Crisis management is generally a costly business. Switching gears away from your forward-thinking strategy and pulling in resource to deal with issues not on the radar can really stymie growth and efficiency.
Especially in the IT management space.
In the past, the role of the IT manager was largely reactive: as soon as a problem occurs, they would have to jump in and manage the crisis. This was, and continues to be, a costly exercise for IT departments – often costing organizations millions of dollars annually.
Investment in predictive analytics has the potential to drastically reduce the surprises faced by IT management. In a recent article in Enterprise Networking Planet, Drew Robb shows how predictive analytics can be used to monitor networks across enterprises and mine behavioral patterns to get out in front of potential issues like usage spikes and plan for them before they occur. As IT moves towards virtualization and cloud models which allow for flexibility in terms of resource allocation, predictive analytics really comes into its own as a tool to help manage these spaces. For instance, with a cloud-based installation, resources can be deployed or changed in minutes, rather than weeks. If you have multiple users and applications on the installation, predictive analytics can be used to determine where resources should be apportioned prior to any impact on service levels.
Maintenance isn’t only the area where predictive analytics play a role.
Steven Sams, IBM’s vice president of Global Site and Facilities Services points out that by 2012 global data storage capacity will need to be 6.5 times what it is today (fueled largely by internet cloud-based services). He recently explained to Forbes’ Quentin Hardy how predictive analytics can be used by data center managers to plan for this growth:
"Tech planners need the same kind of big pattern-finding software more commonly used by designers, chief executives, and finance types. Among the new analytic offerings from IBM are cash flow-based scenario software, for figuring out whether to build, consolidate, or do nothing"
Obviously these decisions can have serious implications on business operations and costs. Sams highlights a Chinese bank that has managed to go from 38 to 2 data centers with a cost saving of $180 million a year using this technology. To better serve this market, IBM has launched a predictive analytics tool for use by the Global Business Services division on data center engagements.
As we move into 2011 and beyond, predictive analytics can play a major role in the way IT departments manage data centers and their operations. Given what’s at at stake, expect to see a lot more interest in this area.