With the rise of the Internet of Things and the connected digital ecosystem, almost everything we touch in our daily lives is producing vast amounts of data, in various formats, and on a rapid scale.
The ability to harness this data to cultivate actionable insights is what innovative companies must do to effectively deliver the best client experience. In order to do this, however, capturing, or "ingesting", large amount of data into a central repository, like an Enterprise Data Lake, is the first step, before any analytics, predictive modeling, or reporting can happen in earnest.
Ingesting data fast, as well as standardizing it, is a real challenge for organizations - but it needs to happen. Without data residing in one central place and serving as the "source of truth", an organization is at risk of having disparate groups performing analytics against incomplete, inaccurate and often opposing data sets, which obviously has the risk of delivering inconsistent results.
Capturing metadata, tracking data lineage from source, and doing this quickly, without different IT support groups writing manual code and ETL/ELT jobs, would be a huge coup for organizations. This creates a streamlined path to analytics, in a fast, automated, governed and cost effective manner.