process of examining large data sets to uncover hidden patterns, correlations, and other insights. This information can then be used to make better business decisions or improve operations.

One of the benefits of big data analytics is that it can help businesses identify opportunities and threats that may not be apparent when looking at smaller data sets.

For example, a company might be able to use big data analytics to predict customer behavior or track competitor activity.

Another advantage of big data analytics is that it can help organizations reduce costs and improve efficiency.

By identifying inefficiencies in their systems, businesses can make changes that will improve performance.

Big data analytics is also useful for understanding customer sentiment. Organizations can use big data to track customer feedback on products and services, as well as social media commentary.

This information can help companies make changes that will improve customer satisfaction levels.

The 5 Vs of Big Data

Big Data is often described as having a number of charcteristics.

The most common form of this is the 5 Vs of Big Data: Volume, Velocity, Variety, Veracity, and Value. It can be extended to include other characteristics too. Variability is the most common additional charteristic considered.

Volume
The size and amounts of data the company manages and analyzes.
Velocity
The speed the data is collected, stored, and managed. This could be the number of social media posts per day, etc.
Variety
The types of data. Including unstructured, semi-structured, and raw. This could include feeds from commercial and government resources, social media, etc.
Veracity
The accuracy of the data. How trustworthy the data is will impact how influential it will be when used to make deisions.
Value
The value to the business from insights provided by the data. The quantifiable benefits could impact improvements in operations, customer relationships, etc.

How Big Data Analytics Works

Big data analytics is a process that uses data science with special software and algorithms to help businesses make sense of all this data.

This software can partition the data into manageable chunks, which makes it easier to analyze.

The algorithms then identify patterns and trends in the data that can help businesses make better decisions about their products and services.

Data Collection
Data collection is the first and most important step, but the process looks different for every business.
Businesses can collect structured, semi-structured, and unstructured data from various sources such as cloud computing and storage, mobile apps, Internet of Things (IoT) gadgets, supply chain software, and other sources.
Some data will be stored in data warehouses where business intelligence tools and solutions can easily access it. Raw data that is too complex for a warehouse can be stored in a data lake and assigned metadata.
Data Processing
After you’ve collected and stored data, you must organize it to ensure accurate results from predictive analytics and other queries. This becomes increasingly important as data sets become larger and are unstructured.
The available data businesses have for decision making is growing rapidly, which makes data processing more challenging.
Businesses can use batch processing, stream processing, or a combination of the two. The way you process data influences how useful the insights from it become.
- Batch Processing
  Batch processing is a technique used in data processing to speed up the execution of a task by dividing it into a series of smaller tasks that can be executed concurrently.
  This technique is often used when the task involves I/O operations, such as reading or writing data, or when the task requires access to resources that are shared among several processors.
  Batch processing allows tasks that are I/O-intensive to be executed on multiple processors simultaneously. This can improve performance by reducing the amount of time required to complete the task.
  Another benefit of batch processing is that it can improve resource utilization by allowing multiple tasks to share resources, such as memory and CPUs.
  Batch processing can also improve reliability by allowing tasks to be executed in parallel. If one task fails, the other tasks will continue to execute.
- Stream Processing
  Stream processing is a type of data processing that deals with data streams as they are generated. In other words, the data is processed as it comes in, in real-time.
  This makes stream processing well-suited for applications that need to respond to changes in data as they happen, such as financial trading or fraud detection. Stream processing can also be used to quickly aggregate and process large amounts of data.
Data Cleansing
No matter the amount of data you have, it requires regular cleaning or scrubbing to improve quality. Your data needs to be formated correctly. Duplicate and irrelevant data needs to be removed or otherwise accounted for. “Dirty” data can result in poor insights that mislead you and
Data Analysis
- Data Mining
  Data mining is a process of extracting valuable information from large data sets. It is used to find patterns and trends that can help businesses make better decisions. Data scientists use various techniques, including statistical analysis, machine learning, and artificial intelligence, to extract insights from data.
  Data mining can be used to identify customer trends, predict future behavior, and improve marketing strategies. It can also be used to detect fraud and other security threats. By analyzing large data sets, data scientists can find correlations that would otherwise be impossible to detect.
  The benefits of data mining can be seen in a wide range of industries. Banks use it to identify fraudulent transactions, retailers use it to determine what products to stock on their shelves, and healthcare providers use it to improve patient care.
  The potential uses of data mining are endless and continue to grow as new technologies are developed.
- Predictive Analytics
  The term predictive analytics is used to describe a number of different analytical techniques that allow businesses to make predictions about future events.
  These techniques can be used to predict everything from the likelihood that a customer will defect to the probability that a particular product will be returned.
  Predictive analytics is made possible by advanced analytics techniques such as machine learning, data mining, and artificial intelligence. These techniques allow businesses to analyze large amounts of data in order to identify patterns and correlations. Once these patterns have been identified, businesses can use them to make predictions about future events.
- Deep Learning
  Deep learning is a subset of machine learning that utilizes artificial neural networks to learn from data. It has been shown to be more effective than traditional machine learning methods in many cases.
  Deep learning algorithms are able to learn feature representations of data that are much more accurate than those learned by other methods. This makes them better at tasks like classification and prediction.

“Big data compiles all your company’s data sources into a central location for processing and analysis.”

Big Data Benefits

Big data has many challenges and opportunities, it has revolutionized the way businesses operate. By analyzing large amounts of data, companies can make better decisions, identify opportunities and threats, and improve their products and services.

Cost Savings

By identifying inefficiencies in business processes, big data can help businesses streamline their operations and save money.
Market Insights

Big data can help businesses understand their customers better. Businesses can gain insights into customers’ needs and wants by analyzing customer data. This helps businesses create products and services that appeal to their customers.

Big data can also help businesses improve their marketing efforts. By analyzing customer data, businesses can identify which marketing campaigns are most effective and which ones need improvement. This helps businesses allocate their marketing resources more effectively.
Product Development

Product development is an important area where big data can be used to improve results. Businesses can determine what products people want and need by analyzing customer data. They can also figure out how to create those products in the most efficient way possible.

Big data is also useful for improving the distribution of products. By tracking sales data, businesses can identify which areas are selling more products and which areas need more attention. This allows them to allocate their resources in the most effective way possible.

Big Data Challenges

Data Accessibility
The promise of big data has always been its ability to help organizations make better decisions by providing insights that were hidden in the vast sea of data.
Volume refers to the sheer size of the data. The amount of data being generated today is staggering, and it is growing at an alarming rate. With high volume comes complex data that makes processing more difficult.
Variety refers to the different formats that the data can take – text, images, video, etc. Velocity refers to the speed at which the data is being generated and changes.
All of these factors create a challenge for organizations trying to make use of big data. The volume alone is enough to overwhelm most traditional analytics tools. The variety makes it difficult to find the relevant data and create a cohesive dataset. However, making big data accessible and usable is a daunting challenge. There are three primary factors that make big data inaccessible: volume, variety, and velocity.
Data Quality Maintenance
The volume and variety of data can be overwhelming, and without proper maintenance, the quality of the data can suffer. This can lead to inaccurate analysis and decision-making, which can be costly for businesses.
Have a plan for data management. This includes specifying who will be responsible for maintaining the data quality, setting standards for how the data will be collected and processed, and establishing protocols for correcting errors.
Another key factor in maintaining data quality is having accurate and up-to-date information about the source data. This includes tracking where the data comes from, how it is formatted, and any dependencies it has on other datasets.
Data Security
As organizations amass ever-larger data stores, they become a more tempting target for cybercriminals. Data breaches can have serious consequences, including loss of customers, damage to reputation, and financial losses.
Implement a data security plan that includes multiple layers of protection. Ensure that your employees are aware of the risks associated with data theft and are trained in how to protect sensitive information.
Use secure methods for storing and transmitting data. This includes using strong passwords, encrypting sensitive information, and using secure networks.
Regularly assess your security posture and make changes as needed to keep up with the latest threats.
Using the Right Tools and Platforms
Big data analysis is great for businesses, but if you’re not using the right tools and platforms, you won’t be able to make the most of your data sources and the information they provide.
New technologies for processing and analyzing data are developed frequently, so your organization needs to invest resources into finding the right solutions to work within your ecosystem. This often means finding a solution that’s flexible enough to grow and scale with you as your infrastructure changes.

Big Data Analytics Tools

Hadoop

Hadoop is a powerful big data tool that can be used to store, process, and analyze large amounts of data. It can be used for various tasks, such as processing log files, analyzing customer data, or creating machine learning models.

Hadoop is designed to scale to meet the needs of large organizations, and it can handle huge volumes of data. It also offers a variety of features and options that allow you to customize it to your specific needs.
YARN

YARN, or Yet Another Resource Negotiator, is a tool that helps manage resources on a Hadoop cluster by negotiating with other services and applications for access to the cluster’s resources.

This allows Hadoop to make better use of its resources and helps keep other services running smoothly as well. In addition, YARN provides an easier way to add new services or applications to a Hadoop cluster since it eliminates the need for them to compete for resources with Hadoop itself.
NoSQL Databases

NoSQL databases are becoming more popular as organizations move to big data solutions. These databases are designed for scalability and can handle large-scale data processing. They are also non-relational, meaning that the data structure is not constrained by traditional relational database models. This flexibility makes them a good choice for big data solutions.
Apache Spark

Apache Spark is a powerful open-source data processing engine built on the Hadoop Distributed File System (HDFS). Spark can run on clusters of commodity hardware and makes it easy to process large datasets quickly.

Spark offers several advantages over traditional Hadoop MapReduce jobs. Spark can execute jobs up to 100 times faster than Hadoop MapReduce, thanks to its in-memory data processing engine.

Spark’s programming model is much more concise and user-friendly than MapReduce, making it easier for developers to write code.

Spark also provides a number of built-in libraries for data analysis, including support for streaming data, machine learning, and graph processing.
Tableau

Tableau is a data visualization software that helps you turn your data into informative and visually appealing graphs, charts, and maps.

Tableau can be used for small or big data and helps you make better business decisions by clearly understanding your data.

With Tableau, you can connect to various data sources, including Excel files, SQL databases, cloud services, and social media platforms. You can then create interactive visualizations with just a few clicks and share them with others in a variety of formats.
MapReduce

MapReduce is a programming model for processing large amounts of data. It was created by Google and has become popular among big data enthusiasts.

The basic idea behind MapReduce is to break down a problem into smaller pieces, which can then be processed more easily. The smaller pieces are then combined to create the final result. This approach can be used for tasks such as sorting data, calculating averages, or finding duplicates.
MapReduce can be run on multiple machines simultaneously. This makes it ideal for processing large datasets. In addition, the code is written in a language called Java, which is widely used in the software industry.

Wrap Up

Big data analytics is an important tool for businesses of all sizes.

By taking advantage of the vast amounts of data that are available today, businesses can make better decisions, improve their products and services, and create a competitive edge.

While big data analytics can seem daunting at first, it is a powerful tool that can be used to give you a competitive advantage.

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How To Create a Data-Driven Culture In Your Business

Keith Murphy — Fri, 27 May 2022 15:35:31 +0000

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How To Create a Data-Driven Culture In Your Business

Category

Business Strategy

Written by Keith Murphy
17 min read

Tags

Big Data Business Intelligence (BI)Management & Leadership

Last edited

June 24, 2024

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Every day, we generate approximately 2.5 quintillion bytes of data. The more data we generate, the more we have to work with to make better decisions for our businesses.

When we can support new ideas with solid evidence, things tend to run smoother.

But, simply having the big data isn’t enough. You must have data scientists and tools to help break the data down into actionable insights before it offers any real business value or competitive advantage.

Today’s companies are investing more time, effort, and money into data analysis in the hopes of increasing customer satisfaction, making business processes and operations more efficient and becoming clear on business strategy. but for many of them, a data-driven culture still evades them.

Why is this the case?

The truth is many obstacles to developing a data-driven business aren’t technical in nature.

They have more to do with your company culture. It’s easy to describe how to include data into your decision-making processes, but it is far more difficult to make this the norm for your employees.

It requires a shift in mindset which presents a major challenge. Let’s look at how you can create a data-driven culture in your organization in 10 steps.

Start at the Top

Companies that have a solid data-driven culture generally have top managers who set the tone.

They expect that all decisions must be anchored in data as the norm. They lead by example.

Executive leaders at one retail bank go through evidence from controlled market trials to decide on their product launches.

At a leading tech firm, senior executives spend half an hour at the start of meetings reading detailed proposal summaries and their supporting data so they can take action based on evidence.

These practices move downward all the way to entry-level employees. It works because employees who wish to be taken seriously must communicate with managers on their terms in their language.

By setting an example at the top, you can foster a shift in what becomes normal company-wide. If you want your team to employ the use of data in everything they do, you should do it, too.

Carefully Choose Metrics and KPIs

Leaders can heavily influence behavior by choosing what they want to measure and the metrics and key performance indicators they want employees to use.

If your company can profit by anticipating your competitors’ price moves, you could use predictive accuracy through time as a metric.

Your data team should continuously make predictions about the extent and direction of these moves as well as the quality of the predictions.

For instance, a popular telecommunications operator wanted to make sure that its network provided customers with the best possible user experience. However, they only collected aggregated statistics on network performance.

Because of this, they didn’t know much about what end-users were receiving and the service quality they got.

By developing detailed metrics on their customers’ use data and experiences, the company could then create a quantitative analysis of the consumer impact of network upgrades.

To accomplish this, the company had to have a better grip on the provenance and consumption of its data than is generally the case.

Don’t Segregate Your Data Scientists

Oftentimes, companies sequester the data scientists within the organization. This results in data scientists and business leaders not knowing enough about each other.

Analytics won’t provide value or survive if it operates independently from the rest of the business. You’ll create silos, which will make it hard to vet the data quality and put it to good use. If your organization suffers from an issue, you can address it in two ways.

First, remove boundaries between the business and the data scientists. For example, a global insurance company rotates staff from their centers of excellence into line roles where they scale up proofs-of-concept.

Then, they return to the centers. One global commodities trading firm has developed new roles in various functional areas of the business to support analytical sophistication.

These roles have direct relationships with the centers of excellence.

The particulars themselves don’t matter as much as the principal. You must find new ways to connect domain and technical knowledge if you want to put the company’s data to good use and improve customer experience.

Leading companies use another tactic. Beyond bringing data science closer to the core of the business, they push the business towards data science by insisting that employees understand code and are fluent in the conceptual aspects of quantitative topics.

Your senior leaders don’t need to be retrained as machine learning engineers, but leaders of data-centric organizations must have some data literacy.

Ensure Data Access is Open Where Necessary

One of the most common business problems organizations struggle with is the ability to obtain basic data.

This situation persists in spite of a variety of efforts to democratize access to data within a company.

Deprived of information, analysts can’t do a lot of analysis so it’s impossible for a data-driven culture to develop let alone thrive. Democratization is crucial to optimize data management.

Top companies rely on a simple strategy to stop this issue. Instead of using large but slow programs to reorganize their data, they provide universal access to a few critical measures at the time.

For instance, a leading global bank was trying to anticipate loan financing needs.

They constructed a standard data layer for the marketing department which focused on the most relevant measures.

In this case, it was core data pertaining to loan balances, terms, and property information along with marketing channel data on how the loans were originated and how that data related to customers’ broader banking relationships.

Regardless of the specific initiative, the best choice for the first data to make accessible is the metrics that are on the executive suite agenda. Requiring other numbers to eventually be tied to the data source will dramatically encourage its use, especially in functions like procurement.

Data is useful in all departments and aspects of a business. Sharing data across the entire company is critical.

Quantify Uncertainty

Everyone knows that it is impossible to achieve absolute certainty. Yet most managers continue to ask their employees for answers without a corresponding measure of confidence. Requiring teams to be explicit and quantitative about their level of uncertainty will produce powerful effects.

It will force the decision-makers to directly deal with potential sources of uncertainty. It forces them to think about whether the data is reliable and if there are enough examples for a reliable model.

It will also ensure that data managers and analysts get a deeper understanding of their models when they have to rigorously evaluate uncertainty. For example, if you fail to adequately adjust to market trends, you should consider building an early warning system to take the trends into account and spot instances that would have otherwise been missed.

Placing emphasis on understanding uncertainty encourages companies to run experiments, which can provide additional guidance on what moves to make next.

Keep Proofs of Concept Simple

With advanced analytics, promising ideas are plenty and you’ll often find that there aren’t as many practical ones as you’d like.

It’s typically not until a company tries to put proofs-of-concept into production that the difference is clear. Starting to implement something only to scrap the idea later isn’t good for morale.

It’s much better to engineer proofs-of-concept where a central part of the concept is viable in production.

Start by building something that is incredibly simple, then work to increase the level of sophistication. Once your foundation is in place, work to improve each component independently.

Use Specialized Training Only When Needed

Many organizations invest in extensive training efforts only to find that employees rapidly forget what they learned because they don’t get to put it to use right away.

Basic skills such as coding should always be a part of fundamental training but it is more effective to train your staff and specialized concepts just before the material is needed.

By waiting until shortly before you need it to train your team on something such as the finer points of experimental design, the knowledge tends to stick better.

Remember Analytics Helps Employees, Too

With such a heavy focus on using analytics to help your customers, it’s easy to forget how data fluency can make employees happier.

But by taking the time to empower your employees to bring gold out of themselves, you can enable them to take care of things themselves.

If the idea of learning new skills to better handle data is presented as an abstract concept, few employees will be excited enough to work to revamp how they do things.

But, if you frame it in such a way that immediate goals directly benefit them, such as reducing the amount of rework, collecting frequently needed information, and ultimately saving time, then that chore becomes a choice, ultimately fostering a stronger data culture.

Temporarily Trade Flexibility for Consistency

A lot of companies that depend on data have different data tribes. Each tribe may have its own preferred sources of information, metrics, and favorite programming languages.

Across the organization as a whole, this is a recipe for disaster. It’s easy to waste hours trying to reconcile subtly different versions of a metric that needs to be universal.

Inconsistencies in how data modelers do their work will also take us home. If coding languages and standards vary across a business every move data analysts make will include retraining, which makes it harder for them to circulate. It can also be difficult to share ideas internally if everything requires translation.

Instead, focus on canonical metrics and programming languages. You can do this by insisting your new hires know how to code in your language of choice.

Explain Analytical Choices

In the majority of analytical problems, there’s rarely one correct approach. Instead, data scientists need to make choices with different trade-offs. It’s a great idea to ask your team how they approached an issue, the alternative they considered, what they understood the trade-off to be, and why they chose the approach they picked over another option.

Making this part of the process will give your teams a deeper understanding of the approaches and often make them think about a wider set of alternatives or completely rethink their fundamental assumptions.

Organizations as well as the divisions and individuals that comprise them Are more likely to fall back on habit because alternatives seem too risky.

Quality data provides evidence to back up hypotheses that give managers confidence the make moves into uncharted territory with somewhat of a safety net. But it’s not enough to aspire to be a data-driven organization.

For a company to be driven by data, you have to develop a data culture where this mindset can grow. Leaders need to promote this shift with an example, practice new habits, and develop expectations for what it means to base business decisions on data.

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Visit our “Solutions” page to see the areas of your business we can help improve to see if we’re a good fit for each other.
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Data-Driven Organization: What Is It, The Challenges and Benefits

Rob Biedron — Tue, 17 May 2022 15:48:16 +0000

Download a free copy of "Preparing Your AP Department For The Future", to learn:

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How AP can improve relationships with your key suppliers.
How to capture early payment discounts and avoid late payment penalties.
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Data-Driven Organization: What Is It, The Challenges and Benefits

Category

Business Strategy

Written by Rob Biedron
19 min read

Tags

Big Data Business Intelligence (BI)Financial Planning & Analysis (FP&A)

Last edited

June 24, 2024

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Today, businesses are producing massive amounts of data at an alarming rate. To be a data-driven organization means that your business is poised to make choices based on data rather than Instinct, hope, General observations, or personal opinion.

It means your business has the data it needs to make decisions that lead to positive outcomes.

But being a data-driven organization goes beyond purchasing and installing installing the necessary apps and tools, hiring your data professionals, and investing in your data infrastructure.

It’s about making data and analytics a crucial part of your overall business strategy, culture, and all of your business processes.

A truly data-driven organization not only recognizes how important it is to collect raw data, but they also understand that they don’t need to make business decisions with raw data alone.

Instead, they take time to collect, analyze, and obtain insights from the data to address business issues, find growth opportunities, and promote profitability.

Data driven organizations use data for several purposes, such as analyzing customer feedback, monitoring consumer behavior and purchase patterns, and more.

These businesses have built a data culture that touches the entire company. It’s not just in silos threw out a few functional departments or at the executive level.

Every organization generates data and as such can become data-driven, no matter how big or small they are, the niche or industry they serve, and the products or Services they offer. What matters is adopting the right strategy and doing things the right way.

Why Data Matters for All Businesses

Data is quite possibly you are most valuable asset. It contains a ton of insights about your consumers, their behavior, and Industry trends that you can use to your advantage.

When used properly, it has the potential to help your business grow, and enable Better Business processes that allow you to work more efficiently getting more done in less time for less money.

Data-driven enterprises don’t scale in a straight line and one of the reasons major tech giants like Amazon and Google report exponential growth is because they based their business models fundamentally around data.

Research from PwC shows that data-driven organizations can outperform their competitors by 6 percent in profitability and 5% in productivity.

Another study found that data-driven organizations are 162% more likely to surpass revenue goals and 58% more likely to beat their revenue goals compared to their non-data-driven competitors.

And this report shows that 81% of organizations agree that data needs to be at the heart of all business decision making.

The numbers make it look great, but it is important to remember that data in raw form isn’t always going to be helpful or informative immediately. To make the most of your data, you need to collect it appropriately and transform it into something you can use.

Challenges of Becoming a Data-Driven Organization

Many organizations have been trying to become data-driven for quite some time now and find mixed results.

That’s because even though there’s plenty of reason to work toward a data-driven culture, there are plenty of challenges to overcome in the process.

According to a recent survey, the biggest challenge for companies working on their data strategy doesn’t have anything to do with technology at all. It has to do more with the fact that people and companies are resistant to change.

When you factor in cultural dynamics such as the COVID-19 pandemic and the rise of self-service, it has become even more difficult for companies to shift to a more data-driven approach.

Today’s consumers get information and data when and how they want it. As information becomes increasingly more decentralized, consumers can choose the news outlets they follow, the social media platforms they engage with, and the information they choose to trust.

Then when you consider the structural aspect since we are creating data at exponential rates, It’s no wonder we see so many organizations struggle with the task of becoming data-driven organizations.

As time goes on, it keeps becoming more difficult for companies to develop a solid data strategy and work toward becoming data-driven.

Today’s corporations encounter massive new volumes of data as well as new data sources every day.

As much as 80% of all new data is unstructured, meaning that it is not easy to capture or make quantifiable.

Companies have to recognize and appreciate that big data is an asset that flows through an organization cutting across traditional boundaries often lacking clear ownership.

The fluidity of the data makes the issue of data management even more complex because you have to find a way to ensure that it can consistently deliver value.

Without the right datasets, data analytics tools, and data scientists to support your business, it can be incredibly overwhelming.

This is especially the case for startups that don’t have the budget to hire a chief data officer (CDO) to handle all of this stuff for them.

Benefits of Becoming Data-Driven

There are a wide variety of benefits associated with Becoming a data-driven organization.

To cover them all would be well beyond the scope of this blog post, so we will cover three of the most important ones.

Better Understand Consumer Behavior and Improve Brand Customer Experience

In today’s market, products and services are unlimited, and consumers have multiple options about where to spend their money.

Competition between suppliers is quite tense.

A strategic way to improve brand awareness and become more relevant is to understand what your customers say about you and how they feel about your brand.

But without data, it is challenging to know who your customers are, how they feel about your products or services, and if your marketing campaigns are effective.

By offering customer surveys, paying attention to social media and online reviews, and more, you can gather data from your customers and analyze the data points to understand how they’re feeling and what they’re thinking about your business.

You can also use the information to develop new products and services or make adjustments to what you already offer to provide a better customer experience.

The insights you generate from customer feedback are beneficial for cross-selling, upselling, product innovation, and even customer support.

Make Better Business Decisions

Decision-making is a critical part of every business that generally involves several people from executives to stakeholders and millions of dollars that businesses can’t afford to lose.

With the right data available to you, your organization won’t have to make significant decisions based on assumptions alone, even in stressful times.

All departments across the organization generate valuable data that can be used to fine-tune your business strategy, improve growth, and boost profitability. For example:

Business leaders have to understand significant trends in the market, such as changes in customer price sensitivity, manufacturing, or shipping.
Product teams need data to understand how their end-users perceive a product after it’s launched.
Sales teams need data about sales performance to understand and differentiate products that are hard to sell from those that are selling well.
Your marketing department needs market segmentation data to find the customers who are willing to buy from them and speed up the sales closing process.
Your human resources department needs people data to manage talent and build more effective teams.
Your procurement team needs to know which products and services you buy most often, which ones are mission-critical, and which suppliers provide the best value to help you save money.

When you constantly collect, monitor, and analyze business data, your organization is in a better position to make smarter decisions and develop ideas that impact the bottom line for more growth and effective strategy.

Data-driven decision-making puts you in a better position to manage risks efficiently and increase your chances of success.

Understand Employee Engagement and Measure Business Performance

Regularly analyzing business data also helps you to measure your business performance to build more reliable and long-term hypotheses along with a strong workforce.

You can use your business data to understand whether you’re meeting your most important business indicators or not.

Analyzing business performance data also helps you to understand strategies that worked and those that didn’t work, business decisions and policies that gave you the highest return, the business operations and costs you can optimize, and more.

Eventually, your organization can leverage those insights to reduce costs, improve services and products, and more.

Many organizations are so stuck on the idea that the customer is king that they only prioritize initiatives that help keep their customers happy.

This is understandable because customers are essential to the success of a business.

That said, it’s also important not to overlook your employees because they are as important as your brand and can have an influence on how customers perceive it.

Taking time to ask your employees questions about how they feel working with you, how they’re coping with new methods of working, whether they have all the tools they need to work efficiently, and so on can help you get more insight into how engaged employees are.

If you notice problems within the workforce, you can Implement initiatives that will improve engagement and morale.

Happy employees are more likely to build better relationships and work hard to make sure your customers are happy, too.

The use of data is no longer something business teams can ignore. The business value it offers can provide a competitive advantage.

How to Become More Data-Driven

Think Long-Term

Data literacy and evolving business processes won’t change overnight. It is a process to become data efficient. Remember that progress rather than Perfection is the goal.

Data-driven companies recognize that success is achieved iteratively. It grows and then spreads. You should expect to be in this process for a while and focus on the long-term.

Offer Training – Prioritize Proficiency

Offer your team both formal and informal training, mentorship, and learning activities that improve knowledge and skills. Teach them how to act on data and help maximize the Investments.

For your team to be able to skillfully analyze data in their job, they have to be data proficient. It’s about more than just having the right skills, but also being inclined toward making data-driven decisions rather than going by instinct.

Organizations that have a successful data culture hire people with the right skills and aptitude to make those data-driven decisions but they also help employees to develop their analytical skills with training and other activities.

When your company regularly encourages and supports employees who challenge the status quo, you will see less complacency.

Encourage curiosity and discovery with data so that it becomes the norm. Invest in self-service analytics because it also plays an important role in empowering your team.

Doing so means your company will eventually see data work its way into all conversations across the entire organization.

Adopt an Agile Framework

Data silos are common across many businesses. Using an agile data management framework to anchor your deployment will ensure that you have ready-to-use, accurate, clean data.

The better data quality you have, the better you can expect your results to be.

The agile framework also makes it easy to ensure the right people have the right data access at the right time.

It’s crucial to strike an appropriate balance between control and freedom with users through your baseline framework. It should generate secure, stable, and trusted analytics.

Ideally, your organization should develop iterative and repeatable processes that will help to minimize issues while also maximizing success before, during, and after deployment.

Keep in mind, however, that this is not a one-and-done process. You’ll need to continually monitor, evaluate, and maintain the process.

It will handle most of this, but it is also important to verify that your analytics performance supports your business needs as they change and mature.

It’s also crucial to ensure that your environment remains secure for everyone. When this is the case, you’ll save time and money, have more efficient business processes, and have stronger customer and partner relationships.

Together all of these things improve your brand’s reputation and profit.

You’re Not Alone in Your Journey to Become Data-Driven

Even if it feels like you’re the only business lost in trying to figure out how to develop a solid data strategy and put the massive amounts of data at your disposal to good use, you are most certainly not the only one.

What matters is that you remain flexible to evolving needs, invest in the right technology solutions, and take the time to develop well-defined processes.

Changing company culture is difficult, but starting from the top down, the evolution will permeate through and your business will be better for it.

Download PDF

What’s your goal today?

1. Use Planergy to manage purchasing and accounts payable

Read our case studies, client success stories, and testimonials.
Visit our “Solutions” page to see the areas of your business we can help improve to see if we’re a good fit for each other.
Learn about us, and our long history of helping companies just like yours.

2. Download our guide “Preparing Your AP Department For The Future”

Download a free copy of our guide to future proofing your accounts payable department. You’ll also be subscribed to our and notified about new articles or if have something interesting to share.

3. Learn best practices for purchasing, finance, and more

Browse hundreds of articles, containing an amazing number of useful tools, techniques, and best practices. Many readers tell us they would have paid consultants for the advice in these articles.

The post Data-Driven Organization: What Is It, The Challenges and Benefits appeared first on Planergy Software.

How Finance Business Intelligence Can Give Real-Time Insight For Better Decision Making

Lyle Del Vecchio — Thu, 12 May 2022 15:07:43 +0000

Download a free copy of "Preparing Your AP Department For The Future", to learn:

How to transition from paper and excel to eInvoicing.
How AP can improve relationships with your key suppliers.
How to capture early payment discounts and avoid late payment penalties.
How better management in AP can give you better flexibility for cash flow management.

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How Finance Business Intelligence Can Give Real-Time Insight For Better Decision Making

Category

Finance

Written by Lyle Del Vecchio
18 min read

Tags

Business Intelligence (BI)Financial Planning & Analysis (FP&A)Strategy & Planning

Last edited

June 24, 2024

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For a long time the finance function has started moved away from doing just accounting and bookkeeping.

The pace of this change has increased in the last few years. It’s no longer a back-office function now.

Finance has firmly moved towards leveraging strategic roles that drive value, innovation, and growth for the organization.

In the current hyper-competitive markets, leveraging the finance function has become the deciding factor between sinking or swimming—organizations with digitally native finance teams show much higher resilience, tighten their market positions, and grow shareholders’ value even if the stock markets tumble.

These organizations can manage crisis better and can recover and excel faster once markets return to normal.

Furthermore, they also mitigate the risk posture better than those organizations that lag behind in financial transformation.

An important question arises: how can organizations turn finance from passive controller to proactive strategist?

It can be achieved by deploying business intelligence in the finance function—a powerful data-driven tool that CFOs and their finance teams can utilize to improve strategic planning and decision-making by analyzing big data, leveraging information to collect actionable insights, and sharing it seamlessly across the organization.

However, here’s a catch—only 21% of CFOs leverage business intelligence to identify new value for their organization.

It is a considerable challenge. Caused partly by the fact traditional business intelligence lacks the forward-looking functionalities and business modelling capabilities required by modern CFOs to add strategic value to the company.

Here, organizations need to understand the vital difference between traditional business intelligence and finance business intelligence to grow the strategic value of finance.

What Is the Difference Between Finance Business Intelligence and Traditional Business Intelligence?

Typical business intelligence primarily focuses on and is designed for data analysis and visualization—conveying historical data in a meaningful format that can be easily understood and interpreted by people without advanced programming or data analytics experience.

It sees a business through a rear-view mirror and improves the efficiency of existing processes rather than the pursuit of change.

On the other hand, Finance Business Intelligence focuses on both past performance and future forecasts to help CFOs inform function-level, entity-level, or enterprise-level strategic decisions.

CFOs require tools like dynamic modelling, forecasting, and predictive analysis that traditional business intelligence fails to provide.

Because of this, finance teams are forced to plaster over those gaps and fill voids by using spreadsheets for different tasks such as driver-based modelling, deployment of different profit and loss and balance sheet schemas, financial reporting, planning, consolidation, and reconciliation.

It defeats the purpose of deploying business intelligence in the first place. Moreover, since spreadsheets are static, difficult to update in real-time, and can’t incorporate data sources from either external or internal sources, they create severe problems for the finance teams.

Troubleshooting becomes challenging in case a fault arrives in data collection or calculation. As a result, inaccuracies in spreadsheets lead to inconsistent BI analytic findings and models.

Dedicated business intelligence in finance solves these inherent issues by integrating multiple ledgers, different entities within the same group, and cross-company data into a single unified view.

Legacy Business Intelligence tools bundled with ERP applications couldn’t meet modern CFOs’ requirements since they were not necessarily designed to operate beyond -their own operating environment. However, today’s application-independent BI tools are intuitive enough for finance teams to manipulate extensively and reduce dependence on IT resources to conduct new and novel analyses.

Finance BI applications, like Microsoft Power BI, Tableau, etc., provide data visualization capabilities as part of a larger platform and can integrate various processes such as planning, consolidation, closure, cost allocation, and profitability analysis.

Finance Business Intelligence has a variety of approaches in its arsenal—from looking backwards to evaluate past financial performance to forward-looking scenario planning and predictive modelling, which makes it an automatic choice for CFOs to wring strategic value from their organizations’ financial activities.

What Is Finance Business Intelligence?

Finance Business Intelligence is a term used to describe methods to collect, process, and analyze financial data from databases in real-time and make better business decisions with the help of professional financial business intelligence software.

Business intelligence solutions extract data and information from both internal data sources (operations, finance, marketing, etc.) and external data sources (market data, competitor data, social media, etc.) for centralized, accessible, and comprehensive data management and analysis.

It positions finance as a business partner with Operations and enables CFOs to grow the strategic value of finance by:

Stepping out from traditional silos
Identifying opportunities to save costs
Deriving new insights through data-sharing capabilities
Working proactively with the enterprise to optimize operational cashflow

When properly deployed, business intelligence in finance appropriately manages consolidation, driver-based allocation, and closure process. It also enables the finance team to determine and analyze profitability across product lines, sales channels, customer pools and geographical regions.

With its support, finance teams can collect, collate, and organize everyday data the Finacne offce needs.

They can also create different P&L schemas (such as IFRS, local GAAP, managerial schemas, or XBRL disclosures) according to different timelines (monthly, quarterly, half-yearly, year-to-date, quarter-to-date, etc.).

An efficient BI solution enables an organization to optimize its processes and use powerful insights like spend analytics to identify opportunities for cost savings.

For instance, Planergy has helped save billions of dollars for clients through better spend management, process automation in purchasing and finance, and reducing financial risks.

How Can Finance Business Intelligence Be Used?

Today’s forward-thinking financial professionals—particularly Chief Financial Officers—can drive innovation by applying digital finance transformation in big data at scale.

They can leverage business intelligence to collect, organize, and analyze information in order to harvest business-critical insights and drive value through process improvement.

Having access to real-time data enables CFOs to move from impulsive to thoughtful decisions and back their responses with accurate, up-to-date financial data. It leads to cost-effective, efficient, and forward-thinking choices and decisions based on objective and unbiased information.

For instance, Planergy provides real-time spend visibility with our Spend Analysis Software which helps finance teams keep a tab on how every cent is accounted for—who spent it and with which vendors.

Besides, it also automates accountability and provides budget vs actual spend reports to better inform business decisions.

What Are the Benefits of Business Intelligence In Finance?

With digital transformation pushing finance into strategic roles, organizations are placing

greater demands on the Finance Function in general—and the CFO in particular—to help them ride out uncertainty.

CEOs, COOs, and other C-level executives are relying heavily on the Finance

Function to not just improve the bottom line and provide an accurate picture of the financial health of the enterprise, but also assist them in understanding how emerging and potential shifts in key market factors and fast-moving strategic decisions will affect the organization.

Business Intelligence begins with helping the finance function achieve its traditional goals such as accounting, bookkeeping, preparation of financial statements, tax filing, etc.

Once traditional goals are met, finance can attend to forward-looking activities such as financial planning and forecasting—modelling and assessing the impact of various events on the cash position, driving portfolio cash investments and initiatives to maximize profitability or supporting the decision-makers in prioritizing, approving, and managing capex investments.

Business Intelligence enables finance to:

Improve forecasting and reduce variance in estimations from different departments – this can encourage finance to shrink the size of reserved cash buffers and free up working capital.

Shorten reporting times, provide timely insights, and speed up time-to-value through faster and optimized automated cashflows

Prevent reporting duplication

Streamline and optimize business processes

Reduce risk exposure

Pursue healthy organizational change

Risk management is another crucial benefit that CFOs can obtain from financial business intelligence.

Dashboards exhibiting key performance metrics that demonstrate the firm’s financial performance and a comprehensive perspective of market and credit risk ensure that management is aware and ready to quickly respond to abnormalities.

Besides, business intelligence also brings more credibility to data by providing periodic data cleansing to prevent bad data from getting in.

BI differentiates between good and bad data and eliminates the chances of inaccurate analysis or negative financial impact arising due to the presence of bad data.

Equally important, business intelligence and data analytics help an organization identify and respond more effectively to customer expectations, improve the overall user experience and turn potential clients into paying customers.

It also improves relationships with vendors by improving the invoice processing and facilitating faster payments with minimum errors or exceptions.

In a nutshell, business intelligence changes the objective of finance from preservation to progression and empowers it to unlock new business value.

Since it sets up a connection between actual results and the planning and simulation phase— finance can focus both on rear-mirror business reporting as well as forward-looking predictive analytics.

Result? CFO can address the company’s business needs through business plans, cascading goals throughout the organization, and implementing proper performance monitoring, measurement, and controlling processes.

How Is Business Analytics Used in Finance?

Real-time Business Intelligence in finance enables an organization to become more agile in the face of unprecedented external events and empowers business leaders to make informed decisions.

Data-driven business intelligence drives finance transformation by:

Consolidating and integrating data into a shared system by pulling datasets from business applications, ERP systems, office tools, and 3rd party system data;.

Providing a platform for scaled data utilization by processing data, transforming data, and enabling insights. Also, since it’s cloud-based, it is infinitely scalable.

Extending to include new data capabilities such as data modelling, machine learning, AI, IoT, and Industry 4.0

Facilitating a 360-degree view of working capital across the business and allows finance to enjoy better control and access over cash movement.

Efficient finance Business Intelligence—seamlessly connected to planning, reporting, and other functions—can predict trends with unbelievable accuracy and speed which humans or spreadsheets can’t even match.

It also eliminates human emotions or bias from the equation and exposes new, never-before-seen financial data that opens up new opportunities to pivot.

Business Intelligence takes analytics beyond charts and graphs and leverages advanced technologies to create powerful, intuitive, and accessible visual representations such as heat maps, interactive augmented reality applications, and dynamic data dashboards.

By slicing and dicing information as required, CFOs can identify opportunities for efficiencies, track performance and revenue streams, and make timely decisions to reduce risk and increase profitability.

Besides, the finance function is responsible for producing various reports in a financial year.

These reports have to be made in accordance with the guidelines prescribed by the General Agreement on Tariffs and Trade (GATT), the International Financial Reporting Standards (IFRS), and the Sarbanes-Oxley Act (SOX).

Apart from external stakeholders, internal stakeholders such as employees and management also require some reports to run the audience.

These reports have to be customized and categorized for each department and aligned to represent a ‘single version of the truth.’

Road Ahead

Once finance becomes accustomed to Business Intelligence, it can address other use cases such as improving payment terms for customers, making timely and accurate payments to vendors, assessing the resilience of the supply chain, and optimizing capex investments.

finance business intelligence can expose an enterprise to unprecedented financial data that opens up new opportunities to pivot. As a result, today’s fast-paced organizations eagerly look up to the CFO to safeguard and strengthen their competitive advantage in the current business climate.

However, finance transformation–deployment and smooth implementation of finance BI—is not a cakewalk.

The five building blocks for transformation are strategy, people, process, technology, and data in any technology adoption journey.

Out of these building blocks, the most important one is people—CFOs must make sincere efforts to combine analytics-savvy people with seasoned business communicators to achieve desired objectives and goals.

Once CFOs persuade their teams to consider themselves strategic advisors instead of traditional custodians, this mindset shift will enable the function to break down siloes, democratize the financial data, and collaborate with the wider business to create new value.

Overall, the deployment of business intelligence in the finance function can enable organizations to achieve financial transformation and earn exponential rewards.

Download PDF

What’s your goal today?

1. Use Planergy to manage purchasing and accounts payable

Read our case studies, client success stories, and testimonials.
Visit our “Solutions” page to see the areas of your business we can help improve to see if we’re a good fit for each other.
Learn about us, and our long history of helping companies just like yours.

2. Download our guide “Preparing Your AP Department For The Future”

Download a free copy of our guide to future proofing your accounts payable department. You’ll also be subscribed to our and notified about new articles or if have something interesting to share.

3. Learn best practices for purchasing, finance, and more

Browse hundreds of articles, containing an amazing number of useful tools, techniques, and best practices. Many readers tell us they would have paid consultants for the advice in these articles.

The post How Finance Business Intelligence Can Give Real-Time Insight For Better Decision Making appeared first on Planergy Software.

Turning Data Into Actionable Insights

Keith Murphy — Tue, 29 Mar 2022 15:11:06 +0000

Download a free copy of "Preparing Your AP Department For The Future", to learn:

How to transition from paper and excel to eInvoicing.
How AP can improve relationships with your key suppliers.
How to capture early payment discounts and avoid late payment penalties.
How better management in AP can give you better flexibility for cash flow management.

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Turning Data Into Actionable Insights

Category

Business Strategy

Written by Keith Murphy
16 min read

Tags

Big Data Business Intelligence (BI)

Last edited

June 24, 2024

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Today, businesses are generating more data than ever before. Till 2025, global data creation is projected to grow to more than 180 zettabytes.

With a plethora of data collection points available at their disposal, it’s easier for business leaders to be lost in the information glut.

However, every business and business leader should ponder for a moment: will collecting such a huge volume of data make sense unless it provides meaningful information and insights? Big data comes with bigger challenges.

In 2017, The Economist published a story titled, “The world’s most valuable resource is no longer oil, but data.”

But unfortunately, this story is no longer relevant in today’s data-intensive world. Big data doesn’t always equate to good data.

Instead, the world’s most valuable resource is the ability to use data to extract meaningful data insights and leverage untapped potential. If appropriately utilized, those insights can help an organization:

Adjust the pricing of products or services
Gain untapped customer insights
Increase employees’ efficiency
Improve strategic decision making
Reduce costs and expenditure
Find potential ways to achieve efficiencies
Achieve a systematic digital transformation
Manage compliance with laws and regulations
Build and improve relationships with stakeholders

However, before discussing the process and ways of collecting valuable insights, it’s important to understand the difference between data and insights.

What Is the Difference Between Data and Insights?

Many people use data, information, and insights interchangeably. However, there’s a vast difference between these terms.

If you look at these terms from a pyramid point of view, data sits at the foundation, information occupies the middle part, and insight is positioned at the pinnacle.

Data: Raw and unprocessed facts in the form of numbers, text, images, audio or video files, etc., which primarly exists in various formats ad systems. On its own, data neither makes sense nor provides valuable inputs to a business.

Information: Information can also be called “data processed, aggregated, and organized into a more human-friendly format.” It provides more context but is still not ready to inform business decisions.

Insights: Insights are generated by analyzing information and drawing conclusions. This step can make or break an organization’s ability to understand its data better and leverage it to maximize profitability, reduce cost, and create value for shareholders.

If you look around, all successful companies like Coca-Cola, Netflix, Google, Spotify, etc., leverage insights to enhance the customer experience and increase their revenue.

In a nutshell, data is the input for extracting relevant information, and then information becomes the input to obtain meaningful insights.

How Do I Use Data To Make Decisions?

As discussed above, data on its own can’t influence business decisions. It has to be first processed and organized in a more human-friendly format and then converted into actionable insights.

For instance, a company receives hundreds or even thousands of invoices from its vendors each month. Those invoices are recorded in the accounting system that results in big data generation.

However, such data is of little use in decision making until it is processed further and actionable insights are drawn from it. Unprocessed data may be limited by severe data quality issues such as:

Duplicate data – Since most organizations collect data from all directions and systems, it may result in duplication and overlap in these sources. A duplicate invoice may lead to a duplicate payment.

Inaccurate data – Human errors, data drift, and data decay can lead to a loss of data integrity. Inaccurate data recording may delay payments which can adversely affect an organization’s relationship with its vendors.

Ambiguous data – Inconsistency in data formats can introduce multiple flaws in reporting and analysis. For instance, phone numbers may be stored in different formats like 9999999999, +1 9999999999, 999-999-9999, or 99999 99999. Even the address may be recorded without following the USPS norms, causing problems in data processing.

Apart from these issues, organizations also face problems with unstructured data, invalid data, data redundancy, and data transformation errors.

It’s almost impossible for business leaders to make decisions based on myriad data sources until this procurement big data is converted into relevant information and then actionable insights.

Only then can leaders uncover hidden patterns and trends and obtain necessary inputs to make informed business decisions.

Organizations often overlook the accounts payable department while trying to make better use of data.

AP manages critical financial data that can provide valuable insights for discovering potential savings.

Organizations can use AP data to optimize their cash flow, create better and deeper relationships with suppliers, and understand trends in the payment data.

Before we discuss further how an organization can make better sense of AP data, let’s understand how raw data can be transformed into actionable insights.

How Do I Turn Data Into Actionable Insights?

Here is a 5-step process that can help you convert raw data into actionable insights:

1. Set Clear End Business Goals and Objectives

It’s critical for an organization to keep an eye on the prize — the end goals to be achieved from data analytics should be clearly outlined.

The goals should align with the company’s strategic priorities. It’s easy to deviate towards vanity metrics that sound impressive and look good on paper, but in reality, don’t add value to a business.

A useful framework for setting goals and KPIs is to be SMART – Specific, Measurable, Attainable, Realistic, and Timely.

2. Ask the Right Questions

Once end goals have been identified, the next step is to figure out key information needed for informed business decisions.

You can ask yourself the following questions:

What are the key drivers of revenue, expenses, and risks in the targeted business area?
Which channels drive the most conversions?
How will specific insights impact the operations and add to the bottom line?
Who will consume these insights? What actions do they want to take based on these insights?

Every user will have different expectations from the data analytics activity. C-suite executives may focus on the big financial picture, while managers may be more interested in collecting insights that improve management practices.

Similarly, executives may want to collect operational insights. Make sure all users requirements are considered ahead of time.

3. Transform the Data

This step is the most critical step when converting data points into insights. Mostly, data is stored in disparate systems with varying degrees of accuracy across sources.

Hence, it becomes important for the organization to collect, combine, and collate this data into a single data model.

Also, the organization has to handle and eliminate common data handling and transformation challenges such as missing values, different output formats, and varying levels of granularity for different levels.

Here, pattern recognition also plays a key role. Not all patterns will be relevant or crucial. Each pattern should be reviewed and moved forward only if it answers necessary questions.

Segmentation is also necessary since it allows you to group data based on common attributes and then process it further.

4. Apply Visual Analytics

Once data has been collected, collated, and cross-examined for accuracy and cleanliness, the next step is to set up visual analytics.

It helps an organization go beyond traditional spreadsheets and uncover hidden patterns and trends.

Visual analytics present information in a highly graphical, interactive, and visual format through interactive dashboards, reports, summaries, graphs, charts, and maps.

Result?

Critical data is displayed in meaningful, insightful ways to help business leaders make informed business decisions such as forecasting, planning, analysis, risk management, strategic sourcing, operational complexcity reductions, and anti-fraud monitoring — to name a few.

5. Translate Information Into Insights

The final step is to derive the required information to make better strategic decisions and generate more value from data.

The insights collected from the entire 5-step process can help the organization manage and enhance profitability, maximise prosperity, and transform risk into value across the board.

This 5-step process is not a law that has to be followed as it is. The main objective behind converting data into insights is to present it in an easy-to-understand, simple, and visual language.

By using accurate data, you can craft a meaningful narrative.

How Do I Present Data in an Actionable Way?

Over the last few years, businesses have understood the importance of being more agile and proactive in getting access to real-time data and insights.

Instead of relying on manual systems where a finance team pulls data from multiple spreadsheets, crunch numbers, and send reports to stakeholders and executives, organizations need to adopt an automated system with robust analytics capabilities.

Ideally, it will be a centralized system that captures data in a systematic, standardized format every time. For example, a centralized Procure-to-Pay software like Planergy.

The automated system should have the ability to use various visual formats to provide actionable insights that help business leaders make informed business decisions.

Data on its own can seem like an alien language to people outside of the analytics team. This is where data visualization can take raw data and turn it into easily interpretable insights.

A few common data visualization techniques include pie charts, bar charts, histograms, Gantt charts, heat maps, Waterfall charts, etc.

Which Tools Are Available To Help Convert Data Into Insights?

An organization has a variety of data visualization tools at its disposal – Power BI, Google Charts, Tableau, etc.

A good data analytics and visualization software is fully customizable and can be embedded right into the core product or ERP.

You can pull data from multiple sources into a standalone data visualization tool but having tools in the various areas of your business processes already equipped with data visualisation can be even better.

You see the relevant data as you are making decisions in the application.

For example, Planergy Spend Analysis software offers a powerful real-time business intelligence software for spend data, equipped with data visualization features such as reports and customisable dashboards.

You can track every purchase in Planergy to power your reporting insights, drill down and uncover hidden patterns to realize savings of up to 15%, and integrate with almost all ERPs.

Besides, a good visualization tool gives you an option to build custom business intelligence reports according to your requirements.

All reports are fully filterable which allows you to drill down and see hidden details.

Bottom Line

By adopting automated systems that convert raw data into insights, the organization can manage their finances more effectively, make better decisions, and earn the best ROI on capital.

Combining data processing with machine learning makes the system more intelligent and capable of handling complex data points.

Steadily business leaders have recognized the need to transform their data into actionable insights and are finding the right tools to capture data accurately, provide information in a way that they can do a deep dive when needed, but also provides the right data at the right time and in the right format to aid decision making.

Download PDF

What’s your goal today?

1. Use Planergy to manage purchasing and accounts payable

Read our case studies, client success stories, and testimonials.
Visit our “Solutions” page to see the areas of your business we can help improve to see if we’re a good fit for each other.
Learn about us, and our long history of helping companies just like yours.

2. Download our guide “Preparing Your AP Department For The Future”

Download a free copy of our guide to future proofing your accounts payable department. You’ll also be subscribed to our and notified about new articles or if have something interesting to share.

3. Learn best practices for purchasing, finance, and more

Browse hundreds of articles, containing an amazing number of useful tools, techniques, and best practices. Many readers tell us they would have paid consultants for the advice in these articles.

The post Turning Data Into Actionable Insights appeared first on Planergy Software.

Natural Language Processing (NLP) in Finance

Lyle Del Vecchio — Mon, 09 Aug 2021 12:52:48 +0000

Download a free copy of "Preparing Your AP Department For The Future", to learn:

How to transition from paper and excel to eInvoicing.
How AP can improve relationships with your key suppliers.
How to capture early payment discounts and avoid late payment penalties.
How better management in AP can give you better flexibility for cash flow management.

Download Free Copy

Natural Language Processing (NLP) in Finance

Category

Finance

Written by Lyle Del Vecchio
14 min read

Tags

Artificial Intelligence (AI)Business Intelligence (BI)Digital Transformation

Last edited

June 5, 2024

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Today’s global economy runs on data. Companies who want to compete successfully need tools to capture, organize, and analyze information faster and more accurately than ever before.

In finance, where business-critical data doesn’t always take the form of convenient numerical tables, a timely and efficient method for sifting through the mountains of unstructured text to extract and leverage insights is more important than ever.

Natural Language Processing (NLP), powered by machine learning, is one way companies are spinning the straw of unstructured textual data into the gold of actionable insights.

By understanding how today’s leading companies are employing natural language processing in finance, organizations can develop and implement their own solutions.

The Importance of Natural Language Processing in Finance

While technologies such as robotic process automation and advanced analytics get plenty of attention and ink in discussions surrounding digital transformation, other data science technologies powered by artificial intelligence are of major importance in both the finance department of your business and the financial industry as a whole.

Natural Language Processing is one of the most important, as it helps professionals tackle the biggest challenge of dealing with Big Data: managing the sheer glut of information flowing into and through a business at any given moment.

In addition to spend data flowing from procurement, financial organizations must also receive and process research reports, transcripts from earnings calls and minutes from virtual meetings, stock market and financial industry intelligence, competitor data, and more.

Much of this data is unstructured, or not readily accessible in digital format. In fact, the International Data Corporation predicts that, by 2025, a full 80% (or more) of company data will be unstructured.

Both major financial institutions such as banks and investment firms, as well as financial professionals at other organizations, are drawn to NLP because it uses powerful machine learning algorithms designed for parsing and converting human language to a format computers can easily understand and use.

Parsing human language for context and meaning is an incredibly complex and difficult task for even advanced artificial intelligence solutions. Literal meanings aside, algorithms processing text or audio must also contend with idiom, homophones and homonyms, tone (including irony and sarcasm), dialect, and more.

Humans need years to master these intricacies, but NLP applications have the power to process unstructured data hundreds of thousands of times faster than humans, whether it’s text or audio.

With help from human trainers, NLP techniques such as named entity recognition (NEM), and high volumes of exposure, accuracy rates improve over time because the algorithm uses deep learning and neural networks (interconnected machine learning algorithms that mimic the human brain) to enhance its knowledge base and performance with every iteration.

The “data firehose” of modern commerce is simply too much for humans to handle. NLP can accurately process text data and audio at volumes and rates far beyond human capabilities, improving its efficiency over time.

Key Benefits of Natural Language Processing

As their importance grows and their usage becomes more widespread, NLP solutions are providing immediate and demonstrable value to businesses in six key areas:

The adage “time is money” has never been truer, and the sooner insights can be harvested from financial data, the more valuable they are in making informed decisions and strategic plans. NLP has speed that can’t be matched by even the most ambitious human staffer, and automation means unstructured data is captured and converted to a usable format in hours rather than days or weeks.
The “data firehose” of modern commerce is simply too much for humans to handle. NLP can accurately process text data and audio at volumes and rates far beyond human capabilities, improving its efficiency over time.
It can also, with help from chatbots, provide a better customer service experience for clients, or provide internal stakeholders with real-time, plain-language search capabilities that still consider all of those linguistic complexities when serving up answers or suggestions.
As with robotic process automation, eliminating human error is as big a priority for NLP applications as improving speed and efficiency. NLP doesn’t need breaks, uses multiple contingencies to verify data accuracy, and incorporates corrections into future iterations to further reduce any errors.
NLP uses machine learning to add context to the data it processes. This metadata can enhance not only response accuracy in searches, but help users refine their data sets during analysis. For example, a user might ask to see all sections of an earnings call transcript where the participants were discussing sustainability, or request a specific section of a financial report mentioning supply chain impact of a particular sourcing decision.
It might be the hobgoblin of small minds, but when it comes to data processing and management, consistency is king. NLP solutions follow strict protocols and contingencies to ensure steady and consistent performance, without the risk of different interpretations from different human readers or listeners.
Risk Management. With pattern recognition and iterative analytics, NLP applications can help companies combat fraud, theft, and money laundering by identifying suspicious activity concealed in unstructured data.

How Companies Are Already Using NLP

Given its wide range of capabilities, it’s not surprising that NLP already has several successful use cases in finance.

1. Financial Sentiment Analysis

Successful stock market investments require informed investors. And while quarterly financial statements provide a black-and-white glimpse of a company’s performance, they don’t provide a complete picture of a company’s position within the marketplace.

NLP applications can analyze not only financial data, but social media streams, financial news, and reports from financial markets to harvest business intelligence.

They use an advanced form of sentiment analysis—i.e., analysis performed to determine whether information is positive or negative—known as financial sentiment analysis, which considers not only the inherent positivity of a piece of information, but how the information will affect the stock market and the price of specific stocks.

For example, a CEO being terminated is generally a negative event (producing negative sentiment), but if their replacement has a track record for rebuilding troubled companies, the net effect might be positive for the company’s stock price.

NLP also enhances a company’s ability to generate value from factoring environmental, social, and governance (ESG) information into their investment decisions.

By incorporating these non-financial factors into their analysis, companies can better identify opportunities for growth and innovation or intercede to reduce risks created by ethical or environmental concerns.

Companies like Bloomberg and DataMinr are already providing financial sentiment analysis services to their clients, while those looking for an open-source machine learning model are turning to FinBERT on github to evaluate financial sentiment.

2. Risk Management

For financial institutions, risk is a part of nearly every business process. In assessing credit risk, the traditional solution relied on credit reports and ratings.

But with nearly half the world avoiding or unable to access financial services due to poverty, financial institutions need a more nuanced and useful way to assess credit risk so they can provide finance to those who request it while still protecting their own interests.

NLP applications can analyze a wide range of data contained within loan documents, as well as more esoteric information such as the emotional state of the borrower and lender during the loan process and signs of a persistent and entrepreneurial mindset within the language used in applications.

3. Capturing and Analyzing Audio Data

In addition to parsing text, natural language processing can, when paired with speech recognition technologies, capture data from quarterly earnings calls, customer feedback calls, seminars, webinars, and more.

Once captured, the data can be analyzed in a variety of dimensions to yield useful insights that go beyond the financial data.

Metadata related to tone and context can provide additional insights on the ways in which a company’s reputation, stock price, or competitive strength will be affected by the calls or events captured.

4. Internal Process Optimization

Ideally, artificial intelligence makes it easier for humans to apply the organic version to more complex tasks.

Natural language processing helps companies collect and manage the data their human employees need to perform higher-value, more strategic tasks.

Deloitte, for example, has incorporated NLP into its Audit Command Language to improve contract compliance. JP Morgan not only implemented NLP in their contract management, but built custom machine learning algorithms to create NLP applications that gauge financial sentiment and offer investment advice to customers accordingly.

In accounting and procurement, NLP can improve the ability of a centralized data management solution to collect and integrate data from a variety of sources.

This not only aids in standardization and collaboration, but provides a richer data set that yields higher-quality insights, improving financial planning, risk assessment and management, and strategic decision-making at the enterprise level.

In addition, incorporating NLP provides internal stakeholders with a more intuitive tool for searching and analyzing information they need to create reports, budgets, and forecasts.

And chatbots can further streamline the guided buying process, speeding processing to improve value and savings across the procure-to-pay lifecycle.

NLP Turns Unstructured Data into Strategic Decisions

Like many other digital transformation technologies, natural language processing hasn’t yet reached its full potential.

But given its ability to reduce risk, improve operational efficiency, and drive innovation with insights harvested from unstructured data, it’s certainly off to a promising start.

Companies who invest in NLP solutions today are setting the stage for better data management, process optimization, and competitive performance in the years to come.

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