Value of using Big Data Discovery

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

Traditional analytics projects force you to spend most of your effort on data preparation. You must first predict all possible business questions, then model the data to match. Next, you locate and get the data sources, and manipulate feeds to fit into the model. Then, 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 even 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 can focus your time and effort on analyzing data, and improving your business.
You can update analytics projects created in BDD with new data, or add newer data to already loaded data.
BDD has tools that let you create scripts for updating data. Also, business users can update and reload data in projects in Studio. As a result, you don't have to prepare data up-front. Instead, you can find data, discover insights, analyze data, make decisions and act on them.