Terminologies in Data Analytics

Terminologies in Data Analytics

Examining massive data sets to find hidden patterns, undiscovered relationships, trends, client preferences, and other relevant business insights is a must. The final product could be a report, a status indicator, or an automated action based on the data collected.

Organizations’ attempts to deal with the data flood and use it to capture value are increasing at a higher rate than either population or economic activity. And so are the methods of data analysis, resulting in an ever-growing vocabulary (including some buzzwords) to describe these procedures. 

This is a developing area, and various people may interpret the terminology differently. Please leave your thoughts about this page and its “definitions.” Because many of these phrases are subsets of others or overlap, the most logical method is, to begin with, the more particular terms and go to the more general ones.

Data Architecture and Design: The Structure of Enterprise Data The actual structure or design differs according to the desired end result. Data architecture consists of three steps or processes: 

(1) conceptual representation of business entities

(2) logical representation of those entities’ relationships

(3) the system’s physical design in order for it to work.

Terminology Applied to Data Collections

1. Data aggregation is a grouping of data points and datasets. In Data-Planet, for example, a search on the broad category “higher education” yields results from a variety of sources.
2. A dataset is a collection of connected data elements, such as survey replies. The word “dataset” is used extremely loosely; the whole Census 2010 Summary File 1 can be considered a dataset, as can any individual table published in the Census 2010 Summary File 1.
3. A database is a collection of data that has been structured for research and retrieval.
4. A time series is a collection of measurements of a single variable taken over a period of time.

Terminology for “Big Data”

1. The term “big data” is widely used in academia, industry, and other fields to describe the increasing availability of all types of data. Big data is defined as having a large volume, a high velocity (the rate at which information is generated), and a wide variety.

2. Data analytics is the term used to describe the analytical techniques and tools required to analyze massive amounts of data.

The act of analyzing, cleansing, manipulating, and modeling data in order to highlight relevant information, draw conclusions, and aid decision-making is known as data analysis. Data analysis is a process that can be broken down into several stages.