Big Data – From Descriptive to Prescriptive

Big Data is the catch phrase of the day.  Everyone has heard of it, most have a vague idea what it means, some have a clear grasp of what it can really do, and few can execute it effectively.  I’ve been doing a lot of reading on Big Data and there is certainly no lack of resources on the topic.  As I read through many reports, white papers, press releases, magazine articles and company presentations (all available via a quick Google search) it occurred to me that visually communicating the value of Big Data is challenging because of the need to convey different concepts simultaneously.  The most popular category by far are plot charts on an X-Y axis.  These charts plot analytical complexity against some sort of business value measurement in a positive correlation that looks entertainingly similar to human evolution charts we’ve all seen, with man becoming more upright and intelligent with time.

human-evolution

Less popular, but also useful, are a bulls-eyes, Venn diagrams and an stacked area triangles. Regardless of graphic representation, they all follow the progression from What Happen (descriptive analytics), Why Did It Happen (correlation analytics), What Will Happen Next (predictive analytics), and What Should I Do About It (prescriptive analytics).  Which one do you prefer?

SAP – Analytics Maturity by Competitive Advantage

Big Data

Source: This graphic is from SAP, a leading provider of business intelligence and predictive analytics software.

Why We Like It: This chart is unique in that it goes all the way back to the beginning when data is first created and gathered in raw form.  So much of the resources needed to develop prescriptive analytics takes place in the very early stages of the process, and it’s nice that this graphic gives it a mention.  The overwhelming majority of data available for analysis does make it to the final predictive/prescriptive model.  If each circle represented the amount of actual data at that stage, the raw data circle (and cleaned data circle) would dwarf all the others, so thank you SAP for giving data its due.

Gartner – Analytical Difficulty by Value

GartnerAnalytics

Source: This graphic is from Gartner Group, a leading information technology research and advisory company.

Why We Like It: It’s clean and simple and gets the job done, with a simple positive linear correlation between analytical difficulty and value.  No longer a nice to have indeed…

Strategy At Risk – Business Value by Analytical Complexity

4Pedictive-Analysis-300x284StrategyAtRisk

Source: This graphic is from Strategy At Risk, a predictive analytics consultancy.

Why We Like It: This graphic shows something many others do not, which is “what is happening now.”  They refer to is as monitoring or “dashboard/scorecards.” One of the things Big Data proponents frequently overlook is the business value of filtered real time data.  Yes, large scale cross-functional company-wide prescriptive analytics projects can have significant impacts on the bottom line, but marketing departments make daily decisions based on the real time data coming from sources such as the websites, social media, digital advertising, apps, videos, etc.  Big Data is a significant strategic weapon but real time monitoring has become the tactical weapon of choice and should not be minimized.  This chart does a nice job of blending it all together.

IBM – Extracting Intelligence Bulls-Eye6IBMForum1

Source: This graphic is from IBM.

Why We Like It: It’s simple and different.

The Ironside Group Quantifiable ROI

63Quantifiable ROI

Source: This graphic is from The Ironside Group, a Massachusetts company that provides business intelligence, data warehousing, predictive analytics, and enterprise planning.

Why We Like It: It is a simple and original use of a Venn Diagram that’s quickly discernible. This chart is an quick way of showing how Big Data ROI increases as a company progresses from descriptive analytics to predictive analytics. I believe some might dispute the quantifiable ROI percentages (predictive analytics being too low), but it’s nice to see that they assigned a quantifiable ROI to descriptive analytics.  Industry estimates are that 80% of all company-wide analytics are descriptive.  With a quantifiable ROI of 80% that’s a lot of business value being created with descriptive analytics alone.  When combined with prescriptive analytics, it’s an impressive 1-2 punch to the competition.

James Sesil’s Stacked Area Triangle

Print

Source: Applying Advanced Analytics to HR Management Decisions: Methods for Selection, Developing Incentives, and Improving Collaboration by By James C. Sesil

Why We Like It: The first thing to like about this graphic is that it’s extremely simple. But the thing we really like is that it’s a little more accurate representation of how often each type of analytics is done by most companies. It shows that descriptive analytics is not only at the base of the analytics food chain, but also represents the lion’s share of analytics applied by companies today.

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One comment on “Big Data – From Descriptive to Prescriptive
  1. Cameron Cramer says:

    If you have favorite Big Data graphics, please let us know?

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