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Data Analytics: What is it?

Updated: May 23, 2019

By Tom Goodwin

Data Analytics and Data Analysis are buzzwords that have become more than prevalent in everyone’s conversations within enterprises and other organizations. The discussions often start with basic strategic questions like “Do we need it?” or “What value is it to us?” Once the discussions go deeper, the mathematicians and IT geeks often dominate the conversation with jargon that delves into the deepest darkest reaches of algorithms and predictive statistics. For the rest of us this could be a mind-numbing experience. The reality is that Data Analytics can help organizations improve how they operate in many ways. This blog is the first in a series that will explore the what, why, how, and the what’s next of Data Analytics.

What Is It?

Data Analytics (or analysis) is a process of inspecting, cleansing (detecting and correcting/ removing corrupt or inaccurate data), transforming (converting data from one format or structure into another), and modeling (defining and analyzing data requirements needed to support processes) data with the goal of discovering useful information, supporting decision-making, and forming intelligent conclusions. There are multiple facets and approaches encompassing diverse techniques under a variety of names. Data Analytics is used in business, science, and social science domains.

What Does It Do?

Diverse types of Data Analytics are employed to generate knowledge and information from the raw data. For example, data mining is a technique that focuses on modeling and knowledge discovery for predictive rather than descriptive purposes. A different approach is business intelligence, which relies heavily on aggregation and focuses primarily on business information.

Data Analytics is also used in statistical applications. The various techniques include:

  • Descriptive statistics - quantitative descriptions or summaries of a collection of data

  • Exploratory data analysis - focuses on discovering new features in the data

  • Confirmatory data analysis - focuses on confirming or falsifying existing hypotheses

  • Predictive analytics - focuses on application of statistical models for predictive forecasting or classification

  • Text analytics - applies statistical, linguistic, and structural techniques to extract and classify information from textual sources, typically unstructured data.

  • Data integration - combines data residing in different sources and provides users with a unified view of them

  • Data visualization - creation and study of the visual representation of data.


Today, HigherGround’s interaction capture and storage platforms focus on descriptive statistics, data integration, visualization and data dissemination (distributing data to users). These are very good starting points for entering Data Analytics. As our customers evolve, then we will evolve with them. Where are you on the Data Analytics journey? Share with us on social media or send us a message so we may reach the future together.

About the Author - Tom Goodwin is the Vice President of Marketing at HigherGround. His background in telecommunications and data networking has been augmented with work in data analytics and automated reporting prior to joining HigherGround. Click here for more information on Tom and the rest of the HigherGround team!​

HigherGround, Inc. provides best-in-class, reliable data capture and interaction storage solutions that enable clients to easily retrieve critical information. Our interaction recording and incident reconstruction solutions transform data into actionable intelligence, allowing optimization of operations, enhanced performance, and cost reduction.

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