Data governance: a two-pronged approach

When you think of the term ‘data governance’, it’s natural to associate it primarily with legislation such as GDPR or CCPA. For example, in 2019, almost half of organizations in the US (45%) and well over half (56%) in the UK planned to make significant changes to their cloud data governance strategy in light of GDPR.

While the legal side of data governance is important, there are other factors that make it a necessary part of building and maintaining a strategic data asset. Loosely put, data governance is making sure your data is an asset, not a liability.

Data governance can refer to a range of internal standards around how your organization gathers, stores, processes, and disposes of data. This, in turn, can indicate how effectively you’re using your data to derive meaningful insights.

Read more: Driving bold business decisions based on strong data governance

However, as a result of its heavy association with legislation, many data ops teams don’t consider it a high priority in their day-to-day operations. As long as they implement the minimum governance to compliance, anything beyond this is often seen as a nice-to-have.

In reality, making data governance a priority can mean the difference between thriving and surviving – and requires a different way of thinking and technology.

So let’s take a look at the two main types of governance that should be occurring: legal and organizational.

1. The legal side of data governance

Privacy laws vary a great deal depending on where your organization is based, but also, where your customers are based, so it’s important to check which legislation may apply to your data. As mentioned, two of the most significant data laws to date are the GDPR in Europe and the CCPA in the US.

Under the GDPR, not having appropriate data governance can cost your business up to €20 m or 4% of your global turnover, whichever is more. For CCPA breaches, you can be fined up to $7,500 for each violation. Making sure your organization has a strong foundation of data governance means you’re far less likely to breach data laws and regulations.

2. The organizational side of governance

This is the side of data governance that is so often overlooked – continual governance of your data for the benefit of business intelligence, and ultimately, your bottom line.

Imagine you go to the airport, and before you get on the plane, they ask you: “Do you know where your bags are? Did you pack your own bags? Do you know what’s in them?” Hopefully, you say yes; you’ve had full, constant control and visibility of your bags the whole time.

It’s the same with data governance. In order to be able to leverage growing volumes and sources of data, you need to understand and audit your data flow. Where is data coming from? What are my business rules? Can I get to them quickly? Can I change them?

Being able to answer these questions not only gives you confidence in the accuracy of your data, but also helps you understand where there may be problems, such as API outages causing gaps in your data.

And then there’s also the benefit of time savings. If you can quickly understand what’s happening with your data, without raising a ticket for the engineering team to investigate at every turn, you can solve problems more quickly. But to do that, you need to have your business rules (or, as we call them, data recipes) in a centralized location, accessible at the click of a button.

That’s where automation comes in.

Turn your data into an asset, not a liability

There are four tenets of automation which help make your data actionable:

1. Aggregation and standardization of your data. This gives you transparency, breaking down data silos so the business revolves around the data, not the other way around.
2. Ability to customize quickly. Without automation, making specific data change requests can cause a huge bottleneck as it requires multiple layers of development. Once you’ve your standardized data, it becomes easy to apply business rules (data recipes) – that allow you to make changes or view a specific slice of the data quickly.
3. Effective scalability. Traditional data ops becomes expensive as your business grows. Using a cloud-based data automation solution is key to helping your business scale in line with your data.
4. Appropriate and accurate data governance. This is perhaps the key takeaway here: Being able to understand and audit your data flow is important for privacy compliance, but it’s also the key that unlocks your ability to find and solve data problems quickly – empowering you to do much more with your data.