How To Analyze Data in 7 Simple Steps: A Definitive Guide

Updated February 3, 2023

Company leaders can examine various types of data to make smart business decisions. The process of data analysis involves transforming numerical values into accessible insights about different business areas. By learning how to gather and assess data properly, you can improve your critical thinking, strategic thinking and decision-making skills in the workplace.

In this article, we explain how to analyze data, discuss the types of assessments you can conduct and describe the benefits of this process.

What is data analysis?

Data analysis is the process of collecting and examining statistical information and drawing conclusions from it. When conducting this process, professionals evaluate large amounts of information from various sources to find patterns and trends. This activity transforms data, such as customer reviews and feedback, into insights that managers can use to make decisions and develop strategic and tactical business plans. 

To gather facts and make smart decisions, businesses collect information from audiences of customers. A large coffee chain, for example, may seek to understand winter sales patterns across several states to develop a new marketing plan for the coming year. With customer data from past years, company leaders can determine which seasonal coffees were in high demand in specific states and whether they could reintroduce them successfully.

Related: Ultimate Guide to Strategic Planning

How to analyze data

Follow these steps to analyze data properly:

1. Establish a goal

First, determine the purpose and key objectives of your data analysis. Think about the questions or concerns you have and the goal you want to achieve by conducting this analysis. For example, your goal may be to increase your customer base.

Related: How To Conduct Exploratory Data Analysis in 6 Steps

2. Determine the type of data analytics to use

Identify the type of data that can answer your questions. If a simple overview is all you want, a descriptive analysis could be useful. You may already have access to data from a previous project. If these numbers are still relevant, you may want to consider using this information.

Related: Guide to OKRs

3. Determine a plan to produce the data

It can take varying amounts of time, effort and resources to collect data, depending on the type of information you're trying to gather. Before investing in this type of project, consider factors like budgetary and schedule needs. Planning these aspects in advance can ensure you have the capacity to complete the data analysis project.

Related: 12 Data Modeling Tools for Data Analysis

4. Collect the data

Set up the necessary data collection processes and assemble a team of people who can accumulate and manage the information. To save time, you may choose to buy access to a data set such as demographic information for a specific segment of consumers. You can also use data analytics software to implement, streamline and track processes with ease.

Related: 6 Methods of Data Collection (With Types and Examples)

5. Clean the data

Once you've built a large data set, the next step is to clean or process the data to ensure it's accurate and free of errors. Check for duplicates and other issues and standardize the data to make it easier to assess later. You can also confirm there are no typographical or format problems.

Read more: Data Cleaning: Definition, Importance and How To Do It

6. Evaluate the data

Investigate the data you've gathered carefully. Use various mathematical interventions to look for connections between points. Interpret what the data could mean and why it could be important for the organization.

Related: What Is Customer Data?

7. Visualize the data

Once you have collected, cleaned and analyzed the data, you can use a method like data visualization to summarize the information and ensure it's meaningful. Visualization tools can help you understand the data and present it to others in an engaging manner.

Colorful charts and graphs can represent hundreds of points that demonstrate a trend or a powerful narrative. A sales data run tied to a geographic region, for example, may help you decide to reallocate marketing funds.

Related: Color Choices in Data Visualizations: Importance and How-To

Types of data analysis

The following are four common types of data analysis:

Descriptive analysis

Descriptive analysis turns raw numbers into information that's easy to interpret and understand. It rearranges, orders and manipulates data to generate simple summaries that show what happened within a study. This method of data analysis shows you the distribution of your data.

It also helps you detect outliers, inconsistencies and errors, allowing analysts to identify how these elements relate to each other. Facts from descriptive analysis can help businesses understand what processes are working well and what areas might require further analysis.

Read more: Descriptive Analytics: A Definitive Guide

Diagnostic analysis

While descriptive analysis tells you what happened, diagnostic analysis determines why it happened. If you want to use data to learn how to repeat a successful project or to reassess a process that may benefit from some improvement, diagnostic analysis can provide you with this information. A diagnostic analysis enables you to discover the reasons a particular marketing campaign was successful, for example, or how you could enhance it in the future.

Related: What Are the 4 Types of Data Analytics? (With Tips)

Predictive data analysis

In predictive data analysis, you can use information from descriptive and diagnostic frameworks to anticipate the future outcomes of a decision or process. Predictive analysis reviews past data to identify potential trends. For example, a department store can use predictive research to determine whether it could be a good idea to hire additional employees during the busy holiday season.

Prescriptive analysis

Prescriptive analysis combines data and helps organizations form a decision on how to improve their work processes. Because the information you gain from this process is on a large scale, prescriptive analysis requires organizational commitment.

Artificial Intelligence (AI) can be a tool to conduct prescriptive analysis. AI consumes a large amount of data and learns continuously, using this information to make recommendations and even implement updates automatically.

Related: Descriptive vs. Predictive vs. Prescriptive Analytics

Benefits of analyzing data

Analyzing data can further an organization's mission, vision and goals. This information can help you suggest continuous quality improvements, product expansions and workplace innovations. It can also help develop:

  • Upgraded software to streamline services

  • Improved inventory control for faster delivery

  • Enhanced leadership training to empower employees

  • New strategic alliances or mergers to stay competitive in the market

  • Added volunteer training to increase outreach efforts

  • Expanded use of machine learning

  • Increased understanding of variables that affect customer satisfaction

By conducting data analysis, you can interpret data that applies to your project. Data insights can support decisions like what new products customers might like, whether to expand into a new region or country or where to reduce specific costs.


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