Where to begin on subjects like business intelligence (BI) and predictive analytics for better performance in management accounting? I remember only two years ago when I was doing a presentation on BI at a regional IMA® (Institute of Management Accountants) conference, people were asking me “Who makes business intelligence?” as though it were a software product I was trying to sell.

We have come a long way. People want to know how they can do BI and predictive analytics better. But awareness and interest are only the first steps to execution. To be effective, we need deeper knowledge. There’s a lot of information out there, but, unfortunately, content that’s easy to understand is typically sponsored by a software vendor or consulting firm, and everything else seems to be impossibly dense and written by those with a Ph.D.

To help you go from interested party to analytics rock star, let’s lay out a general philosophy and review some use cases. In the future, we’ll examine common pitfalls as well as the tools, tactics, and, most important, talents required to move from one stage of analytics to the next.


To understand why BI and analytics are important to management accountants, first we have to understand what BI and analytics are. In fact, using the word “analytics” is a bit confusing because all that really means is the creation of information from knowledge. It’s helpful to understand that there are at least three types of analytics: descriptive, diagnostic, and predictive.

Business intelligence is the use of multiple sources of data, particularly data external to your organization and related to your competitive environment, to enhance the profitability of your business. In simple terms, it’s combining different data sources so you have a better idea of why your business is in its current state. In other words, it’s a diagnosis.

Predictive analytics is taking things a bit further than BI by telling you what state the business will be in given the current state of internal and external factors. It takes the data acquired in BI and uses statistical modeling to give a probabilistic model of the future. In some organizations, this is called quantitative analysis or financial modeling, but predictive analytics typically is now called “data science.”

Contrast this to reporting and its exclusive focus on data that’s internal to the business to provide a view of historical performance. In other words, it’s descriptive of performance and leaves any insights to the reader. Financial reporting, even advanced metrics such as free cash flow, are descriptive analytics. They only state what happened with no indication as to the reason.

Finally, the goal of any analytics initiative is to effect change in your organization. This is called “prescriptive analytics,” which means synthesizing all of the above analytics to create suggested courses of action. While prescriptive analytics is used to a degree at all levels of analytic maturity, it’s only based on speculation (i.e., “We believe changing this process will result in the following…”).

But prescriptive analytics isn’t possible without completing all the other steps of analytics maturing. In order to provide real, statistically validated guidance, advanced capability in predictive analytics is a prerequisite. Unlike the other steps in analytics maturity, prescriptive analytics is a transformative and holistic company initiative; accordingly, it isn’t tied to a particular department or specialization.

As your organization becomes more aware of its data and the opportunities for profitability and growth presented by the data, four levels of analytics (descriptive, diagnostic, predictive, and prescriptive) are like good, better, best, and even better than best in actionable recommendations.


Growth in descriptive analytics, or reporting, hardly needs justification, but small and medium entities (SMEs) tend to not see the value of streamlined reporting. Yet SMEs typically already have the tools and talents in house to answer some common business questions via descriptive analytics. Consider these situations and how diagnostic or descriptive analytics boosted performance.

Here’s an example from personal experience. A small manufacturing firm has a sales team of approximately six individuals. As background, the management accounting team had recently undertaken a massive standard costing initiative. With standard costing in place, each salesperson now has sales targets based on total sales value and sales profitability.

In the past, they could only review their sales targets once a week after a long and manual process where data was exported into flat files and copied into a spreadsheet. The spreadsheet was formatted and filtered for each salesperson. The whole process required about eight work hours. Adding profit targets would increase the process by at least two additional hours.

The purpose of profit-based sales targets is to help salespeople understand the impact the proposed prices have on the overall business. Their standard cost model could now allow them to forecast the profit of potential sales, which, in theory, would shape their sales strategy. Unfortunately, the sales director knew the salespeople wouldn’t appreciate the revenue vs. profit relationship with weekly reporting. Once a week wasn’t good enough; the salespeople needed it once an hour—or better.

After evaluating the current process of report creation, they found they could connect their spreadsheet program directly to the finance database via ODBC (open database connection) with built-in filters and access security for each salesperson. Within the spreadsheet application, a series of pivots and formatting rules were created. Finally, the spreadsheet was locked down, and a specific version was distributed to each salesperson.

All told, the process refactoring required about 80 hours of work. As a result, the salespeople were able to instantly access up-to-date performance metrics. Furthermore, the finance and accounting department had a net gain of 10 work hours per week (or one quarter of a full-time employee).

Sticking with the sales and marketing theme, a regional business services sales office needs to hit a 20% higher sales goal this year. Marketing information is available, but the managing director and sales manager decisions need to be made on market segments to pursue. Using the existing data on target company revenue and employee size, geography, and industries to filter the data produces a reasonable list of companies that are more likely to be open to doing business.

The source includes contact information such as names, titles, email addresses, and phone numbers. The key tools utilized in this process were iSell by OneSource and Excel to download the prospects and all their available data fields. Aggregating revenues by industry in Excel pivot tables enabled the office to pinpoint the highest potential markets for the firm, increasing effectiveness and efficiency of promotional efforts in just a few hours.


As shown by our sales use cases, growth in descriptive analytics alone can save time and make money. The use cases also show how analytics maturity requires communication between departments (i.e., accounting, sales, IT) and process engineering.

With the right analytics framework in your organization—as well as inspiration, tools, and talent—you can initiate analytics growth within your own organization. Over the next few months, we will be writing a series of articles that look at specific examples of analytics maturity in different industries and the steps required in moving from descriptive, diagnostic, and predictive to prescriptive analytics.

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