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Putting The Cart Before The Horse

Putting the “cart” before the “horse”: why a Data Trust Model should come before data and analytics governance strategy


Most businesses today recognize the opportunity to create digital revenue engines and allocate their budgets accordingly. A recent report by Gartner suggests most Boards of Directors have already moved digital-business-related budgets to business functions to accommodate digital investment beyond the regulatory requirements.

What are the expected business outcomes, ROI, and success rate? Apparently, the vast majority of enterprises are predicted to fail while scaling a digital business unless they take a modern data and analytics governance approach.


What stands in the way of a successful governance strategy

The aforementioned predictive failure rate could be attributed to most organizations being rigid and reactive in shifting data initiatives to business, slow to respond, and missing out on growth opportunities. Centralized, IT-led, D&A governance policies often fail because they do not acknowledge the emerging value of data and analytics as a strategic asset produced within business operations functions.

This is a classic “putting a cart in front of a horse” situation, where the demand comes before the organizational governance framework infrastructure that caters to it. For organizations to efficiently leverage their data and build new use cases on top of it, a “Data Trust Model” should be put in place first.


Data Trust Model as a cornerstone of the enterprise governance framework


A Data Trust Model should have the following  components:

  1. A semantic data model of the entire organization across business units silos, ideally automated and active to be able to reflect the up-to-date state; this is the glossary of outcome-critical data and analytics assets;
  2. Lineage and certification of the assets describing where and how the assets have been created, consumed, certified, and by whom;
  3. End-to-end organizational D&A discovery on top of business glossary and lineage;
  4. Governance processes in place for business, data, and IT teams to leverage effective collaboration and transparency and measure their mutual efforts by business impact.


Once all four of these components are in place, the Data Trust Model becomes a cornerstone of the data and analytics governance effort and the driver for your organization’s digital transformation.


The goal of enterprise governance management frameworks is to facilitate trust


As trust increasingly becomes an issue in our space, we decided to conduct our own research amongst data consumers on the trajectory and trust level in D&A. The results were stunning: 

  1. The data consumers trust their data less than before the data access democratization;
  2. Business data consumers are even more skeptical about the D&A they get;
  3. When the D&A comes from the domain expert they trust, it is considered “certified” and perceived correct by the data consumers.

In the light of all the above, we can conclude that an active data and analytics governance initiative with a facilitated certification process performed by data experts is a critical success factor for digital transformation within organizations. illumex was born to automate the Data Trust Model and bring it to the masses.

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