Implementing a Data Integration Center of Expertise

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The Impact of New BI Trends on Data Integration
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Data Standards
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Implementing a Data Integration Center of Expertise

published in Information ManagementIn my earlier column “Do You Need a Data Integration Center of Expertise?,” I discussed these key points:

  • You need to integrate data, not just create another version of it. Data integration is hard work, but it can and should be done.
  • Data integration problems are usually the result of people-related issues such as politics, project ownership and budgets – not technology issues.
  • Many companies don’t admit or even realize they have data integration problems. They only see their investments in enterprise resource planning (ERP), customer relationship management (CRM), supply chain management (SCM) and data warehouse (DW) systems and the terabytes of data stored in them.

These key points established that reining in the proliferation of expensive, redundant data silos requires a methodical approach that includes establishing a data integration center of expertise (DICE). How do we change our approach, using a DICE, and establish data integration as a critical asset to our business?

To establish data integration as a business asset, you need to create the following:

  • Business and IT case for viewing data integration as a fundamental business problem that needs to be addressed.
  • Data integration investment portfolio with data integration as an infrastructure project across all business and IT groups.
  • Data integration center of expertise.

Creating the Business and IT Case for Data Integration

The business is investing in projects and applications that require data integration; but, historically, these are disjointed projects that have created data silos scattered across the enterprise. Someone needs to recognize that this is a systemic problem that needs to be addressed in order to better use IT investments and, more importantly, get the information needed by the business to manage the enterprise. The data integration problem needs an out-of-the-box approach that looks at the problem in a holistic manner.

The first step in this journey is getting help from someone who sees the forest, not just the trees – someone who recognizes the need for change. This is usually an IT person involved in existing data integration efforts who sees the redundancy in data integration projects and understands the business benefit of eliminating it. This person becomes the data integration evangelist who preaches that there is a problem and that something must be done about it. Not having significant budgetary authority and being located deep in the IT organization means that the evangelist generally cannot single-handedly change the momentum of the company.

The next person in the chain to keep the fire going is a champion – someone higher in the organization visible to and respected by either the CIO or CFO. The evangelist enlists the champion to continue selling the vision further up the organizational hierarchy. The champion also may lack significant budgetary authority, but is often the person who creates the business and IT case for budget submissions to the CIO or CFO. The champion needs to justify the solid business case of establishing data integration as an infrastructure program, just as e-mail and networks are treated in most enterprises today.

Finally, the crucial link to success is getting the sponsors, with both the CIO and CFO signed on, to treat data integration as an investment portfolio. There is a wide range of organizational approaches to this – from actually having a single data integration budget to a more realistic approach of budgetary reviews of all projects with data integration components. The budgetary reviews would eliminate redundant or conflicting efforts and, possibly, combine the data integration of multiple projects to enable more expansive and complete coverage.

Create a Data Integration Investment Portfolio

With multiple projects vying for the same budget, you need to evaluate and manage the data integration components by creating an IT strategy and architecture. Although the data integration strategy and architecture are prerequisites to guiding the data integration program, it is crucial to make sure you have funding.

The best way to ensure funding for the right projects is a data integration investment portfolio. With this approach, each project is examined to determine if a data integration component exists. If so, that component is examined in the context of all the other data integration components of other projects. This review will determine if there are any overlapping or complementary aspects between projects, thereby eliminating redundant expenditures and leveraging work across projects. Looking at data integration efforts from an enterprise perspective often enhances these efforts because you are no longer suboptimizing the data integration efforts of each project to fit within that individual project’s budget and resource constraints. A more robust effort can be undertaken when the enterprise is being used as a focus. In addition, some aspects that may have hindered other projects’ efforts may be modified in order to reach a better solution.

Money talks. You absolutely must have financial incentives such as budgeting to get buy-in for data integration projects. Enterprise-wide data integration, while appealing to many in the IT industry, is just an esoteric concept that will be overshadowed by short-term and parochial projects unless backed up by budgetary allocation and review.

Create a Data Integration Center of Expertise

The four basic approaches to creating a data integration center of expertise revolve around best practices, common technology, shared services and centralized services.

Each alternative offers an increasingly greater and more active role for the DICE.

Best Practices. The simplest form of DICE is a virtual team that documents and shares best practices among data integration projects. This allows each project to leverage past work without reinventing the wheel.

Very few corporate cultures lend themselves to this approach, however. The people on the virtual team are often doing this work on a voluntary basis in addition to their primary jobs. In today’s economic times, most people are already working many more hours than they did in the past and may not have the opportunity to invest the time that is needed. Virtual team members are often drawn back into their primary jobs as priorities change or crises emerge. In addition, each project team generally feels empowered to do their project their own way and figures they will do it better than others. New project teams may not have the bandwidth to study and use other projects’ best practices. In fact, they might not even know how they apply. Although this is a very appealing approach from a practical standpoint, it often has little impact on enterprise-wide data integration efforts.

Common Technology. The second form of organizing a DICE is to have a virtual team which not only publishes best practices, but also determines and recommends a common technology platform. This approach is appealing because new project teams can leverage existing technology platform recommendations and avoid the costly and time-consuming selection process.

There are some concerns with this approach. First, how will the DICE team make its selection without using various projects for requirements input? Second, the DICE team often does not have the budget or resources to adequately pick a technology platform. This approach means that an additional budget item must be allocated to a pure IT technology project – often a difficult sell in today’s environment. Third, while the DICE team is examining the technology options, other projects are moving forward without the guidance that the DICE can provide. In other words, more data silos may be built just as the DICE looks at ways to prevent them. Finally, what are the real incentives for each project to adopt the common technology platform? Many corporate cultures will allow new projects to pick another platform anyway, because these projects must still deliver their objectives in addition to picking up the requirements of the technology platform. Many projects will justify moving forward on their own because of their limited budget, resources and expertise.

Shared Services.The third organizational approach is to create a DICE where a group of dedicated people develop the data integration components of individual projects. In this services model, the DICE operates as a subcontractor within the individual projects. The DICE determines the strategy, designs the architecture and selects the technology platform as prerequisites to its development efforts. The shared services model allows companies to leverage work and resources across projects to develop an enterprise-wide data integration program. This organization enables the enterprise to develop deep data integration skills because the DICE specializes in that area. This approach works extremely well, especially when reinforced by strong business and IT sponsorship along with supporting budgetary investments. The main caution is that decentralized companies may be reluctant to use the shared services approach. However, with strong financial incentives and recognition that many IT services already operate in this manner, initial resistance can be overcome.

Centralized Services. The final organizational model is a completely centralized DICE operation. Data integration is elevated as a primary program; projects are independent of the other IT and business application projects. The data integration projects are executed to build a data infrastructure supporting other business projects. All data integration components are pulled out of projects and placed into the data integration program. Just as with the shared services model, the DICE determines the strategy, designs the architecture and selects the technology platform as prerequisites to its development efforts. However, unlike the previous model, the DICE operates the entire data integration project. This is an extremely effective model if the corporate culture supports centralized development and control. However, in a decentralized culture, the shared services model will have a better chance of success.

Data integration needs to become an enterprise-wide infrastructure endeavor to stop the proliferation of data silos and provide your enterprise with the timely, accurate and appropriate business information it needs. The right vision, strategy and architecture will show you where you need to go; sponsorship and organization will help you make it happen.

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