Fundamentals of Data Analysis

Data analysis involves collecting, cleaning, interpreting, analyzing, and visualizing data using Business Intelligence (BI) tools and techniques. This guide aims to jumpstart your efforts to navigate, understand, and bolster your mastery of data analysis.

February 14, 2024
Written By
Rand Owens
Founding team member at Motive (Formerly KeepTruckin) and passionate about all things Marketing, RevOps, and Go-To-Market. VP of Marketing @ Polymer Search.

Fundamentals of Data Analysis

Before we take a deep dive into the fundamentals of data analysis, here’s an empowering quote from the great David J. Schwartz…

“The person determined to achieve maximum success learns the principle that progress is made one step at a time.”

“One step at a time” is a great mindset to have as you journey toward mastering data analysis.

It keeps you from getting overwhelmed.

And it makes the whole data analysis pie so much easier to swallow.

Naturally, there's no better first step to take than to have a firm grasp of the fundamentals of data analysis.

Table of Contents

  • What is data analysis?
  • Importance of data analysis
  • How long does it take to learn data analytics?
  • Data analysis terms
  • Data analysis FAQs
  • Polymer: A Game-Changer for Business Intelligence

What is data analysis?

Data analysis involves collecting, cleaning, interpreting, analyzing, and visualizing data using Business Intelligence (BI) tools and techniques. 

The primary goal of data analysis is uncovering and presenting data insights that inform and shape effective and strategic decision-making. 

Data analytics and data analysis are often used interchangeably, but they’re not the same.

Data analytics is the entire discipline of analyzing data, including data collection, storage, organization, and analysis. 

It involves the data analysis techniques and tools used for deep-diving into data, including the software for communicating the analysis results.

On the other hand, data analysis focuses on transforming raw data into comprehensible and useful insights, including statistics, explanations, and other information. 

To better understand the concept, let’s go over the main stages of the data analysis process.

  • Identify the questions you want answered, such as “What problem does your company want to solve?” and “What key metrics must you track and how will you measure them?”
  • Gather the raw dataset necessary to solve your identified data analysis questions. Your data collection process can be from internal sources such as your Client or Customer Relationship Management (CRM) system. It can also come from secondary sources like social media app programming interfaces or publicly available demographic data. 
  • Clean and prepare your data for analysis. Cleaning your raw data involves removing anomalous and duplicate data, standardizing the data format and structure, reconciling inconsistencies, and handling syntax errors like white spaces. 
  • Analyze your data using various tools and techniques to find correlations, trends, variations, and outliers to get the whole picture. You can use data mining during this stage to spot patterns or leverage data visualization tools to turn your data into easy-to-understand visual formats like charts and graphs. Studies show that data visualization is the best way to present BI data analysis, making it a crucial aspect of analyzing data. 
  • Interpret the analysis results to determine how well your data answered your question. Extract insights, such as the recommended actions and limitations, to your conclusion.  

Importance of data analysis

Big data statistics show that companies that leverage their data effectively get an average annual growth of more than 30%.  

With effective data analysis, you can do the following: 

Spot challenges and opportunities

Data analysis can reveal key insights for new product development, market expansion, business growth, and customer experience improvement opportunities. 

One of the data analysis examples you can perform is trend analysis. 

For instance, you can analyze your sales data to determine which marketing initiatives brought in the most monthly sales.

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Data analysis can also highlight potential business risks and challenges so you can address them proactively, preventing issues from escalating. 

Maintain a competitive advantage

Analyzing your data gives you unique insights and trends to understand your market and competitors better. 

It equips you with the data and insights necessary to stay ahead of competitors.  

Make informed decisions

Data analysis helps you uncover key insights into market trends, business operation performance, and customer behavior. 

You can leverage these insights to make informed decisions based on hard evidence instead of relying on guesswork or intuition. 

Perform predictive analytics

Predictive modeling in data analysis allows you to forecast future outcomes and trends. 

For instance, the sample visualization below uses a predictive analytics model to determine the potential revenue the company can expect from each customer across their relationship’s lifespan. 

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Predicting these factors is crucial for effective and efficient demand forecasting, financial planning, proactive decision-making, and risk management. 

How long does it take to learn data analytics?

Learning data analytics requires knowledge of other related fields, including computer programming, statistics, spreadsheets, and data visualization. 

Most people with a background in these fields can gain basic and core data analytics concept proficiency within three months. 

However, the number can vary depending on factors such as:

  • The method of learning
  • Math background and experience working with data
  • Computer programming skills
  • Working knowledge of data visualization
  • Professional goals
  • Prerequisites, costs, and level of difficulty

Learning more advanced data analytics skills and knowledge, including data optimization and automated data analysis, can take you three to four years to reach a level of mastery. 

Data analysis terms

There are countless data analysis terms and concepts, but you can start with the most common ones that every aspiring data analyst should know. 

Data analyst

A data analyst gathers, cleans, studies, and interprets data to solve a business problem and commonly works with data scientists and engineers. 

Clean data

Clean data refers to complete, accurate, and ready-for-analysis data. 

Having clean data is crucial since it ensures that you remove inconsistencies, biases, irregularities, and inaccuracies from your data.  

Data Architecture 

Also known as data design, data architecture is your company’s data management system. 

Your data architecture can involve all the data lifecycle touchpoints, including your methods of collecting, organising, using, and discarding data. 

Here’s a sample data architecture diagram.

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Quantitative data 

Quantitative data is something you can measure or count. It is objective data that contains a specific numeric value. 

Qualitative data

Qualitative data refers to data that describes characteristics or qualities. It is typically non-numeric, subjective data. 


Queries are requests for information, essentially the questions you ask the database so it returns the data you wish to retrieve. 

In data analysis, database queries are in a query language, such as the Structured Query Language (SQL) example below.

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Relational database

A relational database contains multiple tables with related information. 

While data in a relational database is stored in individual tables, you can use a single query to access related data, such as customer order and inventory information, across multiple tables.

Data analysis FAQs

Below are the common questions asked about data analysis.

What is the main tool for data analysis?

Spreadsheets and spreadsheet programs are some of the common data analysis tools. 

Most spreadsheet programs like Google Sheets and MS Excel can support almost all standard analytics workflows, and you can extend the program with a native programming language. 

What do you do first when doing data analysis?

The first step in data analysis is to set your objective or problem statement.

Identifying your objective is like developing a hypothesis and determining how to test it through data analysis. 

Where do you commonly use data analysis?

Data analysis has many applications in research, banking and finance, manufacturing, media and entertainment, education, transportation, and more. 

In business, data analysis helps companies obtain relevant and accurate information to develop business plans and future marketing strategies while improving operation performance.  

Polymer: A Game-Changer for Business Intelligence

Invest in dependable software solutions to supercharge your data analysis process.

Polymer is one such software.

It is a no-code data visualization platform that makes connecting, organizing, and visualizing your business data quick and easy with AI-driven, intuitive features. 

Polymer eliminates the complexity and steep learning curve many traditional data visualization tools have. 

With the software’s user-friendly and intelligent Block editor, you can build beautiful charts, graphs, and dashboards in minutes, instantly extracting actionable insights.   

Polymer’s core features include:

  • Multiple built-in connectors for Google Analytics, Facebook Ads, Airtable, Zendesk, Jira, Google Sheets, and more.
  • Tags to search and filter data views easily.
  • Sharing options, including a feature that lets you share data views. 
  • Collaborator options that let you add other users who can view or edit your data and visualizations.

If you want BI without the BS, sign up for a free Polymer account today.

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