Four Key Findings to Drive Data-Driven Decision Making
In This Article
It's no secret leveraging data to make strategic decisions -- no matter the industry -- is a smart approach. In fact, a data-driven mindset is closer to table stakes for organizations looking to drive innovation and advance digital transformation efforts.
A recent report from The Economist's Intelligence Unit -- The data driven enterprise: New strategies for better decision-making -- found that while roughly three-quarters of organizations expect profits to rise due to digital strategies over the next few years, fewer than half are confident they are making optimal use of digital technologies.
Decision makers understand and expect to realize the value of a more data-driven culture, but also believe they may be selling their efforts short at the same time.
The Economist interviewed two dozen leading executives, including WWT Chief Technology Officer Mike Taylor, to better understand how enterprises are using data-driven decision making to reap the advantages of digital transformation.
Taylor's remarks shed light on four key findings in the report.
Key Finding No. 1: Rich data sets are effectively useless if they aren't seamlessly incorporated into an organization's analytical platform.
To produce usable data insights, organizations must make sure everyone is speaking a common language through the data.
"We have to look across business lines, business units, at how we ratify a common vernacular and set of terms for our data so that it can be consistently applied across groups and teams," Taylor said. "For us, the beginning of this is not a technology problem at all. It's really a business process."
Key Finding No. 2: Challenges always arise. Adopting a true data-driven culture can overcome those hurdles.
Simply investing in data analytics or digital strategies isn't enough to lead to an organization that embraces a data-first mindset. Many companies struggle to adopt an insights-driven culture due to cultural resistance or a lack in organizational alignment.
Adopting a data-driven culture is a process, but organizations can enable employees at all levels through this process.
"Our employees gain confidence and efficiency from using our more advanced analytics," Taylor said. "Good employees, motivated people, just do not want to do mundane, low-value work. We view it as our responsibility, for both our employees and our customers, to strive to provide something that's better."
Key Finding No. 3: Improving and maintaining the quality of data is paramount. Partnerships play a key role in that.
The more seamlessly data can be analyzed, models built and insights integrated into day-to-day workflows, the more rapidly organizations can innovate and make decisions on the fly.
Easier said than done. Quality of data plays an important role in driving a data-first mindset.
As Taylor notes, data insights are built on complex data webs that extend from the end customer through a company's entire supply chain -- and this affects data quality as well.
"Partnership is so essential to providing unique solutions and capabilities to the customers we serve," he said. "We sit squarely between a group of innovative technology providers and customers that want to use and consume that technology to better their businesses. Our strength in that is we're really good at partnering with companies. So designing systems, processes and data architectures that understand from the get-go that we're going to have data sources coming in from other partners -- that have to be augmented and (rationalized) to our own data -- is critically important for us."
Key Finding No. 4: Data-driven insights can't sit on an island. They must be attached to a business outcome.
Critical to data-driven decision making is understanding the end game.
Organizations should identify and prioritize specific use cases that will create value for the organization before diving deep into which algorithms or platforms should be used. Thinking about the value upfront and pinning it to a specific use case can make a world of difference during implementation and help build momentum for future.
Data-driven insights should help companies answer larger questions that inform strategy and goal setting, such as "How are our teams performing and how effective and high-performing is our culture?" Taylor said.
"This can't be a kind of a science experiment anymore," he said. "It needs to be connected to a corporate strategy and a set of goals and outcomes that ultimately can be invested in and measured over time."
Any company looking to increase competitiveness and agility through adopting a more data-driven strategy should start with an honest current-state assessment.
Don't know where to start? WWT's Data Analytics & AI team can help.
Using our Data Maturity Curve framework, which illustrates the typical stages organizations go through to achieve a data-driven culture, our data experts can help you take the first step of understanding where your organization presently sits so you can chart a path to move up the curve.
WWT Senior Engagement Manager Yoni Malchi does a great job describing the importance of the Data Maturity Curve in his recent article Six Stages of Transforming into a More Data-Driven Organization. You can also watch the video below get a sense for how it works.