Jun 18 2024
Data Analytics

Why Time to Insight Is a Critical Goal of Data Analytics Tools

Companies deploy a mix of analytics software, cloud platforms and other tech to gain advantages fast.

With cars zipping down tracks at more than 200 miles per hour, Formula One racing is one of the most thrilling sports experiences today. The merging of car and driver into a single unit captures spectator interest, but it belies the group effort that propels a successful team such as Oracle Red Bull Racing (ORBR).

The casual fan may think of Formula One racing as an individual sport, but drivers rely on massive teams of engineers, technicians and mechanics whose skill and knowledge are fundamental to delivering victory. 

Behind the scenes, even more team members work to create the sustained success ORBR has seen in recent years. A deep bench of support staffers leverage the power of data and the technology needed to unlock its extensive value, helping to secure a competitive edge not just for driving superstars such as Max Verstappen and Sergio Pérez but also for the business needs of the organization.

“We not only have 60 people who we take to the track, but what people don’t realize is that we have a similar number sitting in our operations room” outside of London, says Jack Harington, senior partnerships manager for ORBR. “No matter where the race is run around the world, whatever time zone it is, we have the same number of engineers in our operations room in Milton Keynes providing live, in-the-loop support to the engineers at the track making on-the-spot decisions based on data that we’re sending back to the factory.”

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Oracle Red Bull Racing Uses the Cloud to Improve Performance

As with any successful organization, technology plays a key role in driving desired outcomes for ORBR. And much of that technology is fueled by the data being generated trackside and analyzed in the Oracle cloud.

“The analytics side of it is run off the computer, but the computer has to stream the data into the Oracle cloud,” says Dan Smith, former technical partnerships executive at ORBR. “During an event, we have virtual machines that we run up with Oracle. We’re running about 45,000 data points a second through the computer into the cloud.”

With all of that data safely delivered to the cloud, analysts can transform it into valuable insights for ORBR’s simulated racing team. “In racing, we’ll be looking at their cornering performance, seeing time lost that they might gain through low-speed, medium-speed and high-speed corners,” Smith explains.

“From that time lost, we can then infer how we can change their setup,” he says. “So, it’s trying to make each setup of the car specific to each driver to gain the maximum amount of lap time. Also, looking at each driver, we can see where one is quicker than another and give that feedback back to them to make adjustments.”

More organizations are leveraging data analytics platforms to remove friction in their decision-making, and the kind of rapid, real-time analysis ORBR does on race day is typical of the most successful analytics programs. Speed matters in every business, says Chandana Gopal, research director of IDC’s Future of Intelligence practice: “The market forces and the way that we operate change so quickly that if you’re not able to keep up and get the right data at the right point in time, you might be able to survive for the near term, but in the long term you’re going to have competitors that will outpace you.” 

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USAA Unifies Data to Get Faster Insights

For more than a century, USAA has provided insurance, banking and other financial services to military members, veterans and their families. In many ways, the company’s core business is processing data to help customers, says Ramnik Bajaj, USAA’s chief data and analytics officer.

Data helps USAA make lending, credit and insurance underwriting decisions. Its data analytics evolution has been focused on making data readily available to business decision-makers and planners as well as frontline employees serving customers, Bajaj says.

“It’s a journey of becoming data-driven in our core products and our processes and in all of our thinking,” he says. “It starts with asking the right questions and having the tools to answer those questions with data and analytics.”

When Bajaj joined USAA in mid-2021, the company had all of its data in an on-premises IT environment. He led an effort to migrate the data to a Snowflake data warehouse on Amazon Web Services cloud infrastructure. USAA built an internal tool to let teams move data sets over an 18-month period, with governance to protect personally identifiable information.

Ramnik Bajaj, USAA


USAA has a unified data environment and catalog, powered by Alation, that lets business users search for data easily and then quickly build Tableau analytics and reports using their identified data assets.

With that, USAA employees can stitch together data from across departments to create a 360-degree view of members, including data on their last interactions with the company, their pain points, what services and products they have, and how extensively they use them. That data can then be used to improve customer service or offer the right kinds of services to meet customers’ needs.

USAA has 6,000 employees who create reports and 14,000 who view them, Bajaj says. Those reports might cover trends on credit card use, which insurance policies are being offered in a specific state or how many claims are outstanding in real time.

“We’re faster in our ability to use the data,” Bajaj says. “We are more creative in asking data-oriented questions because people can actually discover the data and find its use. We’re reducing the number of redundant copies of data by having it all in one place and discoverable, and we know exactly who has access to that data and what they’re using it for.”

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Milliman Builds Powerful Analytics to Empower Actuaries

Milliman, a global actuarial and risk management firm, uses highly complex models to project decades into the future to determine how businesses will perform, the risk they will be exposed to, how capital should be managed and more, all of which is reported to stock market regulators. 

Tom Peplow, principal and senior director of technology strategy at Milliman, says those models, which are critical to the company, can only be built with tremendous amounts of computing power.

Milliman wanted to give its actuaries the tools to build those models themselves, as well as the data within them and the automation to run them at scale in a governable way, Peplow says.

The company’s goal was to reduce the total cost of ownership, speed up actuaries’ time to insights, open up access to data for clients and break down silos of data sets between departments.


The reduction in USAA’s time to production for quantitative models and analytics reports since adopting its Tableau analytics solution

Source: USAA

To do it, Milliman first built an internal data platform called Integrate on top of Microsoft Azure. Actuaries were able to build reports from the data using Microsoft Power BI.

“What we’ve done is glued all the services together for them,” Peplow says. “They run an actuarial model, it produces data, they want a report on that data. We built the stitching to move the data from our models into a database, and they can then build the Power BI reports on top.”

One common report is an income statement, which is more complex for an actuary than it would be for an accountant, because it needs to be projected decades out.

Underpinning that projection are many complicated scenarios and much more data than in a standard income statement.

Milliman is also in the early stages of launching other advanced Microsoft data analytics tools to get insights faster and allow actuaries to dive deeper into reports and use generative artificial intelligence to help make judgments.

“We’re hoping that this technology will have a dramatic impact on that time to insight,” Peplow says.

Ultimately, IDC’s Gopal says, an organization’s business strategy should drive its data architecture and strategy.

“At the end of the day, you don’t have to wait to fix everything to become data-driven,” she says. “You don’t have to wait to have the perfect data environment, because that’s never going to be the case. You’re going to have to ask yourself, ‘What are the drivers that really affect my business outcomes?’”

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