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The Link Between Data Warehousing and MDM

$850.00

The Link Between Data Warehousing and MDM

A New Research Report from The Information Difference – April 2010

“Analytic MDM” has become established as one of the styles of MDM implementation adopted by businesses needing to effect a significant improvement in the speed and quality of their business reporting, often centered around one or more national, regional or enterprise data warehouses.

This is unsurprising since the “dimensions” of a data warehouse are essentially master data (e.g., hierarchies of products, customers, locations, etc.). Despite the close relationship between MDM and data warehousing, a glance at even the recent literature on these topics reveals that these two important areas tend to be treated as entirely separate.

At The Information Difference we were interested in exploring the linkage between master data and data warehouses and to understand the scale, scope and success rates of MDM and data warehousing initiatives in business. We have therefore conducted a survey into the link between data warehousing and master data management.

208 respondents completed the survey from all around the world; the majority from North America (57%) and Europe (27%). Over half the respondents (53%) came from companies having annual revenues greater than US $ 1 billion. The respondents represented a wide spectrum of industries.

Amongst other things the study reveals that almost half of the organizations surveyed have one or more data warehouse and MDM implementations.

The report has 38 pages.

The State of Data Quality Revisited

$850.00

The State of Data Quality Revisited

A New Research Report from The Information Difference – April 2013

Although the issue of data quality has been with us for decades now it still remains an area of concern and debate. In 2009 we conducted a detailed survey of the state of data quality across enterprises. Given the substantial investments in tools and support, has the state of data quality and data management in organizations genuinely improved over the past four years? Or is data management generally still in a ghastly state with organizations feeling that the problem is overwhelming? Despite a surfeit of tools vendors (more than 30), and much press attention, there is still surprisingly little concrete information available regarding the state of data quality in business. In this survey, which was sponsored by SAP, we revisit the topic of data quality and examine what has changed over the past four years.

210 respondents from around the world completed the survey. Just over half of the respondents (53%) were from organizations having annual revenues greater than US$ 1 billion. Respondents represented a broad spectrum of industry sectors.

The report has 40 pages.

The State of Data Quality Today

$850.00

The State of Data Quality Today

A New Research Report from The Information Difference – July 2009

The topic of business data quality has been with us for decades. Given the large number of vendors offerings dedicated to resolving data quality (DQ) issues, one might be forgiven for believing that the problems have all but been resolved. A glance through the current literature reveals, however, that the problem of poor data quality is still very much alive. Has the state of data quality in organizations improved over the past two decades? Or is data management, and data quality in particular, still in a ghastly state with organizations and senior management feeling that the problem is overwhelming? Despite the clear concerns from business and a plethora of software vendors, there is surprisingly little concrete information available regarding the state of data quality in business. In June 2009 we conducted a survey, sponsored by Pitney Bowes Business Insight and Silver Creek Systems, aimed at gaining deeper insight into the views of businesses regarding their current or planned data quality initiatives. Some 193 respondents completed the survey from all around the world, the majority from Europe (47%) and North America (44%). A high proportion (39%) of the respondents were from companies having annual revenues greater than US $ 1 billion; respondents represented a wide spectrum of industries. Some of the main findings from the survey are summarized below:

  • One third of respondents rate their data quality as poor at best and only 4% as excellent. Fully half considered their data quality as good, although this may be somewhat over-optimistic when set against other results from the survey. For example one respondent told us “Poor data quality and consistency has led to the orphaning of $32 million in stock just sitting in the warehouse that can’t be sold since it’s lost in the system.”
  • 63% have no idea what poor data quality may be costing them.
    Surprisingly, 17% have no plans at all to start a data quality initiative, compared with 37% who currently have some form of data quality initiative in place. The remainder plan to introduce data quality in the next one to three year period.
  • Some two-thirds plan for, or currently have, data quality spanning either the entire enterprise or one or more lines of business.
  • A remarkable 81% say that their data quality is focused wider than just “name and address” yet this latter is the area in which most (>90%) vendors currently have their base!
  • The top three data areas for DQ were ranked as: 1) product data; 2) financial data; 3) name and address data. It is interesting that financial data occupies second place but virtually no current DQ vendors specialize in this area. Product data is rated as a higher priority than customer name and address, yet only a few data quality vendors specialise in dealing with product data quality.
  • The top two barriers to adopting data quality were: Management does not see this as an imperative and It’s very difficult to present a business case. This is interesting given that the majority (63%) have not attempted to calculate the cost of data errors.

The report has 33 pages.

What’s the Link Between Data Governance and Success with MDM?

$850.00

What’s the Link Between Data Governance and Success with MDM?

A New Research Report from The Information Difference – December 2011

It has often been said that data governance is key to master data management (MDM), but is it? In this survey we wanted to get feedback from customers who have data governance activities and MDM projects, to see to what extent the two really are linked. In particular, we wanted to identify best (and worst) practice in data governance and MDM.

In 2010, we reviewed these two key areas in detail in our Data Governance Benchmarking, Data Governance Survey Report, and Data Quality and MDM Survey Report. Although many authors are highlighting the crucial importance of data governance initiatives when it comes to ensuring the success of MDM implementations, there is little hard information available on the approach being adopted by organizations that have implemented data governance and MDM.

We therefore conducted a survey, sponsored by Pitney Bowes Software, which was aimed at understanding better the views of businesses regarding their current data governance and MDM initiatives.

Some 110 respondents from across the world completed the survey, with 71% from North America (including Canada), 23% from Europe and the remainder (6%) from the rest of the world. Almost two-thirds (66%) of the respondents were from larger organizations with annual revenues greater than US $1 billion.

The report has 30 pages.