Jenny Schultz has been with Freddie Mac since 1998. “We have tried to do the ‘data thing’ more than a few times, driven by IT, but it never stuck because the business didn’t see the value in leveraging their data and managing and governing it,” she said during her Enterprise Data World Conference presentation titled “Freddie Mac Single-Family Business: A Case Study in How to Operationalize a Data Stewardship Model.” She co-presented with Van Lin and Stephanie Grimes.
After four or five attempts, a 2014 initiative finally gained traction because, according to Schultz, the business was driving the program. The difference with the successful Data Stewardship Model, she said, was getting the right people involved.
Schultz and co-presenter Van Lin spoke at length about how they operationalized a new Data Stewardship Model at Freddie Mac. Both serve as directors of Data Governance for the single-family line of business along with Stephanie Grimes, Manager of Data Governance. The presentation was organized in three phases: (1) Ready, laying the groundwork and establishing the program; (2) Set, identifying and working with stewards and stakeholders; and (3) Go, the execution of the program. Although the presentation spanned the operationalization process from conception through execution, this introduction focuses on the Ready phase: the steps from conception through planning.
What Freddie Mac Does
Freddie Mac’s single-family division provides funding for banks to finance mortgages for single family homes. Since 1970, Freddie Mac has funded 62 million loans. “Every time we fund a loan, we collect 75 data elements on each of those loans. We have borrower information for a 114 million borrowers and 94 million properties” in the single-family sourcing line of business alone, said Schultz. Servicing, their capital markets business, and their multifamily business are not included in those numbers. “The short story is that we deal with a lot of data. That’s all we do.”
Freddie Mac and similar organization Fannie Mae are both government-sponsored programs that provide support to banks for mortgage lending. “We’re in Virginia, and they’re in D.C., so we call them our friends across the river,” Schultz said. “We need to be able to compete with them and respond to new programs and products that they put in the marketplace.” Freddie Mac’s mainframe-based infrastructure is complex, making it difficult to respond nimbly to business challenges. In 2014, they concluded that, “We’ve got to simplify our architecture and have a north star: where we want to get to.”
Program Development – Identifying Pain Points
The head of the single-family business charged the CPA with finding a way to leverage their data and enhance the business value so they could better compete. The CPA, not being a ‘data person,’ decided to consult with key people across the organization to assess the need for a solution. “We went on what we call ‘the listening tour of 2014,’” said Schultz. They consulted stakeholders, co-workers, the internal audit department, compliance, and the privacy office, to determine what challenges they were having and what business problems they needed help with. She sees this as one of the key components to the program’s ultimate success. “Talk to your business stakeholders when you’re starting a program or making a change. We can’t just assume that we know what the business needs.”
As a result of the listening tour, they decided to work with the Enterprise Data Management Council (EDMCouncil) using their Data Management Capability Assessment Model (DCAM) to create a model from stakeholder input. The DCAM allowed them to create a roadmap they could use to take them from where they were to the ultimate goal: a robust data organization.
They identified ten key areas for improvement:
- Single-Family Data Management
- Data Governance
- Technology Engagement
- Data Quality
- Data Asset Management
- Master Data Management
- Reporting Analytics and Modeling
- Vendor Management
The program’s credibility came because the plan was not developed in a vacuum “but with our stakeholders, with our peers, and with thought leaders in the industry. We didn’t just make this up,” Schultz said.
Business Value Through Branding
Schultz said another way they brought credibility to the program early on with executives and business partners was by creating a brand that communicated the data program’s value based on the needs of the business and by publicizing successes along the way. “‘We’ve selected tools.’ ‘We’re helping rationalize data marts.’ ‘We’re retiring legacy systems.’ Each little thing we do, we publicize,” she said.
They used three overarching concepts in their branding:
- Empower: Give users the quality data they need, when they need it, and the independence to use it through self-service. Provide catalogs as the source of truth, robust toolsets, and support for innovation.
- Simplify/Reuse: Decrease hard costs, increase speed to market, share and repurpose data for maximum value. “We buy a lot of data, we create a lot of data — let’s do it once, but you have to share it,” she said. This also creates capacity.
- Control: Ensure rules to allow safe and secure use, align risk management with risk exposure, and reduce data sprawl and testing costs.
Setting Data Standards
Rather than sit in a room with her team and write data standards – a process that could have been done in a few weeks – Schultz said they decided to create working groups to ensure engagement at all levels. They wanted standards that reflected the realities that people worked with. “What bothers them, what challenges do they have, what solutions can we bring to bear?” The first draft took at least 90 days to put together, and they are working on version five presently, “But people are still engaged because we hear them and we make changes based on what they need.”
Different types of data require different handling, so the groups tailored the Data Governance standards to the data’s purpose. Financial data, mission critical data, and data for R&D all have different risk levels anddon’t need to be controlled the same way, she said.
Standards also defined roles and accountabilities, which included an executive committee to oversee high-level priorities and ensure agreed upon road maps. Schultz’s supervisor built in additional oversight to ensure that the planning continued to move forward: she reserved the right to override the process and make a final decision, if needed.
“So, I want to hear from my stakeholders what’s happening, I want to know what’s going on, I want a debate, but when it comes to the end of the day, if we’re not moving, I’m going to make the decision and we’re going to go with what I think is right.”
Whose Job is Data Governance?
“There’s no one in our data centric organization that doesn’t have a role” in Data Governance, Schultz said. Data owners need to provide metadata. Users need to share with owners and stewards what they are using data for and what their quality thresholds are so that data stewards and owners know who to consult if they want to make changes in the data. Schultz said this has necessitated a major shift in the company culture, leading her to conclude that data is not a technology problem, but rather a “people-culture change-communications problem.” “We kind of joke and say, ‘We’d be done with data by now if we didn’t have to deal with all the people.’”
As a result, Schulz considers half of her job as a director of Data Governance to be what she calls “data therapy.” “I listen to people all the time, and sometimes all they need they need is to get it off their chest.” Sometimes there are concrete things that need to come out of those sessions, but often it’s just about feeling heard. “It’s not around data. It’s about the people, relationships and connections – that’s what makes the biggest difference.”
Although they don’t have an unlimited budget, Freddie Mac is fortunate to have a champion in the head of the single-family business who has been willing to provide all the needed resources. Using the roadmaps they created, they were able to approach funding from a business-value standpoint, talk about who in the company was onboard with it, and ask for funding step-by-step.
“He would say, ‘Okay. I’ll fund you one and two. Maybe three needs to take a little longer,’” due to other current priorities. “Every year we have to make our case,” she said.
A participant asked if there were challenges with getting the data owners and stewards to adopt, and Schultz said that there were, and there are still some challenges. During the 2014 listening tour and again last year when they did a stakeholder analysis, they identified detractors and supporters so they could learn who they needed to spend more time with. “Again, that’s a people thing,” spending more time with those who need more data therapy and less with those who don’t.
Another key part of overcoming challenges was having top-down support. “The head of our business said, ‘We’re doing this data thing and you’d better get onboard,’” she said, “so you either get on the bus or get left behind.” She added that as hard-lined as that sounds, the reality is that the process of getting people on board is more about spending time with them, helping them get with the program.
Ongoing Planning Process
As of the end of 2017, Schultz and team have completed another listening tour, but haven’t yet refreshed their Data Strategy. Because the business continues to change and needs continue to change, “We’re going to be on a schedule to do that pretty much every three years,” she said. “We have to keep getting out there, so we have a refreshed version of [our Data Capability Model] that looks a little different” but still has all the capabilities that the company supports.
The most important difference between their previous Data Governance attempts and this one was the level of buy-in they had. By ensuring that the Data Capability Model creation process included input from people at all levels, “They’ll be bought in and engaged right away if they see what they need in that document.”
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