Are You Still in the Dark About the Quality of Your Data?

More and more businesses are waking up to the threat of poor data quality. We’re gradually seeing the risk being taken more seriously as the shockwaves of poor management are felt.

Yet for many businesses, data quality is seen as an abstract concept; difficult to understand, and impossible to value.

When the business formulates its budgets for the year, data quality is often skipped over, because nobody really knows what’s wrong. Sure: they can see emails bouncing, and their customers are drifting away to competitors, but the root cause hasn’t been fully determined.

These businesses aren’t deliberately neglecting data. They just don’t realise how important it is. In fact, it’s the most critical asset that your business currently holds. As your competitors start to take action on data, your business is at risk of losing momentum.

As a society, we are now fully connected. We are reliant on the systems that bind us together. Collectively, humans are generating more data in a day than they have in many thousands of years.

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It’s widely accepted that data decays at a rate of 2 per cent, per month, regardless of how it is stored. So, assuming you are not taking any action to prevent this, your data is slowly and quietly becoming less useful. Today, it’s less accurate and less valuable than it was yesterday, and it’s considerably less valuable than it was last year. You might not see this as an emergency, since you don’t really need to access that data right this second. But when you do, the state of your data could catch you unawares.

Consider a situation where your business embarks on a new marketing campaign. It sends out email marketing messages, and it creates a marketing letter to send out to prospective buyers. After investing several weeks curating content and planning the launch, it finds that its emails are bouncing back in their hundreds. And worse, once the direct mail is sent out, a huge amount comes back as undelivered: fit only for the recycling truck.

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This is a very simple example of the cost of poor data quality for just one department of your business. Yet one inaccurate record is going to impact on every department, given time. Once a record goes out of date, that record is effectively useless, and any attempt to use it is simply a waste of resource.

Once you reveal the extent of your data quality problems, there are two costs to face up to:

Some businesses also go one step further than these two steps.

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