De-Conflicting Terminology of Analytics

  • By Bryan Lapidus
  • Published: 6/14/2022


In attaining an analytics culture, the mindset an organization embraces must be aligned with its attitude toward decision-making.

These are the four levels of analytics intelligence: supporting decisions requires data, contributing to decisions requires analysis, influencing requires insight, and impact requires deep analytics, which increasingly requires artificial intelligence.

In a recent episode of the “AFP Conversations” podcast, I sat down with Larry Maisel, one of the authors of “AI-Enabled Analytics for Business,” to talk about the potential of finance to becoming an analytics powerhouse. Among other things, Larry teased out differences in terms that confuse the discussion. Here’s what we learned …

Analysis versus analytics

Analysis is arithmetic on data — add, subtract, multiply and divide — whereas analytics is mathematics on data, the study of relationships examined through higher level math, such as geometry, calculus, statistical correlations, etc.

AFP Conversations LogoCheck out the full discussion on the AFP Conversations Podcast.
Listen here.

The arithmetic information is useful in explaining what happened in hindsight, such as the variance on a profit and loss statement that might be expressed in dollars and a percent. The increased computing and mathematical power allow analytics to open up predictive and proscriptive estimates.  

Information versus insight

Keeping the above distinction in mind, analysis tends toward comparative or descriptive information.

Insight is different, it is additive to a decision because, when it is uncovered, will affect decisions. Insights are predictions of likely future events or outcomes based on mathematics and will have more of an impact on the decision. Insight is like closing the barn door before the horses get out.

AI versus BI

While artificial intelligence (AI) and business intelligence (BI) are umbrella terms for many things, AI is a machine-learning set of rules that apply mathematics to the data, whereas BI is primarily a way to visualize, represent and present the data that you have.

Say you were looking at a graph where one line shows the target profit – some numbers are above the line, and some are below it. BI tools have the capability to show you that graph, but they can’t tell you why some are above the line, and some are below the line. AI can provide insight into the why.

AI versus IT

AI is the content, and information technology (IT) is the enabling capability you apply to get the results. Think of it this way: AI is the set of codes and algorithms that run on a computer that allows you think and do the work, but the computer, fiber optic cables with flowing data, and the electricity is IT.

Clever versus curious

The clever versus the curious is best explained with an analogy. Let’s say “Bobby in the basement” is a brilliant analyst asked by Mr. President to solve a question. As the clever person, Bobby will collect data in a spreadsheet, construct an answer and give it to Mr. President. We call that the clever because he knows how to do it in a tool, let’s assume a spreadsheet.

If we ask Sally, who is curious, the same question, she will make sure that the question is the right question, may consider other tools, will review competing data sets, and may even ask others for opinions. And then and only then will she proceed to pursue an answer.

Bobby in the basement is clever about the spreadsheet, and will give you what to ask. Sally is curious and wants to make sure it's answering the right question, and then get the data to answer that question — and maybe other questions you didn't even think of asking until you look at the data.

Larry Maisel is president of Decision View, a management consultancy specializing in performance management, data analytics, operational improvement, and IT business management. His past titles have included COO, CFO, controller, vice president of FP&A and senior partner. Maisel previously served in positions with Oracle, KPMG, Gartner and IBM, and has held professorships at Columbia Business School and Wharton. Maisel has also been an AFP conference speaker and has developed and delivered AFP's course on AI-enabled analytics.

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