Data Discovery, Analytics and an Enterprise Data Hub
In a continuation of his series “An Enterprise Data Hub, The Next Gen Platform” Hired Brain’s analyst Neil Raden returns to discuss the important topic of modern data discovery and analytics architectures. Business leaders have always struggled with the limitations of perfunctory reporting and limited data integration. Complex analytical queries often affected performance of operational systems and security concerns often limited accessible data to users. Because of all this many desired analysis were abandoned due to complexity. Getting all the data and tools to knowledge workers became a real struggle (if not impossible). Highly structured business intelligence models have given way to a new way to interact with data that yields fine grain access and variety of analysis options from ad-hoc searches, to detailed “self-enriching” machine learning algorithms. This new type of knowledge work is called “Data Discovery”.
“This is the meaning of Data Discovery & Analytics – rather than pre-arranging data and structures to address known informational needs; data discovery and analytics involves the combination of massive repositories of all kinds of data with the tools and computing power enabling knowledge workers to find patterns, build models, and create new value from “used” data.” – Neil Raden
As Neil points out, a data hub approach allows for agile data discovery over massive sets of data with some of the fastest analytic capabilities in market and has enabled analytic capabilities for derived, blended, and incoming data that previously were not possible. A wide variety of models are supported in a data hub from common SQL to full-text search that allow your existing teams instant access to a robust business intelligence platform.
An enterprise data hub (EDH), provides not only a cost-effective container of “big data,” it supports a myriad of tools and applications to optimize your use and understanding of data. – Neil Raden
A data hub is not only limited to “housed” data but also discovery on incoming streams and real-time generated data (internet of things, high-velocity logs, social, and sensor data). Now users can build complex machine learning protocols that help them identify relationships faster with less manual intervention.
Neil goes into greater depth on these topics and how data discovery is changing the modern BI landscape. Read the full white paper here.
Don’t forget to grab Neil’s breakdown of a next-gen operational data store inside an EDH.
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