Searching Enterprise Data Lakes like Google
Combining NLP and Search to Unlock Insights from Petabytes of Unstructured Data
A recent Aberdeen’s study found that companies are managing an average of 33 unique data sources for analysis and their volumes of data are growing by over 50% annually. As a result, enterprise users’ demand for faster, more efficient data access and analytics has fueled the emergence of enterprise data lakes: repositories designed to hold vast amounts of raw data in native formats until needed by the business (read our blog to learn about data lake implementation best practices).
With enterprise data lakes in place, companies have started to gain various benefits, including:
- Centralize unstructured and structured content silos
- Overcome legacy source systems’ limitations
- Enhance analytics processes
- Enrich data in ways that are not possible in the source systems
However, implementing an enterprise data lake is just a start. The key to maximizing ROI is the ability to derive business insights from that massive amount of data, both structured and unstructured. Thus, as more and more companies put their data in the lake, their next step often involves seeking effective solutions to address two primary needs:
- How to enable users to quickly find the right datasets from billions of records in the lake?
- How to make use of the enterprise data lake to provide answers to natural language questions?
Watch our Innovation Lead, Paul Nelson, discuss how natural language processing (NLP) and search can help companies solve these challenges and unlock the full value of their data lakes.