The terms data science, data analytics, and big data are now ubiquitous in the IT media. Too often, the terms are overused, used interchangeably, and misused. Every vendor is working hard to incorporate as many of these terms into their product and service literature as possible. Many are working equally hard to incorporate related features into their products and services.

Despite the hype and propaganda, these terms are important and useful. Here’s a summary of the definitions and the value the concepts provide organizations.

Data Science

Data Science is a combination of statistics, mathematics, programming, creative problem-solving, and the ability to look at issues and opportunities differently.
Data science produces value through its capability to extract insights from data, often through modeling and forecasting, that do not become apparent from data analytics.

Data Analytics

Data analytics applies arithmetic and algorithms to identify trends, variances, and insights in data. Data analytics is widely used in every organization as demonstrated by the ubiquitous use of Excel.

Data analytics creates value by helping organizations make better decisions about how their assets are used and how they respond to changes in customer trends and expectations.

Big Data

Big data refers to the humongous volumes of disparate forms of data that many organizations accumulate, especially from eCommerce, social media, and IIoT, which cannot be processed effectively with traditional applications.

Big data has no intrinsic value but can be processed to discover insights that can lead to better decision-making.

To explore the definitions and applications of data science, data analytics, and big data in more detail, click below.

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