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Is Your Data And Analytics Process Broken?

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When it comes to data and analytics success, the devil is in the synapses. A new report from EY and Forbes Insights, “Data & Advanced Analytics: High Stakes and High Rewards,” explains that fundamental problems arise at the crucial links between the steps organizations take as they move from identifying new business opportunities to acting on insights and then measuring the outcomes of their data-driven strategies. We liken these junctures to synapses in the brain, where communications pass from one cell to another.

The difference is that in the brain, these communications pass naturally from cell to cell, while in large enterprises, the smooth flow of information isn’t automatic. The way global enterprises handle these links translates into business success or failure. Below we describe each of these junctures and point out the challenges that need to be overcome at each stage to succeed at data and analytics.

Competitive Differentiation

Establishing an enterprise-wide view of analytics requires senior leaders to understand the transformative potential of data in their organizations. “In the past, there’s been a distinction between the use of analytics to improve the current business processes versus the use of analytics to change the way the company is competing,” says Chris Mazzei, chief analytics officer and emerging technology leader at EY. “Many companies started using analytics by focusing on processes, but as they saw success in this area, they realized it can help them in strategic ways, such as determining what to sell, how to sell it, who to sell to, and how to stay differentiated from their competition. This gets to the fundamental role that advanced analytics can play in reimagining the business.”

The key challenge at this point is overcoming the lingering effects of intuition-based cultures, in which decision makers trust “gut feel” more than what data reveals. This is the biggest current pain point, according to 49% of 1,500 global executives surveyed by Forbes Insights and EY.

Operating Model

As enterprises mature in their use of advanced analytics for business initiatives, they must intensify their focus on the underlying operating models that govern these activities. The chances of success increase for enterprises that develop models that support collaboration, so stakeholders from anywhere in the enterprise can work together for business success. Without that holistic approach, companies will continue to see pockets of analytics proficiency.

Lack of alignment and collaboration across functions is the top pain point when creating a data analytics operating model, with the largest percentage (28%) of survey respondents pointing to it.

Initiative Design

When executives reach the point of designing the specifics of business initiatives, they must make a series of critical decisions that will guide their use of advanced analytics and ultimately determine the success of their business imperative. This starts with defining the specific business outcomes leaders hope to achieve. Not surprisingly, the goals that ranked highest overall among all respondents were increased sales or revenue and increased customer satisfaction. These are bread-and-butter objectives every top executive can love.

This important stage defines not only the specific strategies enterprises will use to achieve their desired business outcomes. It’s when enterprises create common nomenclatures and structured processes to frame their advanced analytics efforts, while also determining how to incorporate experimentation and agility.

Lack of collaboration between IT and data and analytics teams are the top challenge (41%) at the initiative design juncture.

Intervention Design

Intervention design, the next step in the analytics process, translates all the upfront goal-setting, modeling and methodology development into action—namely, making the insights derived from advanced analytics an integral part of business operations. At this stage, it’s essential to have a clear and well-defined hypothesis about how value may be achieved.

Also critical for business success at this stage is that companies determine when and how best to apply advanced analytics to realize the value of their efforts. Clearly, earlier is better, since this gives business leaders the most opportunities to shape and test the validity of initiatives based on available data rather than pure instinct.

Lack of required skills is the top challenge (36%) when driving adoption and consumption of insights derived from data and analytics.

Measurement And Learning

In the end, the value of resource investments devoted to devising and activating advanced analytics strategies must be evaluated for how well they are supporting desired business outcomes and contributing to the long-term success of the organization. But many companies still struggle to quantify the benefits of data-driven business initiatives. For example, only about a third of companies overall can accurately measure business value to demonstrate the impact of their advanced analytics initiatives.

The biggest challenge at this juncture (33%) is that too many factors influence the business outcome, and it is difficult to attribute specific results to analytics.

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