Data management projects are often planned without a coordinated, enterprisewide effort to avoid overlaps and optimize efficiency. This research analyzes money-saving measures for CIOs and IT leaders to rationalize these projects, and includes a model for quickly prioritizing them.

• Organizations typically have multiple projects in progress in specific data management disciplines, such as specific data integration projects to support master data management, business intelligence (BI) and data warehousing initiatives in various business units.
• The lack of consistent coordination of data management capabilities and anticipation of business priorities, limits reuse and supportability.
• Disparate, sometimes competing data management projects and activities (such as repeated data warehousing efforts in multiple departments), can lead to redundant project costs of more than 20%.

• Undertake an enterprisewide inventory of all data management related projects, objectives and their impact. Ensure planned data management capabilities are usable to address a wider and more comprehensive range of business priorities to rationalize cost.
• Use assessment criteria (such as improving infrastructure agility and increasing trust in information provided) to determine contributions of data management initiatives and to balance technology-oriented objectives with projects behind business transformation.
• Streamline overlaps, including eliminating some projects, by ensuring each data management program or initiative has a distinct contribution for addressing infrastructure agility issues, while delivering value to help business transformation.

IT organizations and project teams building and supporting data management architectures and infrastructures will be seeking ways to reduce costs during a time of economic pressure. This research describes the cost-saving potential for CIOs, data management IT leaders, infrastructure managers and other roles involved in the deployment and ongoing support of data management infrastructure and processes. The goal is to achieve 20% savings or more in investments annually by rationalizing projects.

Most large organizations approach data management activities in a somewhat opportunistic and fragmented manner, with individual departments, business units and geographies embarking on data management related efforts in a project-specific manner, which results in multiple and overlapping projects from various departments. Additionally, individual project teams often procure their own technology infrastructure (data integration tools and database management systems, for example), on which to execute plans related to data warehousing, data quality improvement, data management modernization, and other projects. Consequently, data management problems are addressed with wide ranging and often conflicting architectures, especially in large organizations that lack enforced standards for how they address their data management needs.

Organizations can save hundreds of thousand of dollars in redundant project costs and staff productivity involved in building and supporting data management projects by the use of a prioritization framework for data management initiatives. The impact of these adaptations is summarized in Figure 1.

According to Gartner inquiries, many clients want to know how they can improve control of the costs and proliferations of data management related projects that have evolved out of uncoordinated efforts of business units and individual departments. This is consistent with Gartner's findings in data quality, for example. A recent Gartner survey shows that many enterprises deploy data quality tools on a single project or departmental basis, even though a more uniformed approach brings greater opportunities to improve data quality throughout the enterprise.

For example, annual investment was initially identified by one organization for a series of projects as part its three-year strategy for improving data management capabilities. These projects include data consolidation for a security unit, data integration for selected BI applications, data warehouse infrastructure replacement, reporting tools for accessing sales information, an enterprise data model and various other data management improvement plans.

A careful analysis revealed significant overlaps in some of these efforts on assessing how various projects contribute to solving infrastructure issues, compared to delivering strategic value to the business. In eliminating redundancy, a set of common capabilities was mapped out and multiple projects of overlapping objectives were rationalized into projects of distinct themes, so that the enlarged scope of a single project had value over the redundancy aspect from multiple project strategies.

Priority was given to having a comprehensive data integration project to replace multiple isolated plans, with a consolidated role integrating the data of multiple business domains. Some limited-impact projects were dropped, such as integration efforts that benefited only a single department or disparate BI applications. An enterprisewide data warehouse project became the infrastructure foundation for a consolidated view of business information, with the development of an enterprise data model as part of best practice adoption, while addressing the broader requirements of stakeholders. The prioritization exercise resulted in a revised set of projects and avoided 20% of the initial project's investment costs, estimated at $2 million in the first year. In other fiscal years, prioritization efforts were able to achieve more than 20% cost savings from revised projects as the IT organization gained better proficiency in prioritizing and coordinating data management initiatives.

Identification of the practicable choices and how to prioritize them required top-level coordination of investment plans in data management projects. CIO and CFO departments had the opportunity to collaborate and establish a direction to augment such activities and gain full visibility of related projects and proposed investments across the enterprise.

Consistency and a cohesive approach in planning are essential, including the necessary buy-in from various stakeholders. Focus on project evaluation that will have a positive impact on the business and customers, such as dropping or consolidating those projects with competing objectives, to maintain business capability.

Every project or initiative needs to produce a lasting and positive impact. Rather than generating hype or producing a temporary spike in productivity or some other measure, actions should create value for the business through the provision of trusted information. The relevance of the data management infrastructure must be continuously maintained, because incremental investments for prioritized projects are expected over time. To gain business buy-in (where rationalization of projects and tools would have business impact), owners of various data management infrastructure and projects need to explain the related cost savings, while also stressing the opportunity to improve the quality of service through better standards, best practices and expert resources.

IT organizations trying to use information effectively to achieve strategic business goals, need an holistic approach to integrate, persist and deliver data assets and the ways in which relevant data management initiatives support overall success. However, data management professionals often lack a planning model they can use to create a "data management capability map" that matches initiatives against essential capabilities. Reducing the cost associates with projects, either with better alignment of focus areas or the avoidance of overlapping or redundant capabilities, requires taking three steps:

1. Assess project capabilities. Identify the impact of each data management initiative associated with the outcome of increasing strategic value vs. advancing technology capability. Each data management infrastructure development project will need to identify how much contribution is envisaged for closing a certain technology gap and how much business impact will be delivered by the project.
2. Allocate each project to its most significant contribution category. The Data Management Capability Map provides a planning model for positioning initiatives (see Figure 2). Consider these themes as part of any data management capabilities as guidance for determining contribution category:

Data management agility. Here, "agility" refers to the readiness of the information infrastructure to fully manage and support the required operational and analytical data and information across the entire organization. The data management infrastructure delivers required resilience, security, accessibility and availability across all usage areas. Infrastructure capability is architected to evolve with the needed ongoing technology offerings and to continuously equip the organization with the essential data management infrastructure to keep data operational across the entire business value chain.

Business transformation capability. The roles of data management and integration must be aligned to a corporate agenda shared by the business value owners, with clear connections to potential business themes, which often need to be tailored to a specific business environment. For example, the nature of the solution offerings should enable organizations to achieve optimal data management and integration, with capabilities in architectural innovation, engaged stakeholders, lasting data management and integration commitment, and a relentless drive toward reliable and accurate data.

Trusted information advisor. Every project or initiative needs to produce a lasting and positive impact. Rather than generating hype or producing a temporary spike in productivity or some other measure, actions should create value for the business through the provision of trusted information. The relevance of the data management infrastructure must be continuously maintained, because incremental investments are expected over time. This ongoing effort will anchor the IT organization and establish it as a trusted provider of high-quality information to the business.

3. Establish a data management program planner and determine priority actions. Prioritize data management programs and projects and agree between IT and business stakeholders the needed balance for technology capabilities with the projects and initiatives behind business transformation (see Figure 3).

Planners should try to rationalize data management initiatives across the three main categories of contributions, based on expected outcome of initiatives. Programs and initiatives achieved along the way need to establish an increasingly positive impact on the business, and not only to grow the capabilities of the data management infrastructure. For every initiative that closes a certain infrastructure gap, a proportion of that initiative must deliver business impact. However, this approach is often neglected in IT programs, such as in a data warehouse capacity upgrade that can be easily perceived as low-business-impact, unless a specific subset of initiatives are linked and communicated formally for corporate involvement in that subset initiative. A corporately shared "journey planner" is necessary to enable the achievement of value drivers and stakeholders' commitment.

Information is a strategic tool when it delivers progressive impact for business change; balanced initiatives are necessary in data management infrastructure development vs. strategic value. Table 1 shows examples of projects and identifies potential contributions, but these are not model-projects, as every organization will defer in requirements.

By revising the approach to data management and prioritizing project requirements, data management project leaders and managers should strive to improve planning, optimize investment and increase the efficiency of project delivery. Data management goals should fuel business transformation in incremental stages, in addition to overcoming gaps in its technical ability to manage and exploit information. Cost reduction targets for optimized and prioritized projects can be realized within the same fiscal year and will affect ongoing financial savings as these initiatives are optimized for future extension and reuse.