Mini-glossary: Big data terms you should knowWhen it comes to assembling a list of key big data terms, it makes sense to identify terms that everyone needs to know — whether they are highly technical big data practitioners, or corporate executives who confine their big data interests to dashboard reports. These 20 big data terms hit the mark.

Analytics

The discipline of using software-based algorithms and statistics to uncover meaning from data.

Algorithm

A mathematical formula placed in a software program that performs an analysis on a dataset.The algorithm often consists of multiple calculation steps. Its goal is to operate on data in order to solve a particular question or problem.

Behavioral analytics

An analytics methodology that uses data collected about users’ behavior to understand intent and predict future actions.

Big data

Data that is not system of record data, and that meets one or more of the following criteria: it comes in extremely large datasets that exceed the size of system of record datasets; it comes in from diverse sources, including but not limited to: machine-generated data, internet-generated data, computer log data, data from social media sources, or graphics and voice-based data.

Business intelligence (BI)

A set of methodologies and tools that analyze, report, manage, and deliver information that is relevant to the business, and that includes dashboards and query/reporting tools similar to those found in analytics. One key difference between analytics and BI is that analytics uses statistical and mathematical data analysis that predicts future outcomes for situations. In contrast, BI analyzes historical data to provide insights and trends information.

Clickstream analytics

The analysis of users’ online activity based on the items that users click on a web page.

Data analytics

The science of examining data with software-based queries and algorithms with the goal of drawing conclusions about that information for business decision making.

Data governance

A set of data management policies and practices defined to ensure that data availability, usability, quality, integrity, and security are maintained.

Data mining

An analytic process where data is “mined” or explored, with the goal of uncovering potentially meaningful data patterns or relationships.

 

 

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Nastel Comments:

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