Q

Build or buy a BI or DW solution?

We are trying to decide on a business intelligence (BI) or data warehousing (DW) solution. What factors should we consider to decide whether to purchase all or a part of the solution (i.e., backend processes, which would include the model and ETL, or front-end processes, which would be the BI tool and reporting) or to build it in-house?

How can I convince management to buy a solution when the team I have is inadequate (either not enough manpower or lack of experience, or both) and there is a hiring freeze?

We are trying to decide on a business intelligence (BI) or data warehousing (DW) solution. What factors should we consider to decide whether to purchase all or a part of the solution (i.e., backend processes, which would include the model and ETL, or front-end processes, which would be the BI tool and reporting) or to build it in-house?

How can I convince management to buy a solution when the team I have is inadequate (either not enough manpower or lack of experience, or both) and there is a hiring freeze?

Foote Partners is fortunate to have analysts and consultants who have been workshop and seminar teachers for The Data Warehousing Institute and DCI Data Warehousing/Business Intelligence conferences. So, you've come to the right place to ask these questions.

First you start with capabilities. Regardless of financial resources available to support the DW/BI solution, does your company have the "chops" to be successful at such a thing? And I don't just mean the technical skills and experience.

We use an multi-part evaluation template with our clients that we developed over the past 10 years specifically to help with the planning and development of DWs. Part 1 focuses on organizational history in building systems; demonstrated abilities in the company in project management, communications, transition and relationship management; an inventory of tech, business and soft skills critical to developing, executing and maintaining a data warehouse; and the history of senior management, the business unit and the IT department support for various IT-enabled initiatives. We compare the results of this evaluation to a collection of better/best practices that we've developed from years of expert analysis and consulting. We do, in fact, split our analyses between back-end processes and front-end decision support pieces.

Part 2 of the evaluation uses job descriptions for DW/BI initiatives that I originally developed in 1994 as an analyst with META Group. They are, in fact, the industry's first comprehensive organizational view of the data warehouse and have been updated and revised several times since then. We have been regularly updating and tracking salaries and skills pay for 19 data warehousing/business intelligence positions four times a year since 1995. We usually do a human capital evaluation using these as a baseline. From this we can judge in-house staffing capabilities, identifying strengths and weaknesses and areas where outside assistance (or new hiring) would be necessary.

The rest of our evaluation is outlined around the following criteria and success factors.

Project management

  • Why a data warehouse
  • Goals and objectives
  • Desired characteristics
  • OLTP vs. data warehouse
  • Architecture
Cost
  • Tangible benefits
  • Intangible benefits
  • Cost elements
  • Budget issues
  • User buy-in
Risk management
  • Examples of failures
  • Challenges to DW quality
  • Risks inherent in DW projects
  • Risk mitigation
Infrastructure
  • Hardware platform
  • Network
  • Tools, software
  • Standards
  • Methodology
  • Staffing
  • Training
Scope
  • Function
  • Selection of first project
  • User expectations
  • Schedule
  • User responsibilities
  • Tools
  • Service-level agreements
Planning
  • Developing the project plan
  • Work breakdown structure
  • Tasks
  • Deliverables
  • Resources
  • Estimating
Vendor management
  • Evaluating products
  • Evaluating vendors
  • Reference checks
  • Getting the most from your vendor
  • Traps
Critical success factors
  • Critical success factors/bare essentials for success
  • Success criteria
  • Measuring results/measures of success
  • Promoting and marketing the data warehouse
As for convincing management to buy a solutions vs. using in-house personnel, that depends on how you go about making your case. Clearly, you first need to establish the business case for the data warehouse. There are all sorts of metrics that you can use, plus the experiences of hundreds of other companies, to help make your case. If you can establish revenue, market share, risk management and/or competitive benefits to having data warehousing/business intelligence capability, it will certainly facilitate a decision to boost manpower either internally or by seeking outside assistance. It's got to pay for itself and more. You also need to develop a solid business plan that includes a fully developed project plan including timeline, resource requirements, risk analysis, stakeholder analysis and plan, etc. No executive will stop any project that will show a good return, has predictable and manageable risk and that may even inspire customers.
This was first published in January 2006

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