Business Intelligence Strategies for the CIOThis article originally appeared on the BeyeNETWORK.
In my initial article on the value of business intelligence
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Hannah Smalltree, Editorial Director
(BI), I discussed a framework to define value attributes within a life sciences firm. This article will review ways to pragmatically measure the value of business intelligence projects. Firms should make a pro forma determination of the value of a BI project before making investment decisions, but they should also revisit the pro forma value model with actual results to validate or change the long-term (2 to 5 years) strategy of the BI project.
The Business Intelligence Value Framework
Many organizations already have return-on-investment models that are supported by their finance and procurement organizations. I am sure that many of those project or IT investment models are financially sound, but they are often limited by the rigor used to develop the model inputs. For example, many models are focused on only IT cost reduction or on value generation that cannot be easily verified or measured. In the past, I have used the “value equation” with clients to help uncover the majority value creation attributes. Here is a high level view of the value equation.
The information value equation:
– NPV = P1 (B1-C1) + P2 (B2-C2) +……
– P is the probability of the scenario occurring
– B is the benefit derived by the business intelligence solution
– C is the cost of the implementation or organizational change
In this equation, NPV is the net present value of the net benefits received from the business intelligence project. Within many business intelligence implementations, there can be several value creation scenarios. For example, a new business intelligence implementation of a targeting or segmentation application may increase the overall revenue of a sales force by targeting the physicians that have the greatest likelihood of increasing or changing their prescribing habits. Another scenario for that implementation would be the reduction of salespeople for that market because you would be able to align sales staff with physicians whose prescribing habits can be positively changed by a sales call. In both value creation scenarios, there is some probability level that value can be realized by the organization. The scenario probability should be derived through discussions with stakeholders who are very familiar with the business environments being analyzed.
The benefits of a BI solution can be derived by many factors. Here are a few examples:
- Number of users of data, which represents organizational impact of the system.
- Percent revenue or cost that can be influenced by the data.
- The amount of organizational efficiencies created by the business intelligence implementation
or realignment.
- Time-specific benefits such as earlier or better decisions.
The benefit factors within the value equation should be validated by developing use-case scenarios for the BI solution. The business intelligence project sponsor must work with his business partners (users) to make sure that the BI solution has practical use in improving business practices. For example, if you are deploying an analytics tool that analyzes prescription behaviors, you must look at what questions the tool can answer and whether the decisions being made will drive high value benefits.
Also, you must also look at the different roles in your organization such as sales operations, brand mangers, strategic marketing, contracting, business development and marketing research. These pharma organizational roles have different decision frameworks and different user types that may or may not be able to leverage the new BI solution to create business value. For example, a highly complex analytics tool to do segmentation for a market research function may not be a highly valuable tool for brand performance reporting for the brand teams. The benefit factor of your implementation should be leveraged by the number of users who can effectively use the tool and the value those individual users can bring to your organization.
Similar rigor should be used when defining the initial investments and ongoing cost structure of a business intelligence project. Following are some of the cost attributes that should be included when determining the value of BI programs or projects:
- Process change costs of implementation, including user training and process documentation costs
- Management costs of operations:
– Service levels required by supported business
organizations
– Service delivery staff
– Data acquisition costs
- Technology infrastructure
- Software, PCs, servers and support, including technology implementation costs
- Future planned events that may require change in the service delivery model
In the past, pharmaceutical companies have been challenged to predict the impact of change management costs. For example, the business intelligence system being implemented is often imbedded into many business processes. This requires investments to redesign the business process and to train the related staff on the process change that includes the new BI system. Addressing these costs up front should allow the economic owner of the BI implementation to accurately assess cost and resource commitments to execute the project as planned.
Once all of the variable factors have been defined for the value equation, you should apply the NPV against those factors to determine the potential value of the project. Figure 1 is a generic example of how to calculate NPV for a specific value scenario.
Pharmaceutical companies often have several potential business intelligence programs or investment opportunities that can help them move their business in a positive direction. A governance structure should be put in place to identify value opportunities for BI projects as well as to revisit and adjust pro forma business models that were used to support existing projects and programs. This type of business intelligence program governance structure will increase business alignment and maximize business value for the organization.
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