DYNAMIC LICENSE AND ACTIVITY MONITORING FOR COST OPTIMIZATION
Our workspace has become fully digitized in the last few years. Productivity suites (for example, Microsoft 365, formerly known as Office 365) leave a digital trace of every single interaction we have with our colleagues, collaborators, partners, customers, etc…
These traces of the interaction between users and workplace services and applications gives the IT managers a detailed understanding of how Workplace capabilities are used and consumed.
When you are one of the biggest corporations in the world, with hundreds of thousands of employees, understanding the usage patterns of a workplace service is a management mandate. And being able to dynamically adjust the purchased capacities – in terms of infrastructure, licenses, and service activations – to the actual needs of users is an enormous opportunity for savings.
HOW WE HELPED
We are working with large customers to help them better understand the patterns behind their Microsoft 365 activity traces. Galeo’s Workplace Analytics platform is helping our customers extract, process, and analyze the workplace interaction data from email to collaborative spaces, calls, and internal content sharing. With these data, the customer can analyze this information to reduce the hidden cost of unused service licenses.
Our solution provided the following benefits:
- Reduce on-going OPEX cost regarding Microsoft 365 licensing
- Reinforce dynamic cost allocation of licenses and capabilities to different business units, based on user activity and profiling.
- Monitor and analyze the impact of change management initiatives and training efforts and the actual adoption of new workplace practices and services.
The modular approach we followed, whose core functionality was oriented to information integration and modular combination of predictive solutions, has confirmed the possibility of accelerating the delivery of a predictive maintenance program. System development gains traction as puzzle pieces shorten time to value, improve the agility to pivot failed approaches, and eliminate the duplication of low-value efforts related to data infrastructure engineering.
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