Among the trends in Big Data lately is the increased emphasis on “business intelligence” not only as a means toward data-driven decision making but also as a commodity for increasing revenue. Yet plenty of people remain confused by the very term, business intelligence (BI). How does it differ, if at all, from “data analytics”? And where does data visualization fit in? In forthcoming posts we’ll explore each of these topics, but here’s an overview.

Let’s start with BI.

Think of your favorite spy thriller, or war movie. Or think of the “I” in “CIA.” (No, not the Culinary Institute of America, the other CIA.) Spies gather intelligence. Military commanders base their decisions on intelligence; that is, information collected from a broad range of sources, then organized and analyzed. It’s a fair analogy to business intelligence. As Wikipedia puts it, BI “comprises the strategies and technologies used by enterprises for the data analysis of business information.”[1]

The military-intelligence analogy is also helpful because of its distinguishes between strategic and tactical planning, both driven by data. Strategic objectives are framed within mid- to long-term time horizons, typically measured in years. Tactical objectives fall within short- and medium-term time frames, usually measured in weeks or months, and support the larger strategic objectives. Strategy and tactics are operationalized by way of day-to-day decision making.

Now break it down.

Data analytics, or data analysis, examines the elements or structure of collected data to enable interpretation. To allow for that, data must be “cleansed” or “scrubbed” to make it usable. That could mean removing data that are incorrect or duplicated, amending data that are incomplete, or mending data that are badly formatted. DA and BI are often used interchangeably. But true analysis is a process of transforming data to derive useful information that supports decision-making. That’s it in a nutshell: Intelligence is information made useful through analysis.

Think of BI and DA as Pat Roche does. He’s the vice president of engineering at Magnitude Software. He says, “Business Intelligence is needed to run the business while Business Analytics are needed to change the business.”[2]

But the reality is that even clean data can be opaque, especially when you have oceans of it. So how to make sense of it?

Make it simple and accessible.

In the news business, visualizations that explain cryptic concepts or statistics are called infographics. Data visualization, like infographics, involves the creation and study of schematic representations of quantifiable information. It can be viewed as a modern equivalent of visual communication.[3]  Among the giants of journalism infographics is Megan Jaegerman, who worked for the New York Times in 1990s. You can see some of her work featured in a book about data visualization by Edward Tufte, statistician, artist and Yale professor emeritus, known as the “da Vinci of data.” His book, The Visual Display of Quantitative Information, is a classic in the field. It demonstrates how good data visualization can be artful.

(BTW, if you doubt that data visualization can be entertaining too, check out—and decipher—some of the worst data visualizations ever or these 16 Useless Infographics.)

Numerous ways of visualizing data exist, ranging from the simplest Excel bar chart to state-of-the-art commercial visualization solutions like the highly regarded Tableau. To be effective, every form of visualization should be customizable to its intended function as these dashboards are, powered by SAS for the University of North Carolina system.

The BI landscape

What’s new in business intelligence is the trend toward monetizing it. Gartner’s 2017 Magic Quadrant for Business Intelligence and Analytics Platforms lays out the BI landscape, fittingly enough, in an accessible infographic. The MQ divides the space among niche players and visionaries, challengers (currently an empty quadrant) and leaders (perennially Tableau and Microsoft, with Qlik hot on their heels). Updated annually, the MQ often stirs controversy, but it remains a useful reference. (Here’s the full 2017 report.)

Source: Gartner

As noted in this blog post from Sisense (themselves a “visionary” company in the MQ), two types of business-analysis and intelligence tools exist: “End-to-end solutions and ones that are merely front-end. An end-to-end solution is made up of a platform backend, basically, the tools and algorithms that handle preparing all the data, and a front-end that creates data visualizations and dashboard reporting.”

The most succinct lesson in BI could be this, from Entrepreneur: “Data-driven decision-making helps improve potential outcomes by reducing speculation in favor of analysis.”

The precondition for speculation is uncertainty, and no one enjoys either one. You can reduce speculation within your organization by speaking with us at tekMountain. We’re a nationally-recognized entrepreneurial and innovation center that recognizes that the BI solution appropriate to your needs depends on the size and maturity of your organization and how you see it being realized.

 

This blog was produced by the tekMountain Team of Sean AhlumAmanda SipesZach Cioffi and Beth Roddy with lead writer Bill DiNome.

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