"Data archeology (finding bad data), data cleansing (correcting bad data), and data quality enforcement (preventing data defects at the source) should be business objectives. Therefore, data quality initiatives are business initiatives and require the involvement of business people, such as information consumers and data originators." (Sid Adelman et al, "Data Strategy", 2005)
"Data strategy is one of the most ubiquitous and misunderstood topics in the information technology (IT) industry. Most corporations' data strategy and IT infrastructure were not planned, but grew out of "stovepipe" applications over time with little to no regard for the goals and objectives of the enterprise. This stovepipe approach has produced the highly convoluted and inflexible IT architectures so prevalent in corporations today." (Sid Adelman et al, "Data Strategy", 2005)
"Dealing with [...] resistance is where social sensitivity, leadership, and power come into play. Social sensitivity is the ability to read the players and respond appropriately to their concerns. Leadership and power can quickly overcome most resistance to change and allow you to establish an environment and convince management to properly support the data strategy." (Sid Adelman et al, "Data Strategy", 2005)
"It is important to remember that the 'single version of the truth' - or enterprise logical data model - is not and should not be built all at once (that would take too long), but that it evolves over time as the project-specific logical data models are merged, one-by-one, a project at a time." (Sid Adelman et al, "Data Strategy", 2005)
"The chaos without a data strategy is not as obvious, but the indicators abound: dirty data, redundant data, inconsistent data, the inability to integrate, poor performance, terrible availability, little accountability, users who are increasingly dissatisfied with the performance of IT, and the general feeling that things are out of control." (Sid Adelman et al, "Data Strategy", 2005)
"The data strategist is responsible for creating and maintaining the data strategy. This includes fully understanding the strategic goals of the organization. [...] The data strategist must know (or learn) the existing environment including the important internal databases, the external data that will be integrated, and the data quality characteristics. The data strategist must be aware of the data volumes expected in the next five years. [...] The data strategist must be aware of changes in the business that will require more complex transactions and queries. He or she must also be aware of governmental factors including regulations and governmental reporting requirements. The data strategist must know about the requirements of service level agreements (SLAs) for both performance and availability and be sure that the data strategy supports those SLAs (it's also likely that the data strategist would have input into creating those SLAs.) And finally, the data strategist must be wired into the politics of the organization so that his or her proposals will be pragmatic and accepted by management and staff." (Sid Adelman et al, "Data Strategy", 2005)
"The folks in IT don't like change if they believe it will diminish the power of the IT group. This is particularly true for managers. Managers put forward countless reasons why the organization should stay as is, especially if a change can decrease the number of employees they control because managers often equate headcount to power in the organization." (Sid Adelman et al, "Data Strategy", 2005) [?!]
"The vision of a data strategy that fits your organization has to conform to the overall strategy of IT, which in turn must conform to the strategy of the business. Therefore, the vision should conform to and support where the organization wants to be in 5 years." (Sid Adelman et al, "Data Strategy", 2005)
"Working without a data strategy is analogous to a company allowing each department and each person within each department to develop its own financial chart of accounts. This empowerment allows each person in the organization to choose his own numbering scheme. Existing charts of accounts would be ignored as each person exercises his or her own creativity." (Sid Adelman et al, "Data Strategy" 1st Ed., 2005)
"You cannot boil the ocean; you have to prioritize your data integration deliverables. An enterprise-wide data integration effort must be carved up into small iterative projects, starting with the most critical data and working down to the less significant data. The business people working with the data integration team must determine which data is most appropriate for integration. Some data might not be suitable for integration at all, such as department-specific data, highly secured data, and data that is too risky to integrate. The team also needs to look at historical data and decide how much of it to include in the data integration process." (Sid Adelman et al, "Data Strategy" 1st Ed., 2005)
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