A report on a recent business survey found that, on average, out of every £6 spent out of their budget, departments wasted £1 because of poor data quality. Business people are aware that having clean data that gives accurate information helps them swim away from their competitors, but many still struggle to keep their heads above water rather than invest in data quality solutions.

Sometimes IT managers feel they could be losing the joust in trying to get the clean data message across. Everyone is so focused on their own aspects of winning the competitive edge, they don't see or understand that faulty tools could be undermining their efforts. Or if they do, it's why can't IT get it right for a change, or we've already invested in all these information systems, why do we need to spend even more money?

Big data is here, and it's getting bigger by the second. It is bringing significant challenges as well as opportunities. The horizons of master data management are changing at record speed.

Got a direct mail campaign coming up? The marketers have prepared an excellent mailing with an eye-catching design, a tempting offer, a powerful call to action and prominent contact details. Everything is lined up to handle the responses and the business is holding its breath for an onslaught of interest.

But even with all this in hand, bad list preparation can virtually kill the campaign, waste the marketing budget and cause lasting damage to the reputation of the business. A bad mailing list that hasn't been checked by data cleansing software can even take you outside the law and result in costly fines.

The Hidden Dangers of Defective Data

The surface dangers of bad data are obvious: it renders mailings a waste of time and money, can cause reputation damage and seriously interfere with customer relationships.

But there are other dangers lurking beneath the surface that could bite harshly into your bottom line and deliver false information that lures you into making business decisions that are financially damaging.

Martin Doyle is a Data Quality Improvement Evangelist and the CEO of DQ Global, which is a UK-based data quality software and services vendor providing data cleansing, international address and email verification, data deduplication, and data matching solutions for Customer Relationship Management, Single Customer View, and Master Data Management. DQ Global has worked with over 500 businesses worldwide on a variety of projects, providing their clients with improved data quality, making their data fit for business use, and enabling them to trust their data and make decisions based on a foundation of fact.

Listen to the full interview here:

http://www.ocdqblog.com/home/the-johari-window-of-data-quality.html

Good content is data which is fit for purpose.  It should be:

A recent report from Enterprise Data Management Council from "What's In and What's Out” in data quality.  Take a look at our DQ360 product which will help you with your data quality issues.

In an ideal world we would have a universal product naming convention.  In practice this rarely happens and it's possible for different users in the same organisation to give different names to the same product.

We recently stumbled across this unintended data quality error as a result of what should have been a simple data processing task.

Just because data defects aren't immediately apparent, doesn't mean you don't have a data quality problem. It's true that there is no easy way to measure or value it, but imagine if your biggest competitor went into receivership, which is their assets would you like to get your hands on? Nine times out of ten, it's their customer database. Surely that gives it a value? And if that's true, why do so many companies neglect their data?

Primary Data Quality Issues

We think you probably already know the answers; however, here are some of the questions you might want to ask yourself.

• Who is responsible for overall Data Quality in your business?
• Do you have a set of data quality guidelines (a DQ-Plan) for your business?
• Does everyone who touches your data know about and adhere to the Data Quality Guidelines?
• Has everyone been trained in the importance of good data management principles and understand the consequences of dirty data?

• It's hard to do
• It's too big a task
• You can't trust it
• And its information quality we want!

These are just some of the reasons why organisations are reluctant to take control of data quality.

Just imagine, if your database held accurate, correctly formatted and duplicate-free data, you would:

• Make better, informed business decisions
• Improve profitability
• Dramatically improve response rates
• Generate increased sales
• Retain customers for longer

Data Deduplication is the removal of unwanted records from a file or database; cross matching is the process of comparing one database against another to identify relationships which lead to accurate data integration/migration and construction of a single customer view.