Why MUST Enterprises Continually Grow Their Corporate IQ? And How?
(Part One of Three)
“As the well-documented ‘data deluge’ deepens, many executives have shifted from feeling overwhelmed (60% say they “have more information than we can effectively use”) to recognizing that the smartest organizations are already capitalizing on increased information richness and analytics to gain competitive advantage”. - Analytics: The New Path to Value, MIT Sloan Management Review
Business Transformation Into Intelligent Enterprise
The challenge to increase corporate IQ, to transform business organizations into smart enterprises, is becoming more acute. There is a growing need to stay competitive, continually improve efficiency, and ensure sustainability. This need drives businesses towards achieving and improving on their status as intelligent enterprises. Various industries feel this need; and oil and gas companies are, perhaps, among those most in need of such a transformation.
What is the single most important indicator of enterprise intelligence? The result of a 2010 study collaboratively undertaken by the MIT Sloan Business Journal and the IBM Institute for Business Value gives the answer: insights. Using this yardstick we may adjudge an enterprise as intelligent ( or “smart” ) to the extent that it is able to quickly and reliably produce actionable insights from the ever-deepening “data deluge” confronting modern businesses.
And there lies the irony: precisely at this day and age (the “Information Age”) which is characterized by the proliferation of all sorts of data (hence, “data deluge“) many organizations, big and small, face the growing problem of disproportionately low turnout of actionable insights from massive data buildup (hence, “insights drought”).
“Water, water, every where,
“And all the boards did shrink;
“Water, water, every where,
“Nor any drop to drink”.
Hindsight, Foresight, Insight
Since insights are so pivotal to enterprise intelligence let’s see how we may better understand what they are. To begin with, a person (human or corporate) is said to have at least three “senses of sight”: hindsight, foresight, and insight. A great discussion on the subject is given here, a blog posted at Strategy Praxis Institute.
Let me share some key thoughts from this source as quoted below ( with boldfaced fonts style supplied and the quotes, themselves, enlivened by images from various sources as credited):
“Hindsight, insight and foresight are critical skills for strategists.”
“Hindsight is learning from experience in order to build expertise.
“Experience alone does not guarantee expertise.
Author cites the United States Post Office which has “…. over 200 years worth of experience, yet still manages to damage, lose and misdirect numerous items each year..” and compares it to Federal Express “…. that only has been in business since 1973 with an on-time delivery rate of 98% plus.
His conclusion: “ The United States Post Office has more experience than Federal Express, yet one can argue that Federal Express has learned more, and consequently has greater expertise..”
“Hindsight involves critical reflection on one’s experience in order to integrate knowledge gained from the experience with knowledge already possessed.
“The goal is to learn from experience in order to build expertise.
Thus: “Hindsight = Action –> Thought “; OR, “Hindsight is where action informs thought”.
“Both hindsight and experience are actions leading to thoughts.
“The shortcoming is that you must experience something to learn from it.”
“Foresight is the capacity to detect and avoid hazards, assess the consequences of action and envision a desired future.
“Foresight is connected to cognitive development.
Thus: “Foresight = Thought –> Action”; OR, “Foresight is where thought informs action.”
“Sensemaking is a deliberate effort to understand reality.
“It is about connecting the dots and generating inferences, but it also involves identifying what constitutes a dot and how to go about seeking out new dots.
“Sensemaking is only possible when we put hindsight and foresight together to create knowledge that can be used for present decision making.
Thus: “One can [correctly] state that: Insight = Hindsight + Foresight + Sensemaking”.
Pardon me for choosing to take this long circuitous route to draw this conclusion: Insight is the result of the interaction of the three key ingredients of hindsight, foresight and sensemaking.
I needed to resort to this analysis largely because the document which I will repeatedly refer to (i.e., Special Report: The New Intelligent Enterprise, by MIT Sloan Business Journal and IBM Institute for Business Value ) does NOT have a Terms of Reference section. Had it done so I would not have bothered with the foregoing analysis. Apparently the report assumes the reader has a fairly good understanding of what insight is; and I beg to differ.
Another term that the report uses repeatedly (and which requires some defining) is “Analytics”. Note the following definitions:
“A simple definition of analytics is it is ‘….the science of analysis'”.
“ A practical definition, however, would be that analytics is the process of obtaining an optimal or realistic decision based on existing data. Business managers may choose to make decisions based on past experiences or rules of thumb, or there might be other qualitative aspects to decision making; but unless there are data involved in the process, it would not be considered analytics.”[boldfaced fonts and underscoring style supplied].
Key Findings of the Study, Analytics: The New Path to Value.
Following are the most important findings of the collaborative research by MIT and IBM (with boldface fonts style supplied):
“ Top-performing companies are three times more likely than lower performers to be sophisticated users of analytics, and are two times more likely to say that their analytics use is a competitive differentiator.
“ Despite the enormous challenge felt by most organizations to “get the data right,” that’s not what executives name as the key barrier to achieving the competitive advantage that “big data” can offer — the top two barriers are “lack of understanding of how to use analytics to improve the business” and “lack of management bandwidth.
“ Over the next 24 months, executives say they will focus on supplementing standard historical reporting of data with emerging approaches that convert information into scenarios and simulations that make insights easier to understand and to act on”.
Finding number  tells us that top performing companies use analytics a lot. Also, these same top performing companies regard the sophisticated use of analytics as a source of competitive edge.
At this point we may rehash the definition of intelligent enterprise as one that uses analytics in a sophisticated manner to produce actionable insights from the flood of data available to it, which insights lead to wise decisions.
Finding number  informs us that the orthodox view that “getting data right” is the biggest stumbling block to deriving competitive edge from using “big data” simply does not hold water. Instead the study has identified two barriers, namely: [a] lack of analytics know-how and lack of understanding how to apply it to business improvement; and [b] lack of management bandwith, a.k.a. management support or sponsorship to adopt an analytics-driven management approach.
BTW, it doesn’t mean data accuracy and data sufficiency are not important; it does say, though, that these are not the most pressing challenges compared to the two problem areas just enumerated.
Finding number  describes what analytics-driven executives would like to see in the next two years to further evolve the process of deriving and acting on insights: they expect to enrich historical data with new approaches that enable the data users to paint, visualize and build scenarios and simulations. Such scenarios and simulations allow decision-makers to look for the smartest and most realistic decisions under varying situations. This way the taking of quick actions (i.e., actionability) on insights is promoted.
The authors of the study have made a key statement saying, in effect, that spreadsheets, data tables, charts and graphs will always play an important role in drawing insights from the “data-deluge”. However, decision-makers will profit greatly from three-dimensional representations, dynamic scenarios, and simulations that will make information as processed much more easily intelligible to all viewers, particularly the always time-constrained executives.
- – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – -
The third finding also brings to mind two recent blogs by my colleague, Natalie –Bakken Part 1, and A Picture is Worth a Thousand Words – which discuss the analysis and visualization capabilities, among others, of Visage; this is an analytics-based software from a partner company of ours. I will definitely revisit these very timely, analytics issue-related blogs for my upcoming sequels to this post.
Other posts with great relevance to data deluge and how to cope with it are: Who’s Dat Baby, by esteemed Compadre Sean (whose name I discovered through the internet is the Irish form of “John”; the name, of course, has the exact Spanish equivalent of “Juan” — pronounced phonetically “Hwahn“); and the two (to-date) very enlightening contributions, To Do List, and The Teardowns , from another distinguished Compadre, Wes Baird. Wes is the latest “enhancement” to the blog team — and I have yet to research his name.
Likewise, I’ll need to revisit previous posts by my other Compadres and Comadres, especially James (or Santiago) and Steve (or Esteban), which speak directly to the subject of data accuracy and timeliness.
- – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – -
At a sequel to this blog I will focus on distinct challenges and opportunities that oil and gas enterprises face in regard to the application of analytics-driven decision-making. For instance, the conclusion that the days of easy and cheap oil are over puts a premium on smart decision-making to avoid or minimize costly errors. A practical example is the requirement for greater precision in horizontal drilling, and in the parallel spacing of injection wells and their twin producing wells; also, greater accuracy is a must in planning and executing multi-stage fracking. I read for example a magazine article on the use of ‘snake wells” in offshore drilling (as pioneered by Shell) which calls for extremely low tolerance for errors as this type of drilling is increasingly dependent on computerized remote controls. Just a sampling of situations which are facilitated by the appropriate use of analytics.
We can come up with an even longer list of challenges and opportunities such enterprises now face, and which — to successfully meet — demands a quantum rise in enterprise brain and not just brawn.
More on this topic next time, with added emphasis on the role of companies, like geoLOGIC, in supporting an enterprise’s push for data-driven, insights-laden, analytics-based decision-making.