Governance: Now That Is a Management Touchstone for MBA Experts

February 27, 2018

I read “Unlocking the Power of Today’s Big Data through Governance.” Quite a lab grown meat wiener that “unlocking,” “power,” “Big Data,” and “governance” statement is that headline. Yep, IDG, the outfit which cannot govern its own agreements with the people the firm pays to make the IDG experts so darned smart. (For the back-story, check out this snapshot of governance in action.)

Image result for wishful thinking

What’s the write up with the magical word governance about?

Instead of defining “governance,” I learn what governance is not; to wit:

Data governance isn’t about creating a veil of secrecy around data

I have zero idea what this means. Back to the word “governance.” Google and Wikipedia define the word in this way:

Governance is all of the processes of governing, whether undertaken by a government, market or network, whether over a family, tribe, formal or informal organization or territory and whether through the laws, norms, power or language of an organized society.

Okay, governing. What’s governing mean? Back to the GOOG. Here’s one definition which seems germane to MBA speakers:

control, influence, or regulate (a person, action, or course of events).

The essay drags out the chestnuts about lots of information. Okay, I think I understand because Big Data has been touted for many years. Now, mercifully I assert, the drums are beating out the rhythm of “artificial intelligence” and its handmaiden “algos,” the terrific abbreviation some of the marketing jazzed engineers have coined. Right, algos, bro.

What’s the control angle for Big Data? The answer is that “data governance” will deal with:

  • Shoddy data
  • Incomplete data
  • Off point data
  • Made up data
  • Incorrect data

Presumably these thorny issues will yield to a manager who knows the ins and outs of governance. I suppose there are many experts in governance; for example, the fine folks who have tamed content chaos with their “governance” of content management systems or the archiving mavens who have figured out what to do with tweets at the Library of Congress. (The answer is to not archive tweets. There you go. Governance in action.)

The article suggests a “definitive data governance program.” Right. If one cannot deal with backfiles, changes to the data in the archives, and the new flows of data—how does one do the “definitive governance program” thing? The answer is, “Generate MBA baloney and toss around buzzwords.” Check out the list of tasks which, in my experience, are difficult to accomplish when resources are available and the organization has a can-do attitude:

  • Document data and show its lineage.
  • Set appropriate policies, and enforce them.
  • Address roles and responsibilities of everyone who touches that data, encouraging collaboration across the organization.

These types of tasks are the life blood of consultants who purport to have the ability to deliver the near impossible.

What happens if we apply the guidelines in the Governance article to the data sets listed in “Big Data And AI: 30 Amazing (And Free) Public Data Sources For 2018.” In my experience, the cost of normalizing the data is likely to be out of reach for most organizations. Once these data have been put in a form that permits machine-based quality checks, the organization has to figure out what questions the data can answer with a reasonable level of confidence. Getting over these hurdles then raises the question, “Are these data up to date?” And, if the data are stale, “How do we update the information?” There are, of course, other questions, but the flag waving about governance operates at an Ivory Tower level. Dealing with data takes place with one’s knees on the ground and one’s hands in the dirt. If the public data sources are not pulling the hay wagon, what’s the time, cost, and complexity of obtaining original data sets, validating them, and whipping them into shape for use by an MBA?

You know the answer: “This is not going to happen.”

Here’s a paragraph which I circled in Oscar Mayer wiener pink:

One of the more significant, and exciting, changes in data governance has been the shift in focus to business users. Historically, data has been a technical issue owned by IT and locked within the organization by specific functions and silos. But if data is truly going to be an asset, everyday users—those who need to apply the data in different contexts—must have access and control over it and trust the data. As such, data governance is transforming from a technical tool to a business application. And chief data officers (CDOs) are starting to see the technologies behind data governance as their critical operating environment, in much the same way SAP serves CFOs, and Salesforce supports CROs. It is rare to find an opportunity to build a new system of record for a market.

Let’s look at this low calorie morsel and consider some of its constituent elements. (Have you ever seen wieners being manufactured? Fill in that gap in your education if you have not had the first hand learning experience.)

First, business users want to see a pretty dashboard, click on something that looks interesting in a visualization, and have an answer delivered. Most of the business people I know struggle to understand if the data in their system is accurate and limited expertise to understand the mathematical processes which churn away to display an “answer.”

The reference to SAP is fascinating, but I think of IBM-type systems as somewhat out of step with the more sophisticated tools available to deal with certain data problems. In short, SAP is an artifact of an earlier era, and its lessons, even when understood, have been inadequate in the era of real time data analysis.

Let me be clear: Data governance is a management malarkey. Look closely at organizations which are successful. Peer inside their data environments. When I have looked, I have seen clever solutions to specific problems. The cleverness can create its own set of challenges.

The difference between a Google and a Qwant, a LookingGlass Cyber and IBM i2, or Amazon and Wal-Mart is not Big Data. It is not the textbook definition of “governance.” Success has more to do with effective problem solving on a set of data required by a task. Google sells ads and deals with Big Data to achieve its revenue goals. LookingGlass addresses chat information for a specific case. Amazon recommends products in order to sell more products.

Experts who invoke governance on a broad scale as a management solution are disconnected from the discipline required to identify a problem and deal with data required to solve that problem.

Few organizations can do this with their “content management systems”, their “business intelligence systems,” or their “product information systems.” Why? Talking about a problem is not solving a problem.

Governance is wishful thinking and not something that is delivered by a consultant. Governance is an emergent characteristic of successful problem solving. Governance is not paint; it is not delivered by an MBA and a PowerPoint; it is not a core competency of jargon.

In Harrod’s Creek, governance is getting chicken to the stores in the UK. Whoops. That management governance is not working. So much in modern business does not work very well.

Stephen E Arnold, February 27, 2018


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