The future of data-centric business.
Data generated by business operations, infrastructure, and research, as well as commercial products and services, streams across the internet and internal networks into storage. This rapidly growing data comes in many forms, and increasingly is stored as very large objects in data lakes. Currently, most organizations report that they either already have implemented some form of data lake, or are actively evaluating the technology. Regardless of industry, companies that have implemented data lakes (and associated analytics) are outperforming their competitors.
Challenges of insight
Over 80% of the execution time for analytics applications is spent on ETL (Extract/Transform/Load) and basic parsing tasks to find and extract relevant data sub-sets, rather than on processing the query itself. Moving large sets of raw data to the analytics platform for these basic operations is complex, expensive, and slow — delaying the adoption of transformative AI solutions, hampering the flow and analysis of scientific information, and hindering enterprises from gaining all the value possible from their big data assets.