CASE STUDIES
How we supported our client in delivering effective workplace analytics to their enterprise customers
CASE STUDIES
How we supported our client in delivering effective workplace analytics to their enterprise customers.
We used proprietary taxonomy and ontology to enhance our client’s AI capabilities and create a quick, iterative process that delivers valuable insights for their customers.
Our client achieved thousands of dollars in savings by implementing an auto-scaling infrastructure and optimizing cloud resource utilization.
The Client & Category
Our client, headquartered in the UK, is a multinational leader in educational publishing and services.
The company was struggling with slow, complex workflows that impeded its ability to deliver timely and accurate workforce analytics to enterprise clients. Factors such as limited agility, high staff turnover, and reliance on specialized skills worsened the issue, particularly with their manual handling of large data sets. They needed a smarter, more automated solution.
The Outcome
We helped our client reduce costs by replacing slow, manual processes with intelligent, real-time automated analytics. Migrating to Databricks sped up processing and made it easier to test new ideas, while a flexible AI system enabled faster, more cost-effective feature launches. Improving the dashboards and evaluation tools also built trust by clearly showing performance results. Now our client can deliver workforce insights to enterprises faster, more accurately, and more affordably than ever.
The Solution
We collaborated closely with our client’s leadership team to rebuild their analytics framework. We migrated their entire data ecosystem to Databricks, which enhanced operational speed and scalability, and helped with AI algorithms for mapping jobs, occupations, and enterprises, facilitating smooth future upgrades.
Real-time analytics were made available through RESTful endpoints, and research into task units and hierarchies enhanced model accuracy. We also introduced user-friendly dashboards to monitor performance and errors, and established a rapid-response process to address any issues swiftly. This has kept the platform fast, accurate, and reliable as it continues to grow.
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