Two Types of Data Analysis for Building a Business Analytics Career

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Knowing what your customers want is key to business growth. After all, success is only attainable if your business strategy aligns with their goals. Data analysis is critical to determining this important information, particularly at a time when it can come from myriad sources. There are two main types of data analysis that companies can deploy to satisfy this need: predictive analytics and prescriptive analytics. For those interested in a business analytics career, it’s important to not only understand the difference between the two types but also when to use them.

Data Analysis Overview

Data analysis describes the process of gathering, examining, and interpreting data from various sources. The goal of this process is to discover information that can be used to develop business strategies. The sources for this information range from telephone surveys and online polls to scrutinizing the purchasing habits of a particular demographic.

There are two primary types of data analysis that can be deployed once the data has been gathered. The first type of analysis, predictive analysis, draws upon past data-driven occurrences to predict future outcomes. For example, predictive data can analyze patterns in the public’s purchasing habits and product loyalty to determine when customers may be ready for a product upgrade. Predictive analysis can use technologically advanced tactics like artificial intelligence (AI) or computer algorithms to determine these predictions.

The second type, prescriptive analytics, uses data to recommend strategies based on predictions. For instance, a data analyst using this method could review six months’ worth of call center data to determine the peak hours, which could then be used to make recommendations on providing optimal call center coverage.

Both types of data analysis can give businesses critical insight on operations in ways that can make it possible to retain old clientele while pursuing new clients. Savvy data analysts will deploy both to create business strategies that can propel growth.

The Importance of an Advanced Degree

Given the technical innovations in data mining, a data analyst must possess several crucial skills to be effective. These include a high level of mathematical competency, advanced technological and computer skills, and an in-depth knowledge of analytical tools and programs. Data analysts must also possess several key soft skills, such as problem-solving, teamwork, and communication competencies.

One of the ways data analysts can cultivate these skills is by completing an advanced degree, such as an Online Master of Business Analytics (MBAn). Having a deeper skill set can help professionals apply data to visionary strategies that can have a profound impact on a company’s success.

For instance, earning an advanced degree can allow students to refine numerous technical skills pertaining to some of the industry’s most innovative data analysis tools, such as Python, SAS, and Oracle. Additionally, they can help refine various soft skills that are equally important, including critical thinking, problem solving, and communication. These skills can help ensure mined data is applied effectively to corporate growth strategies.

What Kind of Jobs Are Available?

A highly skilled data analyst equipped with an advanced degree in data analysis can pursue a number of exciting professional roles within a wide range of industries, from health care and finance to manufacturing and education.

Data Scientist
A data scientist extracts relevant information from analyzed data with the purpose of helping build professional strategies. This can be done through data collection as well as by deploying algorithms to form predictive insights. According to PayScale, the average annual salary for a data scientist is around $90,600.

Data Engineer
Data engineers are charged with the development and maintenance of various data-gathering systems. Their role is essential in helping to protect and secure a company’s data integrity. The average annual salary for a data engineer is roughly $91,300, according to PayScale.

Business Analyst
Business analysts must not only be able to spot deficiencies in various systems as they develop but they must also be able to competently explain how addressing these deficiencies can help improve a company’s bottom line. PayScale lists the average annual salary for this position at about $67,700.

Information Systems Manager
Information systems managers, also known as IT managers, oversee a company’s computer- and internet-related activities. They use data analysis to assess a company’s technological needs, a duty that often requires them to stay on top of the latest tech innovations as they develop. According to the U.S. Bureau of Labor Statistics (BLS), the average annual salary of an information systems manager is around $139,200.

Two Types of Data Analysis, Several Possibilities

If you gain the skills to build strategies through prescriptive and predictive analysis, you can do more than “crunch numbers.” You can play a vital role in developing the type of strategies that can help a business thrive, both now and in the future. Learn more about how Ohio University’s Online Master of Business Analytics program can help you make a fundamental difference in a company’s future.

Recommended Readings:

How to Empower Decision Making with Predictive Analytics

What Is Predictive Sales Analytics? Improving Business with Data

What Is Business Intelligence? Using Data Analytics in Business Strategy


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Forbes, “Descriptive Analytics, Prescriptive Analytics, and Predictive Analytics for Customer Experience”

Forbes, “The Five Most In-Demand Skills for Data Analysis Jobs”

Allerin, “Big Data Analytics: Descriptive vs. Predictive vs. Prescriptive”

Ohio University, Online Master of Business Analytics

PayScale, Average Business Analyst, IT Salary

PayScale, Average Data Engineer Salary

PayScale, Average Data Scientist, IT Salary

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