Gartner defines Data governance as the specification of decision rights and an accountability framework to ensure the appropriate behavior in the valuation, creation, consumption and control of data and analytics.

Data today is ever-expanding. With more and more devices and users connecting to the internet, there is a constant flow of data generated every second. This data includes personal information like name, contact number, and email address; your shopping history like the items you searched for or bought; information about your search history or the websites you visit the most, and the list goes on.

While this data is beneficial for both the users, in terms of enhanced virtual experience, and the organizations, in terms of improved decision-making, it can also pose some complications. When a user shares their information on any platform, the main concern revolves around the privacy of data. Users require the assurance that their data is safe and not distributed to any unauthorized organization or used for malpractices. It becomes the responsibility of the organization to manage, store, use, and secure the data gathered from the user. 

Here is where the Data Governance framework comes to the rescue. Data governance is the process of ensuring the quality and security of data using policies, metrics, standards, and defining roles and responsibilities within the organization.

Why is Data Governance Necessary

In an organization, data sharing is essential as it comprises various departments working together. Data needs to be transparent so that every department can work cooperatively. However, irresponsible sharing of private information can lead to undesirable consequences. Data sharing needs to be governed cautiously in such a way that, every department gets adequate information to work with while also safekeeping the personal information of the customers.

Every organization stores its data in a central location accessible to all. Different departments like finance, marketing, HR, etc. can get necessary data from the shared repository. The function of Data governance is to make sure that no personal information of the customer gets exposed to unapproved departments, i.e., it defines the roles of different departments and assigns what data can be used by which department.

For example, in a banking organization, data about customers transactions, balances, deposits, and other information are kept and collected in a repository. Every department requires some or the other information to do their assigned tasks, however, every piece of information need not be shared with all departments. Data concerning the account number or the address of the customer, and other related personal information must not be disclosed or accessed by everyone and this Data governance framework needs to make sure that privacy control policies are established and followed.

Data Governance Framework for Enterprise

Recognizing the significance of data integration and the imperative of safeguarding sensitive information, we have established a comprehensive data governance framework. This framework encompasses various essential procedures, including:

1. Data Detection: The central repository contains an enormous amount of data. This data can be differently structured and stored like some can be stored in the cloud, some in databases, etc. We need to first analyze this data and understand the different types of data available to us.
2. Classification of Data:  After inspecting various types of data available to us, the next step is to categorize them according to our requirements. The classification can be done on the basis of which department the data is collected from or what is the data about. For example, it can be customer data, financial data, employee data, etc.
3. Policies: Policies are schemes or guidelines formulated keeping the classification of data in mind. These policies instruct about what data to sustain, what data to discard, what kind of data to keep secured, and from which departments, etc. These are basically the guidelines to follow while sharing data.
4. Rules: Rules are the protocols to implement the above-established policies. The rules enforce these above policies through actions. For example, if there’s a database of customer data and the policy is to conceal personal information, so there will be a rule to mask data containing the personal data of the customer.
5. Classification of Rules: Classification of rules is performed using Business Terms. Business terms can be defined as the terminology used to standardize the interpretation of data in the organization. For example, we want to measure expenses made by each department of an organization. However, we realize that every department uses a different unit to calculate its expenses. The technical department uses Indian rupees whereas the marketing department uses dollars. Therefore, a standard term is devised like ‘cost’ and defined to measure revenue in dollars and is then used to measure the expenses of every department.
6. Use of Metadata: Metadata is like a description card that describes the data assets we have. Information like what kind of data it is, where it came from, etc. would be reported by metadata so that it becomes easier for us to use and find desired data.

A good Data governance framework follows all the steps with full clarity and precision and then only the data set becomes trustworthy, useful, and collaborative.

ADVANTAGES OF DATA GOVERNANCE

Data governance can be understood as the core of data management. It governs all the other subunits of data management process and makes using data efficient and insightful. Other advantages of data governance include:

1. Ease of Understanding Data: We read how data governance classifies and standardizes data using business terminologies. This provides the flexibility of sharing, better understanding and ease of use, and consequently better Data Quality, data insights, and decision making. Read more about Data Quality here
2. Trust-worthy Data: Data governance ensures that all policies are well established and followed. As a result, accurate, reliable, and only necessary information is made available to various departments to work with.
3. Cost cutting: Since the data available will be refined and of standardized format with minimal to no chances of errors, there will be lesser expenses to spend on detecting bugs and correcting them. 
4.  Improved Data Management: The task of cleansing and improving the collected data becomes automated with the use of Data governance practices and hence enhances the whole data management procedure.

Altogether data governance becomes a necessity rather than a choice for proper, efficient, and reliable functioning of an organization.

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Sarthak Bhasin