By John Wysseier, CEO and President, The CEI Group
There’s no doubt that, as The Economist proclaimed in a recent headline: “The world’s most valuable resource is no longer oil, but data.” So, here are the questions a business leader should ask about his or her own company:
- Does your company make the most of its own rich reserve of data?
- Does it recognize all the veins of data deposits?
- Is your company mining all of that data to determine the strength of its business pulse?
- Is your company acting on its data to achieve its strategic goals and to help its valued customers achieve their own goals?
If the answers are “No,” maybe it’s because your company doesn’t possess a data-driven culture. And what, exactly, is that? Here’s one definition, from Forbes magazine: “A data-driven culture [is] an operating environment that seeks to leverage data whenever and wherever possible to enhance business efficiency and effectiveness.” Let me expand on this.
A data-driven culture is a set of values and practices centered on the analysis of data. It’s an instinct, from the top of the hierarchy to the lowest level and in every department, to base every business decision on the analysis of data rather than gut feel, personal experience and individual judgment. It’s an environment where every employee is data literate, has access to the right data, and has the tools and expertise to use it.
If your company doesn’t make maximum use of its data, you’re not alone. In fact, according to the MIT Sloan Management Review’s 2017 data and analytics survey, only 17 percent of the more than 2,600 executives, managers and data professionals who responded said they work within a data-driven culture. To their credit, they said their routinely make data-driven decisions and rely on analytics for strategic insights and new ideas.
Another 26 percent said they have adequate access to data and are working to become more data-driven, but use analytics primarily to make operational improves. The survey report labelled these companies “analytical practitioners.” But the report labelled fully 33 percent of the respondents as “analytically challenged,” who said their companies still rely more on management intuition than data for decision making, that they struggle with data access and quality, and that their company lacks key data management skills.
Many studies show that data-driven companies are more successful than those without a culture of analytics. For example, a report released by the Aberdeen Group, ‘The Executive’s Guide to Effective Analytics,’ revealed that data-driven organizations experience a 27 percent year-on-year increase in revenue, compared to only seven percent for other organizations. Furthermore, it reported that more data driven companies were twice as likely to improve their process cycle times and 12 times as likely to cut their operating expenses year over year than companies that don’t possess a data-driven culture.
Here are just a few specific ways in which data analytics have been proven useful. They have:
• Helped marketing teams better understand customer behavior, identify and target preferred market segments, create new products, set prices and measure the ROI of campaigns and distribution channels.
• Enabled fleets to save millions of dollars in fuel usage and avoided accidents, while increasing productivity through more efficient delivery routing and reduced downtime through predictive maintenance analytics.
• Enabled HR to make data-driven hiring decisions based on existing and future talent gaps, identify high turnover rates by location or department and track the effectiveness of employee training programs.
• Allowed sales management to more rapidly and efficiently analyze sales team performance, leads and pipeline data, identify inefficiencies and opportunities, and achieve higher sales volume.
• Provided manufacturers tools to make their production lines more efficient, develop machinery that predicts their need for maintenance and repair, monitor the performance of suppliers and quality of their delivered components, and more efficiently manage their warehouses and inventory.
• Helped financial professionals to improve risk management, build real-time models that enable faster reactions to changing economic and market conditions, automate time-consuming tasks and reporting, and detect fraud.
• Improved auto insurance underwriting through the analysis of drivers’ telematics data.
How to transform your culture
Much has been written over the past five years on how to create or strengthen a data-driven culture. The first step is to order your companies various departments to conduct a data audit by asking the following questions:
• What data do you already have generated and collected?
• Where is it stored?
• How much of it have you used to make improvements in such metrics as efficiency, productivity, revenues and cost control?
• What kind of data do you need that you don’t have?
• What kind of access do you have to data collected by other departments? Is it easy or difficult?
Once the audit is complete, these five basic steps recommended by DataSourceCentral.com for creating a data-driven culture are an excellent way to follow through.
1) Create a single source of truth: Data sources in large organizations are often siloed in independent systems. Staff can pull the same metric from different systems and get different numbers. A single source of truth can be a large investment, but the drag on competitiveness from inconsistent data will only worsen as the industry moves towards greater precision. While this investment takes place, the data team can add great value by acting as data connoisseurs, knowing what’s available and what to recommend for each problem.
2) Write a standard data dictionary: Data scientists and business managers need to agree on a data dictionary with clear, unambiguous and agreed definitions to help team members with different areas of expertise get on the same page.
3) Offer broad access to data: The entire organization, not just data scientists, needs access to data. Without it, you cannot achieve collective business expertise in analyzing data. This requires a simple self-service reporting system with the right governance and access levels based on the needs of customer service, and product and marketing specialists.
4) Teach data literacy: With good access to data must come good understanding of data. Provide compulsory training to reinforce basic data literacy across the whole employee base. There are three basic subjects which every employee should be comfortable with:
Descriptive statistics: Basic ways of summarizing data (e.g. mean, percentiles, range or standard deviation) and knowing when each is appropriate.
Data visualization: Clear communication of insights or concepts to speed up comprehension & collective problem-solving within the team.
Inferential statistical tests: To help detect, for instance, if a difference in sales between weeks is significant or if it is just random variation.
5) Change your decision-making procedures: Many teams are governed by HiPPOs (highest paid person’s opinions). This can be especially bad when HiPPOs decide based on instinct or experience, when the choices could have been tested and supported with statistical proof. One way to counteract this is to cultivate experimentation with A/B testing. For example, in order to decide which website design or marketing messaging is most effective, managers can determine success metrics and sample sizes, and let the A/B tests run and let the data speak for itself.
I think there’s just one step left out here, and it precedes all the rest: senior executives must lead by example. They need to make every one of their decisions based on data analysis and demand the same of everyone in their chain of command. Data strategy expert Brent Dykes put it well in a recent article in Forbes:
“While [many] factors can contribute to shaping a data-driven culture, one of the most influential factors is executive buy-in and support. Whenever I’ve encountered an organization that has made tangible progress toward fostering a data-driven environment, I can usually trace it back to a committed and involved leadership team. If the desire to be data-driven begins at the top, it frequently cascades down through an entire…company.
“When it comes to leading by example, an executive’s responsibility extends beyond just carving out budget and signing off on new analytics tools or hires. Your leadership team must be prepared to immerse themselves in data and exemplify the behaviors that they want to see their organization emulate. A ‘do as I say, not as I do’ approach will undermine your data initiatives.
“Consciously or unconsciously—by design or by default—executives are always leading by example when it comes to being or not being data-driven.”