Data storage involves persistently organizing information within databases, data warehouses, or distributed file systems, ensuring durability and scalability. 

This stage includes cleaning data, deduplicating, transforming and combining the data using ETL (extract, transform, load) jobs or other data integration technologies. This data preparation is essential for promoting data quality before loading into a data warehouse, data lake, or other repository.

Concurrently, data processing manipulates, analyzes, and transforms stored data for meaningful insights, supporting decision-making and innovation. Modern systems seamlessly integrate storage and processing, fostering agile data infrastructure for quick decision-making and advanced analytics.

Here, data scientists conduct an exploratory data analysis to examine biases, patterns, ranges, and distributions of values within the data. This data analytics exploration drives hypothesis generation for a/b testing. It also allows analysts to determine the data’s relevance for use within modeling efforts for predictive analytics, machine learning, and/or deep learning. Depending on a model’s accuracy, organizations can become reliant on these insights for business decision making, allowing them to drive more scalability.

Ultimately, the findings are showcased through reports and visual representations of data, simplifying the understanding of the insights and their business implications for analysts and decision-makers. Data science programming languages like R or Python come equipped with features for creating visualizations, or data scientists can opt for specialized visualization tools.