SACRAMENTO, Calif. -- The California Lottery is using data analytics to drive operational efficiencies and grow an enterprise designed to funnel much-needed money into public education.
From the selling of physical products, lottery tickets, to how they select their retailers, data analytics is bridging many of the gaps between mountains of data and actionable intelligence.
During a panel discussion at the Aug. 11 California Technology Forum, Lottery CIO Chris Riesen explained that data was doing more than simply identifying when and where potential players might be, it is also helping to move the larger enterprise toward its strategic goal of becoming the largest lottery in the country.
By mapping revenue growth over the course of the last several years, officials are confident they will be able to claim the top rank relatively soon. Riesen said revenues have increased from roughly $5 billion in 2014-15 to an unaudited $6.3 billion in 2015-16, and are expected to reach a projected $8 billion by 2018-19.
“And that’s not a small jump to make. It’s not easy to sell lottery products to necessarily the same people and keep growing it. We have to come up with new ways and think about what we do differently,” he said. “We’ve done the math. We use analytics to support the thinking that we can be the largest lottery and we are using analytics to actually take us to that place.”
As Riesen sees it, the numbers only reinforce the fact that California is “nowhere near” meeting its potential for sales and growth. By leveraging historical data and context with business goals and operations, lottery officials are able to tackle a number of issues that they previously relied on people to track.
“We have a predictive model for scratchers in terms of them going out of stock or out of inventory at retailers,” he said. “And we’ve got a model to forecast or predict how well we believe a retailer will perform when it comes to selling lottery products.”
In the larger effort to move product, analytics is also removing some of the human suppositions that once played heavily into which retailers were selected to sell lottery tickets and scratchers, as well as predicting when those products would likely run out. Riesen said empty retail shelves equate to millions in lost revenues.
“If we don’t have a product there that someone wants to come in and buy, that’s lost revenue for us, lost profit for education,” Riesen said, who added that the agency estimates that figure is in the millions of dollars every year.
The organization has pushed past the old days of manually produced outage reports and has focused on identifying when product is likely to go out of inventory. The CIO said that in the near future, the predictive solution coupled with the product ordering system could help automatically allocate product to meet demand.
But the California Lottery is not the only government agency leaning on the business potential of data analytics. Kevin Mergruen, vice president of Vertical Solutions for business intelligence provider Information Builders, said many sectors of government are turning to the technology for actionable intelligence that drives positive outcomes – but this is not without challenges.
“Part of the challenge when we look at most government agencies is that we see a lot of data across the organization. There tends to be a lack of information consistency, a lot of redundancy, a lot of dirty data; there is not a consistent model for maintaining and making sure we have accurate information,” he said.
Disjointed and inaccurate data is one of the major hurdles to conquer before the data becomes useful and can be translated into actionable intelligence for the business side of the enterprise.
One of the case studies Mergruen pointed to as a success was the New Hampshire Department of Revenue Administration. A 40 percent staff reduction across the organization forced the tax collection agency to focus on running as efficiently as possible, which was achieved through the careful consolidation of 14 disparate systems.
Similarly, public safety agencies have also leveraged the technology effectively. The Michigan State Police and the Charlotte-Mecklenburg Police Department are using data analytics to look at traffic safety and incidents across their respective jurisdictions and improve collective public safety.