I stood on stage at our Data and Analytics Summit in Dallas [last week] to talk about data hubs, and said I was very excited to attend and speak with everyone.

Here’s why.

The OECD reports (see Frontier Firms, Technology Diffusion and Public Policy: Micro Evidence from OECD Countries) that national frontier firms are those that are more productive within a country; global frontier are those that are the most productive across countries. Organizations that improve productivity (i.e., produce/serve more with less) do so by effective capital deepening, that is, increasing spend on things like information and technology to improve their business capabilities.

Data and analytics is a critical part of information and technology that is central to digital business and has been a top CIO investment priority for 12 of the last 13 years. As you can tell from the record breaking attendance at our Data and Analytics Summits around the globe (over 3,800 folks in Dallas last week), interest in D&A is at record levels. But what is D&A?

At the current time the investments across the overall D&A platform (see Build Your Digital Business Platform Around Data and Analytics) range across three pillars or discrete platforms*. Two of which are well defined but still evolving and innovating. These two are:

  • Analytics, BI, Data Science platforms
  • Data Management platforms

The third is not yet well defined and is evolving rapidly. It is:

  • Data and analytics governance platforms

It is the developments related to this third platform which excites me so. Nodding to the OECD research and the additional insight that explores how innovations evolve and diffuse an industry, and how value and productivity accrues over time (sometimes a long time), it is the idea that enough of the starts have aligned in the area of data and analytics governance that suggests we are a crossroads or pivotal moment.

Conceptually there are four conditions that need to align at any one time for a productivity-inducing innovation to become massively applicable and usable by many firms. I identified them recently in Forecasting The Next IT-Driven Productivity Take-Off. They are:

  • Complementary innovation (no the original innovation)
  • Sufficient investment in the original and complementary innovation
  • Workforce skills
  • Management capability

Master data management is a very specific innovation that emerged some years ago. It was not overly effective – but it did start the ball moving that implied that firms need to externalize data from business applications if they seek to govern it effectively. But MDM tended to become just another monolithic systems, technically and organizationally. Something else was needed.

Over the last 10 years, and more clearly in the last three or four, a number of items have emerged or crystallized that suggest we have reached that tipping point where the stars are aligning; the four conditions for “take off” are forming up. This is what excites me.

So which starts are aligning?

  1. Right sized MDM. MDM should never have been about all the data in a business application. A firm’s master data is very, very small data: How Much Master Data is there in the World?
  2. Classifying data and emergence of application data management. We noted some years ago that those firms which were able to separate the governance of master data from the application used by specific apps were able to make significantly more progress with MDM. Comparing Master Data Management (MDM) with Application Data Management (ADM)
  3. Beyond the two extremes of global (i.e., master) data and local (i.e., application) data and its governance, we noted a fluid, dynamic, but complex space in between where various amount of re-use was seen across a large array of applications. This came to be known as “shared data management” but it was not bounded or as easily scoped as global and local data. But the concept made perfect sense and emerged a whole back – known as the three rings of information governance. This technique has since been applied to content governance, records governance, and analytic governance. The technique scales and works for departments, applications and business units. See Design an Effective Information Governance Strategy.
  4. The work itself, related to governing data and analytics, was another dimension that touched on workforce skills and management’s ability to organize more effectively. It was only by being explicit here with the three forms of work, and for establishing and recognizing where this work is most effective across the firm, that the operational side of data and analytics became adaptive and effective. See A Day in the Life of an Information Steward. This was expressed in the realization that the work of governing data consists of three aspects:
    1. Setting policy
    2. Enforcing policy (automatized and business-led resolution work)
    3. Data maintenance (the actual work of correcting the data or policy itself)
  5. Most recently discussions shifted from discrete zones such as master data and/or application data to a broader, more flexible, and more connected and holistic strategy – that we call (for better or worse) a data hub strategy. This finally connects the work of governance to integration (application and data) as well as organizational models. See New Research: Implementing the Data Hub: Architecture and Technology Choices.
  6. The final piece, the most important piece, comes last: How to connect data to outcome since this helps determine where to start (the most important outcome) and how to scale slowly (prioritizing) one outcome at a time. This is what makes the whole thing work for the business, by the business. See What’s in Your Data and Analytics Strategy?.

So these are the stars that aligned – as I noted on stage - and why I am so excited to be at the summit and share our research with everyone. I truly believe that when we master these people, process, data and technology skills and capabilities, an effective data and analytics governance platform will emerge.

One wrinkle that will slow organizations down concerns policy types. My colleague Ted Friedman identified some years ago (see Information Governance Requires a Comprehensive and Interrelated Range of Policy Types) the need for holistic visibility across eight overall data and analytics governance policies. The technology vendors remained silo’d; business organizations are silo’d today and slowing connecting the dots. This may be the last star needed to make take-off a long sustaining game.

As it stands, great success is now without our reach. But until we look at unifying and integrating all policies at one, or at least more than one at a time, the take-off and success will lurch forward one victory at a time.

* These so-called platforms are not just technology platforms – they represent people, process, data and technology. So an overall unified strategy and program model is required to align, link and leverage them. We call that the seven building blocks of data and analytics.

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