When we talk about the tech world, there’s a phrase that doesn’t get the spotlight as much as it should: data governance. This term might not sound as exciting as artificial intelligence (AI) or machine learning (ML), but it’s the backbone of successful data projects.

Why is data governance a big deal

Recently, IBM came out with a startling figure: 42% of enterprise-scale organizations have implemented AI in their organizations, which is huge! This means they are using smart systems to make big decisions and innovate. The primary purpose of an AI/ML system is to sift through huge data sets to find answers, ranging from predicting future trends based on historical data to identifying patterns in e-commerce data. If the data from one source is corrupt or biased, it can affect the overall output, making the results unreliable. And you don’t want that when making big business decisions.

To give you another example, have you ever been in the middle of a heated discussion about why one system reports one number while another reports something else? This scenario is all too common, leaving teams confused about which data is correct, who owns the information or how the information got to the right place. While this is a common problem, it is completely avoidable with-you guessed it-data governance.

Source: IBM

The three pillars of data governance

Let’s break down what good data governance looks like:

Ownership

In organizations, assigning clear ownership means someone is responsible for the accuracy, accessibility and lifecycle of data. This role can involve defining policies for data usage and establishing processes for data quality assurance. If no one is responsible for data quality, it will inevitably decline over time. Having someone accountable for the accuracy and integrity of data is therefore extremely crucial.

Transparency

In this data-driven era, we all rely on data to make informed decisions. It’s essential to have a centralized platform where everyone can easily access and comprehend the ins and outs of their data in business terms. This includes clear documentation, metadata management and open channels for feedback and reporting. Transparency is crucial for collaboration, compliance and building trust between users.

Resiliency

Data owners should establish clear policies on privacy, security and data quality. By monitoring compliance with these policies, you can avoid a range of issues in the future and ensure data resilience.

Finding the sweet spot

So, setting up data governance might feel like a bit of a chore, but it doesn’t have to be a drag. But here is the thing, there’s a right amount of governance required for each specific case. The fun lies in figuring out how to navigate and balance the different parameters at play.

Getting data governance right isn’t just about avoiding headaches now. It’s a strategic move that sets your organization up to navigate the twists and turns of the digital world with confidence. So, while it might seem like a bit of a behind-the-scenes role, data governance makes sure that your AI/ML projects are built on a solid foundation.

Keep an eye out for the upcoming part of this blog series where we will discuss how data governance is implemented in data-related projects!

This blog is written in collaboration with Mathieu Renault – Data Scientist at Mediaan Conclusion