Introducing a better way to measure the financial impact of your bad data. In addition to wasted time and sleepless nights, data quality issues lead to compliance risks.
In addition to wasted time and sleepless nights, [data quality issues_](https://www.montecarlodata.com/how-to-fix-your-data-quality-problem/) lead to compliance risks, lost revenue to the tune of [several million dollars per year_](https://www.entrepreneur.com/article/332238#:~:text=Research%20firm%20Gartner%20has%20found,data%20is%20bad%20for%20business.), and erosion of trust — but what does bad data _really cost your company? I’ve created a novel **_data downtime calculator**_ that will help you measure the true financial impact of bad data on your organization._
What’s big, scary, and keeps even the best data teams up at night?
If you guessed the ‘monster under your bed,’ nice try, but you’d be wrong. The answer is far more real, all-too-common, and you’re probably already experiencing it whether or not you realize it.
The answer? Data downtime. Data downtime refers to periods of time when your data is partial, erroneous, missing, or otherwise inaccurate, ranging from a few null values to completely outdated tables. These data fire drills are time-consuming and costly, corrupting otherwise excellent data pipelines with garbage data.
One CDO I spoke with recently told me that his 500-person team spends 1,200 cumulative hours per week tackling data quality issues, time otherwise spent on activities that drive innovation and generate revenue.
To demonstrate the scope of this problem, here are some fast facts about just how must time data teams waste on data downtime:
Based on these numbers, as well as interviews and surveys conducted with over 150 different data teams across industries, I estimate that _**_data teams spend 30–40 percent of their time handling data quality issues instead of working on revenue-generating activities.**
The cost of bad data is more than wasted time and sleepless nights; there are serious compliance, financial, and operational implications that can catch data leaders off guard, impacting both your team’s ROI and your company’s bottom line.
For several decades, the medical and financial services sectors, with their responsibility to protect personally identifiable information (PII) and stewardship of sensitive customer data sources, was the poster child for compliance.
Now, with nearly every industry handling user data, companies from e-commerce sites to dog food distributors must follow strict data governance mandates, from GDPR to CCPA, and other privacy protection regulations.
And bad data can manifest in any number of ways, from a mistyped email address to misreported financials and can cause serious ramifications down the road; for instance, in Vermont, outdated information about whether or not a customer wants to renew their annual subscription of a service can spell the difference between a seamless user experience and a class action lawsuit. Such errors can lead to fines and steep penalties.
Data quality is top of mind for every data professional — and for good reason. Bad data costs companies valuable time, resources, and most of all, revenue.
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Data science is omnipresent to advanced statistical and machine learning methods. For whatever length of time that there is data to analyse, the need to investigate is obvious.