![]() It also allows analysts to determine the data’s relevance for use within modeling efforts for predictive analytics, machine learning, and/or deep learning. This data analytics exploration drives hypothesis generation for a/b testing. Data analysis: Here, data scientists conduct an exploratory data analysis to examine biases, patterns, ranges, and distributions of values within the data.This data preparation is essential for promoting data quality before loading into a data warehouse, data lake, or other repository. This stage includes cleaning data, deduplicating, transforming and combining the data using ETL (extract, transform, load) jobs or other data integration technologies. Data management teams help to set standards around data storage and structure, which facilitate workflows around analytics, machine learning and deep learning models. Data storage and data processing: Since data can have different formats and structures, companies need to consider different storage systems based on the type of data that needs to be captured. ![]() Data sources can include structured data, such as customer data, along with unstructured data like log files, video, audio, pictures, the Internet of Things (IoT), social media, and more. These methods can include manual entry, web scraping, and real-time streaming data from systems and devices.
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