The Impact of Bad Data and Why Observability is Now Imperative

Do you think the impact of bad data is just a minor inconvenience? Think again.

Bad data cost Unity, a publicly-traded video game software development company, USD 110 million.

And that’s only the tip of the iceberg.

Unity stock dropped 37% on 11 May 2022, after the company announced its first-quarter earnings, despite strong revenue growth, decent margins, good customer growth and continued high performance in dollar-based net expansion.

But there was one data point in Unity’s earnings that were not as positive.

The company also shared that its operate revenue growth was still up but had slowed due to a fault in its platform that reduced the accuracy of its Audience Pinpointer tool.

The fault in Unity’s platform? Bad data.

Unity ingested bad data from a large customer into its machine learning algorithm, which helps place ads and allows users to monetize their games. This not only resulted in decreased growth, but also ruined the algorithm, forcing the company to fix it to remedy the problem going forward.

The company’s management estimated the impact on the business at approximately USD 110 million in 2022.

Unity isn’t the only company that has felt the impact of bad data deeply.

Take X.

On 25 April 2022, X accepted a deal to be purchased by Tesla and SpaceX founder Elon Musk. A mere 18 days later, Musk shared that the deal was “on hold” as he confirmed the number of fake accounts and bots on the platform.

What ensued demonstrates the deep impact of bad data on this extremely high-profile deal for one of the world’s most widely-used speech platforms. Notably, X has battled this data problem for years. In 2017, X admitted to overstating its user base (link resides outside ibm.com) for several years, and in 2016 a troll farm used more than 50,000 bots (link resides outside ibm.com) to try to sway the US presidential election. X first acknowledged fake accounts during its 2013 IPO.

Now, this data issue is coming to a head, with Musk investigating X’s claim that fake accounts represent less than 5% of the company’s user base and angling to reduce the previously agreed upon purchase price as a result (link resides outside ibm.com).

X, like Unity, is another high-profile example of the impact of bad data, but examples like these are everywhere—and it costs companies millions of dollars.

Gartner estimates (link resides outside ibm.com) that bad data costs companies nearly USD 13 million per year, although many don’t even realize the extent of the impact. Meanwhile, Harvard Business Review finds (link resides outside ibm.com) that knowledge workers spend about half of their time fixing data issues. Just imagine how much effort they could devote elsewhere if issues weren’t so prevalent.

Overall, bad data can lead to missed revenue opportunities, inefficient operations and poor customer experiences, among other issues that add up to that multi-million dollar price tag.

The fact that bad data costs companies millions of dollars each year is bad enough—and that many companies don’t even realize this because they don’t measure the impact is potentially even worse. After all, how can you ever fix something of which you’re not fully aware?

Getting ahead of bad data issues requires data observability, which encompasses the ability to understand the health of data in your systems. Data observability is the only way that organizations can truly understand not only the impact of any bad data but also the causes of it—both of which are imperative to fixing the situation and stemming the impact.

It’s also important to embed data observability at every point possible with the goal of finding issues sooner in the pipeline rather than later because the further those issues progress, the more difficult (and more expensive) they become to fix.

Critically, this observability must be an imperative for C-suite leaders, as bad data can us impacthave a serio on company revenue (just ask Unity and X). Making data observability a priority for the C-suite will help the entire organization, not just data teams, rally around this all-important initiative and make sure it becomes everyone’s responsibility.

This focus on end-to-end data observability can ultimately help:

• Identify data issues earlier on in the data pipeline to stem their impact on other areas of the platform and/or business
• Pinpoint data issues more quickly after the pop-up to help arrive at solutions faster
• Understand the extent of data issues that exist to get a complete picture of the business impact

In turn, this visibility can help companies recover more revenue faster by taking the necessary steps to mitigate bad data. Hopefully, the end result is a fix before the issues end up costing millions of dollars. And the only way to make that happen is if everyone, starting with the C-suite, prioritizes data observability.

Learn more about IBM Databand’s continuous data observability platform and how it helps detect data incidents earlier, resolve them faster and deliver more trustworthy data to the business. If you’re ready to take a deeper look, book a demo today.