Understanding Data Governance: A Guide for Beginners

Understanding Data Governance: A Guide for Beginners
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I work with data, but when people talk to me about data governance, I invariably think it's boring and too complex, so I never spent time learning and understanding what it encompasses.

I had a recent thought, unclear what sparked it, but it dawned on me that some of the things I've been doing at work are actually data governance work. That thought led me to ponder the tech giants, commonly referred to as FAANG, an acronym for Facebook, Amazon, Apple, Netflix, and Google. One of the reasons for their success is that they have robust data governance in place. These giants also recognize that data governance is not just left to the back-office function; to them, it's a strategic imperative to get it right. Data governance is a crucial aspect that determines the value of data. Quality data enables better decision-making, helps create better products, builds user trust, ensures compliance, improves security, and increases operational efficiency.

With this realization and renewed appreciation, I carved out some time to better understand data governance. In this blog, I'm going to share what I've learned. I aim to help demystify this commonly perceived complex humdrum topic in an accessible and straightforward way. We will look at the following:

  • What is Data Governance?
  • Data Governance Processes
  • Value of Data Governance

Let's being.

What is Data Governance?

Data governance is the overarching strategy that ensures the availability, usability, consistency, data integrity, and data security in a company. It incorporates a set of rules, processes, and responsibilities aimed at managing and protecting the organization's data effectively. With proper data governance, businesses can ensure that their data is accurate, consistent, and secure, thus making it a reliable resource for decision-making.

Data Governance Processes

Understanding data governance can be easier if we break it down into simpler processes. Let's use the analogy of cleaning out a house to elucidate these processes. This house-cleaning task involves three major stages - discovering what's in the house, classifying the items, and finally, deciding what to keep, donate, or discard.

1. The Discover Process: Unveiling What's Hidden 😮

In the context of our house-cleaning analogy, the first step involves exploring the house and identifying all the items you have. This could include various objects that have accumulated over the years - clothes, photographs, heirlooms, and much more.

Similarly, in the world of data, the first step is "Data Discovery". This process entails understanding all the different data assets you have across various repositories. These could be stored on local servers (on-premises), in the cloud, or derived from various Software as a Service (SaaS) applications.

Data discovery, however, involves not just cataloguing the data you're aware of but also unearthing the data you weren't aware of. Uncovering these hidden treasures of information is crucial to realizing the full potential of your data.

2. The Classification Process: Sorting and Labeling 🔖

Once you've discovered all the items in the house, the next phase involves sorting and classifying them into different categories. Family heirlooms, photographs, financial records - each item finds its place.

Similarly, once you've discovered your data, the next step in data governance is "Data Classification". This involves assigning your data to different categories, such as customer data, product data, financial data, etc. Providing these labels or classifications helps manage the data more efficiently, enabling quicker access and better utilization.

3. The Policy Enforcement and Metadata Management 👮‍♀️

After classifying the items in your house, you then need to decide what to keep, what to donate, and what to throw away. This decision-making process equates to enforcing data policies in the data world. Data policies serve as guidelines and standards dictating how to handle and manage your data. For example, a common and crucial data policy is that personally identifiable information (PII), like Social Security numbers, must be protected or masked, with specific rules enforcing this policy.

Once you've decided what to keep, these items are repackaged and labelled - a process akin to managing metadata in data governance. Metadata provides descriptions of what a data asset is, its origin, and its characteristics, akin to a library's card catalogue describing a book. Proper metadata management is crucial to make data easily findable, understandable, and usable.

Value of Data Governance

Just as you might stumble upon a valuable heirloom during your house cleaning, effective data governance can help you discover valuable insights within your data that can be leveraged or even monetized. The real beauty of data governance in today's technologically advanced world is that much of this process can be automated, ensuring data remains relevant, usable, and valuable over time. 😎

There you have it, that's data governance likened to the house cleaning process. I know it's not always thrilling, but I think (at least I hope) you agree that data governance is undeniably vital. As we've seen, the treasure to be found in well-governed data is invaluable.