Data Governance

Everybody is talking about Data Science and Big Data, but one heavily ignored topic is Data Governance and Data Quality. Executives all over the world want to invest into doing data science, but they often ignore Data Governance. Some month ago I wrote about this and shared my frustration about it. Now I’ve decided to go for a more pragmatic approach and describe what Data Governance is all about. This should bring some clarity into the topic and reduce emotions.

Why is Data Governance important?

It is important to keep a certain level of quality in the data. Making decisions on Bad Data Quality leads to bad overall decisions. Data Governance efforts are increasing exponentially when not done in the very beginning of your Data Strategy.

Also, there are a lot of challenges around Data Governance:

  • Keeping a high level of security is often slowing down business implementations
  • Initial investments are necessary – that don’t show value for month to years
  • Benefits are only visible “on top” of governance – e.g. with faster business results or better insights and thus it is not easy to “quantify” the impact
  • Data Governance is often considered as “unsexy” to do. Everybody talks about data science, but nobody about data governance. In fact, Data Scientists can do almost nothing without data governance
  • Data Governance tools are rare – and those that are available are very expensive. Open Source doesn’t focus too much on it, as there is less “buzz” around it than AI. However, this also creates opportunities for us

Companies can basically follow three different strategies. Each strategy differs in the level of maturity:

  • Reactive Governance: Efforts are rather designed to respond to current pains. This happens when the organization has suffered a regulatory breach or a data disaster
  • Pre-emptive Governance: The organization is facing a major change or threat. This strategy is designed to ward off significant issues that could affect success of the company. Often it is driven by impending regulatory & compliance needs
  • Proactive Governance: All efforts are designed to improve capabilities to resolve risk and data issues. This strategy builds on reactive governance to create an ever-increasing body of validated rules, standards, and tested processes. It is also part of a wider Information Management strategy

The 4 pillars

4 data governance pillars
The 4 pillars of Data Governance

As you can see in the image, there are basically 4 main pillars. During the next weeks, I will describe each of them in detail. But let’s have a first look at them now:

  • Data Security & Data Privacy: The overall goal in here is to keep the data secure against external access. It is built on encryption, access management and accessibility. Often, a Roles-based access is defined in this process. A typical definition in here is privacy and security by design
  • Data Quality Management: In this pillar, different measures for Data Quality are defined and tracked. Typically, for each dataset, specific quality measures are looked after. This gives data consumers an overview of the data quality.
  • Data Access & Search: This pillar is all about making data accessible and searchable within the company assets. A typical sample here is a Data Catalog, that shows all available company data to end users.
  • Master Data Management: master data is the common data of the company – e.g. the customer data, the data of suppliers and alike. Data in here should be of high quality and consistent. One physical customer should occur exactly as one person and not as multiple persons

For each of the above mentioned pillars, I will write individual articles over the next weeks.

This tutorial is part of the Data Governance Tutorial. You can learn more about Data Governance by going through this tutorial. On Cloudvane, there are many more tutorials about (Big) Data, Data Science and alike, read about them in the Big Data Tutorials here. If you look for great datasets to play with, I would recommend you Kaggle.

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