The future of data-centric business.
Data generated by business and research operations, as well as by commercial products and services, streams across the Internet of Things (IoT) or internal networks into IT environments where it is often stored in data lakes. Currently, the majority of organizations surveyed report that they have either already implemented some form of data lake, or they are actively evaluating the technology. No matter the industrial sector, companies that have already implemented data lakes are outperforming their competitors.
Challenges of insight
In fact, over 80% of the execution time for analytics applications is spent on ETL and basic parsing tasks to find and extract relevant data sub-sets, rather than on processing the query itself. This approach 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.