Insights-driven decision-making is more than a nice-to-have strategy; it’s an essential part of competitive businesses. The reason is simple: A savvy use of data can help companies differentiate their products and customer experience.
In fact, Forrester Research estimates that insights-driven businesses will earn $1.2 trillion by 2020. But to get there, companies need a culture that embraces data-focused methodologies and practices. Here, we highlight best practices for cultivating a culture that thrives on data insights.
Do Make the Data (and Necessary Training) Accessible
In addition to making it easier for teams to share data, empower staff to become more comfortable working with corporate data even if they don’t have a background in statistical analysis or business intelligence. “Resist the temptation to relegate data analytics to the specialists and statisticians,” says Don Peppers, founding partner of Peppers and Rogers Group. “It’s important to educate the managers and staff on how to make more informed decisions. Also, don’t isolate the analysts and statisticians, but popularize their findings within the organization and encourage discussions about the findings.”
Do Invest in Scalable Tools
There are myriad data analytics and business intelligence tools to choose from; the key is to be realistic about the tools your team can use today while leaving room for growth, notes Benjamin Bisson, associate broker and marketing manager at Bisson Real Estate.
The real estate agency selected Placester, a CRM platform that includes digital marketing tools, to help it capture leads and get a unified view of its customers. Within a few months, the Maine-based company was receiving about 12 qualified leads each month, up from one per month. “We’re very pleased with the results,” Bisson says. “But I still have to provide a lot of evidence to justify every tool we use before we can upgrade to more features and make sure that it makes sense for our team to use.”
Do Provide a Compelling Value Proposition
Employee buy-in is essential, yet it is sometimes overlooked, observes Duncan Lennox, CEO and co-founder of Qstream, a sales analytics software provider. “For any initiative to be successful, leaders need to provide employees with a compelling answer to this question: What’s in it for me?” Lennox explains. “Ultimately people want to be successful at their jobs and they’re open to new things, as long as the value is clear.”
Don’t Stop at the Surface
There is a difference between measuring outcomes versus measuring the work it took to get there, Lennox points out. “You could say this person is the top salesperson because you have data that shows he hits his quota regularly but that is not being data-driven,” Lennox maintains. “Smarter companies look under the hood to find the underlying data that explains what makes that person successful and how to replicate it.” Learning how success is achieved and not just whether it’s been achieved is far more valuable. “One of the benefits of data is it can help identify desirable behavior,” Lennox says. “The question is, can you also identify patterns in that behavior and replicate them?”
Don’t Use Data to Confirm what You Already Know
“When data is used to confirm a decision that was already made, that’s a waste of data,” Peppers notes. “Data is best used to uncover new insights but more often than not, executives use it to support decisions they’ve already arrived at.”
Statistical bias also leads to lopsided and misleading conclusions resulting in poor business outcomes. However, even with the best intentions, some biases are difficult to identify and several biases may occur in a single use-case. Because bias is pervasive, business leaders and analysts should be aware of it and take steps to at least minimize the effect. Indeed, “you should always question the data you’re looking at,” says Jay Wilder, director of product marketing at Datorama, a marketing analytics solutions provider. Be wary of vanity metrics that don’t yield meaningful insights and remember that extracting value from data “is an iterative process.”
Don’t be Afraid to find Mistakes
One of the challenges to adopting a data-driven culture is the acceptance of mistakes, points out Tom Davenport, a professor at Babson College and research fellow at the MIT Center for Digital Businesses. “Some people are afraid of the errors or deficiencies that the data will reveal and so they try to avoid [reports and analytics] as much as possible.” However, “good management means constantly looking back to find opportunities for improvement.”
Another mistake is to hide errors from the rest of the organization, Davenport adds. Employees look to upper management and the C-suite for guidance and if a CEO is transparent about his or her successes and failures, that behavior is likely to have a trickle-down effect. “Even data-driven decisions won’t always be right,” Davenport notes. “But by sharing those mistakes, for example an acquisition that went badly, others may benefit from those lessons.”
Don’t use Data Punitively
On a similar note, nothing kills motivation faster than the fear that you could be punished. “It may sound counterintuitive, but using data punitively is the biggest mistake a company could make in trying to establish a data-driven culture,” Lennox says.
Using data to identify and remove weak performers makes sense and may even be necessary, but making it a frequent practice can lead to poor employee morale, low retention, and shoddy work as employees do whatever they can to meet those goals. A more effective approach is to use data to “help make everyone as successful as possible,” Lennox advises. “Everyone has weaknesses, so think of it as finding the areas that an employee needs help with and working with that person to hopefully find a solution.”
Do Think of it as a Journey
Developing a company culture that balances data-driven insights with human, i.e., instinctual input is an ongoing process. Davenport explores the challenges of finding that balance in his new book, Only Humans Need Apply: Winners and Losers in the Smart Age of Machines. And one of the lessons Davenport says he learned in researching this topic is that “most organizations can benefit by taking an attitude of augmentation.” There are clear benefits to “taking human decision-making out of an equation and automating processes,” Davenport notes. “But it’s key for people to understand how those systems work because even an algorithm can be wrong.”