Best Data Analysis Methods

Everything measurable that has happened, is happening, and will happen in a business can be boiled down to data. But not all data is immediately comprehensible to the human mind, so data analysis steps in to elucidate meaning. 

Data analysis is the practice of cleaning, modifying, and examining data to find helpful information. The function enables business analysts to understand how variables connect and derive actionable insights from data sets. 

The practice can be performed by machines and software, but it often requires human interpretation to make the insights contextually relevant. 

See below how data analysis works, what it could mean for your business, and some of the top players in the data analysis software space:

Data analysis

• Data analysis vs. data analytics
• How can you analyze data?
• Benefits of data analysis
• Data analysis use cases
• Data analysis market

Data analysis vs. data analytics

It’s easy to confuse data analysis with data analytics, especially since many of the available data software tout their analytics capabilities more often than their analysis capabilities. 

Think of data analysis as one piece of the data analytics puzzle: Data analytics is a practice that involves far more than simply analyzing data. You are also collecting, storing, organizing, and using tools like data visualization to tell meaningful business stories about your data sets. Data analysis is the part of data analytics where you focus solely on cleaning and initially understanding the data at hand.

See more: Qualitative vs. Quantitative Data

How can you analyze data?

Before you analyze data, you should consider which types of data you are analyzing and what outcomes you want to achieve. Different categories and methods of analysis can be applied depending on how you want to use your data.

Categories of data analysis 

• Text analysis: One of the most common types of qualitative analysis, text analysis involves translating human text, so it can be processed through machine learning for more valuable insights. Text analysis, or text mining, is a text-based form of data mining.
• Descriptive or statistical analysis: The earliest step in data analysis, descriptive analysis involves organizing and manipulating raw data to be understood and interpreted in further analysis. It is usually numerical and statistical in nature.
• Inferential analysis: This form of analysis focuses on a sample group within the whole, making hypotheses based on generalized trends across a data set. It is often used in market research and statistical analysis.
• Diagnostic analysis: If you’re faced with a problem tied to numbers or outcomes, diagnostic analysis helps you to find the “why” behind events and relationships in your data set. This is considered one of the most important forms of analysis in business settings.
• Exploratory analysis: Often used in combination with text analysis or data mining, exploratory analysis involves examining the relationships between the data and its variables. This is a key category of analysis for problem solving.
• Predictive analysis: Relying on a mix of the other analysis categories as well as machine learning (ML) and artificial intelligence (AI), predictive analytics uses existing data to forecast data outcomes.

• Prescriptive analysis: As the most complex form of data analysis, prescriptive analysis combines all of your data and analysis to determine potential outcomes and actions to take based on your findings. AI is a growing subfield within prescriptive analysis.

Methods of data analysis 

• Cluster analysis: grouping data based on similar features, in order to find further contextual insight on relationships in the data set.
• Cohort analysis: dividing data by user categories to compare information across those groups.
• Regression analysis: analyzing how a dependent variable changes in relation to one or more independent variables to predict outcomes.
• Factor analysis: taking several observed and related variables and connecting them to broader and usually unobserved variables.

See more: Challenges and Best Practices in Data Analytics

Benefits of data analysis

Data analysis helps your business make sense of a mess of letters and numbers, and it offers several notable benefits to organizations that regularly analyze their data:

• Business intelligence (BI): Artificial intelligence, machine learning, and basic forms of data analysis provide key insights and actionable ideas for business improvements.
• Competitor knowledge and analysis: Analyzing your data with proven methods teaches you about your business performance in the marketplace more than assumptions and gives you metrics to compare with competitors’ data.
• Customer data and relationship building: Without data analysis, much of your customer information would stay as raw data and never inform business decisions. Data analysis teaches you about customers’ habits and preferences, allowing you to build connections with your base.
• Interoperability of data analysis software: Data analysis and data analysis software are meant to work alongside your other business software to bring meaning to varied databases. Most data analysis software is interoperable with your ERP, CRM, and other company software, allowing you to mine deeper data insights from those platforms.

Data analysis use cases

Most data analysis software offer more than analysis capabilities, usually delving into other top areas of data analytics. Here’s how some customers have used this software to derive meaning from their data:

• “We use SAS for predictive modeling, ad hoc statistical analysis, and automated report[ing], all of which were absent before the software arrived here. This tool has allowed us to get to answers more quickly, while utilizing our data the best we can.” Nathan Och, product review of SAS Analytics at TechnologyAdvice
• “[Birst’s] end-to-end BI on the cloud, including storage, database, modeling and GUI development. One-stop shop for all our analytical BI. The most important consideration is that I do not have to maintain DR and HA procedures. I do not have to wait for DBA, storage or system guys.” Nir Carasso, product review of Birst at TechnologyAdvice

Data analysis market

The data analysis and greater data analytics markets have grown exponentially over the past several years, most notably in industries like retail, health care, government, and real estate — where actionable customer insights can change overnight. 

The data analytics market will hit $132.9 billion by 2026, growing astronomically from its 2019 value of $23 billion, according to Market Research Future. Growth will be driven by key factors like the widespread adoption of advanced business technologies. 

You can expect to see a lot of this growth in manufacturing, retail, and healthcare, where increased use of AI and machine learning through predictive analysis will enable quick, precise results and personalized customer experiences.

Data analysis software makers

How can your company stay competitive with adopters of data analysis programs? Check out the 10 data analysis software providers listed below, or get a deeper look with this data analysis product selection tool by TechnologyAdvice.

• Tableau
• Microsoft Power BI
• R Analytics
• Knime
• SAS Analytics
• Minitab
• ThoughtSpot
• Sisense
• Stata
• IBM

See more: Data Analytics in 2021: Key Trends

See more: Top Data Analytics Tools & Software 2021