What is data governance?
• Data governance usually requires a framework.
• People and tools help run the day-to-day governance.
• Procedures and rules help bound the way the governance works.
Data governance is a phrase increasingly heard in business circles as companies realize exactly how much data any enterprise-level business generates every day. But what is data governance, beyond a catch-all phrase for any system that manages the availability, usability, integrity and security of a company’s data at any time, so that it’s accessible by the “right” people – and not accessible to anyone else?
Frameworks for success.
First of all, data governance is a process that works within a framework. It combines people, tools, procedures and rules, and if the framework is sufficiently well written, it should allow for all those things we mentioned – data availability, data usability, data integrity and data security – to go on day in, day out, powering better business decisions and more effective steering of the enterprise through the ups and downs of any economy.
But a data governance framework can’t exist in a vacuum. Typically, it’s part of a wider data management strategy, and it needs writing with the data goals of the organization clearly in mind.
An organization has to know what it needs its data to do, both on a day-to-day basis and in terms of overall added value to its strategic goals, before the data management strategy can be nailed down, and the data governance framework written to make those goals attainable.
Always respect the power of your data.
The components you need.
So what is data governance? A process within a framework, made up of people and tools, procedures, and rules.
But what people, tools, procedures and rules?
Naturally enough, the exact specifics are like to change from organization to organization, but there are fundamentals that apply across the board.
Usually, successful data governance frameworks include a governance team, a steering committee (the governing body of the process), and a group of data stewards. There’s usually at least one data analyst in the mix, so that data can be assessed for categorization and correct location.
The steering committee will largely take the goals laid out in the data management strategy and guide the work of the governance team towards creating a data governance framework that achieves those goals. Whereas the implementation and enforcement end of the equation is usually the purview of the data stewards.
Ideally, there will be members of the organization’s business operations team – or even potentially the board – on either the steering committee or the governance team, to connect the organization’s principal stewards with its use of its available data.
Without recommending particular products, it won’t surprise you that the tools organizations use to perform their data governance vary depending on the data goals the organization has.
There will typically be tools to handle data workflow management, so data ends up stored in the place and the way it should be. There is increasingly an element of automation in the system, so as to take the work of manual data management off human hands – especially as the volume of date involved increases exponentially.
Many organizations will also include tools to help with the creation of data catalogs, governance policies and even, where appropriate, to automate the process documentation procedure, so as to minimize the risk of human error where human interaction in the system is unnecessary. Tools might also be deployed to handle metadata management.
While each organization will have its own procedures, dictated by its overall data management strategy, there are several procedures that will be common to most data governance frameworks.
There will likely be a data mapping and classification procedure – again, without knowing what you have and where you keep it, not to mention on what kind of data storage, your higher-level data retrieval and usage tasks will flounder fast.
In particular in the modern age, this procedure also lets an organization classify and properly protect any personal identifying information, to secure it against the possibility of data breaches or intentional intrusion.
As mentioned, there will be a need for a data catalog, and the creation and maintenance of that catalog will depend on establishing procedures around access, data lineage, search functions and other important in-roads to the data.
It’s also usual for the data to have a glossary, a set of term-definitions within an organization. How these are made, stored and accessed will also be the business of an established set of procedures, to ensure the consistency of both the stored data itself, and the way in which it’s accessed and used.
Rules are useful to ensure that the data governance framework is not only updated, but that it works in a seamless way. Rules allow for transparency in terms of both decision-making according to the organization’s principles, the assignment of data access privileges (usually, but not always, on a zero-trust basis), the establishment of data quality and much more.
These people, tools, procedures and rules are the basis of any effective data governance framework, whether the organization is in high-level banking or high-volume widget-supply. Whenever you need to apply data management to your organization, first, identify the data you have. Then set your data management objectives, and define your strategies accordingly.
If you ever find yourself at that point wondering “What is data governance, exactly?”, think of it as your nuts and bolts toolkit to achieve those objectives, and remember: people, tools, procedures and rules will see you right.
30 November 2023
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