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Data Analytics: About Data Analytics

Data Analytics

 

Data Analytics:  Refers to qualitative and quantitative techniques and processes used to enhance productivity and organizational gain. Data is extracted and categorized to identify and analyze behavioral data and patterns, and techniques vary according to organizational requirements.

Types of Analytics and Disciplinary Area

Data analytics is an evolving and multidisciplinary area of study in which there are as many different definitions as there are definitions.  Despite these differences, data analytics generally seeks to support the decision making through:  

(a) Application of organizational understanding to identify the issues and outcomes of import to decision makers

(b) Process of acquiring and analyzing data that leads to information based insight.

Analytical Applications and Disciplinary Areas:

 

Organizational / Business Analytics:  Application of analytical techniques to determine and understand the effectiveness of organizational or business functions.  Analytics can be either focused on internal or external processes.  Different specializations exist, encompassing most major aspects of organizational action, including risk analysis, market analysis, and supply chain analysis.

 

Rule-of-Thumb:  As the organizational value of the data analysis increases, so does the challenge of conducting that analysis.  Keep in mind to be successful requires:  (a) Defining goals and outcomes before the analytical process begins; and (b) allocating time and resources to ensure that the analysis meets the ethical standards of your profession and community.

Organizational / Business Analytics

Data Analytics:  Organizational Value vs. Difficulty

Descriptive Analytics:  Describes or summarizes raw data and makes it something that is interpretable by humans. It uses descriptive statistics to understand at an aggregate level what is going in an organization and to summarize and describe different aspects of that organization. 

Diagnostic Analytics:  Examines to answer why the data is what it is and why it happened from past performance. It is used for discovery or to determine the cause of an occurrence.

Predictive Analytics:  The use of data, statistical algorithms and machine-learning techniques to identify the likelihood of future outcomes based on historical data. It is used to identify trends, understand customers, improve business performance, drive strategic decision making, and predict behavior. Some common examples of predictive models are those used by credit bureaus for developing credit scores.

Prescriptive Analytics:  Is the final step in data analytics that answers specific questions about the data. Examines data to answer what actions should be taken.