Ten years ago I asked if you wanted to trim $500,000 per month from your IT budget while increasing productivity. Guess what? Ten years have passed and virtually nothing’s changed, except that it’s now more important than ever to automate your service level management!
Informal studies show me that an average large enterprise spends upwards of $500,000 per month for service level management reporting—reporting that is error prone, late, reactive, and virtually useless. Given the increasing importance of cloud computing and service orientation, one of today’s top IT issues for large enterprises remains how to migrate from expensive, manual, and error-prone service level reporting at the end of the month to automated service level management during the month.
Today, most enterprises perform service level reporting—a monthly report that documents service levels (and outages) during the preceding month. The typical enterprise has dozens to hundreds of people performing these tasks. This is one of the last non-automated functions in IT today, and it’s one not done very well in most IT departments. Automating this costly and manually intensive task frees resources for other projects and increases the usefulness of the process at the same time.
Aside from freeing dozens to hundreds of valuable human resources, automating service level reporting delivers the additional benefit of becoming proactive. Instead of documenting that “IT hit the rocks” during the last month, automated solutions can predict that “rocks are coming” during the month and provide the guidance to “steer around the rocks” altogether, resulting in higher productivity.
Plus, with cloud computing’s OPEX vs. CAPEX, service level management takes on an entirely new meaning: it can make you money.
Migrating from manual IT service level reporting to automated service level management requires two elements: a process or practice for improvement (things like ITIL come to mind here) and metrics to guide the process. As shown throughout history, organizational improvements happen reliably when these two factors are present: a well-understood measurement of quality and a process whereby services are improved as a result of those measurements.
The closer these measurements align with customer experience (perhaps SERVQUAL) the more impact the changes will have on the organization.
Now lets throw big data predicative analytics into the equation: artificial intelligence that can PREDICT how a service is going to perform. Today, migrating from reactive service level reporting to automated proactive service level management should be the highest priority for any IT executive who manages services or service providers.