The Real Cost of Dirty Data

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“The world’s most valuable resource is no longer oil, but data”. In the 2 years since The Economist ran this headline[1], there has been plenty of debate about whether data is the new oil or not.

What is undisputed is that raw data can be refined to add value, just like oil. And just like dirty fuel in your car, dirty data in your business will lead to poor performance, which adds to your costs.

Financial Cost

According to a Gartner survey, poor data quality is costing organizations an average of $15 million annually each.

IBM[2] estimates bad data is costing U.S companies over three trillion dollars per year.

Dirty data can also lead to GDPR compliance issues, which risk fines of up to €20 million, or 4% of annual global turnover – whichever is greater.

The financial cost of bad data is the most obvious one, but there are consequences that run much deeper.

Opportunity Cost

Accurate and complete prospect data are crucial to effective and efficient targeting of potential new clients. Opportunities will be missed if your sales and marketing team don’t even know they exist, and opportunities will not be pursued if they can’t be segmented and prioritised.

Accurate data is essential to understand what your ideal prospect looks like in order to prioritise effective marketing and drive sales.

Productivity Cost

Bad data doesn’t just affect your audience, it affects your employees as well. Salespeople waste time dealing with junk leads; service delivery people waste time correcting mistakes in customer orders; data scientists spend time cleaning data; analysts get misleading results; IT struggles to synchronise data across disparate platforms; and executives don’t trust the numbers from finance.

Keep your employees engaged by giving them the tools they need to be successful, which includes access to clean, up-to-date data.

Execution Cost

Poor data quality undermines digital initiatives, weakens competitive standing and leads to lack of customer trust. There is also a direct execution cost that comes with using bad data – wrong or sub-optimal decisions being made, wasted calls to wrong numbers, deals lost, etc.

Reputational Cost

Most businesses only learn of their dirty data when their customers and prospects tell them, not always privately.

Once the issue goes public, the internet never forgets. A mis-step driven by bad data will live on in search results, news articles and social media for years to come.

Your brand, customer and email sender reputations can all can be tainted by your (mis)use of bad data.

What to do

Rhetorik recommends a 4-step solution to address the problem of dirty data:

  1. Cleanse – stop dirty data from disrupting your business. Cleansing data involves the removal of duplicate, invalid and inactive records, as well as ensuring the data in those records is accurate and up-to-date.
  2. Enrich – incomplete data can be just as costly to the business as inaccurate data, so the next step would be to improve the fill rates of those fields that are most valuable to you.
  3. Add – extend your reach by adding new data records. Append additional contextual data – firmographics and technographics – to aid segmentation, prioritization and messaging.
  4. Repeat – B2B data decays at a rate of around 30% per year, so data cleansing should be viewed as an on-going requirement, not a one-time event.

The business impact of dirty data is considerable and addressing your data hygiene issues will mitigate its impact. Click here to learn more about how the Rhetorik DataCliniq™ can help restore the health of your business data.

[1] The Economist – World’s most valuable resource

[2] IBM – infographic

Kevin Savage

Author

Former VP of Sales & Marketing at Yankee Group with research expertise for the technology sector Previously: Successfully growing new data and analytics markets for CEB/Gartner as well as Communispace, Jupiter Research, Forrester Research.

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