In this blog we shortly go over what data governance and data quality exactly is and why every company should embrace it.
Data governance has a wide variety of definitions. If you look it up in a dictionary you will find the definition as the Data Governance Institute provides: “Data governance is the exercise of decision-making and authority for data-related matters.“. Simply put, it is about managing data with internal data standards and policies.
But why is this important? It is not an essential part of the implementation of data management initiatives. Well, that is where many people are wrong! It is actually one of the most essential things to do before you even start a new software implementation. It is actually increasingly important, as organizations have to deal with new data regulations and rely on data analytics and dashboarding to set KPIs, make better decisions, and to understand their operations better. Eventually, in order to create a competitive advantage and an increase in revenues and profits.
One of the important goals of data governance is breaking down the data silos (this is the lack of communication between departments). Another aim is to ensure data is properly used, to avoid introducing data errors and to block potential misuse of sensitive data (e.g. personal information of customers). Furthermore, data quality is also an integral part of data governance, or to put it with a well-known saying: “garbage in, is garbage out”. Data quality is a measure on the condition of data, based on the following dimensions: completeness, conformity, consistency, accuracy and integrity. Without good data quality, rules and a system of checks, the data could have duplicates, be incomplete, inaccurate, and have inconsistencies (e.g. too short for the required field, a string value and not a numerical one, or too much text for a description).
Data governance can, among others, be improved by arranging to have conversations with all involved stakeholders (these are e.g. the data governance committee, data stewards, the management team and the end users) to agree on the same business definitions and to create a business glossary (e.g. what is a customer, etc.). Furthermore, creating clear user roles and rights is important. Additionally, data governance can be improved by creating data mappings and classifications that help document the available data assets and data workflows, which again influence how data governance policies are applied to individual data sets. Also, data catalogs should be created, which can best be seen as a library of all data assets available within a company or a certain user role, with an easy search functionality in order to find the correct data needed. Creating the proper data quality rules that automatically can detect and block the incomplete, inconsistent, inaccurate or duplicate data and send a message to the right data owner and/or to the person that tried to upload the data, so they can correct the data they tried to add. Here, Axon Data Governance from Informatica and Collibra Data Governance are some good examples of data governance tools from two data management software companies that have many of the just mentioned capabilities and more.