This, the fourth in a series of four articles, is about the definition of analytics to resolve the confusion vendors create with their claims about the capabilities of their tools. Here, we’ll discuss analytics, as many software vendors claim this capability.
The information in the article and more will be covered at JPK Group’s 2019 Analytics Academy. Click here for more information.
What is analytics? Broadly, as we use it in the context of business, it’s the application of mathematics and statistics on data for analysis, forecasts, and predictions. Of note is the word mathematics and, to be a stickler, arithmetic is not mathematics. The importance of this distinction will reveal itself shortly.
The toolbox for Finance to do Reporting and Analytics can be split into four categories of Reporting, Visualization, Desktop Statistical and Analytics tools. It is important to emphasize that Analytics starts with Visualization, but that Reporting is not Analytics. The more analytics intelligence Finance wants, the higher Finance needs to climb the arrow in the figure below.
Reporting Tools vs. Analytics
BI and EPM/CPM tools do multi-dimensional reporting. Valuable for consolidated financials. However, this activity primarily employs arithmetic (add, subtract, multiply, divide). For example, YoY variance, variance to budget, dimensional consolidation, etc. Therefore, these tools are not analytics tools.
Vendors claim analytics capability because, they say, you can download or program a mathematical formula into the software that can then be applied on the data . . . BUT this is cumbersome, requires programming, someone skilled to know the mathematics and how to apply it multi-dimensionally, and greatly impacts system performance. These difficulties significantly restrict the analytical adventures by these tools.
Data Visualization Tools vs. Analytics
Visualization primarily shows trends – nothing mathematical about that. However, it is also used in dashboards, and these can have analytical applications. For example, in the image below, the doughnut chart on the left compares the current month Distributor Unit Sales for the Monitor 19 product at the distributor Fry’s, in its Seattle store to its KPI, then the arrow beneath is a statistical predictor of the direction of the trend for the future next month. Dashboards can have good analytical capabilities. To gain an analytical output, some visualization tools require IT/consultant support to implement analytics and others less so.
Analytics vendors provide, you guessed, analytics software. There are three classes of these vendors (1) desktop statistical, (2) enterprise, and (3) platform. We separate the three classes into two groups – Desktop Statistical tools and Analytics tools. Desktop Statistical works well with small dataset whereas Analytics tools operates with Big Data.
Desktop Statistical Tools vs Analytics
The entry level to more advanced analytics tools are Desktop statistical tools. These tools are often add-inn to excel and use small data sets and require mathematical and statistical knowledge to properly understand. These tools are powerful for high level analytical insight like correlations, clusters and some even have simple predictive algorithms included. Visualization capabilities are very limited in these tools so data needs to be visualized through other tools.
Analytics Tools vs. Analytics
Enterprise tools are for large multi-source data and can have many users. These tools often require data scientists and programming. Platform tools are enterprise cloud platforms with a collection of visualization, formula, and ML libraries and functions. These require data scientists, programmers, consultants, and application developers to deliver a solution. Analytics tools have a range of visualization, like that above, that can be very powerful including cluster charts, decision trees, etc.
Finance vs Analytics
Analytics are very important because they can give an unbiased prediction. They tell you that which you do not know by creating data that you do not have. For example, in the image above, the arrow on the left makes a prediction from applying a statistic on the Distributor Unit Sales data. The analytics are unbiased and 9 of 10 times correct. Thus, better decisions can be made with these unbiased predictions.
As mentioned, most of the analytics vendors provide tools where Finance needs a data scientist to link databases, to build and operate the application, and even to interpret the analytical outcome. As such, these systems often have no value, even for the savvier Finance professionals. For Finance to get value out of analytics it either need to settle with a Desktop Statistical tool or use one of few enterprise systems that is built for Finance.
For Finance to be able to operate an enterprise analytics tool, it is preferred not to have to rely on consultants or data scientists. As important, the tool needs to be as intuitive to use as Excel with analytics to be easily and quickly applied, as assisted with built-in AI to automatically trend, correlate, and predict. In my opinion there are very few systems on the markets to meet these Finance criteria’s – LightZ™ from Aurora Predictions is one of the few which places Finance in control of their analytics.
If you are interested in this topic, then join us at the Analytics Academy in San Diego from September 18-19th 2019.
Click here for more information and to register!
This article is a collaboration between Robert J Zwerling and Jesper H Sorensen from the organization Finance Analytics Institute (www.fainstitute.com) and is an excerpt from their book, Implementing an Analytics Culture for Data Driven Decisions – A Manifesto for Next Generation Finance. Robert and Jesper are the content creators behind the Analytics Academy and will teaching at the Academy in September!
Copyright 2019 Finance Analytics Institute, Robert J Zwerling & Jesper H Sorensen. All rights reserved. No part of this document may be reproduced without this copyright notice.