Simply put, data is the lifeblood at Express Scripts, a $44 billion pharmacy benefits management company based in St. Louis.
The Fortune 100 company processes close to 1.5 billion prescriptions for some 300 million consumers per year, all the while analyzing the wealth of information that accompanies each order.
"As we track a prescription through data entry and the pharmacy process and into the fulfillment system, we're tracking all sorts of information that gets fed to an analytics team that is focused on process improvement," says CTO Jim Lammers. Internally, it's how the company speeds delivery and cuts errors, he says.
But Express Scripts also processes more than 1 billion pharmacy insurance claims annually, and they represent a gold mine of information that could help cut healthcare costs and address the multibillion-dollar healthcare problem created by people who don't take their medications as prescribed, says Lammers.
Computers, mobile phones, tablet devices, sensors, tweets, texts and posts to social networks, not to mention run-of-the-mill retail and registration transactions online, are all generating potentially valuable data. A lot of data. By 2020, IDC estimates that the number of business-to-business and business-to-consumer online transactions will reach 450 billion per day. We took a look at three organizations that are ahead of the curve in generating big business value from big data and analytics technology. At the top of their lists of lessons learned: A deeply-rooted culture of analytics and a relentless focus on cost efficiency and process improvement are invaluable.
The Win: Lower Healthcare Costs
At Express Scripts, claims data can show whether patients are filling their prescriptions in the most cost-effective way, which is frequently by mail order. If they aren't, Express Scripts can intercede by providing the patient with additional cost information and offer to switch delivery fulfillment methods for them with a minimum of hassle.
"If they're taking a maintenance medication for high cholesterol and we know they've been taking it but they've been taking it from a retail pharmacy, we know if they move to a mail order, they can save," Lammers says. "We'll do proactive emails and drive the patient to our website and use specific messaging to get them to make [a mail order] decision."
What it boils down to is "doing the data analysis, creating the interaction and getting out the right message so that the patient can make a different choice," Lammers explains. "One of the key tenets is that if we offer people the right choice, they'll take the right path."
It sounds easy, but behind the seemingly effortless redirection is a massive amount of technology, not to mention a strict culture of analytics that permeates virtually all of Express Scripts' operations.
One of the company's largest IT investments has been in IBM's master data management software, which is critical to creating a single record that connects all of a customer's actions, regardless of whether a transaction is made via email, on the Web, by phone or in person at a retail pharmacy.
"One of the biggest challenges is linking all information together across all these different sources," says Lammers. "We've made very heavy investments in master data management. We invested early on and we've been through two or three iterations."
Express Scripts also created what Lammers calls a federated analytics model that includes a business analytics team embedded in each key functional operation, such as supply chain, sales and finance. A single data warehouse and centralized data governance are two other keys to the company's analytics success, he says. "With a centralized core, everyone is looking at the same data," Lammers notes.
With a proven data governance model and a data management foundation in place, Express Scripts recently expanded into predictive analytics, introducing an application called Screen Rx that's designed to reduce the problem of patient non-adherence to prescriptions for chronic conditions such as diabetes and high cholesterol. At a cost of more than $317 billion annually, non-adherence is the most expensive healthcare-related problem in the U.S., according to Express Scripts.
For example, skipping doses of a prescribed cholesterol medication might trigger heart attacks for some patients. Using predictive modeling based on 400 factors, such as a patient's location, family situation and the number of medications involved, Express Scripts can now identify, and proactively intervene with, patients who are likely to skip doses. Interventions might include a timely reminder to the patient to take his medication or a referral to a patient assistance program to help him pay for his medications. A third option is a referral to a clinical pharmacist who can assist with questions or concerns about a drug's side effects.
"This is really one of the key things we've been building to -- to change behavior," says Lammers. He adds that striving to foster healthy behaviors in patients is especially important in light of impending healthcare reform as millions of people gain access to consistent healthcare for the first time.
"We have to train them to take care of themselves," he says. "When we can put Screen Rx into a population that hasn't had consistent access to healthcare, we can get them to get the right stuff right away."
The Win: Fuel Savings and Better Driver Safety
Transportation and logistics giant UPS, which has annual revenue of $54 billion, invests roughly $1 billion per year in IT, and a very hefty portion of that is devoted to data analytics, according to Juan Perez, vice president of information services. The goal -- for now -- is to improve business processes, cut costs and increase efficiency.
The effort has been a success. By analyzing a continuous stream of sensor data from its thousands of delivery trucks, the global company has eliminated 5.3 million miles from its routes, reduced engine idling time by almost 10 million minutes, saved 650,000 gallons of fuel and reduced its carbon emissions by more than 6,500 metric tons.
At the heart of these eye-popping metrics is ORION, which stands for On-Road Integrated Optimization and Navigation, a data-intensive system that lays out the most efficient routes for individual drivers to deliver their loads via a series of complex algorithms. Additionally, the system taps into the mountain of sensor data to predict when a truck part might fail so that preventive maintenance can be scheduled and completed.
ORION also lets UPS managers peer into the habits of individual drivers, pinpointing, for example, the number of times a driver backs up a truck or makes a U-turn. This information can be used to identify drivers who need additional training.
"We have sensors that capture information about the vehicle and the driver's behaviors. We marry that information to delivery and acquisition information, and we can get a complete picture of how a driver is completing his work, day in and day out," Perez says. "That has incredible consequences for the way we manage the business across the board."
Now, the company's appetite for data is extending outward. Its goal is to get closer -- much closer -- to its millions of customers with another analytics-intensive service called UPS My Choice, which lets people set individual preferences for how they interact with the company.
Customers using the service can, among other things, give specific instructions about how and precisely where to deliver their packages to specific addresses, reroute packages if they change locations, and sign up to receive status alerts.
"What we've done is take a new approach to managing personal supply chains. Having that level of connectivity with our customers is going to change our business now and in the years to come. The integration with consumers is what is enabling revenue growth," says Perez. In the first year UPS My Choice was available, more than 2 million customers signed up for the service, and more than 25 million packages were delivered under its auspices.
Data about customers' delivery preferences helps UPS to continue to refine its internal processes in response to those preferences "so we can build a one-to-one experience," Perez says.
But even more critical is the insight that the data provides into what new products and services to offer.
"All of the [tracking and delivery] notifications we provide and how customers respond to notifications tell us what they want so we can create the products and services they want. It's a lot of data to define new products and services."
The next step, as Perez sees it, is to tie everything together and create a graphic picture of UPS's various big data systems so the company can uncover new uses for the data -- and thereby derive more business value from it.
"It starts with process improvements, but once you start tying all of this together, it can mean very big changes in the business," Perez says. "That's what we're getting at."
The Win: Millions in Added Sales
Traditional business intelligence is alive and well at Intel, but big data mining and predictive analytics are the forces driving design and manufacturing efficiencies, and uncovering new revenue sources that added up to tens of millions of dollars in 2012 alone.
"It starts with believing that you can change outcomes," says CIO Kim Stevenson of the chip manufacturer's massive success with analytics. That, she says, requires less time spent on historical questions, which is the purview of traditional BI, and more focus on the future, which is what predictive analytics is all about.
Predicting the future at $53 billion Intel requires analyzing massive amounts of data to discern patterns and then applying predictive algorithms to solve high-value business problems.
In 2012, for example, Intel IT created a new reseller sales tool that worked to increase the chip maker's revenue by enabling its sales team to identify, then strategically focus on, larger-volume resellers. The new software engine mines large sets of internal and external data, then applies a predictive algorithm to pinpoint the most promising resellers. So far, it has helped identify three times as many high-potential resellers in the Asia-Pacific region as manual methods typically would have uncovered, according to Stevenson. That translates to about $20 million in potential new and incremental sales. More gains are expected as the tools are rolled out to other geographies.
On the manufacturing front, Intel is using a predictive analytics tool to reduce microprocessor testing time. The company saved about $3 million in testing during a proof-of-concept period. By 2014, as the tool is implemented more widely, Stevenson expects it to rack up another $30 million in savings companywide.
Intel's analytics success has been fast-tracked, to say the least. The key, Stevenson says, is tackling big-money problems with relatively small and swift-acting teams.
"To get the business to focus on the future and ask better questions that would lead to better outcomes, we knew we would have to do things quickly," she explains. "We were coming out of a traditional BI environment where solving master data is the unsolvable problem. People work on it forever and the business doesn't necessarily see the value."
So Stevenson came up with the "six months and $10 million" rule. "A $10 million problem solved in six months is important. Any general manager would say they'd invest six months if we could save them $10 million," she says. (At Intel, business managers must support and fund IT projects.)
Stevenson recruited five-person teams made up of a business expert, a statistician, a predictive modeler, a machine learning expert and a data scientist. "Each person on the team had a slightly different perspective on the problem we were trying to solve. Doing it in six months was our way of earning the right to prove the capability was there to really change the way we do things," she says.
In addition to the projects that reduced testing time and pinpointed lucrative resellers, 13 other analytics projects have been completed using that approach. So Stevenson has upped the ante by finding $100 million problems and challenging teams to solve them.
"When you have a track record, you can ratchet up," she says. Other ongoing projects include a predictive engine for streamlining Intel's chip design and debugging process and another to predict new information security threats.
But Stevenson cautions enterprises not to underestimate the skills required for analytics initiatives and the time it may take to nurture those skills.
"When I think about our learning curve with Hadoop and some of the more advanced presentation layers that are very different from SAP or traditional BI, I'd emphasize that there is a learning curve there for technical skills that isn't insignificant," she warns.
Her other piece of advice: "Develop an appetite for experimentation," especially since analytics technology is still evolving. "The winners and losers on the tech side are not completely shaken out yet," she says. "Keep your aperture wide."
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