Using Systems Capacity Data for Business Intelligence

In the modern economy, important business decisions are normally made after analyzing data of some sort.
In large companies, the importance analyzing data for decision-making has created a whole field in IT called BI (business intelligence). Many vendors provide very sophisticated BI software specifically to address this area. Complex data analysis for BI can incorporate many components including, large data warehouse, ETL (extract, transform, and load) tools, OLAP cubes, dashboards and scorecards, etc...

Most of these systems focus on finance, inventory, assets, and other business performance KPIs (key performance indicators). Using system capacity data in a capacity-planning role has long been a part of making intelligent business decisions for expensive hardware purchases, like mainframe hardware.

As low cost distributed systems became an option, many applications have moved onto these platforms, without much regard for capacity planning. The costs for doing capacity planning were seen to be more costly than the equipment, so it was not given priority. After many years of operating this way, many distributed environments have grown unwieldy. Looking at these environments as a whole, they now appear most costly and less efficient.

Using capacity data for making intelligent business decisions has not changed. What has changed is the realization that it still needs to be done.

Looking at the distributed environment as a whole, and determining what areas are under-utilized is now a key area of discussion. Of course ensuring that key resources do not run out of capacity is still the most important goal. However, achieving this dual mandate is not easy with so many moving targets. Having a sound plan of attack, and the proper tools, is critical for success.

We're running a free to attend webinar on March 8th which will cover high-level topics related to constructing a plan of attack, and then examine specific examples for implementation. The end goal is to bring back visibility that was once a precise science on the mainframe, and create usuable business intelligence from the mountain of data now being created. In some shops, even the mainframe has slipped into the cracks of poor capacity management brought on by the distributed deluge. In this case, the mainframe may need to be included in the plan as well.

Join in as we take a top down approach to creating business intelligence using systems capacity data.

• Business intelligence
• Evolution of capacity data in the systems environment
• KPIs
• Costs
• The plan
• Implementation

Register for your free place now http://www.metron-athene.com/training/webinars/index.html

Rich Fronheiser
SVP, Strategic Marketing