Business Analytics Project Prioritization - ©Copyright 2010 Alberto Roldan

As companies get involved in the area of predictive analytics, the issue of project prioritization has come more important. Companies need an analytics project prioritization framework that allows them to maximize resources while simultaneously reduced costs and improve profitability. Traditionally companies look at predictive analytics as an isolated function within an organization. As predictive analytics, data mining techniques, and other advanced methodologies are starting to permeate throughout an enterprise, the issue of prioritization has become a keystone in their analytics strategy.

Initially, companies looking for a prioritization framework look at their IT projects or IT reports processes in an attempt to adapt those processes to business analytics. Companies tend to find out that those processes are sufficient since an analytics project is more a combination of IT, reporting, and research and development (R&D) project.

Let me suggest the components of a framework to help companies prioritize their analytics projects:
1. Identify analytics projects prioritization committee composed of members from:
a. Executive sponsor
b. Business sponsor
c. IT lead
d. Analytics lead
e. Identify other interested parties – they may not vote but need to have input in the process of prioritization – for example project management lead, finance lead, human resources lead, and administrative support.

2. Develop standard operating procedure for meetings (agenda, minutes, decision making criteria) – meetings could be by teleconference and members of the committee can give their documented input by email

3. Follow the six M’s for the entry point for the prioritization of all analytic projects:
a. Must be Easy
b. Must be Friendly
c. Must be Accessible
d. Must be Efficient
e. Must be Documented
f. Must be Consistent

4. Identify how the project aligns with corporate and department-specific strategic goals

5. Prepare business case – the key is to quantify the business case: investment costs, quantifiable savings, changes and disruptions, soft benefits and ROI.

6. Prioritize analytics using a quantifiable framework approach in the following dimensions:
a. Strategic and Tactical initiatives
b. Costs
c. Business Impact
d. Disruption of other initiatives
e. Impact on organization financial health
f. Analytics maturity level of end-users
g. Resource availability
h. Budget availability
i. Technology availability
j. Leverage current IT environment
k. Leverage current business environment
l. High-level execution Plan
m. Exceptions
n. Process improvement suggestions

7. Document prioritization methodology so that the process is transparent

8. Notification of project prioritization with quantifiable scores and/or exceptions reasons
a. It should include the high-level execution plan
b. All stakeholders, interested parties and committee members should be notified.

9. Process improvement analysis and recommendations

Although the aforementioned process is not all inclusive, at least it gives a general overview of how to develop an efficient and documented business analytics project prioritization framework