2.2 Data quality issues within businesses

As we saw previously accurate data is the most important dimension of data quality. Data is the heart of any good businesses or organizations. Some companies such as financial ones are only living on information.

The use of the Internet increased the flow of information and now company's data are used by other companies to make decisions such as purchasing and selling.

So if company A is providing bad quality data which afterward are retaken by company B it enters in a vicious circle where the flow of biased information never stop.

As Jack E. Olson mentioned it in his book "Data quality":

"Data is generated by more people, is used in the execution of more tasks by more people, and is used in corporate decision making more than ever before." (Olsen, J. (2003). Data quality: The accuracy dimension. p.5)

Data quality is critical.

Even though databases are recognized as the most important asset, companies tolerate enormous inaccuracies in their databases.

According to the same author this issue is not only present within businesses but as well in governmental organizations and educational systems:

• Businesses and organizations are aware of data issue;
• They all underestimate the consequences of it;
• They have no idea of the cost linked to those issues;
• They have no idea of the potential value in fixing the problem;

Jack E.Olsen gives us as well in his book an estimation of the loss associated to data quality fixing it at 15 to 25% of the operating profit.

Those losses are of different kinds: transaction rework costs, costs incurred in implementing new systems, delays in delivering data to decision makers, lost customers through poor service, lost production through supply chain problems.

Those issues are normally not coming from the data management system (DMS are conceived to answer a specific request). The failure is mainly coming from its users.

To avoid this they need to be aware of three things:

- what are the system capabilities;

- how to use it properly;

- how to interpret its results.

The main remedy of this issue stands to be a long term strategy in which teams within the organization are trained in the concept of data quality management.

The concept of data quality is very relevant when dealing about search engines. Most of the search engines we know as consumers are commercial search engines. But as we know the main objective of a commercial company is to make profit and from this a lot of issues are raising.

According to a study untitled "Findability" (cf. The Association for Enterprise and Content Management. (2008). Findability: The Art and Science of Making Content Easy to Find.) most of businesses (62%) agree that finding information is critical however on the other hand most of them do not know the criticality level of finding information and this due to a general lack of awareness. It shows as well that strategy are almost mainly not defined (49%):

Enterprise findability goal

And proper goals not clearly expressed. It draws the same conclusions as some authors on this topic. (Olsen, J. (2003). Data quality: The accuracy dimension. p.7-8)

As we saw technology is not responsible of quality issues but the use of technology and the interpretation made out of the information retrieval is a source of quality problems. This can be reduced by implementing methodologies such as:

– Putting in place a better information research management strategy (Kehoe, M. (2009). Overview of the Enterprise Search Market.) mainly based on employees training. It does not only mean to train employees on how to use technologies but as well how to develop a pro efficient behavior when making

research. It means reconsidering the information process and participating in the improvement of the all research information system (cf.chapter:2.3.4). Computer users are expecting too much from technologies waiting to be fed with the most rational solution whereas it is not yet on the market;

– Implementing a more user oriented research application. Studies are showing that regarding libraries too many of them did not investigate enough in this field, focusing on the size of their database rather than how to retrieve the information. (Cf. UCL. (2008). Information behaviour of the researcher of the future.) This is one of the reason why people move from libraries to the Internet as an information provider;