The average employee may not be wildly excited about data governance. The term may seem dry, perhaps vague, probably really complicated. It has something to do handling information, right? Even upper management tends to be confused about the topic.
Yet despite its apparently sleepy profile, data governance is a dynamic, rapidly changing endeavor that plays a crucial role in a company’s operation. Data governance is about properly planning and managing data flow, all while monitoring compliance and safety; without it, no organization can effectively function. Moreover, data governance enables a company to mine its data for maximum competitive advantage.
In short, data governance will – increasingly, in the years ahead – separate the winners from the losers.
Among the topics we’ll discuss:
- At what stage are companies, generally, in terms of data governance? Where do you think the biggest pain points are among companies in their data governance practice?
- If we look at cloud computing, it appears that trends like multi-cloud and cloud native are shaping its development. What are some key trends or technologies in data governance that are playing a similarly key role?
- What role does AI and ML play with data governance? Is it mainstream yet?
- If a company asked your advice for a few essential best practices for data governance, what would you recommend?
- Data governance in the future, say 3-5 years out? What will the sector look like?
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Where do you think companies are – generally – with the idea of data governance? What do you see as some of the big pain points with data governance?
Let’s take a walk down history lane for just a bit. I’ve seen in years past 2017, 2018, go back that far, largely data governance was about regulations. It was about more of the risk-based approach to data.
Fast forward now to 2020, we started to see this in 2019 and 2020, and certainly in 2021, it is really about helping customers to see the value of data. And in fact, many of these organizations that are looking at self-service analytics say, “We need data governance in order to effectively deliver that.”
The challenge is with the sheer volume of data that companies are generating today, the whole concept of delivering transparency and trust with a balance of right data ethics, data governance and privacy has now also become even more critical.
And in general, data protection rules have definitely kept a lot of our customers awake at night. And so we’ve helped them with their data governance or framework that enables them to define and document standards, the norms and the accountability and ownership that’s needed.
No, [data governance] is not under control and what [companies] are finding is that they’ve got these goals to make data more accessible but in a trusted way. And so the ability to know the data they have and to have the appropriate quality are things that are very much top of mind for them. And so they’re seeking our help, not only in our technology, but also the best practices.
What are some technologies or trends that are shaping data governance these days?
We live now in the cloud AI era. What fuels the cloud AI economy is data, right? Businesses are increasingly becoming more and more data driven in how they run their supply chain, their marketing, their customer service. It can’t be off of gut feel.
I’ve gotta use data to make these decisions especially with these new market conditions too that we’re all dealing with. And they are also increasingly moving their data to the Cloud. Especially during this global pandemic, we saw massive transformations there.
So new data types and sources are contributing a huge amount of information that needs to be assessed, curated, cleansed and protected so that anyone, whether it’s business or IT or any system, can also use it.
So the answer really is an intelligent data governance solution that can help you with that. The transparency, the quality, the protection, the data access, one that is automated and can scale to handle the largest data lakes and is intelligent not only in just name only.
AI and machine learning is in our company’s DNA. We were born with it, [chuckle] with all of the metadata management that we’ve invested in over decades, and it’s really at the core in everything we do at Informatica.
And we doubled down on AI machine learning, especially over the last three years with continuous investment in our R&D efforts and launched the industry’s first metadata-driven AI engine called Claire. And in fact, the way that it spelled is C-L-A-I-R-E. [chuckle]
Data Governance Best Practices
I have this conversation just about every single day.Basically the clinic hours with Susan. [laughter]
How do we derive more options? How do I really make this sticky ’cause there’s a lot of data governance programs. They’re at their fifth attempt and they’ll lose their credibility with their enterprise.
And so I always say always start with the business problem that you’re solving for. Always think about the experiences of the consumers, of people that you need to benefit from solving this business problem, and that might be a business analyst, that might be a data scientist. It could be your executive team. And so having, I call it the wisdom, the “What’s in it for me” in mind of that individual is critically important. Don’t just say, “I’m going to implement the full glossary to terms for findings.”
What’s the end in mind? It’s all about what’s the business problem I’m solving for and how do I make it stick it and build from there? Because listen, data is now recognized as one of the most important strategic assets and enterprise has to manage.
And data is a powerful asset when it’s governed, meaning I understand it, I have the context rendered, its quality I can trust. And that’s critical for any data-driven transformation and initiative.
So I always say, “Look for the data initiatives in your organization that you can have the biggest impact but mediates a small amount of effort – so find quick wins, right? And those quick wins could be with self-service analytics. It could also be with a regulation. It could also be helping to break down the data silos across maybe finance in your research organization.
So it’s looking for those data initiatives and really using that as an opportunity to start small: “Think big, start small, scale fast” and find those opportunities and use them and to grow your program over time.
Future of Data Governance
Looking at my crystal ball, automation is going to be playing a bigger role in data governance and privacy in the years ahead, and customers will get more and more value out of it. We’re building a lot in. We’re becoming a lot more aware of what a customer is doing, what the customer needs, the broad set of capabilities.
It’s not like the Cloud. We know things about your systems. We know what you’re doing with the data. We have the data supply chain in mind. We also understand the business objectives, so we’re putting it in the R&D space more to get more out of the value of data for companies. So more is gonna be built into the focus on establishing a solid foundation now and getting more people engaged with that trust of data.
Governance programs will also support more projects in every corner of the organization because they’ll be able to scale faster. They will be more automated. We’ll be able to provide more insights into how they can connect ’cause there’ll be a lot more intelligence about data from finance, data from operations.
And the technology that supports these programs will also likewise scale, replacing more and more of the manual tasks with automation and machine learning methodologies to help reduce that friction and enable customers to get more accurate and consistent answers out of their data.
To get the data governance right, though, they’ll need to go live with their applications and services faster and empower more lines and business to do great things, make faster, better decisions and unite their customer data. In short, we really do believe that for data that’s properly governed, the sky is really going to be the limit.