How Risky is Bad Data for Your Organization

Inplace #2

Have you watched the latest Avengers: Infinity War? *spoiler alert*

Thanos intended to capture all the stones in order to achieve his goal. Before launching his campaign, he researched and knew where the stones were or with whom they were.

But, what if, he did not have the correct information about the stones. Would he reach his goal?

No, right?

Bad data could do exactly that. While you may think you are heading in the right direction, you are in fact, misled.

Result? Not as great as you expected it to be (or planned it to be).

I ran a quick Google search for cost of bad data to the company and found this –

As a data cleansing expert, these stats dishearten me. While there was a time data wasn’t everything (and you could afford inaccurate data), today, it is more valuable than gold.

But, again, your data has no value if it is not cleansed and updated regularly.

What is bad data?

In simple terms, bad data is an inaccurate set of information. It also includes missing data, wrong information, inappropriate data (for example, data entered in wrong columns), non-conforming data, duplicate data, and poor entry (misspells, typos, variations in spellings, format etc).

Do you know that 25% data decays annually?

Over time, lead change positions, contact numbers, email addresses, shift companies, retire etc. Companies may cease to exist in some cases. So, the changes in the data can be drastic!

Such data is not only unhelpful but hurts any campaign which is based on it. According to Gartner, the average financial impact of bad data on organizations is $9.7 million per year.

That’s a lot! A LOT!

Oh, but I am a small business owner with a tiny dataset. I won’t require data cleansing!

Unfortunately, you are wrong! Don’t think you cannot be a victim of bad data just because you are small. According to Blue Sheep research, smaller companies lose 6% of their revenue each year due to poor-quality data.

Now, that’s a lot of revenue lost for you, isn’t it?

Along with the revenue, you also lose time which could have been spent on nurturing relationships and lead generation.


study by DiscoverOrg study by DiscoverOrg found that sales teams lose about 550 hours each year updating old data.


HBR calls it the “hidden data factory.”

(image source)

According to HBR, 50% of employee time is wasted in these hidden data factories, hunting for data, finding and correcting errors, and searching for confirming sources.

Phew, that’s not what you hire talent for! Isn’t it better to spend resources on cleaning data regularly instead?

Definitely, yes and this gets us to our next point

Why is clean data important for any marketing activity?


We live in a data-driven world today.

Source: E-consultancy

Most marketing activities have gone digital. We have an entirely new section for social media, internet marketing, and the likes.

In fact, 63% of marketers report spending more on data-driven marketing and advertising last year, and 53% said that “a demand to deliver more relevant communications / be more ‘customer-centric’ is among the most important factors driving their investment in data-driven marketing”.

For all of this to yield results, you need to clean your data.

We understand. Data cleansing is, in fact, tedious and time-consuming. However, there is good news. Businesses that do not have an existing process, technology, or bandwidth to do this themselves outsource it to agencies.

What are the benefits of keeping clean data?

  • Knowledge of customer base: Accurate data on your buyers can be used to understand buyer persona, customer behavior, preferences, and sales journey that helps in effective lead generation. Also, personalized communication and segmentation (of campaigns and audience) becomes easier.
85% of organizations have seen improvements in timely and personalized customer communications by improving data quality.
  • Reduces duplication and redundancy:  No one likes to receive the same email or call twice. If your data is clean, you can avoid this and other such redundancies that cost you money, time, and reputation.
  • Rescue from inaccurate marketing decisions: Outdated or inaccurate data will always lead you in choosing wrong platforms, strategies, and tactics.

Not only this, if you run reports on this data, you will always get a fragmented picture of what is working and what is not. Not only do you run the risk of reporting inaccurately, but you will end up spending the marketing budget on wrong channels.

  • Increases ROI on email marketing: Data cleansing is central to email marketing success. If your data is old, you will have lots of unopened and bounced emails.

A significant percentage of email addresses inevitably become bad over time (from 20% to 30% on average)

One of the benefits of data cleansing and scrubbing is that it keeps email list hygiene and maximize ROI (in the form of revenue, click through rates, qualified leads, open rates etc) from email marketing campaigns.

 In short, bad data

  • Kills your marketing campaigns
  • Decreases customer satisfaction
  • Damages brand reputation
  • Effects lead generation as data is obsolete
  • Results in bad decisions
  • Results in missed opportunities

So, how to cleanse it?

Data cleansing is an art and requires a strategic approach. While we have discussed the process in detail here, in short –

First, start by understanding the composition and quality of your existing database. Ask yourself

  • Does the data conform to expected patterns?
  • Is the data complete?
  • Is the data usable?
  • Is the data accurate and unambiguous?

Secondly, integrate the data you have from all your sources. You can either merge data from various sources (for example, excel sheets of various teams) or merge new data (like purchased data). It helps eliminate duplicate records, dormant and junk contacts.

Thirdly, start correcting, updating, and validating existing data using standardization rules. For example, Only B2B ITES Pvt Ltd can also be recorded as Only B2B Ltd or Only B2B Private Ltd etc. Once data is standardized it becomes easier to analyze the business relationship with the entity. Similarly, dates, social security numbers, and contact details should be in a specified format.

Last but not the least, conduct regular audits and automate the process of cleaning data.

Now you know that bad data hurts.

Think about it this way: Would you rather have sales and marketing teams chasing down qualified leads or sorting email addresses?

I think the answer the is obvious and for that to happen, you need to ditch bad data and implement data cleansing regularly.


About Vikas Bhatt

With 10+ years of B2B Lead Generation, Vikas Bhatt now runs OnlyB2B, a reputed B2B Demand and Lead Generation company from India that serves most European nations, the US, Mexico, and Canada. Vikas is a renowned Demand Generation expert, motivational speaker, and a B2B entrepreneur. You can connect with Vikas over email: