Mastering Data Analysis Techniques

Key takeaways

Data analysis techniques help companies across many industries collect and evaluate data to gain valuable insights. 

• Fortune Business Insights projects that the global big data analytics market will grow from $447.68 billion in 2026 to a value of $1,176.57 billion by 2034 [1].

• Four common data analysis techniques are descriptive, diagnostic, predictive, and prescriptive analysis.

• You can prepare for a career in data analysis by earning a bachelor’s degree in computer science and gaining experience through an internship.

Explore why data analysis has become an indispensable tool for modern companies and learn more about various techniques. To start building your skills, consider enrolling in the Wharton Business Analytics Specialization. In as little as two months, you can learn how to use data for recruiting and performance evaluation. Upon completion, you’ll possess in-demand skills to use in your career.

What are the 4 types of data analysis techniques? 

Depending on varying needs and business objectives, companies use different types of data analysis. Four common types of data analysis you might utilize include descriptive analysis, diagnostic analysis, predictive analysis, and prescriptive analysis. Depending on the technique used, the insights gained from data analysis can help you improve workplace operations, detect patterns and trends, enhance customer service, grow revenue, and more. 

1. Descriptive analysis

This type of data analysis involves interpreting data at various points in time and seeks an answer to the question, "What happened?" Descriptive analysis can help companies understand business changes and trends and define company strengths and weaknesses. 

2. Diagnostic analysis

This type of data analysis seeks to explain the cause of an occurrence and help you understand why it happened. Diagnostic analysis can help your company make better decisions because you have data to back them up. 

3. Predictive analysis

This type of data analysis uses past data and statistical models to make predictions about the future. Predictive analysis can help companies gain clearer insights to map out smarter data-based strategies. 

4. Prescriptive analysis

This type of data analysis involves the use of algorithms that analyze data and make recommendations based on the analysis. Prescriptive analysis can help your company decide what moves to make to achieve your objectives. 

Read more: 7 Data Analysis Methods to Know

Who uses data analysis techniques?

Data analysis techniques and tools help organizations, large and small. According to a report from Fortune Business Insights, the global big data analytics market will grow from $447.68 billion in 2026 to a value of $1,176.57 billion by 2034 [1].

Types of industries that use data analysis, and corresponding applications, include:

• Entertainment: Streaming platforms use data analytics to make recommendations to customers based on past choices and keep them engaged. 

• Finance: Financial analysts use data analytics to help manage client investment portfolios.

• Health care: The health care industry uses data analytics to manage patient records and monitor patient health. 

• Manufacturing: The manufacturing industry uses data analytics to boost production and prevent maintenance-related delivery delays. 

• Retail: Retailers use data analytics for inventory control and to drive sales through personalized customer recommendations.

• Tourism/travel: Hotels use data analytics to create customized guest experiences, and various travel and tourism companies use data analytics for targeted customer marketing.

What are some advantages and challenges of data analysis?

Although data analysis offers your company valuable benefits, such as better customer service and inventory control, it can also present some challenges, like increased costs and data privacy concerns. Knowing about common advantages and challenges can help ensure more effective use.

Advantages

In a world now driven by data, companies, communities, and governments across the globe rely on data analysis. Common benefits of data analysis include:

• Better customer service

• Better inventory control 

• Evidence-based decision-making

• Improved risk identification and management

• Increased operational efficiency

• Greater productivity

Challenges

Certain challenges do arise when you decide to implement a data-analysis strategy within your company. Common challenges of data analysis include:

• Costs for data analytics staffing, technology, and infrastructure

• Changing technology, which may require new costs and training

• Concerns about data privacy and security

• Difficulties with data access

• Incomplete or inaccurate data

• Bias in data sets, which can create biased results

How do you pursue a career in data analytics?

If you choose to pursue a career in data analytics, it may help to follow a few key steps. Consider building related skills, getting an education, and gaining valuable work experience.

Build related skills.

Workplace skills (which transfer to most jobs) and technical skills (which help you perform data analysis tasks) will help you find success as a data analyst. 

Good workplace skills to build for a data analyst role include:

• Attention to detail

• Communication

• Critical thinking

• Problem-solving

Good technical skills to build for a data analyst role include:

• Experience with data visualization software

• Familiarity with Microsoft Excel software

• Knowledge of programming languages like Python, R, and SQL

• Understanding of machine learning

Get an education.

Fortunately, if you’re interested in this field, you have several educational options when pursuing a career in data analytics. Some of these include:

• Taking online courses or participating in data analytics boot camps or certificate programs

• Getting a bachelor's degree in computer science, information systems, statistics, or a related field

• Obtaining a master's degree in data analytics

Gain valuable work experience with an internship.

An internship allows you to gain experience doing data analytics work in a company for a short period of time (usually one college semester). Depending on the position and company, you may or may not receive pay for internship work, and typically, internships offer three hours of academic credit for 10 to 20 hours of weekly work. To find an internship, consider visiting with your academic advisor or inquiring at your college career center. 

Average data analyst salary

According to Indeed, the average salary for a data analyst in the United States is $85,267 per year [2]. To get an idea of what you might make in different parts of the country, look at the average salaries for this position in some of the highest-paying cities [2]:

• McLean, VA: $112,094

• Washington, DC: $94,359

• Austin, TX: $93,830

• Shelton, CT: $88,642

• Atlanta, GA: $80,857

Discover free resources for a data analysis career

Join us on Career Chat on LinkedIn, where you can explore industry trends and gain career insights. Then, check out these resources as you consider a career in data analytics:

• Hear from an expert: 7 Questions with a Data Analytics Professor 

• Learn your vocabulary: Data Analysis Terms and Definitions 

• Consider your path: Data Analysis: Where to Start and How to Build This High-Income Skill 

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