<p>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.<br />
Zig Ziglar, a renowned motivational speaker, famously said this:<br />
<em>“Every sale has five basic obstacles: No need, no money, no hurry, no desire, no trust.”</em><br />
The same could be said about data quality projects.<br />
So why are businesses so reluctant to take data quality seriously – and how do we tackle that malaise?</p>
<h2>1. No Need to Fix Poor Quality Data</h2>
<p>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.<br />
Until data quality becomes a massive problem for the organisation, the need for change could be swept under the carpet.<br />
In the meantime, the business becomes inefficient, begins to lose money and risks infringing the law.</p>
<h2>2. No Money to Fix Poor Quality Data</h2>
<p>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.<br />
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.<br />
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.</p>
<h2>3. No Hurry to Fix Poor Quality Data</h2>
<p>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.<br />
And a shoddy database full of poor quality records is hardly the same as a leaky pipe or a hole in the roof.<br />
This means businesses can excuse themselves and deal with data at some undefined future date.<br />
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.</p>
<h2>4. No Desire to Fix Poor Quality Data</h2>
<p>When it comes to everyday business activities, maintaining data quality may not be the most exciting task on the to-do list.<br />
Often, there are tasks stacking up that feel more urgent, more pressing or simply more achievable.<br />
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.</p>
<h2>5. No Trust in the Quality of Our Data</h2>
<p>The end result of a neglected database is a disillusioned workforce that can’t see an end to the chaos.<br />
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.<br />
In short: nobody trusts the data.<br />
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.</p>
<h2>How to Deal With Data Quality</h2>
<p>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.<br />
With de-duplication, phonetic matching, cross checks and even international data cleansing, companies can clean up massive databases in minutes.<br />
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.</p>
<h5>Featured images:</h5>
<p><span class="license">Photo provided by: Guest blogging community</span><br />
Martin Doyle is CEO of DQ Global, specialists in data quality and de-duplication software.</p>

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