Cost of Bad Data for Organizations

Cost of Bad Data for Organizations

Modern world entrepreneurs rely on data heavily for successful business ventures. However, innumerable issues can still cause it to fail. Good entrepreneurs may be able to identify a lot of them. However, only a few point fingers at bad data.

According to IBM’s estimate, the US lost $3.1 trillion yearly due to bad dataThis mind-boggling factor is why bad data visualization and data quality improvement should be every businesses’ primary concern. Nearly 2.5 quintillion bytes of information are generated every day. Naturally, bad data gets mixed up with useful data. Thus, ensuring you spend money on good data is precious!

One such case is illustrated below.

Suppose a company’s internal department Y is on a deadline to do their jobs. When department X gives them faulty information, department Y takes up dual responsibility. In addition to performing their own tasks, they now also correct department X’s mistakes. As a result, a few unnoticed issues seep into the consumer’s deliverables. These issues infuriate customers and deteriorate the company’s brand image.

The above case is just one among many others!

What is Bad Data?

Bad data has a devastating influence on businesses. For those who need clarity on bad data’s definition, here’s what we consider bad data to be.

Bad data is characterized as: –

  1. Inaccurate
  2. Incomplete
  3. Inappropriate
  4. Non-conforming
  5. And/or Duplicate

Bad Data Losses

Now, let us compute how much bad data actually costs you. Here is some information that will shock you: –

  • Gartner.com states that organizations lose $13.3 Million yearly average on poor data.
  • Cio.com states that 77 percent of companies believe they lost revenue due to issues.
  • Integrate states 40 percent of all leads have inaccurate data. 
  • SiriusDecisions states it costs $1 to prevent a duplicate, which when left untreated amounts to a $100 expense.
  • MITSloan states employees waste 50 percent of their time coping with mundane data quality tasks.
  • Econsultancy.com states companies having mail delivery issues lost 28 percent revenue. Furthermore, 21 percent of businesses experienced reputation damages due to bad data.
  • Kissmetrics states businesses lose up to 20 percent of their revenue because of bad data.
  • CrowdFlower states data scientists spend 60 percent of their time cleaning and organizing data.
  • Pragmaticworks states 20 to 30 percent of operating expenses are due to bad data.

These statistics reveal a clear pattern of losses due to poor data quality. As stated above, 2.5 quintillion bytes of data are generated every day. This exponential data generation only exacerbates bad data issues.

Consequences of Bad Data

Nowadays, all companies use data to make big decisions about their business. Heavy reliance on data puts businesses in vulnerable positions when bad data is used.

Poor data quality causes innumerable significant issues, such as: –

  1. Greater Consumption of Resources
  2. Increased Maintenance Costs
  3. Higher Churn Rate
  4. Product/Mail Delivery errors
  5. Distorted Campaign Strategy Success Metrics
  6. Negative Reputation on Social Media
  7. Lower Productivity
  8. Poor Decision-Making Capabilities
  9. Missed Opportunities

Thus, poor quality data harms all facets of businesses.

How do Companies Cope with Bad Data?

However magnanimous bad data issues may be, they are solvable. Here is a list of steps (recommended by HBR) that aid businesses fix bad data: –

  1. Confessing having bad data issues: Every solution begins with an honest acknowledgment. Fixing bad data is no exception.
  2. Focusing on data exposure to external agencies: Meticulously monitor systems to ensure sync with the latest data for your customers, regulators, and other agencies.
  3. Formulate and execute advanced data quality programs: Ensuring quality data filters is a viable long-term option to prevent future poor data quality issues.
  4. Carefully assess the way you treat data:  Deep dive into current data management practices gives a good insight for future optimization purposes.

Though steps 3 and 4 seem simple, they are tremendously complicated and need expert assistance. Be assured that this investment will be an asset moving forward.

Conclusion

Data is the New Oil – Adeola Adesina. This quote, though true, is somewhat incomplete.

Not all information is useful and some even harm companies. Bad data wastes time, resources, and most importantly, revenue! Good data, like oil, should go through various purification processes to be deemed useful.

Data cleaning may seem like a big business’ game. However, all businesses lose revenue due to bad data. Start-ups dealing with aggressive burn rates should not compromise on data. Doing this will ensure they never become cash-flow positive and grow. 

Good quality information is difficult to extract and retain. However, as proved above, this aspect of your business is too critical to ignore. So, seriously consider investing in good data processes. After all, bad data does mean bad data out eventually. We promise it will be worth your while!

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