The ability to easily create new transformations allows the business to try more analytic approaches to find the unexpected, but valuable, winners. “In the old world, if we had 10 ideas for useful analytics, we only had time to work on four of those,” he says. “We wanted the team to try every idea even if 60% of them failed.”
Maintain and secure the data over time
Despite enterprise-wide needs for more and better data, it can be difficult to convince business units or boards of directors to fund the ongoing work to ensure data is accurate, timely, and secure.
Yao Morin, chief technology officer at commercial real estate services firm JLL compares data maintenance to plumbing, which nobody thinks about until it malfunctions and creates a messy, urgent problem. To get the needed funding, data practitioners must continue to show business leaders the value of data, and how if we don’t maintain the data, it’ll be useless, she says.
In JLL’s case, such value includes meeting demands from clients (and the renters who occupy their buildings) for new types of information as workers return to the office after COVID-19 lockdowns. This includes whether employees are sitting isolated at desks or meeting in crowded conference rooms, the quality of the air in them, and what amenities, such as restaurants, are open near their offices to lure them back.
While senior management backing is crucial for ongoing data management, Lenovo’s Hoogar calls doing the work a collective responsibility for everyone. One way to build ground-level support, he says, is finding data enthusiasts in each department and building their skills through courses and regular meetings with other data champions or data councils. Continual education, training, and upskilling are also critical to better data management, he says.
“The issue CIOs run into is that many boards and bank CEOs are reluctant to hire data analysts over commercial lenders because they don’t see them as revenue generating resources,” says Garcia at First Commerce Bank. “But a financial institution armed with a dozen data analysts properly weaponized with real-time data can be more effective than a legion of lenders aimlessly trying to grow their portfolios without the proper analytics to guide them.”
Start early
The time to standardize everything from data modeling to its security is when the data is acquired. “We template a lot of our data ingestion processes,” says Morin, requiring the addition of metadata and a data dictionary so business leaders can know what information they can get from the data lake. “Without those templates, it’s hard to add such information after the fact.”
Robbins, at First Service Credit Union, urges holistic, up-front data modeling to create well understood data that can be analyzed easily and in new ways. For example, a query asking how many deposits a credit union received each month will only draw on the elements required to get the data for that report, he says. Generating a related report and asking for the number of new accounts receiving deposits requires starting from scratch, which wastes valuable time. “With a metadata platform, you assemble all the data adjusted to those elements in one view so you can simply do any one of a number of reports on that data,” he says.
Along with such day-to-day benefits, companies such as Comcast say the right data architecture and infrastructure allow them to develop exciting new generative AI applications much quicker than they expected. But before reaping such benefits, “you’ve got to get the infrastructure right and the data clean,” says Davis. “It takes a lot of grunt work, but with that work done, one can do amazing things.”
