Workplace Analytics: Dynamic license and activity monitoring for cost optimization
Our workspace has become fully digitized in recent years. Productivity suites (e.g. Microsoft 365 (formerly known as Office 365) leave a digital trail of every interaction we have with our colleagues, collaborators, partners, customers, etc.
These traces of the interaction between users and workplace services and applications provide IT managers with a detailed understanding of how workplace capabilities are used and consumed.
When you are one of the largest companies in the world, with hundreds of thousands of employees, understanding the usage patterns of a service in the workplace is a management mandate. And being able to dynamically adjust the acquired capabilities – in terms of infrastructure, licenses and service activations – to the actual needs of users is a huge savings opportunity.
HOW WE HELP
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 workplace interaction data, from email to collaboration spaces, calls and internal content sharing. With this data, the customer can analyze this information to reduce the hidden cost of unused service licenses.
Our solution provided the following advantages:
- Reduce ongoing OPEX cost related to Microsoft 365 licenses.
- Strengthen the dynamic cost allocation of licenses and capabilities to the different business units, based on user activity and profile.
- Monitor and analyze the impact of change management initiatives and training efforts and the actual adoption of new practices and services in the workplace.
The modular approach we followed, whose main functionality was oriented to the integration of information and the modular combination of predictive solutions, has confirmed the possibility of accelerating the delivery of a predictive maintenance program. Systems development gains traction as pieces of the puzzle shorten time to value, improve agility to pivot failed approaches, and eliminate duplication of low-value efforts related to data infrastructure engineering.