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In our previous article, we have discussed that Data Governance plays a key role in Data Management. But to build a proper data governance system, we need to define Data Governance’s Сore principles, Roles, and Responsibilities.
That, of course, has already been done by probably the biggest name in the data governance field out there, Robert Steiner or Bob Signer:
The first principle is that data must be recognized as a valued and strategic enterprise asset – your organization must shift from thinking “my data” to “our data”. You see this quite often in organizations where, for example, the marketing team believes that the marketing data is their data and becomes protective of it.
As a result, the marketing department may encounter conflicts when they are reluctant to share data with other departments, such as sales. While it is understandable if the data in question pertains to sensitive HR information, it is important for an organization to have a framework that enables the sharing of data based on need and access levels to ensure smooth operations and minimal friction between departments.
When the senior leadership of an organization recognizes the strategic value of data as an enterprise asset, they should do everything in their power to facilitate the widespread use of data within the organization. This enables the organization to improve sales and provide better service to customers.
Unfortunately, in many organizations, senior leaders view data governance as the sole responsibility of the IT or data teams, without acknowledging the need for wider involvement.
However, with the rise of regulations and hefty fines for noncompliance, it is crucial for everyone in the company to understand the proper use of data and to follow established procedures. Any single individual within the organization who mistakenly shares sensitive data with external parties could cause serious legal trouble.
Therefore, it is essential that all employees participate in and adhere to data governance policies to prevent potential risks and ensure proper data management across the organization.
To ensure compliance with the diverse range of industry-specific legislation, the data governance team must collaborate closely with all business units. This is necessary to maintain conformity to internal and external regulations across the organization, thus minimizing the risk of noncompliance and associated compliance issues.
And the last core principle is that data quality must be defined and managed consistently across the data lifecycle. So the idea here is that data is going to be correct to use all the time and every time.
If we want to follow the first core principle where data is recognized as an enterprise asset, then we need to make sure that our data is accurate in order to make good decisions. Good data is great for making great decisions, but when you have bad data, you can make really bad business decisions.
So these are the four core principles. And when you start a new data governance program, your first job is to make sure that the senior leadership in the company understand these four core principles and actually approve of them. They actually see why they’re important. If they see why this is important, the sponsorship of the program and the allocation of resources from senior management is going to be so much easier.
In order to fully understand data governance and how to go about implementing these four principles, you should really first have an in-depth knowledge of who is involved in a data governance program. What are the different levels? What are the different responsibilities, and how to go about securing these resources?
The displayed diagram represents the conventional structure of data governance prevalent in most large organizations. This model has remained largely unchanged for the past decade and features several levels of authority within a data governance program, namely the executive, strategic, tactical, and operational levels.
Although the diagram may appear overwhelming at first glance, we will cover each level in detail in this article. One notable aspect of this diagram is the triangles in the middle, which indicate the frequency at which each level must make decisions regarding data. As the operational level handles data on a day-to-day basis, they are required to make decisions more frequently than the tactical level.
Let’s take a closer look at the various levels of the data governance program.
Senior management is responsible for driving the data governance program and ensuring that the tactical level has the necessary bandwidth and focus to support it.
This is where we find the data domain stewards and coordinators. There is usually one data domain steward for each subject area of data, responsible for managing data within their respective domains. They document data classification, compliance, and business rules, communicate these rules to stakeholders, and escalate any issues to the strategic level. The data stewards coordinators serve as communication officers, distributing rules and regulations to the operational stewards, identifying data stewards, and coordinating data governance program-related activities with them. While they do not have decision-making authority, they facilitate the program.
The data stewards define how the data will be used and managed within the organization. They participate in creating, reviewing, and approving data definitions and use data on a daily basis.
We have the Data Governance Office responsible for participating in program development, architecting solutions in the data governance framework, and assisting with the program. They facilitate Data Governance Council meetings for the strategic level, participate in the development and delivery of data governance policies, standards, and procedures, define data quality metrics, and support data quality issues and audits.
Data governance partners are also part of the support level, and they collaborate within the data governance model, including being responsible for technical data handling, securing infrastructure, and meeting business needs. PPMO assists in managing projects to ensure appropriate data governance, and other departments serve as data governance partners as well.
Finally, we have the data governance working teams responsible for improving data definitions, production and collection, classification and protection, and usage and understanding of business data rules.
To tackle organizing proper data governance, you need the right data management tools that will offer the following features:
AINSYS offers these and other tools and syncs data between every tool and platform your IT team employs, helping you get an accurate picture of your software. By implementing AINSYS tools, any business can organize proper data governance to make the right decisions for your organization and keep up with the ever-changing technology landscape.
