Recently, Gartner revealed its top predictions for IT organizations for 2018. Among the expected buzz around technologies like artificial intelligence, blockchain, and the Internet of Things, was something a bit surprising to me. Gartner predicted that by 2022, most people in mature economies will consume more false information than true information.

“Fake news has become a major worldwide political and media theme for 2017,” says the Gartner statement, with the caveat that the ability for false information to perpetuate extends beyond what we traditionally think of as news. “For enterprises, this acceleration of content in a social media-dominated discourse presents a real problem.”

Fake News in Your Organization

When you think about it, on some level, you deal with this “fake news” idiom within your organization already. Perhaps something that starts with some validity is completely blown out of proportion by the time it lands in your inbox or on your desk. A customer takes to Twitter to vent over a bug, and suddenly, a leader within the marketing organization is firing off urgent emails and then standing at your door. A recent public announcement of retiring some functionality had a customer take to a user community forum to lament, and you’re being told (after likely months of carefully communicating the decision at trade shows, user group meetings and key accounts) that “all customers are upset.” Or a blog has landed in the inbox of senior management, detailing the loss of customer who didn’t renew a subscription for your company’s software, and why they moved to a competitor – and suddenly, the product roadmap is being called into question.

It all has the ability to whip internal staff, customers and even partners into a frenzy – and ultimately, take the business away from strategic roadmap development and the important tactical steps you had planned for your team to take to that end.

The solution to combating the “fake news” – at least in a product organisation – is good intelligence. Having reliable data available in real-time allows you to quickly and accurately sort through what is a valid concern and what is not, keep your team on task, and let data-driven product development reign. Let’s dig into three examples and look at how usage data can help.

1. Prioritize a bug fix

Twitter has the ability to make a mountain out of the proverbial molehill. One disgruntled user can garner support within minutes, even from people who aren’t affected by the issue.

With usage data, you can quickly get a sense of just how pervasive the bug is, and what sort of effort needs to go into fixing it. Usage data allows you to segment the user base by a huge number of machine attributes, geographical parameters, and feature usage, to pinpoint the depth and breadth of the problem. Out of thousands of users, you can quickly see the attributes of those affected by the bug – and work on targeted fixes to speed resolution.

It means your marketing team can also reliably message the Twittersphere and send out the necessary collateral to the rest of the customers who are not affected, reassuring the base and protecting brand value.

2. Engineering Backward Compatibility for a Legacy Release

As a product manager, some of the most difficult decisions you have to make surround when and how to retire functionality, especially when legacy features are used in key accounts. It is often a decision driven by an account representative who is fearful of angering a customer – and not by data or what is best for the rest of the customer base

With usage data, you can look at exactly how pervasive use is across your customer base –and work to make a value-based analysis on whether keeping this feature is a viable business decision. For instance, by drilling into data on usage by version and further drilling into that metric by runtime session, you can reliably determine exactly how many users are leveraging the old functionality and in what capacity. If, say, only a handful of users are using the functionality daily –it is a viable approach to make targeted offers to them to move to the latest version, rather than delay the possible new release by working to support backward compatibility.

3. Identify and Remedy Issues Surrounding Churn

Robust usage data not only informs planned obsolescence, it helps to ensure that nuanced information is read the right way. Perhaps users aren’t renewing contracts for a version with additional functionality, but you’re not seeing the same trend occur with the lower-tiered, less expensive and less functionally rich editions. One blog on a competitive win over your product puts the whole development cycle in focus – when it’s not at all clear whether all customers are eager to jump ship.

With usage data, you can dig into the use of the product over the year, and drill down into usage of the feature in question by runtime. Perhaps you discover that most of those with this particular licence don’t use the main feature all that much – maybe a handful of times during the year – and clearly aren’t seeing the return on investment to renew. With this knowledge, you can start to dig into the reasons why, and make an insight-driven decision on how to reevaluate licensing options so that users will be happy with their purchases and stay with you. Perhaps you bundle the richer functionality in with a lower-priced edition, or make a call to change or stop development of the functionality altogether if it no longer makes business sense.

Usage Data Sheds Light

Usage data can help shed light where things can get foggy – and ensures that your team tackles the right problems and remains on task, sorting out what is a major news event, from what is not.