Business Intelligence: Staffing Pros and Cons

There are pros and cons for retooling staff, laying off existing non Data Warehousing staff or hiring a new team of technically qualified individuals.

Before a decision is made, organizations need to consider best practices around Data Warehouse knowledge and techniques, critical success factors, and the effectiveness and speed of getting a solution in place and running effectively. Organizations may have several options and view it as a luxury; however, the common pitfalls are extended timelines for getting a solution up and running, increased costs and not having the information available and useful for the business.

The Data Warehouse team can make or break an organization. It requires a shift in thinking and technology alone won’t make the project successful. What happens when you don’t have the skilled staff in place? What happens to the staff that has been deemed as having a “skill set mismatch? 

These individuals are typically redeployed to other positions in the organization if there is a role and a budget to support the headcount.  When the headcount and budget are not available to redeploy a staff member, these individuals separate from the company.  Often times companies lose the business knowledge that these employees have obtained during their tenure, so it is important to weigh the pros and cons of losing an employee that previously added value.  What are the pros and cons of re-training the staff?

Pros: Business knowledge is retained.  This is critical because companies need individuals that understand the operational aspect of how the business runs.  Often companies retain a few individuals that have strong business knowledge and acumen and retrain them to operate effectively on a Data Warehousing team.

Pros: High potential employees view this as a challenge and invite the opportunity to be retooled in a new skill.  These individuals have a higher rate of success for transitioning onto a Data Warehousing team.

Cons: Data Warehousing architecture, analytical skills, data modeling skills and report development require special skill sets that can’t be easily attained by attending a few training courses.  Many companies employ staff with advanced degrees in statistics, fuzzy logic, pattern matching and Boolean logic. Individuals may become good analysts with a significant amount of training; however, they will most likely not become a specialist in predictive analytics which means the value they add may be inadequate.

Cons: Retraining staff to think in terms of being able to analyze data in various dimensions is difficult.  The staff may not view the new way as the right way of thinking.