Data is one of those things that many businesses still neglect. I see it far too often, but then again, that’s probably because I work in the Data Marketing Industry. Then again, in the past year or two, there has been a massive rise in awareness of the importance of having good data.

What do I mean when I say Good Data and Bad Data?

  • GOOD DATA = all data sources are defined, standardised and tracked, data values are normalised where feasible (eg. USA vs U.S.A. vs United States, etc.), customers and prospects are easily defined and segmented, marketable vs non-marketable leads are defined, etc. etc. etc…
  • BAD DATA = no/poor standardisation and normalisation efforts in place, excessive fields with no data value definitions, no segmentation practices in place, inability to manage unsubscribes vs subscribes, etc. etc. etc…

Data Quality What's That Darth Vader

As the world continues to evolve into a digital world, the critical part for all marketers and businesses as a whole is to gain insights from the data they collect. You may have heard of the massively hyped-up concept of ‘Big Data‘. Big Data is in effect bringing the concern of bad data to light.

But why do businesses have bad data in the first place? In my experience, it’s typically due to technological limitations and poor data management planning. The systems and tools we have available to businesses today just simply did not exist 15, 10 or even 5 years ago. So, businesses that have grown in size over the years, naturally grow in complexity as technology advances faster than they can keep up with. It’s unlikely this will ever change, but now that businesses have the power of technology at their fingertips (literally), they are now faced with infrastructure and technological limitations to adapt and migrate to more efficient systems of data management. Quite often it’s too hard and too expensive to evoke change within a large organisation to put greater focus on improving data quality. It’s a shame, but with the emphasis of Big Data coming to the table, the message feels like it’s really starting to sink in.

The purpose of this post is to highlight some of the significant costs that businesses face if they choose to ignore their data management and quality.

On a side note, I think it’s also important for businesses out there to consider their objectives when approaching a Big Data concept into their operations. I recently read an article about the dangerous trend of Big Data storage and one quote caught my attention that I believe all businesses need to consider:

“These days there’s a lot of hype around the idea of Big Data – and with it the notion that we should capture and store every bit of data we can get our hands on. The “capture-it-all” approach raises serious questions of privacy,” states Chief Scientist, Martin Fowler.

The stats and infographic below paint a good picture of the scale that bad data can have on a business’ revenue.

The true cost of bad data

  • Corporate data grows about 40% a year
  • Business cost of bad data may be as high as 10-25% of an organisation’s revenue
  • Bad data costs the US economy over $3 trillion a year – over twice the amount of the 2011 Federal Deficit

How can bad data cost money?

  • A lack of visibility into the right data caused one major retailer to lose more than $3 million a year
  • As much as 50% of a typical IT budget may be spent in “information scrap and rework”
  • The average company wastes $180,000 per year simply on direct mail that does not reach the intended recipient because of inaccurate data

Benefits of good data

  • A global consumer electronics organisation calculated that lost sales decreased by 27%
  • A UK Financial Services company gained 20% to 30% in revenue due to clean data
  • A large institution realised a 12% sales benefit and a 15% productivity improvement, totalling $64 million

Conclusion

If you run a business or work for a company that has bad data practices, I’d highly recommend you change your focus on the tactical operations and start considering how your marketing efforts could be improved if you had good data. Start asking yourself (and your stakeholders):

  • What would good data mean to your business?
  • How much do you think having good data could improve your bottom line if you could target the right people at the right time?
  • What information would you need to qualify your customers more efficiently?
  • If knowing a prospect’s Industry or Location (for example) would mean that your Sales team could qualify easier and generate more opportunities and pipeline, how are you working to capture that information?

There are so many questions that could mean the world of difference to your business. Have a think and then take the first step to good data.

The Cost Of Bad Data
Source: http://lemonly.com/work/the-cost-of-bad-data
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