Data is everywhere. It’s in our personnel records, our win–loss reports, our customer demographics. It’s in our accounting ledgers, our employee-sentiment surveys, and our competitive analyses. We have vast quantities of data from many parts of the business, which we can analyze to make informed decisions about hiring, mergers and acquisitions, product development, and much more. The many tools available provide a rich pool of financial, operational, and human resources data that allow us to move faster and with more-predictable results.
At least, that’s the theory.
The reality is a bit more nuanced. Because although the data is there, many of us can’t access it. We can’t contextualize it. And even if we’re lucky enough to understand it completely, we can’t implement our findings because our organizations are siloed, wary of change, and risk-averse.
Data is everywhere. But taking advantage of it may require some fundamental changes. We need easy access to it. We need technology that helps us interpret what we find. And we need a culture where teams and individuals work together to put our findings to good use. Like so many other aspects of business, culture is the hidden backbone of a successful analytics strategy. Build the right culture and your advanced analytics can thrive.
In 2002, manager Billy Beane of the Oakland Athletics baseball team changed the game of baseball forever. The cash-strapped team had lost three key members the previous season, and Beane was tasked with drafting a winning team on a shoestring budget. Since throwing money at top players wasn’t an option, Beane had to think strategically. He turned to data.
Beane theorized that a team with a high on-base percentage was more likely to score runs and therefore win more games. Based on that theory, Beane drafted college players with high on-base percentages, strong baserunning skills, and solid defensive abilities. Many of those players didn’t fit the stereotype of a star: they didn’t always have the best physique or impressive batting averages. But for Beane, the data was clear: they were the right players for his unique strategy.1
The Athletics finished first in the American League West with a record of 103–59. Their payroll for the year was $41 million. To put that in perspective, the payroll for the New York Yankees, who won the American League East with a record of 103–58, was $125 million.2 After 2002, nearly every team in the majors adopted a similar statistics-based draft strategy.3
Oakland’s story underscores something we know intuitively: The answers to so many of our business woes are buried in the data. But bringing that data to life requires perspectives from across the business: from finance, HR, operations, sales, and so on. Perhaps we need to understand, for example, the relationship between our talent pipeline and our R&D budget for coming years. We might ask questions like: Will we have the right people to work on those business-critical projects? What will be the cost to acquire them? What kind of training will they need? And how long before their contributions translate into ROI?
Getting to those answers requires aggregating data from performance management systems, workforce management systems, learning management systems, and finance and compensation. And it requires the right analytics engine to decipher the aggregated data. But even the most elegant algorithms are worthless if we can’t put the yielded insights to work. Those insights will be cross-cutting—having come from a unified set of data, they will suggest a unified solution across the business. And implementing a unified solution means even the most disparate parts of the business need to be adept at working together. Business units need processes and workflows that are complementary and mutually beneficial. They need the ability to integrate and break through organizational silos. They need an inclination toward collaboration.
As HR professionals who want our analytics to thrive, we need to develop that inclination. We need to create a culture that invites and repays collaboration and that rewards teams and individuals for being curious, resilient, and open to change.
It’s estimated that by 2021, 80% of midsize to large companies will change their cultures to speed up their digital transformations.
To gain the competitive advantages we seek, analytics must be adopted throughout the organization. If only human resources, finance, or operations are using data, the business will limp along, hobbled by its weakest leg. To move surely and quickly, everyone must have access to data, everyone must use it, and everyone must contribute to it. This democratization of data is essential. The singleton insights gleaned from a sole department might offer a small return, but small returns are not the goal. We want to knock it out of the park. Advanced analytics are poised to make high-dollar contributions to our businesses, but to earn game-changing results, the data must come from and be used across the business.
“You have to think long-term and say, ‘These singles and doubles are not going to bring us home,’” says Ted Colbert, CIO of Boeing. “If you really believe you can take billions and billions of dollars off the bottom line, you have to stratify the opportunity…and democratize the capability.”4
But democratization alone is not enough. Teams must work together to build and share datasets gathered from disparate sources: from employees, customers, business partners, and each business unit. If our work cultures haven’t historically valued collaboration, those muscles are probably weak. But without collaboration, our pan-organizational analytic initiatives aren’t likely to be adopted. The key to gaining companywide adoption is culture.
67% of organizations are currently undergoing culture-change initiatives.5 And why? Because too many companies are finding that their culture is a barrier to digital transformation.
Marcus Blosch, research vice president at Gartner, identified lack of partnership as a prime cultural barrier to transformation. “Politics and misunderstandings often block or limit potential partnerships on the way to digital,” says Blosch. And without these crucial partnerships—such as between HR and finance—companies simply don’t have the infrastructure to implement their analytical findings.6
Next Chapter: Collaboration
