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But the data repository options that have been around for a while tend to fall short in their ability to serve as the foundation for bigdata analytics powered by AI. Traditional data warehouses, for example, support datasets from multiple sources but require a consistent data structure.
Namely, we’ll explain what functions it can perform, and how to use it for data analysis. As the topic is closely related to businessintelligence (BI) and data warehousing (DW), we suggest you to get familiar with general terms first: A guide to businessintelligence. An overview of data warehouse types.
Dedicated fields of knowledge like data engineering and data science became the gold miners bringing new methods to collect, process, and store data. Using specific tools and practices, businesses implement these methods to generate valuable insights. If the amount of data is small, any kind of database can be used.
Mark Huselid and Dana Minbaeva in BigData and HRM call these measures the understanding of the workforce quality. The platform provides “ businessintelligence, planning, and predictive capabilities within one product” and uses AI and ML. Tools for data integration.
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