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Is Your Business Making 3 Simple Mistakes With Data?

Martin Doyle October 29th, 2013 Data Quality
Data Typo

All too often, businesses think they know the ins and outs of their own data quality issues. But many of these assumptions are myths. As data quality specialists, we see the same scenarios crop up time and again. Are you making the same false assumptions?

1. One Mistake Means All the Data’s Wrong

When reviewing your database, you might be alarmed to see the odd mistake here and there. You might be tempted to extrapolate this over the entire database. The truth is, over time, it’s natural to see the quality of data diminish.

There are various causes to duplicate data. Data quality can suffer due to the ageing process, where the records simply go out of date. It can also suffer through human error; this is where deduplication software can often help to match and merge entries.

If you start to see flaws in your business data, don’t panic. It’s common for our customers to throw the baby out with the bath water and assume their data quality is poor overall, therefore making the problem feel much larger than it is. In every case, data quality software can put right past mistakes: there’s no need to abandon the lot.

2. Bad Data: It’s Someone Else’s Problem

In many businesses, people assign data problems to the IT department. This is a curious quirk of the modern business environment; we automatically assume that IT personnel are responsible for securing, backing up and storing our data, so we assume they’re also going to sort out any quality issues at the same time. Perhaps it’s because the task seems somehow very technical.

The truth is that the IT department is rarely responsible for data quality. They may well maintain the servers and hard drives that hold it, arrange the tape backups that protect it, and monitor the firewalls that keep it safe, but they can’t be expected to wade through the data and pick out flaws. And even if they were, they wouldn’t necessarily be able to pick up duplicates and mistakes.

Whose problem is poor quality data in business? In truth, it’s everyone’s problem, because every department in the organisation needs to do its bit.

3. Once I’ve Fixed My Data, the Job’s Done

The final point is one of the most common outcomes we see after a business has completed the first phase of its data deduplication project: it assumes the job’s done and dusted. In fact, nothing could be further from the truth. B2B data decays at an alarming rate: around 24 per cent per annum, according to industry averages.

A good dataset begins to decay the moment it’s fixed, and this final point therefore brings us full circle. Consider these questions: whose details are stored in the database, and will they change? Who works for the company today, and who will work for the company in a month’s time? Whose responsibility is it to maintain data quality, and will it always be their responsibility to do that?

All of these influences can sabotage your database. As such, it’s a mistake to assume that your data set is static and a one-off fix is all that’s needed.

Using Data Quality Tools

Picking out three very predictable issues may make for depressing reading, but data quality tools are readily available and can alleviate your data quality problems. Whether you are looking to develop a single customer view, or you simply understand the value in cleansing data and data deduping, there’s software out there that will make the job simple. Investment in deduplication tools may seem rather abstract, but in the long run, that investment always pays dividends.


Written by Martin Doyle

Martin is CEO and founder of DQ Global, a Data Quality Software company based in the UK. With an engineering background, Martin previously ran a CRM Software business. He has gained a wealth of knowledge and experience over the years and has established himself as a Data Quality Improvement Evangelist and an industry expert.