Has your Company Caught the Data Flu?

The temperature has plummeted, the heating has been fired up and the nasty cold and flu viruses are thriving. Unfortunately, last week I was one of their victims. It started with a sneeze and a sniffle.  I knew something wasn’t right. I tried to ignore it and hoped that it would go away. 12 hours later I was in bed with a fever and completely unable to move, thanks to room spinning nausea.

Your business is your body

The issue of data quality within businesses is much the same; the erroneous data representing the virus, the organisation being the body, and the employees being the immune system. How the immune system functions determines the survival of the body.

With data quality, the initial infection could be something as simple as a spelling mistake, such as the difference between a  “Y” or an “N”, giving a completely different meaning that which was intended.  If the infection isn’t dealt with quickly, the data will be stored and potentially replicated within the organisation. That replicated ‘infected’ data will then be used for operational activities with potentially harmful consequences for the business.

On a small scale, errors like this might be tolerated, however, the real issues arise where either through lack of validation, training or proper process, multiple errors creep into the database overloading the immune system and “hospitalising” the patient.

The data death

The company’s reputation will soon deteriorate as its carefree or lacklustre approach to handling personal data spreads, staff will begin to feel demoralised and  wasted on campaigns that are sent to the wrong prospects.

So, on the basis that prevention’s better than cure, our advice would be to start taking the medicine by making sure that your organisation’s immune system is healthy by using validation tools to tackle any data nasties before they get deep in to your company’s ecosystem and start replicating.


  • What a fantastic analogy Guy! I’ll be using this one with my clients and colleagues.

    Trying to describe and explain issues with data quality is often difficult, often because it’s so technical and, frankly, dull. I like to compare data to that of bricks. Pretty boring on their own, but when lots are put together something amazing can be created for a variety of purposes. That usually gets the attention of who ever I may be talking to!