LSEG, Head Enterprise Data Governance: Data Governance Is a Truly Business-Aligned Function

Diane Schmidt, Head Enterprise Data Governance, London Stock Exchange Group, speaks with Leonard Maganza, Chief Customer Officer, Syniti, about organizations taking risks to reap benefits, the misconceptions around data governance, the importance of automation, and suggestions for data leaders.

At the interview's outset, Schmidt asserts her belief in “rolling the dice” for greater organizational benefits. She acknowledges the much-needed factors of resources, backing, and funding, but still urges organizations to take on the biggest challenge out there. "I do realize that the dialogue has been, ‘Take off pieces that you can't manage, and think small– win big. And I understand that,” she says. “But I say, go for it. Take the risk. Because the benefits are so much sweeter and greater for the company." 

Schmidt emphasizes that the need to have a well-managed and controlled ecosystem is non-negotiable. "We're going for the gusto and want to make sure that we've got this trusted ecosystem because it’s critical for the financial infrastructure," she says.

She points out that considering data governance as a back-office function usually handled by IT is the first misconception. "Data governance is truly a business-aligned function,” she states. “IT plays a critical role in that they are partners and they support solutions based on requirements and instructions." 

Secondly, Schmidt pinpoints that data governance is not just about data and governance. It is also about behaviors. It is about transforming the way people think about data, and how they manage and control data. 

Delving into a more practical aspect, she speaks on execution through automation. Schmidt cites the example of metadata management as one of the areas of automation. She takes pride in the group data policy framework at LSEG, which includes the policies, standards, and procedures adopted and followed through the data journey.

Shifting the perspective, she discusses how as an organization, they review change opportunities alongside the policies and standards before automation is implemented. "We had set this initial preliminary process to do that,” she adds. “We have created and developed an in-house tool that is an automated form, where we have people come to our website and they essentially can fill out a form. It triggers events and we get it through email." She feels positive about not needing excel spreadsheets, due to the tools that are used to automate not only the internal operations but also initiate the overall change management process.

She says that it is okay to use non-automated processes while starting as an organization because those are the requirements at that point. "Those are the initial ways you want to capture how you do things, how you want to do things, and then use that for your tooling,” she adds. “Use that to build out your tools and your use cases. That's what we've done."

Schmidt then voices her experience in the form of suggestions to today’s data leaders. She states, "It is paramount as a data leader in a company that you understand your company's businesses, your products, your services, and figure out the problems that you're trying to solve … and how those proposed solutions are supporting the corporate mission, the line of sight."  

She reiterates the fact that as a data leader, understanding the company comes first. Understanding what fuels the company to do what it does is important, knowing that each company has its own complex legal entity structures along with different products and services. In addition, she urges leaders to know the reason behind choosing data as a career. Answering questions such as “Why are we here?” and “Are we solving the right problems?” or “Are the problems prioritized?”  are a good starting point.

"Do your assessment, your strategy, and all those classic steps, but taking the time to understand your own company is something I think people forget about sometimes," Schmidt concludes.