10 Stats about Data Quality I Bet You Didn’t Know

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Today, data is no doubt the most essential and valuable resource available for marketers, publishers, media companies, agencies, and more. But of course, data can only be deemed as useful as its quality.

At its best, bad data can be inconsequential. However, at its worst, it can scar the reputation of a brand and even lead to making costly mistakes.

Did you know the cost of bad data as estimated by IBM is around $3.1 trillion each year? And that’s in the U.S economy alone.

Let’s admit it, poor data quality isn’t helping anyone and the more updated a business is with its presence, the more likely it is for them to successfully escape the grasp of bad data quality.

Here are 10 fresh statistics on data quality that will revolutionize your perception of data.

• According to DiscoverOrg, the sales & marketing teams of companies lose approximately 550-hrs while each sales rep loses about $32,000 from utilizing bad data.
• Marketing Sherpa reports how 25-30% of data is deemed inaccurate each year, leading to ineffective sales & marketing brand campaigns.
• Kiss metrics estimate businesses losing as much as 20% of their revenue because of poor data quality.
• According to Sirius Decisions, the cost of handling duplicates when left untreated is about a dollar for preventing, $10 for correcting, and $100 for storing.
• A study from MIT Sloan reveals how employees waste about half a percent of their time while handling mundane tasks concerning data quality.
• Integrate estimates how 40% of leads consist of inaccurate data
• Dun & Bradstreet made reports on how a massive 41% of companies cited inconsistent data across technologies such as Marketing Automation or CRM as their biggest challenge.
• Chief Marketing states, only a small portion of companies, about 16% characterize the data they’re utilizing as ‘good’.
• According to Reachforce, within the next hour, about 59 business addresses will be changed while 11 companies will change their name, and 41 new businesses will open along with the ones that will close down.
• Integrate reveals how 15% of leads are known to contain duplicate records

As seen from the stats, poor data quality is not only a monetary issue but it can also heavily damage the reputation of a brand/company.

According to a report from Gartner, organizations often make erroneous assumptions regarding their data state while calculating the business value of their data quality. This leads to experiencing excessive costs, inefficiencies, customer satisfaction issues, and compliance risks. In effect, their business data quality is left unmanaged.

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Yes, we live in a digital age but how much knowledge do you have regarding data quality? As the majority of people do, you probably are aware of data as something important and must be managed, however, apart from seeing it as something manageable, most individuals shy away from it.

This is because data management is often seen or associated with manual, inexhaustible, and tedious work, hence, it is typically ignored or not seen as part of one’s responsibility. But the thing is, if you’re unable to discern the implication poor data may have on your Marketing Automation System or CRM, your company will likely suffer a huge loss.

Let’s admit it, our current customer-centric landscape calls for more attention from your business as compared to before and this includes anticipating the needs of your customers.

What’s the Cost of Poor Data Quality?

Erroneous decisions resulting from bad data can not only be inconvenient but extremely costly as well. According to research from Gartner– the financial impact poor data quality has on organizations rounds up around $9.7 million on an average each year.

Take, for instance, the Hawaiian Airlines incident. When various individuals booked flights, they were surprised to discover their free award flight tickets actually cost thousands of dollars. The main culprit turned out to be a defective airline booking application.

Instead of the usual airline miles, the application had accidentally charged the customer accounts in dollars. As a result, redeemable tickets for 674k miles had turned into a massive price of $674,000.

However, financial cost is only the tip of the iceberg and it, in fact, goes beyond dollars & cents. Bad data can also slow employees down to the extent that their performance may start to lack or suffer.

For example, each time a salesperson initiates a phone call, they rely on the power of data like a phone number. However, when that data is deemed invalid or false, they’ve contacted a person that probably doesn’t exist – something that wastes over 27% of employee time.

As such, accommodating bad data can be both time-consuming & expensive. The data you require may contain tons of errors. This means during critical deadlines, many employees will correct those data themselves to complete their given task.

Bad data quality also impacts customer satisfaction – something that may undermine your company’s reputation as customers can instantly take matters to social media and share any of the negative experiences received from your business’ end.

And let’s not leave behind the employees. They can start questioning the validity of any underlying data when or if data inconsistencies remain unchecked. They may also request customers to validate service, product, or customer data, thus, further increasing handle time & eroding customer trust.

This makes data quality such a pervasive issue, in fact, a report from Forrester reveals how almost one-third of analysts invest over 40% of their time validating and vetting analytics data before it’s utilized for strategic decision-making.

Wrapping up

If you want to stop the negative impact poor data has on your business, it is crucial to reduce the factors that cause bad data quality. Whether or not an individual has direct supervision over data, the data quality of an organization ultimately falls on everyone.

Hence, it’s important to manage proper data quality. These statistics are something that can help businesses realize the severe effects of poor data, so as to avoid turning a blind eye to this grave issue.