5 essential elements of Data Governance

What is data governance? Well, for one, it is a buzzword. Particularly with the GDPR around the corner. And, with buzzwords, we often forget to slow down and investigate what they actually entail. But you know us, we are always looking out for you. So we have decided to dedicate this article to exploring 5 essential elements of Data Governance as well as a general definition.

What is Data Governance?

A broad definition is always a good place to start. So, what is data governance? According to good old Wikipedia, it’s defined as “[the] process an organization follows to ensure high quality data exists throughout the complete lifecycle”. But this doesn’t capture the entirety of data governance. We have therefore collected some of the most important elements of data governance – Data Quality, Security, Master Data Management (MDM), Data Stewardship and Data Architecture.

1. Data Quality

Much of governing your data involves ensuring that your data is of high quality. In turn, data quality refers to the perceived appropriateness of data in a specific situation, encapsulating elements such as accessibility, relevance, and timeliness. We could go on, but in an attempt to keep this Insight as short and sweet as possible, we won’t. But you can head over to our article What is Data Quality? And why should you be thinking about it? to find out more.

2. Security, Privacy, and Compliance

Security, privacy, and compliance are further must-haves. Not only does the data that you are handling need to be secure from outside threats but you also need to comply with privacy laws governing the collection and use of personal data. Find much more of this in What is the GDPR, really?.

3. Master Data Management (MDM)

On to the term that is often mistaken for being the same as data governance. While similar, MDM refers to the processes, standards, tools, and policies that are involved in managing data. Basically, it deals with the organizational and strategic elements of data governance and is a central aspect.

4. Data Stewardship

In line with MDM, data stewardship is a term that describes the person or persons responsible for establishing and maintaining data governance. We included this as it is crucial that roles and responsibilities are assigned in order to ensure a successful data governance initiative.

5. Data Architecture

Correct data architecture needs to be established in order to optimize governance. What is data architecture? Put best by Techopedia, it is

“a set of rules, policies, standards and models that govern and define the type of data collected and how it is used, stored, managed and integrated within an organization and its database systems. “

A handful, sure, but we thought their definition encapsulated all of its elements. In an attempt to simplify, data architecture is the structure that your data stands on throughout its lifecycle, from collection through to integration. If the structure of your data, i.e. your data architecture, is optimized to enhance data governance, your life will be a lot easier. In fact, the GDPR will require that your data governance be a central aspect of your data architecture.

Conclusively, we’ll admit that there is still much left to say about data governance. So consider this an introduction.

If you are new to NodeGraph, first of all – welcome. We are a data quality platform for QlikView and Qlik Sense. To find out how we can help you take control of your data, get started today.