Below are a selection of excerpts from independent sources to highlight some of the true costs and effects of poor data quality. These create a powerful argument that investing in data quality is a low cost low risk route to improving your enterprises revenues and results.

“End users spend as much as 40-50% of a typical IT budget reworking data in one application to make it work with another”. The high cost of low data quality. English, Larry P. (1999) Improving Data Warehouse and Business Information Quality, John Wiley & Sons, Inc., ISBN 0-471-2538-9 (see www.infoimpact.com for more information).

“As corporate IT departments struggle to implement customer relationship management (CRM), business intelligence (BI) and data warehousing (DW), trade magazines and convention presentations echo the same painful stories. Years and dollars later, the systems are in place but the results are not worth the effort. The most common underlying reason - poor data quality.” Why data integration projects fail
DM Direct Newsletter June 25 2004, Nancy Rybeck
(see: http://www.dmreview.com/article_sub.cfm?articleId=1005619 for more information)

STAMFORD, CONN February 24, 2005 - Data warehouses play a crucial role in the success of a business intelligence (BI) program. However, through 2007, more than 50 percent of data warehouse projects will have limited acceptance, or will be outright failures, as a result of a lack of attention to data quality issues, according to Gartner, Inc. http://www.gartner.com/press_releases/asset_121817_11.html

The Data Warehouse Institute estimates that bad customer data costs American companies upwards of $600billion dollars per year 5/1/2002 By Wayne W. Eckerson
(see http://www.idma.org/valuePropositionGeneral.pdf )

"Garbage in, garbage out (GIGO)", is a common explanation for the mediocre performance of database operations. Dirty data still plagues every database and that means that an extraordinary amount of valuable information is lost forever in the mega-sized databases that are commonplace today. Data errors represent a much larger percentage of our data stores than ever before. DM Direct Newsletter Aug 27, 2004 By John C. Hermansen (see http://www.dmreview.com/article_sub.cfm?articleID=1009131 for more information)

Victoria Climbiè, murdered by foster parents, Every Child Matters (see http://www.dfes.gov.uk/everychildmatters for more information) The murder of a young child which effective multi agency data sharing should have prevented. 3 housing authorities, 4 social services, 2 child protection teams, 1 NSPCC team, 2 hospitals had data on Victoria and 12 separate opportunities to intervene.

Soham Murders: Bichard Inquiry Report (see http://www.bichardinquiry.org.uk) Huntley’s details passed through 4 police systems and the Police National Computer. Social services had records of Huntley being suspected of making sexual advances to 11 under age girls before 2001.

“No government anywhere in the world has successfully introduced and maintained an authenticated unique identifier for each citizen. Many claim success but cannot provide hard evidence. For example, in the UK there are 81m National Insurance numbers but only 60m eligible citizens”
Why multiple unique identifiers are common. Hanrahan, Mat. Jan 2004, One Card, One Database, Government Computing Magazine (see www.dalcais.com form more information)

In 2003 META Group estimated there was a 50-70% failure rate for large IT projects. Data layer problems were a major contributory factor (see www.line56.com/articles/default.asp?NewsID=2808 for more information) “A decade of intensive business change has left many organisations with a multitude of poorly integrated business units minus the processes needed to make them work together and a 50%-70% failure rate across the board for SCM, ERP and CRM. If data coming out of IT is clearly wrong, end users can’t say if it’s a data, an IT or a business process problem”.

‘Data Quality issues have come to represent perhaps 70% of the effort of data warehousing over the last few years. It’s not the technical stuff that’s the challenge, it’s the data quality: it’s cleaning the data, its….For example, different words have different meanings within an organisation. When the data was stored separately, the meanings were separated by bureaucracy. Put the data together - which is exactly what a data warehouse does - and for the first time different people can be seeing data and expecting it to mean separate things. This is a non trivial issue with serious cultural implications for a company… if you don't get data quality right, someone will be doing analysis and drawing conclusions from it that are totally irrelevant.’ Humans and Data Quality”. Dr Mark Whitehorn, author, academic, columnist and one of the world's pre-eminent database experts. 6th May 2003

Read how some of our clients have benefited from improving their data quality.

If you would like to find out more please contact Jane Keys by email: sales@infoshare-is.com or telephone the office on 020 8541 0111.