These last few months, it has become more and more difficult to attend a meeting without hearing the expression data governance. However, this subject is nothing new! Be that as it may, with the arrival of Big Data technologies: data and its use become the cornerstone of approaches to innovation. An old subject evolving in a very new context…
Data and governance: One can’t be without the other
The data craze over the last years is such that enterprises invest a lot of time and money to try to break down data silos and to unify their asset thanks to new, ever more efficient, and less costly storage infrastructures.
Nevertheless, enterprises understood rather quickly that the promise – to innovate through data – was going to be much more complicated than previously expected. Despite the latest technological advancements, data are still scattered on both sides in the enterprise with a militant legacy. New storage systems implemented are, ultimately, “only” additional technical stacks in the enterprise’s IS landscape and don’t allow, on their own, to manage data’s life cycle, guarantee rules allowing the best data usage and thus, maximize the creation of data value. We are talking about data governance here.
The objectives of data governance
In the pursuit of innovation, enterprises are rethinking their organizations to move towards a “data-driven” culture. Information systems must become the profession’s strong arm by placing refined, secured and quality data at the center of strategic decisions.
To achieve this transformation, organizations construct what we call data governance. This project pursues quite clear objectives, among others:
A strategy of agile data governance
The way to approach the subject of data governance is evolving. Our experiences have brought us to promote data governance based on the following four pillars:
Such an approach can be successful where many larger “data governance” initiatives have failed.
Agile data governance conclusion
The same as how software developments have gradually shifted away from traditional methods (V-model, Waterfall, etc.) to agile methods, data governance must be rethought.
Such an approach is not only iterative but also applied incrementally to your data governance strategy allowing greater flexibility, necessary to take into consideration the ever-increasing complexity of your IS.