“Oh, people can come up with statistics to prove anything. Fourteen percent of people know that.” – Homer Simpson
The era of big data is here, the nerds proclaim.
Computers are powerful enough to gather and synthesize terabytes of information to answer questions ranging from how best to compensate employees to how risky is that mortgage-backed security.
But while the numbers don’t lie, how people use them is extremely subjective. Quantitative analysis played a part in the financial crisis of 2007, after all, and companies that think a room full of analysts crunching numbers can solve their problems can do damage to not only their profits and losses but also to their corporate culture and employee well-being.
“Making the decision at the end of the day can be aided by data, but the thought that computers will make all the important decisions is just not true,” says Shvetank Shah, executive director of the Corporate Executive Board (CEB), which recently published a study titled Overcoming the Insight Deficit: Big Judgment in an Era of Big Data. “Saying that I’ve got 10 quant jocks who are going to solve all my data problems is the wrong way to go about it.”
There is little argument that many competitive advantages in the future will go to those who most effectively use analytics to guide decisions. Having the data, though, is not enough, Shah says. According to the CEB, only 38% of employees in a 4,941-person study were considered “informed skeptics” who rely on data but not so much that they are afraid to question the results and solicit feedback from others. The rest of the workforce either trust data without question (43% of the study participants) or rarely trust analysis and prefer to go with their gut (19%).
“We have to face up to the fact that our education system isn’t preparing us to analyze data effectively,” Shah says. “We showed people graphs and asked them what they mean, and even folks with Ivy League educations struggled to make sense of the data.”
There are ways to bridge the education gap, though. Shah’s research has shown that training is ineffective when students are told to focus on the analytic tool or software being used. Instead, they should be taught how to interact with the data or, in other words, how to think critically.
One way for a company to ensure that they turn their employees into “informed skeptics” is to work on creating a data-driven (but not data-enslaved) culture, starting with the CEO on down. If the chief executive is on board, others will likely join.
Another effective method, according to Shah, is to hire quants not only for their analytic abilities, but also for their ability and willingness to explain what they do to others. “You’ve got to hire quants for their coaching skills,” he says. “Each quant can improve the decision making capabilities of 10s or 100s of others.”
is using analytics to roast several sacred cows, says Joe Touey, senior vice president in IT at its North America Pharmaceuticals division. The company has embraced data analysis to redesign its sales operations, moving what was once a field based solely on relationships to one that’s now based on data.
“We are building a reputation by making hard decisions about what data we use to compensate our sales people and what we don’t use,” Touey says.
Since the early 1980s, pharmaceutical companies have used physician prescription data acquired from vendors to determine how they pay their sales force — sales reps would make more when doctors wrote more scripts. As any doctor who has scored a catered buffet or free sports tickets can attest, this strategy led to some questionable relationships and caused some patients to wonder why doctors were prescribing one treatment over another.
Beginning last July, GSK scrapped this model. The company now relies on surveys of doctors and managers, as well as tests of product knowledge and business acumen, to determine sales bonuses.
Selling this new model to the sales force became easier thanks to support from top management, the transparency of the incentive system, and the favorable reaction from customers, Touey says. “It has become less about relationships with doctors and more about facts and science,” he says.
GSK also is letting its data guide how it negotiates with governments and insurers. They compete with other giants like Merck
for a very limited pie, and Touey says that analytics are providing an advantage to the firm.
In the ever-changing world of healthcare, effective data analysis can provide cost savings in multiple channels when applied correctly, says Susan Helm-Murtagh, vice president of information management and analytic services for Blue Cross and Blue Shield of North Carolina.
“The only way to do this is by examining information and analytics,” Helm-Murtagh says. “Where are costs coming from? What cares are effective? Where is it delivered?”
Last year, BCBSNC brought many of its analysts from different units into a centralized group where they could collaborate. The company also looks to hire analysts who are capable not only of crunching numbers but also of serving them up in ways that others can understand and accept.
When executives realized the absurdity of having Ph.D. statisticians wasting time hunting for data, they made a large investment in a Center of Excellence (COE), which allows analysts to focus on asking and answering questions instead of looking for data and verifying its accuracy.
These changes have resulted in significant cost savings and increasing levels of customer satisfaction, Helm-Murtagh says. The investment in a COE has already resulted in medical expense savings worth 19 times the initial cost.
While the focus on data has been a resounding success for BCBSNC, the company still faces the challenge of convincing some employees that this is where the company’s future success resides.
“We have a job to do in helping people understand that this is a different place to work today than it was three or four years ago,” Helm-Murtagh says.