Many large modern enterprises are data-aware. They deploy processes to transform raw data into information using a variety of data integration, data management, and business intelligence (BI) tools. However, being data-aware, or even data-driven, does not necessarily mean that they are insights-driven.
A recent DBTA webcast covered what it takes to move a BI environment to the next level by harnessing the power of a data lake to drive new insights and business agility. Featured speakers included Boris Evelson, VP, principal analyst, Forrester; Alex Gutow, director, product marketing, Cloudera; and Steve Wooledge, VP, marketing, Arcadia Data.
According to Evelson, enterprises must transform themselves from being data-driven to insights-driven. Most companies have a data strategy that includes analyzing, integrating, and “getting signals” from data, but the ability to extract actionable insights from data is the next level of maturity in an insights-driven business. Many companies are drowning in data but starving for insight, Evelson said, observing that companies that are able to transform themselves to insights-driven businesses will grow 8-10 faster than the competition.
According to Evelson, most companies are only getting insights from a subset of all available data, and typically are using only about 20% of their structured data and about 10% of their unstructured data for insights. In addition, the majority of analytical apps are still being built using spreadsheets. What companies need is a modern BI architecture to get insights from all of their data, he said. Earlier generation BI architectures brought the data to the BI tools but today, what is needed is the ability to bring the BI to the data.
Discussing how to get more value from data with technology, Cloudera’s Gutow outlined the advantages of a modern analytic database. A modern analytic database enables flexibility with iterative modeling and self-service accessibility as well as portability with no proprietary formats or storage lock-in. It is also offers cost-effective scalability with the ability to elastically scale in any environment; cloud-native integration for optimized pay-per-use costs; and is proven at massive scale. Going beyond SQL, it can consolidate data silos with an open architecture, and cover data across SQL and non-SQL workloads. A final advantage of a modern analytic database is a hybrid, decoupled architecture, spanning multi-cloud and on-prem for zero lock-in and supporting multi-platform storage over S3, ADLS, HDFS, Kudu, Isilon, and other platforms.
Providing a list of the five keys to success in analytics, Gutow said companies need to: build a data driven culture, develop the right team and skills, be agile/lean in development, leverage Dev/Ops for production, and right-size data governance.
Wooledge from Arcadia Data, concluded the webcast presentations by explaining why companies should seek to get value from data lakes with the added agility of visualization tools or a BI platform to surface data that is not being leveraged and bring it to a larger audience. Arcadia Data was built from inception to work natively on data lakes, he noted. The top use cases for ntive BI and analytics on data lakes are customer intelligence and Customer 360 initiatives, financial services and insurance, risk and security optimization, and IoT analytics. Advantages of native BI on the data lake are one security model; no movement of data; the ability to discover and take action; and model-based on usage.
To watch an on-demand replay of the 1-hour webcast, titled "Take Your Enterprise Analytics to the Next Level with Native BI Platforms for Data Lakes," including presentations and audience questions, go to www.dbta.com/Webinars/1188-Take-Your-Enterprise-Analytics-to-the-Next-Level-with-Native-BI-Platforms-for-Data-Lakes.htm.