Why culture is so important for data-driven companies

The biggest obstacles to creating data-based businesses aren’t technical, they’re cultural, said David Waller, a partner and head of data science and analytics for Oliver Wyman Labs.

“It is simple enough to describe how to inject data into a decision-making process,” Waller wrote in 10 Steps to Creating a Data-Driven Culture, a blog published on Harvard Business Review Feb. 6. “It is far harder to make this normal, even automatic, for employees – a shift in mindset that presents a daunting challenge.”

Waller’s tips were written for general business audiences, and not insurance specifically. Among them was to not pigeonhole data scientists, use analytics to help employees (not just customers), and start data-driven culture at the very top of the organization.

Data scientists are often sequestered within a company, with the result that they and business leaders know too little about each other. “Analytics can’t survive or provide value if it operates separately from the rest of a business,” Waller wrote, noting that those who have addressed the challenge successfully have generally done so in two ways:

Make any boundaries between the business and data scientists highly porous, Waller recommends. For example, one global insurer rotates staff out of centres of excellence and into line roles, where they scale up a proof of concept. A global commodities trading firm has designed new roles in various functional areas and lines of business to augment the analytical sophistication; these roles have dotted-line relationships to centres of excellence. “Ultimately, the particulars matter less than the principle, which is to find ways to fuse domain knowledge and technical know-how.”

Leaders pull the business toward data science – mainly by insisting that employees are code-literate and conceptually fluent in quantitative topics. “Senior leaders don’t need to be reborn as machine-learning engineers,” Waller wrote. “But leaders of data-centric organizations cannot remain ignorant of the language of data.”

Waller also recommends companies use analytics to help employees, not just customers. If the idea of learning new skills to better handle data is presented in the abstract, few employees will get excited enough to persevere and revamp their work, Waller wrote in the blog. But if the immediate goals directly benefit them – by saving time, helping avoid rework, or fetching frequently-needed information – then a chore becomes a choice.

“Years ago, the analytics team at a leading insurer taught itself the fundamentals of cloud computing simply so they could experiment with new models on large data sets without waiting for the IT department to catch up with their needs.” The experience proved to be foundational when IT remade the firm’s technical infrastructure. “When the time came to sketch out the platform requirements for advanced analytics, the team could do more than describe an answer. They could demonstrate a working solution.”

Companies, and the divisions and individuals that comprise them, often fall back on habit, because alternatives look too risky, Waller noted.

“Simply aspiring to be data-driven is not enough,” he wrote. “To be driven by data, companies need to develop cultures in which this mindset can flourish. Leaders can promote this shift through example, by practicing new habits and creating expectations for what it really means to root decisions in data.”