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Boost Data Management to Stop Data Anarchy

By   /  July 25, 2017  /  No Comments

data managementData anarchy has serious consequences, with the financial crisis of a decade ago being one of them. There was a “break in the data supply chain,” according to John Bottega, Principal and Managing Member of Data Management Advisory Services and senior advisor and consultant at the Enterprise Data Management Council. Important mortgage-related data, such as information about first-time homeowner loans, was missed as it flowed through processes, affecting the ability to measure true instrument risk.

“We were losing data,” Bottega recently told an audience of data professionals at the Enterprise Data World 2017 Conference as he made the case for the discipline of Data Management in rapidly changing landscapes. There was no way to gain better insight into underlying collateral even as new instruments were created faster than data functions were able to manage them. As housing values started to decline around 2006, even as teaser rates were ending, mortgage loans began defaulting as the pockets of homeowners who had questionable credit to begin with were increasingly strained. Banks panicked and the situation deteriorated, culminating in the failure of Lehman Brothers that is credited with setting off the financial crisis of 2008.

At the time, Bottega was a CDO at Citi Institutional Client Group, and he recalled the questions swirling around the event. What was a company’s exposure to Lehman’s collapse? How to even gauge it given Lehman’s extension to encompass other entities and sub-entities and complex relationships? “I was on the floor that day and someone threw a Lehman’s prospectus at us and said, ‘Who is Lehman’s?’” he said. “Nobody knew.” The data that could have helped assess that and the systematic impact of the collapse “may have been there, but it was not actionable.”

The industry couldn’t fully react in a systematic fashion, he said, as Data Quality was poor, disparate, and inconsistent, lacking in harmonization and standardization and unique identification. “We didn’t have Data Management at the time, we had data processing,” he said. “We had fallen into a malaise.”

Lesson Learned, But A Long Road to Go 

Could there be a stronger lesson than barely avoiding a complete collapse of the financial system to make the business case for strong Data Management? It’s not just about adhering to regulations, though that’s an important part of it, but about having coherent data sets and business processes that leverage them to deal with complex issues, questions, and predictions, whether it’s the financial industry or any other sector.

Technology is a part of the picture, he said, as every industry today faces the challenge to understand and acquire insights from data — even farming. Consider that “there is not a tractor built in the U.S. today that is not wired to the Internet and GPS,” he said. “When you sit on a tractor, it knows where you are, the soil makeup, how deep you have to plant seeds” and more.  But with every piece of farm equipment representing data in a different way, aggregating it for a holistic view can be complicated.

That’s not an uncommon situation, no matter the industry. It’s just another case where there’s “lots of progress but still a long road ahead” when it comes to dealing with data. People, processes, concept definitions, content curation and discovery are as critical as technology if businesses are to extract real value from their data assets, he noted.

“We want trust and confidence in data,” he said. Achieving that end also requires a flexible, adaptable data infrastructure that enables organizations to respond appropriately to threats as well as opportunities.

More Than a Project

Pulling together strong Data Management isn’t easy, and it won’t happen fast. Expect business executives to constantly ask when the work is finished, and be prepared to answer that it never will be. “Data management,” he said. “Is a behavior, not a project.”

Does that create challenges for funding efforts? Of course. “We live in an environment based on annual financial cycles, and you have to show benefits to get money for next year,” he said. “But this stuff doesn’t often show benefits quickly.”

Nonetheless, it’s important to establish CDO leadership that can drive best practices for holistically managing data, both vertically and horizontally, and build a strategy that articulates the business case for it. This is the lynchpin on which so much else rests: Building a common and measureable framework based on standards, rooted in a common language and documented with evidence-based models; implementing Data Governance; marrying Data Architecture with technology; building control environments to create trust in data; and teaching, training and benchmarking. “This kind of progress turns Data Management into a science,” he said.

As guidance, Bottega pointed to the EDM Council’s Data Capability Assessment Model for the financial services sector as a standard set of evaluation criteria for measuring Data Management capability. Its tenets include:

  • Content Management: Identifying, defining and locating data;
  • Program Management: Determining skill sets, creating appropriate governance approaches and driving culture change;
  • Data Quality: Gauging data’s fitness for purpose; and
  • Collaboration: Considering the role of the Chief Data Officer to also be the Chief Diplomacy Officer.

“We’re at an inflection point for the CDO role,” he explained. Think of it as CDO 2.0, as the position evolves to driving the agenda for true Knowledge Management. That requires taking a real proactive approach to facilitating the implementation of the management of content and the meaning of data. Analytics can’t be high quality unless the data that flows in is of good quality, too. In addition to facilitating an infrastructure to enable analytics, the CDO also has to have information security in his or her sights, which is where Metadata assignments and classification come into play.

The goal is to “Build an infrastructure based on getting the right data to the right people, collaborating on technology and making data discoverable, actionable and accurate,” he said. “The opportunity is there to actually get value from data, and firms now expect CDOs to have a vision for that.”

 

Here is the video of the Enterprise Data World 2017 Presentation:

 

 
For more on Enterprise Data World, go to: www.enterprisedataworld.com

Photo Credit: Africa Studio/Shutterstock.com

 

About the author

Jennifer Zaino is a New York-based freelance writer specializing in business and technology journalism. She has been an executive editor at leading technology publications, including InformationWeek, where she spearheaded an award-winning news section, and Network Computing, where she helped develop online content strategies including review exclusives and analyst reports. Her freelance credentials include being a regular contributor of original content to The Semantic Web Blog; acting as a contributing writer to RFID Journal; and serving as executive editor at the Smart Architect Smart Enterprise Exchange group. Her work also has appeared in publications and on web sites including EdTech (K-12 and Higher Ed), Ingram Micro Channel Advisor, The CMO Site, and Federal Computer Week.

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