How to Not Build Castles on Digital Quicksand

If data is a modern currency, data governance is its version of banking.

Developing a data governance framework is a complex process that needs to be custom designed for each organization, looking at the best data governance practices is a wise place to start.

A typical rule of thumb in developing a data governance program is to start with a minimum viable deployment and iterate from there – based on the feedback derived from the organization where it’s been deployed. This allows for keeping the process manageable and reliable, while also making it as comprehensive as possible.

So if you’re looking to implement data governance best practices, it’s well worth spending some time learning about concepts such as data quality, data stewards, sensitive data, and data domains. Understanding these key concepts will make it easier to build a business case for establishing a robust data governance strategy that suits the entire organization.

Preliminary Steps for Outlining Successful Data Governance Policies

In general terms, the preliminary steps to outline data governance programs involve deciding what types of data will be managed. This involves deciding who will get to manage each type of data, where it will land, and why.

In the next section let’s overview the key roles; for now, here are the key concepts:

You can think of data assets as tools of your trade. This is whether they are obtained via a database, document, web page, or any other preferred file or software program. These include the data security tools that will allow your governance program to be manageable.

Data Quality: a structured evaluation of the contents of the subsequent data catalog, including considerations of accuracy, reliability, completeness, and consistency, as well as timeliness. Establishing such guidelines and boundaries will be essential to making sure the data being handled is relevant for the intended purpose, as well as identifying possible errors and limitations.

Data Domains: a simple way to think of this concept is to regard it as a form of data catalog that will lay the outline for all data assets to be developed. This includes a delineation of what is sensitive data, personal data, and customer data. Which data governance tools will be needed, who gets data access, and who makes sure all regulatory requirements are fulfilled?

Key Roles in good data governance

Chief Data Officer: this will be the head of your data management team; the CDO is the head of the project, and the key person designing the frameworks for effective data governance. Having this title in the ranks of an organization signals a commitment to progress and an understanding of the latest data policies.

Data Owners: when it comes to fleshing out the data governance team, delineation of roles is the key aspect; in order to keep the data protected and valuable as a business asset, it must be structured on a need-to-know basis, which in turn implies defining the suitable data owners (along with their privileges in data dissemination) across the entire data domain.

Data Stewards: these are the people who will train the data owners, and help orchestrate their efforts toward fulfilling the data governance practices established by the head of the board. The upper management of your data governance team can serve as your data stewards.

The Data Lineage tool is a type of data governance tool that allows you to trace data originating from inputs such as applications and source code.

Key Considerations for Well-Structured Data Governance Policies

While it’s a smart idea to look at how smart organizations approach data governance, we really can’t carbon copy what they’re doing. This is because we can’t expect to get the same results. In fact, we can’t even expect whatever information they divulge to be fully accurate, since misdirection itself can be a form of data protection.

What is more reasonable is to instead look into developing data governance as an organic project that is best rolled out gradually. This is best rolled out and iterated upon until it covers the specific needs of your organization.

Rather than rushing things through, it’s much wiser to aim for developing a proper structure framework over a reasonable period of time. This is done by starting with a general overview and adding complexity with iteration.

This may seem at first like a needlessly complex approach, but it’s actually the most straightforward, and one that can avoid losing time and money in the long run. We want to think of this as the development of a blueprint for the future, rather than something to be rushed into for completion’s sake.

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itdirection

Tech & Sustainable Innovations at IT Direction

Ian McKenzie is a passionate technology writer and advocate for sustainable innovations. With a background in environmental science and tech trends, Ian brings a unique perspective to the intersection of technology and sustainability.