Key takeaways
Every step in the data analysis process is essential. Getting the most out of collected and cleansed data requires data analysis methods and techniques that generate accurate and reliable data to make informed decisions. The methods and techniques used in data analysis are essential in the data analysis process, and it’s what will be covered as a topic in this article.
The importance of the data analysis process
Every step in the data analysis process is essential because each step can be considered a building block to the other steps further into the process. If one of the initial steps is wrong before you use a technique or a method, your results will be flawed. The adage “garbage in, garbage out” will be true and applicable if one of the data analysis steps is incorrect. Using a technique or method on flawed raw data is a wasted effort.
The purpose and importance of the data analysis process is to help businesses improve sales, reduce costs, understand their customers better, or improve any aspect of a business through the data analysis process. The entire data analysis process is important; no step is more important than any other step.
The purpose of the data analysis methods and techniques
The data analysis techniques are the steps in the data analysis process, including data governance, data analysis tools, establishing key performance indicators (KPIs), integrating the technology, and interpreting the data. The data analysis methods are considered the tools that manipulate the data through a mathematical, statistical, or artificial intelligence method to derive accurate, actionable data that leads to businesses making insightful and informed decisions.
Types of data analysis
The types of data analysis can be categorized in multiple ways without necessarily being incorrect, for there are varying opinions on categorizing data analysis, methods, and techniques. When looking at data analysis from a technique perspective, it’s divided into two categories. One category is the mathematical and statistical methods that derive actionable and insightful data to make business decisions. The other category is based on artificial intelligence (AI) and machine learning tools that create insightful information for decision-making.
The two categories used to explain the methods of analysis or data analytics techniques are not adequate enough to explain the processes, methods of analysis, and data analytics techniques. Multiple steps need to occur before data is analyzed, and post-actions involve sharing analyzed results that are not represented in using two categories to define types of data analysis. Both categories are integral in the data analysis process.
A method is defined as a particular form of procedure for accomplishing or approaching something, especially a systematic or established one. A technique is described as a way of carrying out a particular task, especially the execution or performance of an artistic work or a scientific procedure. These definitions sound eerily similar and can easily be exchanged in a sentence, as they both define carrying out a procedure or task. The terms used for data analysis, such as analytic methods, data analytic techniques, and methods of analysis can meet the definition of method or technique.
Common types of data analysis techniques and methods
The data analysis process must be executed before manipulating data with a method or technique. The process involves the following:
The first three steps will always be the same in this simplified explanation. An AI tool, a mathematical, or a statistical calculation will generate insightful data for the last two steps.
It varies greatly when defining common data analysis techniques and methods. The most common data analysis techniques and methods used are:
Each business can define methods and techniques differently as long as it is consistent in an organization. To simplify the definitions, methods are the analysis tools used to manipulate processed data into actionable information that helps companies make better decisions. The techniques are the data analysis processes, data governance, interpreting the results, and sharing the information, including data analysis tools. Methods are any AI tool, mathematical or statistical equation used on processed data that creates informed decision-making information. The technique can be considered the entire data analysis process.
What are data analysis methods?
The data analysis methods will cover all the mathematical, statistics, and AI tools used to analyze data to extract meaningful information to make data-driven decisions. Here are some examples of data analysis methods.
Cluster analysis
Cluster analysis is a data mining method that groups similar data points into clusters. The analysis segments or categorizes a dataset into groups based on similarities.
Regression analysis
Regression analysis estimates the relationship between a dependent variable and one or more independent variables. The regression formula is Y – f(X,B) + e.
Time series analysis
Time series analysis is a statistical method used for analyzing a sequence of events over a specific period. Figure 3 shows the number of items sold over a particular period.
Data mining
Data mining involves analyzing large data sets to find patterns or trends using programming software like Python or R that can extract useful information to help businesses solve problems through data analysis. Data mining analyzes historical data used in business intelligence for decision-making, too.
Decision trees
Decision trees are a decision support tool displayed in a flowchart diagram, mapping out all potential solutions to a specific problem. Decision trees are used in machine learning for classification and regression tasks.
Narrative analysis
Narrative analysis is a qualitative research method used to study and analyze people’s stories about their lives and experiences. To help understand someone’s narrative story, a listener must understand and know the plot, characters, point of view, setting, theme, conflict, and delivery style. The goal is to interpret the human experience and motivations by listening closely to people’s stories.
What are data analysis techniques?
Defining data analysis techniques is very subjective because the definition includes the entire data analysis process and the methods used to extract patterns, trends, or produce intelligence information for business decisions. The data analysis definition contains the methods used to clean, transform, and model data to discover valuable insight for business decision-making.
Data analysis techniques include data governance, integrating technologies, and autonomous technology. These techniques are all important aspects of data analysis for maintaining data integrity, exploring solutions that require integration with another IT solution, or automating IT solutions that can function without being controlled by a human. The best way to think of data analysis techniques is to include the entire process, from the first step that defines the problem as a question to sharing the results visually or in narrative form. The consideration of using autonomous technology or integrating an IT solution with an existing solution comprehensively covers any technology that may be implemented based on the data analysis results.
What data analysis methods and techniques are used in different businesses?
The methods and techniques discussed barely scratch the surface of all the methods used to derive smart data-driven information to make intelligent business decisions. Every business industry has data analysis methods that they use more than others to make smart decisions. Here are some specific business industries and the methods they use to make data-driven decisions.
Financial firms
Financial analysis methods focus on growth rates and use regression analysis to analyze monthly or year-to-year growth rates. They also use top-down analysis, bottom-up analysis, or revenue driver analysis, which are activities, products, services, or markets that generate income for businesses. Financial firms also use vertical analysis, horizontal analysis, and rations and trend analysis as required.
Retail business
Retail businesses use retail analytics that focuses heavily on AI and machine learning tools to help predict trends, propose offers, provide the basis for pricing inventory items, measure the effectiveness of marketing campaigns, and make inventory allocation decisions. Visualization charts are preferred over spreadsheets or plain-text reports when showing retail results.
Manufacturing
Manufacturing industries use diagnostic analytics to identify manufacturing equipment failures and anomalies in the manufacturing processes. Diagnostic analysis also helps identify specific problems, events, and behavior. Along with diagnostic analysis, other methods such as drill-down, data discovery, data mining, and correlations are popular methods used with diagnostics. Data analytics methods are used to help streamline warehouse operations.
Hospitals
The healthcare industry uses common data analysis methods for patient care. Hospitals use descriptive, diagnostic, predictive, and prescriptive analytical methods to care for patients. Doctors rely on data to make informed decisions about the best treatment options for their patients. Hospitals also use online health-related mobile applications and electronic health records to serve their patients better.
Sales
Organizations focusing on sales use multiple data analysis methods to track sales information. Sales businesses use machine learning, decision trees, sales performance analysis, sales analytics, and Predictive sales analysis methods. These methods help identify sales trends and patterns, predict future sales success, improve sales performance, and track Key Performance Indicators (KPIs).
How to select a data analysis method
When selecting a data analysis method, you want to be sure the method used will draw the correct conclusions from the data and the results are free from bias or errors. As you go through the data analysis process, you will start narrowing down the best methods based on the data type, such as continuous or discrete. You will also want to minimize the uncertainty of the results produced from the method selected.
To verify you selected the correct method, formulate a hypothesis and test it on sample data using the selected method. The method should produce an expected result. Look for any anomalies or trends in the results that are suspicious. If you think the results are incorrect, ask a high-frequency user to look at the results to verify your findings. If all the participants agree the results are skewed, repeat the hypothesis testing with another method until the results are in the expected range.
Get started by evaluating some of the best data analysis software solutions available. Remember that industry-specific data analysis tools are available to meet industry-specific data analysis business needs.
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