Data Governance what is it and why Its Important

Data plays an important role in every business. Proper data governance helps organizations of all types to be more successful in key areas of their businesses.

Sam Maddula

7/1/20236 min read

Data plays an important role in every business. Proper data governance helps organizations of all types to be more successful in key areas of their businesses. This article will help you understand why your business may benefit from implementing good data governance.

I will go through how any organization can kick start data governance, what are those key roles and responsibilities needed to implement in your business.

First things first, let's look into why we need data governance.

We all know that our increasingly connected, digital world generates a lot of data. Data by itself may not be all that intriguing. However, what it can do and the answers it can supply are critical. Fundamentally, data assists us in making better decisions. Data must be viewed and used as a critical tool for the twenty-first century. You're either making it, consuming it, or leveraging it, no matter who you are. To realize the clear benefits of data, we need good quality, accurate, well managed, and properly maintained data.

We need to know where data is, who owns it, whether it's kept current, and how to store it properly for later use.

We also need the tools, and skills to work with this data. If we do these things right, the value of all this data will increase and be much more useful in the years ahead.

Data is important to our organizations. For example, let's agree that sales data is important in a for-profit business including who buys your products and services, when, how often, and where. Without this data, an organization would be blind to market behaviour. It won't have the necessary information to upsell or to find new customers; we can go on with more examples like above.

Using data to inform our organizational decision-making is called data management or information management. To some degree, all organizations take advantage of data management. However, we must ask ourselves if we understand and manage our data correctly, and if we are getting the most value from it. Every organization falls somewhere along a continuum where weak data management is on one end and high-performance data management is on the other. Where your data management performance falls is in how well you govern the management of data.

To achieve high performance in managing your organizational data, we need policies, standards and frameworks defined and accountabilities drawn with help from management, implementing all these tasks nothing but Data Governance.

Always have an organizational mission to empower and align your staff with Data governance. Some key foundational principles are Transparency, Accountability and Standardization.

As the word suggests, in transparency, everyone within an organization and those who interact with the organization from the outside should be able to clearly understand the processes and impacts of data governance. Transparency is essential to avoid surprises, to gain buy-in from all those impacted people and to build trust in the organization. As an example, an organization should have an information security policy that clearly states what kind of data must never be stored or displayed on a public website. This could include data on employees' birth dates or home addresses.

The second principle is accountability which encompasses the responsibilities of anyone who has a role to play regarding data in an organization. The person has a role to play in data governance; it should be fully described, understood and agreed upon by all involved parties. Within a mature data governance environment, we will hold people accountable for taking specific actions at specific times.

In the third principle, standardization of data, we are concerned with such things as how data is labelled, described and categorized. An example of data standardization is how we might handle US state designations in an organization. When we capture California, is our standard to use CA or to spell out the entire state name. This can be a really big deal, why? Well, for one thing, if you wanted to do a search for all data that includes information in California, a lack of a standard will result in incomplete results. That is, if you search for CA only, you may not get the results for the full word California. Done right, standardization solves many challenges. Standardization improves the quality of data, makes it more usable across systems and, subsequently, makes it more valuable. What all these core data governance principles have in common is that they support data quality which is a central goal of data governance.

So far we discussed background and motivation for data governance. Let's check when an organization need data governance, To answer this we have to know how important is data right now to the success of your organization? Some high-value data may provide a competitive advantage. That is, by leveraging the insights in the data you collect, you may have access to valuable and unique knowledge that can be used to win in the marketplace. If this is the case, you need data governance in your organization. Another scenario is compliance; not meeting certain compliance requirements can have far-reaching legal consequences for organizations and for individuals. If your organization is subject to rigorous requirements for mandatory data handling, you should be looking at data governance.

Let's talk about roles in data governance. The two most important roles are data owners and data stewards, but there are other roles. When asked who has responsibility for data governance in an organization, the answer must be everyone. If an organization employs data governance, the expectation must be that all staff agree to support the function of data governance. What other roles have particular importance regarding data governance? There is a range of technology roles, such as a database administrator, or a software developer, both of which, for example, manage many aspects of creating, reading, updating and deleting data. They may have special requirements relative to data governance, such as adherence to industry regulations. We know, for example, that everyone who handles health care data must follow specific data handling rules, like what can be stored and who can access it. There are auditors who are tasked with making sure that all of the organization's data is being handled in accordance with policies; a critical role for management. All levels of management must help to communicate value and support enforcement. If data governance is not supported, and messaging from management isn't clear or inconsistent, it will be less successful.

We covered some key foundational concepts in data governance above. To implement and succeed in data governance we need engagement from the senior leadership team from the start, with their involvement we can define key data governance mission and vision for the organization. Training your staff helps the data governance adapt much faster, meaning we need to consider fundamentals to expert level concepts to be part of your training material. Nowadays collaboration across organizations is highly improved with new tools and technologies used to engage all parts of the business to align on data governance. Measuring data governance adoption and KPI's across governance components will help you to create a roadmap and plan the next steps.

We should acknowledge that an organization that begins a data governance capability doesn't need to be perfect on day one. In fact, most organizations will evolve their data governance over a period of time. We refer to this as the process of maturing. In fact, we'll reference the data management maturity model for thinking about how we plan and develop data governance. The data management maturity model is a product of ISACA; it's a not-for-profit professional organization for IT governance, assurance, and cyber security professionals. The data management maturity model consists of six categories of processes. Keep in mind that there are large differences between those organizations that are mature in each area, and those that are just beginning. Maturity could be considered on a six-point scale: 0 is no maturity, 1 is initial, 2 is managed, 3 is standardized, 4 is advanced and 5 is optimized. This is how we need to think about the design process. Get the basics in place for each of these areas, and then build on each of them as your organization gains confidence and experience.

a person holding a hand with a diagram of a data system
a person holding a hand with a diagram of a data system