The mounting pressure on businesses to increase fiscal data compliance, accountability and transparency has compelled businesses to realize and accept that ERP systems and data warehouses alone are insufficient to really tackle the problem of inconsistent, inaccurate and unreliable data. In particular, there is a growing awareness that the processes that create and update corporate data need to be addressed if the data dragon is ever to be slain. This involves understanding, documenting and controlling the business rules that surround the creation of new business classifications (such as a new customer code, a new product line or brand, an updated hierarchy of engineering assets or organizational structure). This is commonly termed "data governance".
Data governance is the process of establishing and maintaining cooperation between lines of business and management to establish standards for how common business data and metrics will be defined, propagated, owned and enforced throughout the organization. It is closely related to master data management (MDM), which is the management of data that is shared between computer systems, such as customer, product, asset, location or contract.
Although a growing number of organizations have put a tentative toe into the waters of master data management and data governance, there is scant concrete information relating to business motivation, level of maturity and adoption of data governance by business.
Against this background The Information Difference conducted a survey in August–September 2008 into the take-up, adoption and present level of maturity of data governance in business. The research addresses the following questions:
- What plans have business to adopt and deploy data governance?
- What is the current level of adoption of data governance by business?
- How are these initiatives funded?
- What is the nature of the data governance organization and how is it managed?
- Who is responsible for driving data governance in business?
- What does business expect from data governance?
- What are the key business drivers, perceived benefits and roadblocks?
The purpose of the study was to gain understanding of, among other factors, the level of take-up, business motivation and the preferred approach to implementation.
The survey was sponsored by four vendors: Datanomic, Initiate Systems, Kalido and Silver Creek Systems and by media sponsors DM Review and Obis Omni.
233 fully qualified participants took part in the survey, 60% from companies with over USD $1 billion in revenue. 20% of respondents hold the job title of "Chief Architect" and 16% are CxOs or VPs. 64% were from North America, 20% from Europe and the rest elsewhere. 38% were drawn from the business and the remainder from IT.
Some key insights included:
- There is clearly significant interest in data governance by businesses
- To date data governance is relatively immature
- The majority of businesses are failing to count the cost of poor data
- Most businesses are not measuring data quality
- Most data governance initiatives are currently focused on improving BI and reporting
- A very clear requirement is to move beyond "Customer" and "Product" to encompass a broader range of (master) data
- In general success with data governance initiatives is limited.
For details of the full survey see the following link - The Information Difference.
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4th November 2008: 'Judy Ko' said:
The experience I’ve had while at Informatica working with customers on data governance programs is very much in tune with this study’s findings. While there is a lot of interest in data governance, the practice is still quite immature and many organizations are struggling with a) building the business justification for a data governance program and b) getting the program off the ground. One successful tactic I have seen is to first identify a couple of data elements that are very important to a key business stakeholder (e.g. “bill-to” data is very important to the collections and cash management function; customer contact data is very important to the marketing department). Then you profile that data, an exercise which can be done in a couple of days, to uncover the depth and breadth of the data quality issues which are inevitably lurking in the data. You’re not trying to fix the issues—merely bring them to light. In most cases, the business is unaware of the severity of the data quality issues. So when the simple profiling report is shown to the business stakeholder, indicating x% of their business-critical data is incomplete or incorrect, all of a sudden, they get very motivated to do something about it. They then lend their support to getting a data governance pilot off the ground. And by focusing on just a handful of data elements, the scope is sufficiently narrow that the chances for a quick success are much higher than a boil-the-ocean data governance program.
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