It’s like healthy eating, if you’re not used to it, there’s a crazy up-and-down cycle of trying to eat healthily,
succumbing to fast foods, trying to eat healthily, succumbing to fast foods, and on it goes.
Then one day, you crossed that pivot point, you’re now a healthy eater, and you have the self-control to avoid and manage eating fast foods.
Or, you just give up and stay unhealthy!
You justify this by noting that you only live once or what’s the point of living unhappily or something along these lines. Either way, you use logic to back-justify your results of not being able to eat healthy foods all the time.
This yo-yo effect is similar to why companies tolerate bad data. There’s some effort in cleansing, then it’s tempting to buy some data and import that into your CRM without checking its quality, or you let salespeople enter poor data because it’s too much hassle complaining about the same problem, and the list of reasons goes on.
Over time, the data becomes unhealthy, then you decide to cleanse it or create new processes or buy some software to solve your problems, but then again the data gets dirty as you take your eye-of-the-ball.
Just like eating, some companies overcome the challenges and live in the good data quality zone, others continue the crazy loop of trying, ignoring, trying, ignoring, etc.
So, it got me thinking, how do we back-justify why we keep poor data in our system, and these are the most common ones:
- “Our revenues are great at the moment so no need to worry about data” – until the revenues go slower then we’re in a panic to perform some marketing but that requires good data, but a lot of the data is now old and we didn’t keep in touch, oh dear! All those lost opportunities.
- “It’s too complicated” – but it only gets more complicated as you use more data, as you ignore centralising it or managing it proactively or as CRM users lose confidence. Find a partner who can help.
- “Can’t justify the cost” – The cost of cleansing is so small compared to the opportunities you have with your data. The data holds that vital information about prospects and customers: what they brought, why they bought, how much they bought, why did they buy, whose buying, whose likely to buy etc. The devil’s in the details but so are all the answers. Analyse good data and the answers will show up. Analyse poor data and the wrong answers show up.
- “Don’t have the resources” – Data needs to be a higher priority. Hire someone, or find a competent partner.
- “Our systems don’t let us manage data” – Make sure the systems don’t dictate your quality of data; there are other systems out there, but that data is there only once and it needs to be of good quality.
There are many ways to back-justify poor data, but don’t let this get in your way. Fight through the challenges, measure your data, cleanse your data, find the best tools, partner with experts and maximise your sales and profits.
“Analyse good data and the answers will show up. Analyse poor data and the wrong answers show up.”