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.