Many IT leaders aim at providing Data Quality as a Service (DQaaS) to business users, but few succeed at realizing that goal at scale. It is possible, though, if IT execs standardize their approach to the challenge.
Carl Gerber, Head of Enterprise Data Transformation at Teachers Insurance and Annuities Association of America – College Retirement Equities Fund (TIAA-CREF), is one of the success stories. Employees across the financial services firm’s business units today can tap into the DQaaS offering built by Gerber and his team, in order to better leverage corporate data for competitive advantage. A dashboard provides visibility into the data important to any particular department’s operations, which is determined by each business unit’s Data Steward, so that every steward can understand the quality of the information that will inform his or her group’s business processes, as well as be more efficient and effective at continuously improving on the data’s accuracy.
All the work behind this – the defining of quality measures and the controls that ensure that the quality of data in scope persists as the organization’s information assets evolve – is mechanized and automated. The outcomes of the continuous sampling and testing that takes place are regularly updated to the dashboards, and alerts generated as the new results are published.
“The service provides insight into the data quality so that the stewards can focus on running business processes or a functional project, rather than having to themselves do data quality discovery and putting controls in place,” says Gerber. In internal benchmarking, the people who use the Data Quality services (for example, DQ measuring, impact analysis, and data discovery) realize a 4 to 10x faster improvement over manual, business-as-usual approaches to Data Management.
Before the debut of the DQaaS platform, Data Quality measurement/visibility varied across business units, and the effort to measure it was mostly manual. Previous to the platform’s launch, the marketing department, as an example, did have visibility into the quality of the customer data it may have pulled from various sources to use in its campaigns. But it wasn’t able to gain that visibility as quickly or efficiently as it now does. Today, “they have an incredible visualization tool for them to actually see the level of data quality,” Gerber says, as well as a way, via the analytics platform his team also spearheaded, to see what are the positive benefits in terms of business outcomes if they improve the quality of, say, email addresses over time to make marketing campaigns more effective.
The stewards, he says, can be very proactive in fixing and cleansing data – not just at the point where the gap is evident but systematically at its source, lowering the amount of ongoing manual reconciliation that is required, he adds.
Blueprint for Data Transformation
How did TIAA-CREF reach this point? It started when Gerber came on board in 2011 to accelerate, refocus and re-energize a data transformation program that was already underway, the goal being to enable the firm to use its data assets and analytics for competitive advantage. Doing so, he says, has been a team effort and partnership with the company’s Chief Data Officer and business leaders.
The blueprint behind this covers creating and growing Data Management capabilities so that it can offer these to users as enterprise data services. Such capabilities enable the company’s business strategy and business imperatives, lower operational risk due to data quality, optimize the data landscape to drive down costs by having less redundancy and to drive up re-use for speed to market, and mature Data Governance and stewardship practices. To these ends, the company has focused on building enterprise data platforms and metadata repository platforms that integrate and unify data assets to replace redundant information silos; institutionalize pockets of best Data Management practices as enterprise best practices; and make Data Governance/Stewardship a part of the day job for individuals in each business unit, versus an IT function.
“We saw that as an opportunity to nurture business data ownership,” says Gerber. The lead Data Stewards, he says, are close to business opportunities and processes. So, it makes sense for these individuals to target the data that matters to those efforts, and for the enterprise data transformation team to work closely with them and provide the tools to continuously improve their data and link it to business outcomes.
For services that live on the enterprise data platforms – tools like the dashboard that is key to the DQaaS initiative’s success at enterprise-scale – TIAA-CREF put into practice a playbook to formalize a continuous improvement cycle around data. The Data Steward Playbook, as it’s called, is the how-to component that shows the processes of exactly what to do and how to do it when it comes to cataloguing data with stewards, mapping it to key business terms and applying ongoing quality and change control monitoring.
It leveraged K2 Solution’s data playbook for increasing Data Management maturity in its work, which includes as part of its plan the creation of agile Data Sprints to identify and remediate data quality issues.
“That is for us to take a specific business problem – usually a horizontal one across multiple lines of business – and do a discovery phase to quantify what is the challenge; link that to underlying root causes in the intersection of business processes, systems, data flow and data quality, in order to provide visibility to the horizontal business problem; and then recommend a course of action,” says Gerber.
These action courses can be packaged into rapid-implementation services, like TIAA-CREF’s data quality offering.
“If we look at the flow of customer data across the firm, we can find any gaps leading to lower quality data and remediate them,” says Gerber. Consider that in context of the fact that if you look at all the business processes that handle customer information, “you want to be able to have a consistent outcome.”
What makes an enterprise service successful as TIAA-CREF’s is, “is not just that it’s a great service idea, but that you can execute it at scale across the enterprise,” adds K2 Solutions founder Dan Meers. With this approach, organizations can “persist the value” of all the work they put into driving Data Quality for the enterprise at large to use, rather than treat a Data Quality effort as an individual project that’s shelved once completed. “That service is now added to the managed data portfolio. It’s a one-time cost, and annuitized value because you don’t have a team of people reworking the project every year to re-determine quality issues,” Meers says.
TIAA-CREF’s enterprise data transformation program, with its DQaaS component, has received industry-recognition for how far it has gone to leverage technology to move its business forward. In 2014, it was one of the recipients of the CIO 100 Innovation Award for its data transformation program and DQaaS solution, as well as the sole winner of the Data Warehouse Institute’s Best Practice Award for Enterprise Data Management Strategies.
Within TIAA-CREF, DQaaS – which has been in production for two years – now is seeing between 40 and 80 percent compound annual growth rate in the amount of critical business data monitored for business units. “The amount of critical data for the firm that we measure and monitor goes up every year,” says Gerber.
That’s a good thing, very much in line with his team’s strategy of focusing on offense – that is, “using the services to get to good business outcomes that make us more competitive,” he says. Defense is covered too, of course, in that DQaaS supports necessities such as regulatory data compliance requirements in a streamlined fashion – something which Gerber says consumes a lot of bandwidth in other financial services firms.
It’s never too soon both to shift focus to getting a line to business outcomes and to getting started on the journey to developing enterprise data services that support that. As for TIAA-CREF, “we’ll continue to grow the number of data services we offer, and continually mature and improve our practices,” Gerber says. “We’ve driven great adoption that gets returns and outcomes, and we want to continue on that success.”