Change control exists to review and approve important modifications, but done wrong, you chance confusion, chaos, failures, and outages. Poorly run change control wastes everyone’s time, but far worse is the missed opportunity to assess and manage risk. Here, Bob Aiello gets you up to speed on the lost art of change control.

Continuing to manage highly complex IT environments in a reactive mode leaves IT specialists vulnerable, when really they need to understand the actual causes and effects of what’s happening among the many technologies in use across the enterprise. Instead of constantly fighting fires, IT operations teams should aim to prevent the fires from starting.

Implementing change can be a colossal challenge. People tend to prefer what's familiar, safe, and predictable to that which is new, unfamiliar, uncertain, confusing, or potentially risky. But the timing of a change effort can influence how readily people accept the change and adjust to it.

No matter how well we plan and execute software development, defects are generated and can escape to the customers. Failure to quickly resolve software problems leads to negative consequences for our customers and increases internal business costs. A quick deterministic
method to prioritize problems and implement their solution helps to reduce cycle time and costs. Achieving this goal requires several steps. The first is to determine a model that links problem resolution performance to institutional variables and problem characteristics. Statistical Design of
Experiments (DOE) is a tool that provides data requirements for estimating the impacts of these variables on problem resolution. Once data has been gathered, the results of statistical analysis can be input into a mathematical optimization model to guide the organization.
This paper describes such an analysis.