The Cost of Data Bias is Shared by IT and the Business
Opinionated collection of data leads to missing data, which means you’re going to miss insights.
The results are in. Every year we survey the market on a wide variety of technology and business trends and topics. And every year we are surprised by some responses. One response we were not surprised by was the pervasiveness of missing insights. Out of more than 1400 respondents, nearly everyone – 98% – report they are missing insights. Unchanged are the nature of those insights: root cause of performance degradation, root cause of incidents, and indication of a possible attack. Performance. Availability. Security. The “big three” concerns of IT when operating a digital business continue to drive a need for more data.
The impact of those missing insights is troubling. It is no longer merely just a problem for IT professionals held accountable for meeting SLAs related to performance, availability, and security. Outages, issues, and security incidents have a very real and negative impact on a digital business.
Some effects are obvious. Loss of reputation and revenue, decreased productivity, damaged relationships with partners. Others are not so obvious, such as the loss of unrealized opportunities that might have otherwise been uncovered through analysis of data across operational and business lines.
But knowing insights are missing is just half the battle. No one is surprised by this today. Growth of digital business leads to scale and expansion of the systems, services, and environments required to support demand. With so many distributed components across multiple clouds and, now, the edge, it’s no surprise that missing data was cited as the most common challenge faced in the quest to find those missing insights.
This is, in part, the cost of data bias: the opinionated collection of data.
“The cost of deploying—and managing—agents introduced data bias because it limited the systems from which you could collect data. Opinions—experienced or not—regarding the worthiness of monitoring a given application or system often drove the deployment decision.” – Most Missing Insights are Due to Data Bias
Those that have surmounted the challenge of missing data are well-positioned to take advantage of the insights analysis that data can unlock. These organizations are twelve times more likely to uncover timely business insights that lead to tangible benefits like an increase in revenue per user.
They are also more likely to correctly identify bots, which in turn means they are less likely to fall victim of bots that exploit digital channels and can wreak havoc on the business itself.
What we can’t quantify yet is the impact of “false” insights caused by missing data to the business. We know that misclassification—false positives—of legitimate users as bots is a problem, one that often leads organizations to forgo active security to avoid potentially blocking a revenue-generating user. The Aite Group estimated that losses to false positives would far exceed projected fraud loss of $6.4B.
And the impact goes beyond revenue to other business outcomes such as customer acquisition cost and lifetime value. It is not unthinkable that similar “false positives” might arise in the future with respect to business insights.
That’s why it’s so important to develop a data and observability strategy before you begin relying on insights to operate your digital business.
We are on the cusp of a digital as default world, in which every business is a digital business. Digital business runs on data, which means developing a data and observability strategy is critical to future success. Without a strategy the insights—and subsequent decisions—made on data may very well be the wrong ones.
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