The evaluation and selection of a data modeling tool for your organization can be a daunting task. Not only must you consider numerous technical criteria and requirements, but political challenges require attention as well. For example, a database administrator may have a favorite tool that she used in the past. The corporate standard might dictate yet another technology, which may not align technically with your particular project. There may be a push to use technical checklists or request for proposals that may not apply to your individual needs. Information from vendors may be flavored with their own particular strengths, which may not be relevant to your requirements. How do you sort through all of these conflicting messages to choose the tool that is right for you?

It is imperative that your organizations’ requirements be fully understood, documented and prioritized, and that the team responsible in the decision process clearly highlights the implications of requirements before the evaluation process gets too far along.

Consider these 10 steps when evaluating a data modeling tool:

1. Identify Requirements

Document the requirements for data model support within the modeling tool. This should include the major functions to be performed along with the frequency of their execution and the extent of their usage. The main focus at this stage is to have the core evaluation team highlight the possible implications of particular requirements. While documenting these requirements, be careful not to include too many mandatory items. Keep in mind that no perfect solution exists that can address all of your company’s current and future needs, so don’t create a list of requirements that will eliminate every tool on the market.

2. Identify Organizational Constraints

Create your list of organizational constraints. These are usually aligned with the requirements and should not require excessive work beyond interviewing the users. Typical types of organizational constraints are cost, hardware and existing software, operating system, migration effort and timeframe, risk, staff skill levels and training availability.

3. Customize the Evaluation Criteria

After you review the requirements and organizational constraints, it is time to customize the evaluation criteria to align with specific information that your company will need to make its decision. Scoring spreadsheets and questionnaires should complement each other.

4. Prioritize the Evaluation Criteria

Assign weights to all criteria elements and be sure to take into account representation from those team members who will actually be producing the various high-level data models.

5. Create a Product Wish List

Create a reasonable list of desired products and vendors that will meet the critical requirements and not exceed the budget. If it’s necessary to prequalify vendors in advance of the main evaluation questionnaires, then a vendor prequalification questionnaire and briefing should be issued. At this time, it is critical to keep and maintain a log of all correspondence and contact information. With members from your team and the vendors’ teams checking on a variety of requirements and questions, it is easy to lose track of progress.

6. Evaluate the Product Pros and Cons

Evaluate the products on your wish list with the methods your team feels necessary to secure the information that you need to make your selection. Vendor visits and product demos should be conducted during this stage, and be sure to keep vendor visit reports and meticulous records of all visits and conversations in the log book. To maximize everyone’s time on these visits, be sure that the vendor has the questionnaire well in advance of the meeting, with a deadline clearly indicated for both completion of the questionnaire and demonstration of the product.

7. Apply the Evaluation Criteria Against the Products

All vendor questionnaires and product demos should be complete by this time. The team can apply the evaluation criteria to the products and score how well each product meets the evaluation points. The evaluation team should have an understanding of how each individual on the team scored and rationalized his or her decision. At the end of this stage, there should be one cohesive evaluation per vendor or product that represents the group’s collective opinion. Before ranking products in the next step, make sure there are no more than three products on the list to be considered in the final evaluation.

8. Rank the Products

Summarize the evaluation scores and indicate the rationale for exclusion or adoption of a particular product. If the products are close in ranking, consider an in-house trial that would go into more detail than the product demo conducted earlier in this process by the vendor. Use your own organization’s data models to show how well the product performs in the user environment. At this point, see if any additional information is needed before preparing the final recommendation.

9. Perform What-If Analysis

This is the step where the team agrees on the final recommendation. Use the weighted product evaluation spreadsheet to evaluate the sensitivity of the scored items in the final shortlist of products. Note that if changing a few items dramatically affects the final ranking of the products, then the weighting assigned to them must be re-evaluated. It is critical that the impact of the details of the product in the affected areas is clearly understood.

10. Present the Your Case

Even if not explicitly required, an outline of the implications and next steps for use of the product should be included in the findings that you present to the management team that is responsible for signing off on the investment. The findings should include a product findings summary, evaluation spreadsheets, product pros and cons, recommended product and implications for its intended use as well as recommended next steps.

To avoid frustrations and streamline the decision-making process, leverage this 10-step guide to evaluate each data modeling solution best suited for your unique business needs. It will enable the evaluation team to make a strategic and sound decision, and maybe even make you the data modeling evaluation hero.

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