Financial Analytics vs. Financial Reporting

Financial analytics and financial reporting – two seemingly similar processes with very different purposes and outcomes. Financial reporting vs analytics does not imply a sporting event, nor does it suggest legal action. In our world of finance, it simply denotes two specific processes conducted in the finance department of most organizations. On the surface, one may conclude that there is no need to discuss financial reporting vs analytics, both processes are one and the same. You use data supplied by your accounting system and perhaps also your budgeting and forecasting software to produce financial reports. These reports are then given to management to review or perhaps used to satisfy external reporting requirements, e.g. government, banking institution and shareholder compliance.

The Differences: Financial Reporting vs. Financial Analytics

What is financial reporting?

Financial reporting, such as at period end (month, quarter, year), is a process where consolidated or non-consolidated data is used to construct statements required for management or legal review and distribution to shareholders, government entities (e.g., SEC) or lenders and other persons who have a need to receive this information. The data is extracted from the ERP or accounting GL and is either used with a built-in report writer or exported to an external tool, such as a spreadsheet (not a good or reliable choice but used by many companies). The final reports and statements are constructed, reviewed, edited, formatted and finally published and distributed. No company can escape producing financial statements and other compliance reports, but the distribution of these reports greatly varies from organization to organization.

What is financial analytics?

Financial analytics, on the other hand, is a process that is not regulated nor is it mandatory and many smaller organizations simply ignore it, either by choice or ignorance. But common sense and experience dictate otherwise: Like many other business and work-related processes, just because you don’t have to do it doesn’t mean you shouldn’t do it. According to the Business Dictionary: “Analytics often involves studying past historical data to research potential trends, to analyze the effects of certain decisions or events, or to evaluate the performance of a given tool or scenario. The goal of analytics is to improve the business by gaining knowledge which can be used to make improvements or changes. ”Another important use of analytics is to compare actual business results with budgets and forecasts and see how actual performance differs from its stated budget. This is useful in making decisions, realigning strategic and operational plans with new realities and making sure the business continues to function as envisioned and planned. So why do many smaller organizations fail to recognize the importance of financial analytics? There’s nothing mysterious or complex about the fundamental approach to regularly using analytics. You can certainly define what you want to see and at what intervals and to whom the results should be reported. Where it becomes complex and often impossible for many organizations is the process itself, especially after extracting all available and relevant data.

All That Data for Financial Reporting and Analytics

Nowadays, with computerized accounting functions, capturing transactional data is simple and automated. The process, however, usually generates vast amounts of information that’s stored in the application’s database and can be archived indefinitely. Here’s a typical example of data that many ERPs (and even low-end accounting software) can capture: A customer invoice is generated. The invoice lists on each of its lines a product or service. The product data is automatically retrieved from the product (or inventory) master file and may have several or more attributes: Product Code (Item ID), Product Type (e.g., Made-In, purchased), Product Group (e.g., Defense, Commercial), Product Category (e.g., metric fasteners, U.S. fasteners), or perhaps a lower-level classification of the item, such as Product Class (e.g., Phillips Flat Head, Slotted Oval Head, Phillips Round Head, etc.).Customers, as captured in the Customer Master File, have their own attributes such as: Customer Number, Customer Type, Customer Class, Customer Category, Customer Region, Customer Territory, Sales Person and more. In this example, a single invoice line will capture twelve attributes plus the date and time the invoice was recorded, the GL accounts the revenue and cost were recorded to, the inventory control account, the AR control account, the GL source category and perhaps other GL attributes for a total of at least eighteen (18) attributes (or data dimensions).The system database will retain all this information for however long you want past the current fiscal year. It’ll amount to a lot of data, but given the low cost of  electronic storage space, the data can and should be archived indefinitely. Smaller organizations certainly realize the data is there, assuming all these common data dimensions were set up and standing records (Customer Master Records and Inventory Master Records) were properly created, but often are unable to extract the needed intelligence from these databases, either due to lack of qualified staff, management direction or technology-based tools. In the next part of this article I’ll examine what can be done with the available data and how even smaller companies can leverage certain tools to perform in-depth analytics with little effort and cost.

So What Do You Do with That Data?

In the first part of this article we saw the distinct differences between reporting and analytics, the purposes they serve and the large amounts of data used in both processes. So what do you do with this data? How do you use this data to support financial reporting vs analytics in your organization? First, GL data is used to construct compliance and other financial statements, reports and schedules. Transactional data and other business intelligence aren’t used, nor are they needed to construct financial statements. The assumption here is that all required account reconciliations have been performed and needed adjusting or reclassifying entries have been made. All that is needed is GL account balances and activities. A single entity set of financial statements is certainly easier to produce than consolidated financial statements, but both fundamentally use data from the GL, with consolidated financial statements using the data residing in the GLs of the entities participating in the consolidation. Now, for the financial analytics, assuming you can easily extract data from your ERP and budgeting/planning software databases and have the proper analysis tools, you can create specific reports that can be manipulated to reveal data trends and unusual variances (period over period, actual vs. budget, etc.). You can analyze your customers’ buying habits, product performance over time, the performance of certain product classes or categories, suppliers’ performance, job and project costing and a lot more.

Critical Requirements of Financial Analysis Tools:

Whether you are carrying out financial reporting or financial analytics, the tool you choose will go a long way in making the process easy and in some cases even possible. Below are critical requirements to check before choosing any FP&A tool:

1. Ease of data extraction. A direct interface between your GL and the financial analytics software is ideal and will transfer the needed data accurately and reliably and at the specified time interval. Some systems allow the transfer of GL transactional data in addition to account balances or account activities within the specified period or period ranges. Similarly, a direct interface to other systems (e.g., CRM, inventory or warehouse management, etc.) can also be established and maintained.
2. Ability to create multidimensional versions of the extracted ERP, CRM, and budgeting and forecasting solution data. Each authorized analytics user should be able to create their own data cubes (e.g., OLAP Cubes like MS Analysis Services cubes) without assistance from IT. Smaller organizations don’t have large IT departments, and some only have one person in finance who is also the “go-to” IT person. That person may not be fully qualified to deal with complex databases and only expected to perform routine IT tasks.  Larger organizations may take days or weeks before such requests are fulfilled. You want to get the data right when you need it, and every delay will only slow down the decision-making process and may financially hurt the organization.
3. Self-service business intelligence. Like #2 above, the analytics solution must allow all authorized users to independently create their own reports and be able to manipulate the data (i.e., slice and dice) in the analytics tool on their own, on a desktop computer, mobile computer or mobile device. Reliance on IT to generate desired reports is no longer a viable option. Users must be empowered to generate their own reports and select the right data to answer specific questions as they arise.
4. Ability of each analytics user to format reports and distribute them to report users when data is available and answers to questions can be given. For example, if you discover a large variance in an expense account, period over period, you should be able to quickly drill through the summary number and get to the detailed transactions that make up that number. A detailed report can immediately be sent to the right person with specific questions for them to answer. Additional, follow-up reports can be sent to further clarify the questions and assist in arriving at answers. As in other areas of analytics, IT should never be involved in this process.
5. I’ve worked with complex enterprise solutions where finance staff are at the mercy of IT. First, requests for reports must be authorized, then queued for execution.  Often, the resulting report, arriving days and weeks following the request, is not exactly what the user requested and often, the process must be repeated one or more times. The delays can be significant, resulting in management not getting critical pieces of information needed in the decision-making process.

I hope by now you see the distinction between reporting and financial analytics clearly. Both processes start with much of the same fundamental data, residing in the ERP or accounting software database, but their purposes and results are markedly different. While reporting is often mandatory, financial analytics isn’t but should become a routine process, required by managements of all organizations, regardless of industry and size.

‍Alan Hart, MBA, is Principal Consultant at Pacific Shine Group in Portland, Oregon, with responsibility for client business development and hands-on client project implementations. Prior to starting Pacific Shine Group, he worked in various executive accounting and finance positions with technology and growth companies. Notable is his 18 years in the hi-tech manufacturing industry where he served as Controller, Vice President of Finance and CFO of several privately as well as publicly held companies in the Hi-Tech industry, such as Hybrid Arts, Inc., Hamilton Bay Associates and Syncronys Software.  In his role in management consulting, Alan has worked in diverse industries and with a variety of clients, including fortune 1000 companies such as Boeing, Delta Airlines, Intel, Wyndham Worldwide and others, as well as many mid-market organizations such as Guitar Center, Ducommun AeroStructures, Cypress Semiconductor, TriQuint Semiconductor and others.