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Data is crucial to running a successful business. Gone are the days of spending hours filling filing cabinets, jotting down endless notes by hand, and making key decisions on a hunch. 

The digital revolution and the data it brings have crucial importance for sales, from gaining customer insights and streamlining the customer experience to optimizing sales operations and tracking performance levels. It’s becoming hard to imagine a time before data. 

Most businesses now understand the importance of data. It’s common to see businesses asking questions like, “How can we combine qualitative and quantitative data to learn more about our customers?” and “Should we use a dataframe, dataset or a resilient distributed dataset (RDD) sample to process our data?” These terms were unheard of until the digitization of business. 

According to Forbes, data interactions, the creation, capturing, copying and consumption of data, increased by 5,000% between 2010 and 2020. This percentage is likely to continue rising throughout the next decade. 

But for all the positives of data, “bad data” threatens to undo the good work and damage your sales efforts. But don’t worry; we’ve complied this guide to help you understand bad data and how to stay clear of it. 

 

What is “bad data?” 

Imagine you have stockpiles of data to help your sales team close deals and increase conversions. Great, but have you ever thought about where that data came from? 

Data collected from prospective clients is usually accurate when it’s first collected at touchpoints. But over time, the accuracy of your stored data can change. People may have changed their email address, their home address, their job, their department, or any other crucial bits of information. 

Then there are other errors that regularly appear, such as: 

  • Outdated information 
  • Typo and spelling mistakes 
  • Data input in the wrong field
  • Missing contact information
  • Duplicated data
  • Incorrect data format

The result is bad data that is no longer accurate or useful. 

 

Should you be worried about bad data? 

Yes, especially if your sales team relies heavily on data to gain insights and drive sales (as they should). 

It’s easy to dismiss bad data; after all, how much damage can a few typos really do? The answer is quite a bit. Bad data can have devastating consequences for the bigger picture of your sales, especially in times when data is being transferred among several platforms.

Even if you’re satisfied with the results of your current campaigns, always be aware that bad data is potentially lurking in the shadows and holding your full potential back. Customer information can change very quickly after it first enters your database, quicker than your CRM keeps up with. 

 

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Let’s say, for example, your sales team has been tasked with sending follow-up emails to customers who opened your promotional email two months ago. How many of these customers still have the same email address or phone number? 

If as few as one in 100 changed email addresses and phone numbers, it could be enough to hit your figures. Time and money are wasted sending emails that will never even arrive at their intended destination. Each time you run a new campaign to reach out, you’re reaching out to someone who isn’t there. 

Imagine if all that wasted time could have been spent generating new leads. That’s the importance of having accurate data. 

 

How does bad data affect sales teams? 

Bad data is bad news for sales. 

While data is a good thing that enables sales forecasting to help accurately predict revenue, bad data can do the opposite. Here are four issues sales teams have encountered when working with bad data:

1. Bad data causes a bad experience all around

Bad data can cause problems for your business, sales reps, customers and prospects. 

Whether it’s a name spelled wrong, an undelivered email or call, or a mix-up with accounts, bad data is going to have negative consequences all around. Your customers may feel like you’re disorganized and be less inclined to trust or continue working with you, or they’ll miss out on exciting new content or key information. 

2. Bad data gives your competitors the advantage

With so many businesses vying for customers, standing out from the crowd, especially online, is no easy feat. 

Generating leads and connecting with customers in the modern digital era requires accurate and reliable data. The valuable insights data brings to sales reps allow them to start organic conversations with prospects and customers about their pain points and what they need. 

Having too much bad data will make things unclear and inaccurate. The result? Competitors can swoop in where you fall short. 

3. Bad data creates higher costs

Having bad data in your databases being used by your sales reps ultimately increases costs. Think of bad data as an unruly employee. They get things wrong, they’re unreliable and they’re hard to track down. To get them back on track, you’ll need to spend time and money to fix the situation. 

It’s similar with bad data. Fixing the problem is costly. The time and resources spent fixing bad data really ought to be spent elsewhere. Left untreated, bad data issues can snowball out of control and affect other departments. All of this requires money to get it right. Avoiding and minimizing bad data in the first place is much more cost-efficient. 

 

What can you do to fix bad data? 

Collecting bad data is somewhat inevitable. But don’t worry; there are steps you can take to fix it. 

1. Merge data from sales and marketing

Sales and marketing teams need to work together to maximize the chances of success. 

While these two departments will have their own systems and ways of working, it’s important to merge data from sales and marketing. Having access to a single database will allow reps to spot mistakes or query things that may appear wrong. Give access to both of these departments to edit and update records to fix bad data. 

2. Practice good data hygiene 

Not only does unreliable data affect sales, but it also affects your brand’s reputation. Practicing good data hygiene helps avoid overwhelming amounts of bad data at any one time. 

Data hygiene includes cleaning up, verifying and organizing data to spot common mistakes like duplicate fields, mismatches and missing bits of information. You’ll need to go through your CRM to fix them.

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Most CRM and other analytical tools already have cleanup options for data hygiene purposes. In other cases, you may wish to outsource your data cleaning efforts to experts who provide this as a service. 

 

Maximize your efficiency with good data

Modern businesses are all about maximizing productivity and efficiency throughout. Data lies at the center of achieving this, so the data you collect must be good data. Data helps sales teams in many ways, from giving insights into your target prospects to helping close sales and boost your bottom line.

Keeping on top of bad data will take the stress off your sales team and allow them to build a successful sales pipeline that’s backed by data. 


This article is part of the Crunchbase Community Contributor Series. The author is an expert in their field and we are honored to feature and promote their contribution on the Crunchbase blog.

Please note that the author is not employed by Crunchbase and the opinions expressed in this article do not necessarily reflect official views or opinions of Crunchbase, Inc.


  • Originally published August 11, 2022, updated September 22, 2022