A few months ago giving a lecture I was asked one of the most uncomfortable but easiest questions to answer since I hung up my consulting suit again, “Moisés, could you tell us what benefits data governance brings?“
My answer, clear and powerful: “In itself, none. Data governance has no value in itself, it is not an objective, it is not an end, it is a means”. And months later I still have the same opinion, which does not detract from my belief that without being a ‘fruit’ in itself, it is essential to sow its seed in the organization (and the sooner the better).
In a previous post, in which we played to relate the 4 key capabilities of digital business according to GARTNER (people, processes, data, technology) as 4 different pillars to achieve operational efficiency, let’s do a little double click on the DATA pillar. Data Governance is not an initiative per se within DATA, it is a program. A program that, as we will discuss at another time, can be broken down into different ‘pieces’ (initiatives, activities, phases) that will capture operational efficiency improvements. It is more a means than an end in itself, it is an enabler of data management improvement, which will bring indirect returns derived both from the improvement of data and from a change of mentality and a cultural reinforcement of data as an asset (which, as such, must be taken care of).
If we approach the issue from a ‘defensive’ point of view, without investing in data governance it becomes very difficult to achieve sustainable and scalable data management over time (not to say impossible). Data management ranges from data registration to data transformation, sharing, use and consumption. An endless number of processes that involve a wide range of actors and interlocutors, with the need for coordination and cooperation, as they depend on each other for their knowledge and information. Data governance seeks to implement a structure of responsibility for the data, making it possible to identify those who are ‘responsible’ for the data, for consultation, approval and support in the different requirements of the data in the performance of their functions in the day-to-day work of the organization. Without data governance, better data management will not be possible, so we will not capture efficiencies through this pillar..
From a more ‘offensive’ point of view (thinking beyond the mere efficiency of current operations), without data governance there will be no data quality (which, as we will also discuss later, is the main KPI to monitor a quantitative improvement of data). And without data quality, there will be no increase in data value (one of the most interesting and topical issues today, as there are still no regulations or standards in place to help quantify data value, but – almost – all the literature* on data quality uses data quality metrics as part of its calculation formulation). But we will also leave this for later (the to-dos are piling up…).
Not long ago, in conversations with a large Spanish manufacturing company, which had as one of its main annual objectives to improve operational efficiency, they told us that they had an ‘initiative to implement data governance’ to achieve efficiencies. Going into the matter, we observed that the expectations (very important to align them, always) were that data governance itself would bring efficiencies (as if it were an end in itself). The approach that was being given was that of a project (not a Program), looking at finding returns in the short term (not in LP) and without considering fundamental implications such as a change of mindset towards the concept of data as an asset or a cultural change towards data accountability. In this case it was the rush (?), the lack of perspective and understanding of how data governance is an enabler, not a value in itself, for the capture of efficiencies of the DATA pillar that prevented an orderly and success-oriented deployment in the long term of a Data Governance Program as the foundation of this pillar and precursor of high-impact initiatives and capture of efficiencies through better data management. We will discuss these in another post.
Indeed, initiating the deployment of Data Governance will bring efficiencies from better data management, but it is a long road, not an immediate one, and these efficiencies will come as a result of DATA lever initiatives, not from the implementation of a data accountability model per se.
*Recommended reading: INFONOMICS (Doug Laney), The Value of Data (Rafael Fdez, Javier Mtnez), Data Juice (Doug Laney), several reports and readings by renowned analysts such as FORRESTER or GARTNER.