Value of using Big Data Discovery

In BDD, a wider number of people can work with big data, compared with traditional analytics tools. You spend less time on data loading and updates, and can focus on actual data analysis of big data.

In traditional analytics projects you spend most of your effort on data preparation. You must first predict all possible business questions, model the data to match, locate and get the data sources, and manipulate feeds to fit into the model. You build pipelines for extracting, transforming and loading data (ETL). Only after these tasks you can engage with the data. As a result, only a minimal effort is actually focused on data analysis.

The complexity of big data compounds the up-front costs of data loading. Big data increases the amount and kinds of data. You need more time to understand and manipulate new source data, especially unstructured sources, before it is ready for analysis.

Big data also changes fast. You must update your existing analytics projects with newer data on a regular basis. Such data loading and update tasks need qualified engineers with expert skills. As a result, you spend more time and money on up-front data loading and data updates.

Big Data Discovery addresses these problems:
  • More users can engage with the data and contribute to big data analytics projects.
  • Data preparation becomes a small part of your effort. You don't have to prepare data up-front, and can focus your time and effort on analyzing data, and improving your business.
  • You can update BDD projects with new data, refresh already loaded data, or add newer data to existing projects.
  • You can work with data as the data scientist, and rely on a range of ways for data shaping and visual analysis in BDD.