All successful businesses all rely on good quality data. Data is at the core of everything a business does: it’s critical in decision-making, marketing, support and procurement. It inspires trust; it stops the business infringing the law and letting its customers down.
Zig Ziglar, a renowned motivational speaker, famously said this:
“Every sale has five basic obstacles: No need, no money, no hurry, no desire, no trust.”
The same could be said about data quality projects.
So why are businesses so reluctant to take data quality seriously – and how do we tackle that malaise?
1. No Need to Fix Poor Quality Data
The issue with bad data is this: it’s everyone’s problem, yet nobody feels they have ownership of the cause. This can mean departments continually neglect the matter, each thinking it’s someone else’s problem to fix.
Until data quality becomes a massive problem for the organisation, the need for change could be swept under the carpet.
In the meantime, the business becomes inefficient, begins to lose money and risks infringing the law.
2. No Money to Fix Poor Quality Data
Many businesses are strapped for cash, so inevitably any non-urgent expenses get put to the back of the queue. Data quality isn’t a tangible expense: it’s ephemeral, and therefore easy to ignore when it’s time to set the budget.
In truth, the quality of data in any business should be seen as an investment – not an expense. Sadly, the benefits of clean data – increased productivity, more efficient marketing, better customer service and happier staff – are all very difficult to quantify, making data quality easy to ignore.
Good quality data shouldn’t be something you have to justify as an expense. It should be something businesses aim for to keep costs low on a day-to-day basis.
3. No Hurry to Fix Poor Quality Data
A little money only goes so far, and businesses almost always have a list of priorities that feel more urgent than the data they’re working with.
And a shoddy database full of poor quality records is hardly the same as a leaky pipe or a hole in the roof.
This means businesses can excuse themselves and deal with data at some undefined future date.
In the meantime, the quality of the database is deteriorating further. Staff are becoming more frustrated; they may be unwittingly capturing information in such a way as to make the situation even worse. And so the cycle continues.
4. No Desire to Fix Poor Quality Data
When it comes to everyday business activities, maintaining data quality may not be the most exciting task on the to-do list.
Often, there are tasks stacking up that feel more urgent, more pressing or simply more achievable.
Businesses need to have the desire to please their customers by keeping good records about them. They have to desire a contended workforce that can rely on good data. And they must want a better return on investment for sales and marketing activity, too.
5. No Trust in the Quality of Our Data
The end result of a neglected database is a disillusioned workforce that can’t see an end to the chaos.
Customers may lose faith in the brand; complaints start to pile up. Marketing staff complain that their budget is being wasted sending direct mail that’s never read.
In short: nobody trusts the data.
By dealing with the problem sooner, and putting proactive measures in place, data can be the lifeblood of a business rather than the reins that hold it back.
How to Deal With Data Quality
Sophisticated, affordable data quality tools can dramatically improve a business’ use of data, its response to customer enquiries about data, and its capturing of data going forward.
With de-duplication, phonetic matching, cross checks and even international data cleansing, companies can clean up massive databases in minutes.
When it’s time for your business to decide its annual spend, consider just how much time, money and hassle you’d save by making data quality a top priority.
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Martin Doyle is CEO of DQ Global, specialists in data quality and de-duplication software.