Data science is the theory and practice powering the data-driven transformations we are seeing across industry and society today. Artificial intelligence (AI), self-driving cars, and predictive analytics are just a few of the breakthroughs that have been made thanks to our ever-growing ability to collect and analyze data. Just as with big data (see my terminology pieces here: Part I,
This is the Part II of my blog series that simply distils the key terminology of Big Data (see Part I here). These are the remaining 16 key concepts that you should understand if you want to learn more about Big Data. Structured Data Structured data is data that can be arranged neatly into charts and tables consisting of rows,
These definitions are for anyone who wants to know more about Big Data and of which they should have a general understanding. As-a-Service Infrastructure Data-as-a-service, software-as-a-service, platform-as-a-service, these all refer to the idea that rather than selling data, licences to use data, or platforms for running Big Data technology, it can be provided “as-a-service,” rather than as a distinct product.