Chris Mazzei, Chief Analytics Officer at EY, looks at why you must build capabilities and change culture to embed analytics into your business.
Data analytics is in essence a science. But, in several important ways, it is also an art. Powerful technology can extract insights from data, but real value isn’t derived until the insights are put into practice. It is people who see to the latter, and their ability to make change happen that will determine the effectiveness of those actions. Successful analytic strategies therefore entail investment not only in capabilities, including technology and recruiting talented data scientists, but also in the development of organizational design experts. They also rely on giving due attention to the very human challenges posed by analytics, and the support of a powerful change agent, such as the CIO. I will explore these issues in two related posts.
At the outset of major analytics initiatives, organizations typically pay far greater attention to building technical capabilities than to changing culture, or developing behavioral assets. In a recent EY and Forbes Insight survey, while 60% of executives say their organizations actively train their staff in analytics skills, no more than a third say they are striving to build a strong analytics culture.
Building physical capabilities is of course critical to embedding analytics into the fabric of an organization. In the first instance, this entails data excellence, which means achieving the elusive “single version of the truth” through robust data collection, strong governance and the destruction of silos. Tools and technology are also a fundamental component of capabilities – not only hardware and software, but also the ability to identify, procure and deploy the right tools consistently. Achieving process improvement through analytics requires a robust methodology – a series of defined steps and clear milestones to be reached along the way. Management must understand the types of talent needed to drive business value from analytics, and know where to acquire it (35% of survey respondents say they have hired “advanced analytics talent”). The final building block is organizational design, in which talent is aligned and structured in a way that drives business value.
Tougher nuts to crack
Most organizations, however, struggle with the behavioral alignment required of analytics. Focusing on the following five “softer” assets will help companies leverage the true value of their analytics efforts:
- Mental models: the representation of thought processes that chart how something works in the real world
- Learning orientation: the group-held beliefs that prescribe how members should behave in a given context
- Holistic thinking: understanding the interplay of components within a system and how they influence one another within a whole
- Collaboration: understanding how constituents can best work together to achieve individual and collective goals
- Incentives: ensuring there is alignment between desired actions and how people are rewarded
These may be seen as a set of “soft” assets that are less vital to the success of analytics than the “hard” capabilities outlined earlier in this post. And developing these is tougher because they are harder to define and it is harder to set concrete targets against which to measure progress.
But it is only when an organization begins to understand and develop these behavioral assets that a better balance can be achieved between the science of analytics and the art of managing change. And when this balance is achieved, the organization stands a real chance of deriving substantial value from its analytics.