How Data Governance Links Master Data Management and Data Quality

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1 Brochure More information from How Data Governance Links Master Data Management and Data Quality Description: This Information Difference survey investigates the relationships between data governance, master data management (MDM) and data quality (DQ). Many authors have recently highlighted in the media the crucial importance of data governance and data quality initiatives to ensure the success of MDM implementations. There is, however, scant information on the approaches being adopted by organizations that have implemented or plan to implement MDM, or indeed those who have chosen not to implement data governance. We were therefore interested to explore the linkage between data governance, master data and data quality. In particular, to discover how organizations are tackling this area in practice. Additionally, we wanted to understand the scale, scope and success rates of data governance in relation to MDM and data quality initiatives in business. To achieve this, we have conducted a survey to examine the link between data governance, master data management and data quality. The aim was to find the current state of practice in data governance, the levels of effort involved and its success rate, and understand how well integrated it is with data quality and MDM initiatives. A total of 257 respondents from across the world completed the survey. 56% came from North America (including Canada), a further 26% from Europe and the remainder from the rest of the world. Fully two thirds of the respondents were from larger organizations having annual revenues greater than US $ 1 billion (62%). The respondents were drawn from a wide spectrum of industries including banking, finance and manufacturing. The key findings from the survey are summarized below: - 31% have already implemented data governance and have had active data governance implementations for a median of 2 years. A further 40% plan to implement within one year. - One third of organizations (both in production (27%) and planned (26%)) selected supporting business intelligence and data warehousing initiatives as the key driver for implementing data governance. - A significant number of organizations (39%) are electing to implement data governance alongside MDM (and data quality). - On a positive note, only 18% of organizations with data governance in production have implemented this stand-alone ; most have done so in combination with data quality and/or MDM. - Organizations are ensuring that business plays a central role in owning and driving data governance. Roughly one third had ownership in the IT camp, the rest jointly or solely with the business camp. - Only half of the organizations in production with data governance had prepared a formal business case with a similar figure (54%) for those planning to implement data governance. - 80% told us that they were measuring data quality (93% of those planning to implement data governance intend to measure data quality!). This compares favorably with our earlier study in 2008 in which 42% recorded that they were not measuring the quality of their data. - 68% of those in production admitted they had no monetary measurements for the cost to the business directly attributable to poor data. - 39% were relying on spreadsheets and the like to support their production data governance processes while 32% had actually taken the step of building their own in-house tools. Among those planning to implement data governance, 67% told us they intended to use functionality which they expected to find in data quality or MDM tools, 24% plan to use spreadsheets and only 14% considered building their own tools. - Of those in production, 37% have already implemented both data quality and MDM, and a further 32% have already implemented data quality or MDM and plan to complete implementation of both within the year. - The top two benefits of data governance (for both those already in production and those planning implementations) were strongly focused on the speed and quality of decision-making ( better quality and faster decision making and ability to respond faster to business change ). - Encouragingly, there is a predominance of business people/functions on data governance steering groups. Respondents told us that data governance initiatives ought to be led by business (29%) or jointly by business and IT (50%). Those planning implementations took a very similar view. - In general, data governance groups had (or will have) enterprise-wide scope and authority (42%). They usually covered all business data, not just high-level data objects. Indeed, only some 11% told us their

2 steering group had an advisory role. - 68% had named individuals with the authority to resolve the inevitable disputes regarding data definitions and ownership. Also about half those in production had a formal job description for their data stewards (or equivalent roles). - Those already in production with data governance reported that a mean of 11 people (median 6) from business together with a mean of 5 people (median 4) from IT were required to maintain their data governance implementations (an ongoing resource). This compared with 10 (median 9) people from business and 5 people (median 4) from IT for those planning implementations. This is a significant level of investment. - The top five (master) data domains that were managed under data governance (or planned to be managed) were: customer, financial, product, location and sales & marketing. The results were broadly similar for those planning implementations, with sales & marketing being replaced in fifth place by supplier. This demonstrates that organizations recognize the need to manage a broader range of data domains than the traditional ones of customer and product. - Those organizations already in production with data governance are publishing their data governance policies (45%) for want of any better system on their intranet. - 48% of those with production data governance told us that in their view, implementing data governance prior to embarking on implementation of MDM and data quality is key to the success of an MDM initiative. - 60% of those organizations with data governance already in production considered it to be moderately successful and 19% thought it was very successful. Only 6% considered it to have been fairly unsuccessful. - Among those planning to implement data governance, the highest ranked roadblock was competing business priorities are more pressing. This was also given as the main barrier for those not yet planning to implement, ahead of lack of support from other areas of the business. Other roadblocks cited were difficulty to demonstrate the need and difficulty in justifying the cost. - 74% of those planning to implement considered that implementing data quality was essential to a successful MDM implementation. Contents: - Executive Summary - Background To The Survey - The Approach - About The Respondents - Businesses That Have Already Implemented Data Governance - Businesses Planning To Implement Data Governance - Businesses With No Plans To Implement Data Governance - Conclusions - Enterprises - Vendors - About The Information Difference - Questionnaire - How Data Governance Links Master Data Management And Data Quality - A We Have Already Implemented Data Governance - B We Plan To Implement Data Governance In The Coming Year - C We Have No Plans To Implement Data Governance In The Coming Year - D Common Questions - E End Survey Ordering: Order Online - Order by Fax - using the form below Order by Post - print the order form below and send to Research and Markets, Guinness Centre, Taylors Lane, Dublin 8,

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