TOP SIX CAUSES OF ANALYTIC VALUE GAPS

So what are some of the issues underlying this value-delivery conundrum? It helps to compare internal solutions with customerfacing ones to get at some of the root causes. Here are the top six we’ve identified:

There Is Little To No Solution Governance

External products are generally well defined. But internal solutions are often developed haphazardly whenever a problem is identified. That can lead to solution proliferation in which expert users take matters into their own hands, developing point solutions to specific problems and publishing them to a wider audience. In a larger organization this can cause real headaches: eight versions of the same shipping report, for example, or hundreds of dashboards with little clarity regarding which one users should turn to. As priorities shift, the problem is only exacerbated. Changes are made with minimal oversight, and solutions may be abandoned without being formally decommissioned.

Feedback Loops Are Ineffective

Market-facing applications are usually built with a direct and fast customer feedback loop that helps companies continually improve their products by adding features that customers want. This, in turn, increases usage and consequently revenues. Amazon’s famed recommendation engine is a great case in point. The AI-driven capability gets smarter with each user interaction, making it more likely that customers will trust and purchase from Amazon.

The situation is quite different when you’re dealing with operational solutions—particularly the analytics solutions that support decision-making. Why? We see a handful of common reasons:

Most internal dashboards are built on visualization platforms that don't come with a native feedback mechanism other than simple usage statistics (i.e., who used the dashboard how many times).

It can be difficult to gather feedback directly from constituents who are scattered across the organization with multiple competing priorities.

Unlike with external products, there is no market pressure to continually improve the offering.

The data that feeds various dashboards and visualizations is often messy and stale.

Outcomes Are Not Always Clear

Consumer-facing products are designed to solicit a specific outcome from a customer— whether it’s measured in terms of clicks, revenues, likes, or some other metric. Data analytics solutions, on the other hand, are about improving decision-making. That’s a lot harder to measure. For example, if the solution is intended to help users select one vendor over another, how do you measure success? How do you even know what outcome you should be measuring? If the desired outcome hasn’t been clearly articulated, it’s impossible to measure and track progress.

Attribution Is Extremely Difficult

Developers of externally facing products are continually tweaking their offerings based on feedback. They are able to quickly incorporate the most valuable features, in part because they can test them simultaneously and determine which ones generate the “best” response, often in the form of more clicks or increased sales. In other words, each change in customer response can be attributed back to a particular change made to the product. Many social media companies, including Facebook, Tik Tok, and Instagram, make highly successful use of this approach: They release new features, monitor the response for each, and compare responses with one another. This enables them to quickly determine which new features produce the biggest “bang for the buck.” It is unusual for analytics solutions to have such a clear and high degree of value attribution. Furthermore, once an internal solution is live, the development team has usually moved on to the next project, making it unlikely that attribution will garner the attention it deserves.