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.

Like solving riddles? This one should be easy for any IT specialist familiar with this website. We are all about data quality, which comes from clean data; and clean data means fewer records because all the duplicates have been weeded out.

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:

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?

Forget the six wise men. More often than not, it's the four dumb men—Everybody, Somebody, Anybody, Nobody—who look after data quality.

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?

The need for accurate business intelligence is becoming increasingly critical as decision-making becomes more complex daily and customers demand ever higher levels of service.

The function of the data warehouse is to consolidate and reconcile information from disparate business units and systems to present a consistent picture of the business at a single point in time.

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.

Well, the first problem is the definition of "Single Customer View" (SCV). In my opinion an active SCV certainly shouldn't be considered a service, it's a capability underpinned and delivered by technology.

A true SCV should be dynamic, so as any piece of data changes anywhere the business can see all data linked to the single customer instance they are interested in.

What bureau appear to be selling is simply a deduped and merged file, which they are now labeling a single customer view; this is not a living and breathing representation of the data. It's more akin to a company's audited accounts which are only true at one specific point in time.

Furthermore, this typically only delivers name and address data, leaving important linked transactional data, product information, contact history etc. out of the equation.

Data-deduplication can help businesses to make the most out of their IT storage space.