Sometimes considered to be part of data preparation, exploring the underlying data helps data science teams understand the business context of a problem and formulate better analytics questions. Data explorationīig data analytics frequently involves an ad hoc data discovery and exploration phase. The data wrangling and preparation process involves seamless integration across disparate data sources, plus steps that include data collection, profiling, cleansing, transformation and validation.īig 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 product and industry marketing for data platforms at InterSystems, a database and healthcare software vendor. Data wrangling and preparationĭata scientists tend to spend a good deal of their time cleaning, labeling and organizing data to prepare it for analytics uses. Such features should include the ability to create insights in a format that is easily embeddable in decision-making platforms, which should be able to apply them in a real-time stream of data to help drive in-the-moment decisions.
"It is of utmost importance to be able to incorporate these insights into a real-time decision-making process," said Dheeraj Remella, chief product officer at in-memory database provider VoltDB.
0 kommentar(er)
