Blog

Bad data: a costly roadblock to your success?

Last updated: September 26, 2023

Imagine leaving for a road trip with a print map from 1985. You might get to your destination eventually, but not without a lot of wrong turns, wasted time and unnecessary stress. Bad data has a similar impact on businesses. They depend on customer data to make important decisions about their strategy, customer experience and product offerings. So if it’s outdated, inaccurate or spread across multiple platforms, they can’t provide the right offers to their customers – and miss out on important sales and revenue.   

This has staggering financial and reputational costs: According to a survey by Gartner, organizations believe poor data quality to be responsible for an average of $15 million per year in losses. Even worse, nearly 60% of those surveyed didn’t know how much bad data costs their businesses – or how to fix it  – because they don’t measure it in the first place.  

In this article, we’ll present the costs of bad data, then present ways to overhaul your data strategy.  

Streamline your tech stack: Boost efficiency, unify data & retain talent! Get expert tips now:

Work Email *

First Name *

Last Name *

Company *

Job Title *

Read Now

What is “bad data” anyway?

Data is “good” when it’s prescriptive and actionable: It tells what action you should take, and allows you to take it without needing to re-query or restructure the findings. For instance, if you want to query your American audience for a country-specific campaign, you can enter a single term, like “USA,” into your database and immediately pull up every customer located in the United States.  

As long as your database is small and standardized, that process is pretty simple. But when data isn’t stored in one place, or it’s stored in inconsistent formats, or it’s outdated, it takes a lot more time and effort to activate your data. Or you might not be able to use it at all. This has major costs to both productivity and revenue.  

Your data quality might undermine your marketing efforts if: 

1. Your data doesn’t answer the right questions: Say that you’re a marketer trying to figure out why your email unsubscribes rose last quarter. If your opt-out form doesn’t ask people why they’re choosing to unsubscribe, you’re missing the most direct answer to your question – and the clearest solution to your email churn issues. 

2. The same data point or value isn’t stored in a standard form. As tech stacks get more complicated, the chances of pulling inconsistently formatted data from each source increases. That makes it harder to target all members of that demographic. (If you’re not collecting addresses via a drop-down form, American customers might enter their home country as “America,” “USA,” “United States of America,” etc.)

Another complicating factor: Most time and data is standardized Coordinated Universal Time (UTC) instead of reflecting the user’s local time zone. So now it takes a lot more math to figure out if that person in Fiji opened your emails at night or during the day (and God help you if you have to account for Daylight Savings on top of that).

Day, month and year formatting also isn’t consistent across the globe: American customers do MM/DD/YYYY whereas the DD/MM/YYYY convention is more common globally. Nor is it consistent across different software.

3. It doesn’t keep up with your audience: 70% of your customer data may become outdated each year because of job or name changes, relocations, etc. So if you don’t have a mechanism for recording updates and merging existing customer profiles, your sales and marketing teams will be stuck with outdated contact lists each year – and waste time reaching out to defunct email addresses/phone numbers, etc.

4. Customer purchase history is outdated and/or incomplete, making it harder to target them appropriately.

(Note: There are a LOT more nuances to this subject than we can get into here – check out Bertil Hart’s article on Medium to learn more.)  

What is the cost of poor quality data?

1. Distorts decision-making 

88 percent of marketers say that being data-driven helps them stay on top of customer needs and market trends. So if that data is misleading or inconsistent, it drives bad decisions. That can set off a negative butterfly effect for your entire business.

On the micro level, you might stick with an ineffective automated campaign for too long – or misallocate your ad dollars based on incomplete information. More broadly, you might prioritize the wrong products, fail to react to market conditions in time, or even hire and fire the wrong people. (And if you’re in a highly regulated industry like pharmaceuticals, you could even be fined for presenting false results.)

Customer satisfaction is one of the biggest predictors of success for any company: Not only is it 5 times less expensive to retain a customer than to acquire a new one, but repeat customers spend more money and drive more word-of-mouth business over time.  

Bad data makes it much more difficult to achieve this. That’s because companies increasingly depend on data to figure out what their customers want, what content and resources they need, what steps they take to learn more about the product, etc.   

If your customer experience is grounded in inaccurate data, you’ll drive them away. Your offers become irrelevant at best and annoying at worst, which will have more people unsubscribing from your emails or even moving to your competitors.

3. Derails and disrupts marketing campaigns  

Marketers need to execute marketing campaigns across an increasing number of platforms – email, social media, paid ads, events, webinars, etc. – that each generate their own customer and campaign data, often with different data points, fields, and formatting. Standardizing all that data is difficult and time-intensive, and that can result in fragmentation and siloed data.

This leaves teams without the information they need to create segments, personalize their outreach and make consequential strategic decisions.

4. Increases security and regulatory risks  

The expansion of tech stacks has also opened companies up to additional – and potentially unnecessary – cybersecurity risks.  

That’s because every time you take on a new product, you open yourself – and your customer data – up to that company’s security risks, either from the company itself or any of its third-party vendors. Considering that companies use anywhere from 25 to 250 software products to conduct their business, this opens companies up to enormous risk.  

The more disparate your customer data, the higher the likelihood that your customer data will be exposed in a breach – and that it’ll blow back on your own brand.   

Besides harming productivity, disparate data also makes it harder for companies to keep up with the GDPR and American state-level data privacy laws, both of which require companies to maintain a certain level of data accuracy and integrity. They’re also required to provide customers with a copy of their personal data if requested – at an acceptable level of quality for it to be understood.

The standards are higher than ever, and so is the cost of falling short: Companies whose data doesn’t meet these standards risk fines of 20 million euros or 4% of annual revenue (whichever is higher) under the GDPR.  

5. Limits impact and opportunity  

The dirtier your data, the more time your data scientists need to spend on “data janitorial tasks” like cleaning, vetting, and validating data instead of higher-level work.  

According to Gartner, data quality affects overall labor productivity by up to 20%. 

Data scientists spend about 60% of their time verifying, cleaning up, correcting, or even wholly scrapping and reworking data (a.k.a “data janitorial work”). They also spend about 19% of their time hunting and chasing the information they need.   

That’s a sobering stat, but even incremental improvements in data collection can bring major benefits – and revenue opportunities: If the typical Fortune 1000 business could increase data accessibility by just 10%, it would generate more than $65 million in additional net income. 

How to improve your data strategy

Siloed data drives bad decisions and missed opportunities. But by storing and unifying your data in one place, you can maintain the quality of your data, activate it more quickly, and ultimately drive better results.   

Omeda’s CODIE Award-winning end-to-end customer data platform helps companies improve audience insights and drive revenue by gathering all their customer data – from email, print, social media, paid ads, events and more – into a single database.

Subscribe to our newsletter

Sign up to get our latest articles sent directly to your inbox.

Email *

Subscribe