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

In today’s fast-paced business environment, companies deploy a combination of analytics software, cloud platforms, and other technologies to gain competitive advantages swiftly. This principle is vividly exemplified in Formula One racing, where cars zip down tracks at speeds exceeding 200 miles per hour. While the spectacle of the race is thrilling, the real magic happens behind the scenes with teams like Oracle Red Bull Racing (ORBR), where data analytics play a pivotal role.

Formula One might seem like an individual sport focused on the driver, but each victory relies on the efforts of a massive team of engineers, technicians, and mechanics. These professionals use their expertise to fine-tune every aspect of the vehicle and the strategy. Even more team members work behind the scenes, leveraging data analytics to provide real-time insights that give ORBR an edge on race day.

Oracle Red Bull Racing: A Data-Driven Success Story

At the heart of ORBR’s success is a robust data analytics infrastructure powered by Oracle Cloud. Jack Harington, senior partnerships manager for ORBR, highlights the team’s extensive support system: “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. These off-site engineers provide live, in-the-loop support to trackside engineers, making real-time decisions based on data streaming back to the factory.

During races, ORBR processes around 45,000 data points per second through Oracle’s cloud infrastructure. This data includes everything from cornering performance to time lost in various speed corners. Analyzing this data allows the team to optimize each car’s setup specific to each driver, ensuring maximum performance. Dan Smith, former technical partnerships executive at ORBR, explains, “From that time lost, we can then infer how we can change their setup…trying to make each setup of the car specific to each driver to gain the maximum amount of lap time.”

The Broader Impact of Real-Time Analytics

The rapid, real-time analysis that ORBR performs on race day is not unique to motorsports. Businesses across industries leverage similar data analytics platforms to enhance decision-making processes. According to 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.”

For example, USAA, a company providing insurance, banking, and other financial services to military members, veterans, and their families, has integrated data analytics deeply into its operations. Ramnik Bajaj, USAA’s chief data and analytics officer, emphasizes the importance of making data readily available to both business decision-makers and frontline employees. This data-driven approach allows USAA to process data for lending, credit, and insurance underwriting decisions more effectively.

USAA’s Data Evolution: From On-Premises to the Cloud

When Bajaj joined USAA in mid-2021, the company relied on an on-premises IT environment for its data needs. Recognizing the need for agility and scalability, USAA transitioned to a Snowflake data warehouse on Amazon Web Services (AWS) cloud infrastructure. Over 18 months, they migrated data sets with strict governance to protect personally identifiable information. This move to the cloud enabled a unified data environment and catalog powered by Alation, simplifying data search and report creation using Tableau.

With this streamlined data environment, USAA employees can now create comprehensive reports covering trends such as credit card usage, insurance policies offered in specific states, and real-time claims status. The ability to quickly access and analyze this data has significantly improved USAA’s customer service and operational efficiency. Bajaj notes, “We’re faster in our ability to use the data… reducing the number of redundant copies of data by having it all in one place.”

Milliman: Empowering Actuaries with Advanced Analytics

Another example of effective data analytics implementation is Milliman, a global actuarial and risk management firm. Milliman uses complex models to project decades into the future, determining business performance, risk exposure, and capital management strategies. These models require vast amounts of computing power and sophisticated data analytics tools.

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.

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?’”

The Future of Data Analytics: Continuous Improvement and Adaptation

The journey towards data-driven decision-making is ongoing. Organizations like ORBR, USAA, and Milliman demonstrate that investing in data analytics tools and infrastructure can yield significant benefits. However, as IDC’s Gopal points out, achieving a perfect data environment is not a prerequisite for becoming data-driven. Instead, businesses should focus on identifying the key drivers that impact their outcomes and continuously adapt their data strategies to meet evolving needs.

In conclusion, the ability to quickly derive insights from data is a critical goal for any organization. Whether it’s optimizing race car performance, enhancing customer service, or managing risk, real-time data analytics provide the competitive edge needed to succeed in today’s dynamic market. As technology continues to evolve, companies that embrace and innovate with data analytics will be better positioned to navigate the challenges and opportunities of the future.

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