Not all data is born equal
Two commonly attributed causes of failure or slow adoption of data governance are strictly related to determining a scope and defining a corporate data governance strategy.
When preparing a data governance program there are two critical factors that need to be considered:
A strict alignment with business goals and objectives — Keeping in mind that “data exists to serve business”, this means that any data governance process must be supported on strong business cases, with objectives anchored on business objectives, otherwise it will be viewed as another siloed IT project with no perceived value from the business side.
A strong focus on strategic data — At an operational level, most of the organizations rely on dozens of different systems, which handle massive volumes of data of every kind of typology daily. Approaching data governance in a global perspective will inevitably lead to a lack of focus, resulting on a misalignment with the business objectives and incapability to deliver value. Again, being supported on a strong business case that identifies and prioritizes business critical and strategical data is paramount for success.
Looking at any organization’s data landscape, we can see that these defining priorities, to design a strategy for data governance can be a challenge.
One of the tools that can help determine the scope of data to be included and prioritized is a Criticality Matrix.
In this context a Criticality Matrix is a method that allows the classification of data based on its usage and the criticality, allowing us a better understanding of data’s effective business value.
The main objective is to determine what data can create the most value for business or the biggest risks.
At the core of this tool there are two basic concepts:
Criticality — This parameter determines how critical data is for business. It a measure of the impact, positive or negative, this data can have. This classification can include regulatory requirements, data protection and privacy requirements, security risks, anti-money laundering or know your customer due diligence, etc.
Usage — This parameter determines the business purpose of data. Why and how data is used in the organization’s processes. This can include commercial, marketing, financial or legal processes, but also customer experience, or analytical applications of data. This includes how often data is used in these processes and how often this information is collected, updated, etc.
This application of this matrix gives us the tools to determine which data should be address with highest priority (the top-right quadrant), but additionally it might give some indication on how to act on the data that falls on the bottom-left quadrant, typically the less critical and less used data (this information is important when adopting data minimization principles).
It is not that hard to build a Criticality Matrix, so it is important to understand why it is essential to do this in the first place. The Criticality Matrix allows the clustering of data into priority groups, leading to a more focused data governance strategy, but also to identify data that doesn’t bring any value to business allowing some gains in efficiency and resources.
And finally, it can make the data governance processes easier to succeed, giving a clear picture of the priorities and direction.