Data governance: Definition
In the race for digital transformation that we have seen in recent years, enterprises are looking to become “data-driven”.
The influx of information and the massive amount of stored data are leading companies to initiate data governance and to consider it a strategic area of focus.
We like to define data governance as an exercise of authority with decision-making powers (planning, surveillance, and rule enforcement) as well as having control over data management. Its main objective is to transform data assets into a set of shared and shareable assets in the enterprise.
5 ATTRIBUTES
Towards a new data governance
In the past, implementing data governance within organizations has rarely been successful. Zeenea’s aim is to pragmatically shed some light on data governance attributes that are capable of taking on this new era’s challenges, and this, with the help of our data catalog.
We want it to be bottom-up, non-invasive, automated, collaborative, and iterative data governance. In one word, agile.
Bottom up
“Data governance must be as close as possible to your enterprise’s operational reality.”
A bottom-up data governance strategy encourages putting individuals and their interactions in front of processes and tools. A data governance approach can only be successful by involving all the employees of an organization, thus benefiting from collective intelligence.
Non intrusive
“Data governance must adapt to your enterprise’s context and not the other way around.”
Data governance must not be an obstacle to innovation in your enterprise. It is a matter of giving your collaborators freedom to use the tools, among others, adapted to their usages.
Automated
“Data governance must accurately reflect your data assets.”
Automating metadata collection on your data assets allows your tools to accurately reflect reality. On the other hand, this automation ensures that such governance is kept up-to-date and it can be scaled up.
Collaborative
“Data governance must involve and federate all employees of an enterprise.”
The practice of having one person or group arbitrate data governance has fallen into disuse. We believe that the sustainability of data governance is by creating communities around various data-related domains within your organization.
Iterative
“Data governance must deal with changes quickly.”
To best match the company’s expectations and its operations, data governance must be built step-by-step. Adapting to change must be at the heart of the company’s data governance strategy.
Why start an agile Data governance?
For the past few years, on the trails of GAFA (Google, Apple, Facebook, and Amazon), data is perceived as a crucial asset for enterprises.
How does Data Democracy strengthen agile data governance?
In this second edition, we decided to tackle the organization of this new, agile data governance and its scaling process using this same mindset.
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