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Embracing the Future of Data Governance with our Clients, Part 1

3 minutes read

We had engaging and enlightening conversations with our customers at our Ataccama Innovate event. Here are excerpts from these conversations, highlighting use cases, innovations, and best practices. Read on to learn about managing data in the cloud, using AI, and making data accessible to more people in the enterprise.

Jason Wright
Senior Manager Data Quality and Governance, T-Mobile

What has your experience been of making data available to the business?

We’ve been trying to make data self-service with a DQ as a Service model for our business and technical partners, based on the dimensions of DQ and making it visible to the business so that they can have a high degree of confidence and trust the data via scores. 

How do you currently undertake access management at T-mobile?

Having an interface in place that is well-managed and for the business is critical. We keep the use cases separate so that business users and technical users can’t interfere with each other’s policies. 

Can you tell us why Data Quality is at the center of what you do?

The cost of storing as much data as we do is astronomical so you need to make sure it is not bad quality because garbage in, garbage out! We’ve gone through an exercise to create service levels around data controls.

What does it mean to have speed and agility?

We’ve broken the lifecycle down into 8 simple steps and made them as automated as we possibly can. We want this to happen as quickly as possible in a reusable way. 

Miroslav Umlauf
Chief Data Officer, Avast

How are you using AI in Avast?

AI plays a critical role in threat detection. In regards to data quality, being able to show the quality of the data that is used and visualizing it is one of the critical elements that allowed us to improve it, and AI automation in this area helps us spot problems. 

Could you comment on having to automate policy enforcement and the benefits you’ve seen from it?

Being in a security business we need to have ambitious privacy and data usage policies that go beyond regulations like GDPR and this is where automation, as we get from the Ataccama platform, takes on a mission-critical role. 

Can you tell us more about how you’re exposing data across the organization?

For us, it is about balancing the offense with the defense. Our decisions are driven by improving our competitive position, and some of this is about being as defensive around data as a bank, for instance. We had to learn how to create a controlled environment for data while also still being innovative. We need to protect and retain the data but also use it. 

What is the right approach to data governance in the cloud?

We enjoy that Ataccama is cloud-agnostic because in the future there will be more than the big three. In the future there’s going to be more “cloudification” and it is good that Ataccama doesn’t care where the data is. For us, the move to the cloud is also a move towards being a more mature organization in terms of data governance. 

How can you ensure you have continuous funding for data governance?

To do this you have to show the value of work already done and what issues might arise if you don’t continue and you need to keep doing this. You could create a dashboard for example of the value protected against risks by your program. 

Heather Richardson
VP Data Management, Farm Credit Services of America

Can you expand on how you see AI support your business initiatives?

No matter how brilliant our data people, if it is taking a long time for them to get the data they need, we’re going to lose competitive advantage, so we were struggling with our data maturity inside the organization at the beginning. Today we have an automated evaluation model so that our farmers can make better decisions. 

How are you managing the different needs of the different users in your business?

We need to rapidly get the right data in front of the right people in the organization and automation can really help with this.

What’s your take on AI-centred data enablers?

My goal is to provide the context to the business to do the right things with data. We put it in business language and translate it into a repeatable process de-coupled from any specific application. Now we have a whole library of rules we can use.