Analytics

Leading cybersecurity enterprise demonstrates how technical and organizational reforms can democratize data after a merger.

Data analytics is nothing new. For decades, businesses have been deploying a "stack" of data analytics tools to collect, transform, evaluate and report on data. However, as data has grown larger in volume, and as the ability to analyze data quickly and accurately has become ever-more important to business success, the data analytics stacks that businesses depend on have evolved significantly. So, if you haven't taken a look at data analytics stacks recently, they're worth revisiting.

The buzz around digital transformation and the shift toward an API-first approach is prevalent in today’s Employee Benefits industry. However, after engaging in over 30 detailed conversations with industry professionals over the last three months, it has become clear that the reality on the ground diverges significantly from the prevailing discourse. Throughout 2023, I observed a notable increase in the number of companies within the Benefits space using our integration platform.

A 2021 Educase report shows that 75% of surveyed institutions have started or are planning extensive digital transformation strategies to improve their data utilization. The importance of a strategic approach to data utilization in this context cannot be overstated. Effective data management frameworks ensure that data across the institution is accurate, consistent, and used in alignment with internal policies and external regulations.

Data ingestion is important in collecting and transferring data from various sources to storage or processing systems. In this blog, we compare the best data ingestion tools available in the market in 2024. We will cover their features and pros and cons to help you select the best software for your use-case.

Building top-tier enterprise-grade intelligence using LLMs has traditionally been prohibitively expensive and resource-hungry, and often costs tens to hundreds of millions of dollars. As researchers, we have grappled with the constraints of efficiently training and inferencing LLMs for years.