10 must-have features for big data analytics tools
September 25, 2018
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Searching for a big data tool? You’ll want to make sure it allows you to embed results, can easily integrate with other apps and offers version control, among other capabilities.
Analytics features- Big data analytics involves a complex process that can span business management, data scientists, developers and production teams. Crafting a new data analytics model is just one part of this elaborate process.
The following are 10 must-have features in big data analytics tools that can help reduce the effort required by data scientists to improve business results:
Big data analytics gain value when the insights gleaned from data models can help support decisions made while using other applications.
“It is of utmost importance to be able to incorporate these insights into a real-time decision-making process,” said Dheeraj Remella, chief technologist at VoltDB, an in-memory database provider.
These features should include the ability to create insights in a format that is easily embeddable into a decision-making platform, which should be able to apply these insights in a real-time stream of event data to make in-the-moment decisions.
Data scientists tend to spend a good deal of time cleaning, labeling and organizing data for data analytics. This involves seamless integration across disparate data sources and types, applications and APIs, cleansing data, and providing granular, role-based, secure access to the data.
Big data analytics tools must support the full spectrum of data types, protocols and integration scenarios to speed up and simplify these data wrangling steps, said Joe Lichtenberg, director of marketing for data platforms at InterSystems, a database provider.
Data analytics frequently involves an ad hoc discovery and exploration phase of the underlying data. This exploration helps organizations understand the business context of a problem and formulate better analytic questions. Features that help streamline this process can reduce the effort involved in testing new hypotheses about the data to weed out bad ones faster and streamline the discovery of useful connections buried in the data.
Strong visualization capabilities can also help this data exploration process.
Support for different analytics
There are a wide variety of approaches for putting data analytics results into production, including business intelligence, predictive analytics, real-time analytics and machine learning. Each approach provides a different kind of value to the business. Good big data analytics tools should be functional and flexible enough to support these different use cases with minimal effort or the retraining that might be involved when adopting different tools
Data scientists typically have the luxury of developing and testing different data models on small data sets for long durations. But the resulting analytics models need to run economically and often must deliver results quickly. This requires that these models support high levels of scale for ingesting data and working with large data sets in production without exorbitant hardware or cloud service costs.
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Article Credit: TechTarget
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