Top 3 reasons why data, analytics and insight are all dressed up and have nowhere to go in your enterprise

The buzz around big data and analytics has been doing the rounds of IT corridors for a decade now, but truthfully, if you have even seen a handful of data-driven improvements in your business function, you are part of a slender minority of businesses that can really make that claim.

But how can it be that, on the one hand, you have reputable technology companies virtually certifying high ROI on data and analytic products, and yet we see time and time again that such endeavors seldom bring meaningful change to the business organization? What could possibly explain this?

We have the technology to identify issues in a system through data and analytics that can turn that data into insight, and yet there seems to be little in terms of measurable action and outcomes in the business community. It feels like even though the technology is producing signals, business organizations are somehow inhibited or cannot respond to those signals. In neurology, a condition called ataxia manifests as a lack of muscle control or coordination of voluntary movements, which may be an apt metaphor for this dissonance in the business organization in relation to data.

So what is the solution? What can business leaders do to turn the tide on this trend?

Here’s a list of some of the key reasons we see business organizations failing to action insights from technology and suggest a set of strategies that can help based on what rings true for your organization.

The business lacks belief in its data.

This is perhaps the most common reason technology fails on its promise of delivering value through data-driven insight. If the primary consumers of the data product really don’t believe the ingredients to be healthy and wholesome, why should we expect them to consume it? And, if you have instances where your managers run reports in many systems to collect and aggregate data and then manipulate the data in excel to make it a “truer reflection” of operations, then this applies to you.

We recommend running a data and process hygiene exercise to help your teams understand the value of good data, what good process and data look like, and follow up with analytics on how the team is progressing through this improvement journey. There are, of course, more solutions one can avail in this space – that include business event monitoring, process chain analysis, and process mining that can help promote a more comprehensive understanding of the data context of a line of business.

The signals are not actionable.

This is a pervasive issue that afflicts most data and analytical initiatives. To define actionable insight, one must possess an understanding of what is vital to a domain in an industry. This is a skill usually only available on the business side. And to produce this insight, one must be consummate with a vast slew of technology and software development disciplines. And lastly, meaningful insight is often created as an outcome of exploratory analysis. It is rare for a company to execute a data and organizational strategy that addresses the nuances of this delicate balance well enough to have these three factors lined up. The result of most efforts either skew too heavily toward technology level thinking or are not reliable since they are an exercise in validating a domain experts’ opinion on a given subject.

A good option to consider in this case would be to bring in a specialized consulting firm with experience in data-driven analytics that can create value not just in producing the insights through data but also in guiding that insight towards actions and outcomes through leadership coaching.

Inadvertent culture of rewarding gut-based decision making

Another factor that plays a role is the company’s history and culture – especially the companies that have enjoyed success over decades operating well enough in their class not to have to introspect or re-instrument their thinking around the role of data in the enterprise.

And while data-driven thinking cannot be imposed on management directly, leaders can choose to redefine the very things their managers are being measured on to force a change in thinking. For example, a leader can suggest that going forward; they plan to measure the manufacturing team on customer feedback on social media platforms like twitter rather than only looking at their output targets.

There are, of course, more reasons for the failure to apply insights in business organizations, but overwhelmingly, these are among to most common.