What Is Data Analysis? The Complete Guide

From Gut Feeling to Data-Driven Decisions!

Data analysis or data analytics might sound like a modern concept for a new field, but the core idea and skillsets – using information to make better choices – has always been around.

Think about it: even basic decisions like what to wear or when to hunt involve analyzing some data, like the weather or animal tracks. Historically, leaders used spies to gather information about enemy forces, a prime example of data collection and analysis driving strategy in warfare.

Fast forward to today, data analysis is no longer a luxury, it’s a necessity. Companies across all industries leverage data to make informed decisions. From social media platforms personalizing your ads to Netflix recommending shows you’ll love, data analysis is everywhere.

In this comprehensive guide, we’ll delve into the world of data analysis. We’ll explore the essential skills, exciting career paths, and powerful methods used to transform data into actionable insights.

What Is Data Analysis!

Data analysis is the art of uncovering valuable insights from data, This process involves collecting, cleaning, transforming, and organizing data to in a form that is easier to analyze, in order to find trends, insights, conclusions, and make predictions . Which empowers data-driven decision-making in various fields.

Why Data Analysis Is Important!

Companies in various industries and of all sizes use data analytics in order to increase their profits, but money is not the only aspect data analysis can improve, find out why below:

• Making data-driven decisions: Data analysis helps you uncover trends, insights and facts about any aspect of your business in order to make informed decisions that lead to better outcomes.
• Improve efficiency and productivity: Data analysis helps Uncover areas of improvements in processes and resource allocation by identifying trends and patterns in your data.
• Gain a competitive edge: Analyze customer behavior, market trends, and competitor strategies to develop targeted approaches and stay ahead of the competition.
• Solve problems: Data analysis helps pinpoint root causes of issues and develop effective solutions.

Data Analytics Life Cycle

The data analytics life cycle describes the phases that data go through from even before collecting the data to making informed decisions. While there isn’t a single defined and agreed on structure to be followed by all data analysts, one that I find very useful and easy to follow is the Google data analytics life cycle which include the following phases:

1. Ask:

The ask phase is the first step and it starts even before collecting any data. In this phase you need to ask questions in order to define very clearly the business challenge you are trying to solve, the project stakeholders, the project objectives, data you need to collect and how to collect it if it is not yet available.

You can follow the SMART Framework for highly effective questions.

2. Prepare:

Once the business problem is clearly defined, the prepare phase starts which include data generation, data collection and retrieval, store the data, and perform data management.

3. Process:

Data quality is an important factor in data analytics, if your data is not of quality then you risk to make a wrong decision, the process phase focus on cleaning and transforming data into a shape that is easier to analyze.

4. Analyze:

The Analyze phase is where the fun starts, it is where you get to do Data exploration, visualization, and analysis; and finally make sense of the data you have.

5. Share:

You have done a great job finding trends and insights in your data, but if you can’t clearly  communicate these insights in a form that is easy for management to understand then no one will be able to make a decision, the share phase is all about communicating and interpreting results.

6. Act:

The Act phase is where you Put your insights to work to solve the problem.

Note that this process is an iterative process, for example if you have discovered something wrong in your data during the analyze phase, you can go back to the process phase to further clean the data or to the prepare phase to collect more data.

Data Analysis vs Data Analytics

Although there is some arguably technical differences between Data Analysis and Data Analytics, however we are using both terms interchangeably through out this post.

In simple words data analytics describe the complete process of turning data into actionable insights (phases 1 through 6) while data analysis is a subset focusing on collecting, transforming and analyzing data (phases 2 through 4).

Data Analysis vs Data Science

Data analysis and data science are both fields that deal with extracting knowledge from data, but they have some key differences:

Focus:

• Data Analysis: Analyzes existing data to answer specific questions and identify trends. It’s more about understanding what happened in the past and why.
• Data Science: Uses data to create models that can predict future outcomes or develop new systems. It’s about using data to uncover hidden patterns and create tools for future use.

Process:

• Data Analysis: Focuses on cleaning, organizing, and visualizing data to communicate insights to stakeholders. Often uses pre-built tools and techniques.
• Data Science: Involves building new models and algorithms to solve problems. Requires more programming and statistical expertise.

Skills:

• Data Analysis: Strong in data manipulation, communication, and data visualization. Needs a solid understanding of statistics and business acumen.
• Data Science: Requires programming skills (Python, R), statistical modeling, and machine learning expertise.

Analogy:

Think of data analysis as inspecting the ingredients and leftovers of a meal to understand what was cooked. Data science is like using those insights to create a new recipe or improve an existing one.

Here’s a table summarizing the key differences:

In short, data analysis is a core skill within the broader field of data science.

Data Analysis Tools

There are literally hundreds of tools that are used today in Data analytics, they are mainly categorized as following:

• Spreadsheets: Spreadsheet tools are used mainly to save date in rows and columns format and can be used in calculations and basic data analysis. They can be even used for basic data exploration and data visualization and work like a charm for small to medium datasets. The most famous spreadsheet tools include Microsoft Excel and Google Sheets.
• Programming Languages: For more complex tasks and larger datasets, Programming languages like Python and R offer powerful libraries specifically designed for data manipulation and analysis. They offer the capability to do statistical analysis and data visualization.
• Query Languages: A powerful language specifically designed for extracting and manipulating data from databases. SQL (Structured Query Language) is the most widely used query language, allowing you to effectively retrieve and analyze data stored in relational databases. Some popular SQL programs include MySQL, Microsoft SQL Server, IBM DB2, and Google BigQuery.

Learn SQL with our 14 Best Resources For Learning SQL For Free

• Visualization tools: Softwares like Tableau, Microsoft Power BI, Google Looker Studio, and IBM Cognos Analytics excel at data visualization, and creating interactive dashboards and reports that make data insights easily understandable and sharable.
• Business Intelligence (BI) Tools or ETL Tools: Data often resides in various formats and locations. ETL (Extract, Transform, Load) tools streamline the process of extracting data from disparate sources, transforming it into a consistent format, and loading it into a data warehouse or another target system for analysis. Popular ETL tools include Apache Kafka, Microsoft SSIS, Google Cloud Dataflow, and Informatica PowerCenter.

• Statistical Analysis Software: Tools like SPSS, SAS, and Matlab are geared towards in-depth statistical analysis, hypothesis testing, and uncovering complex relationships within data.
• Data Cleaning Software: Before you can analyze your data, you often need to clean it. Data cleaning software helps identify and rectify errors, inconsistencies, and missing values within your dataset. Popular data cleaning tools include OpenRefine (formerly Google Refine), Trifacta Wrangler, Tableau data prep, and WinPure Clean & Match. These tools can automate many cleaning tasks, saving you time and ensuring the accuracy of your analysis.

Data Analytics Types

Data analysis isn’t a one-size-fits-all process. There are various techniques used to extract valuable insights from data, each suited to answer specific questions and achieve different goals. Understanding these different types of data analysis empowers you to choose the right tool for the job and unlock the full potential of your information.

Here’s a roadmap to some of the most common types of data analysis:

• Descriptive Analysis: This is the foundation of data analysis. It provides a summary of your data, describing its central tendencies (like average or median) and variability (like range or standard deviation). It often uses basic statistical measures and visualizations like charts and graphs to paint a clear picture of your data’s characteristics.

• Diagnostic Analysis: As the name suggests, diagnostic analysis delves deeper to diagnose the root causes of problems or identify areas for improvement. It leverages techniques like data mining and drill-down analysis to explore specific trends, outliers, and patterns within your data that might be contributing to an issue.

• Exploratory Analysis: This type of analysis is all about discovery. It’s an open-ended journey where you explore your data to uncover hidden patterns, relationships, and trends that you might not have anticipated. Exploratory analysis often involves data visualization techniques and statistical methods to identify interesting questions and guide further investigation.

• Inferential Analysis: This approach takes you beyond your initial dataset and allows you to draw conclusions about a larger population. By using statistical tests like hypothesis testing, you can make inferences about the broader population based on the sample of data you have analyzed. Inferential analysis helps you determine if the patterns you see in your data are likely to hold true for a larger group.

• Predictive Analysis: Looking forward is a key aspect of data analysis. Predictive analysis leverages statistical modeling and machine learning techniques to forecast future trends and make predictions about what might happen next. This is critical for tasks like risk assessment, sales forecasting, and targeted marketing campaigns.

• Prescriptive Analysis: Prescriptive analysis goes beyond prediction, it takes the insights from your data and uses them to recommend specific actions or courses of action. By leveraging optimization techniques and scenario modeling, it helps you identify the best course of action to achieve your desired outcomes.

Remember, these types of data analysis are not always linear and distinct stages. They can often be iterative, where you might move between them as you explore your data and refine your understanding.

The key is to choose the right type of data analysis for the specific questions you’re trying to answer and the goals you’re aiming to achieve. By mastering these diverse techniques, you’ll be well-equipped to unlock the hidden gems within your data and make informed decisions that drive success.

Data Analytics Techniques

We’ve explored the various types of data analysis, but how do we put those into action? This is where data analysis techniques come in. These are the specific methods and algorithms data analysts use to manipulate, explore, and model data to extract meaningful insights.

Here’s a glimpse into some of the most powerful data analysis techniques:

• Statistical Analysis: This is the foundation of many data analysis techniques. It involves using statistical methods to summarize, describe, and analyze data. Common statistical techniques include measures of central tendency (mean, median, mode) and dispersion (variance, standard deviation), correlation analysis to identify relationships between variables, and hypothesis testing to draw inferences from your data.

• Regression Analysis: This technique explores the relationship between a dependent variable (what you’re trying to predict) and one or more independent variables (factors that might influence the dependent variable). Regression analysis helps you understand how changes in the independent variables can affect the dependent variable and even predict future values.

• Clustering Analysis: This unsupervised learning technique is used to group similar data points together. It’s like sorting data points into categories based on their characteristics, helping you identify hidden patterns and segment your data for further analysis.

• Classification Analysis: In contrast to clustering, classification analysis is a supervised learning technique. Here, you use a labeled dataset (data where the category or group is already known) to train a model to classify new, unlabeled data points. This is commonly used for tasks like spam filtering, fraud detection, or customer segmentation.

• Time Series Analysis: When you’re dealing with data that’s collected over time (like sales figures or stock prices), time series analysis comes into play. This technique helps you identify trends, seasonality, and patterns within the data over time. It’s critical for forecasting future trends and making informed decisions.

• Text Analysis: The world is full of textual data, from social media posts to customer reviews. Text analysis techniques, also known as Natural Language Processing (NLP), help you extract meaning from this unstructured data. You can use NLP to identify sentiment (positive, negative, neutral), classify topics, and even generate text summaries.

• Machine Learning: Machine learning algorithms learn from data without being explicitly programmed. They can identify complex patterns, make predictions, and even improve their performance over time. Machine learning is a powerful tool for a wide range of data analysis tasks, from image recognition to fraud detection.

These are just a few examples of the many data analysis techniques available. The specific techniques you’ll use will depend on the type of data you have, the questions you’re trying to answer, and the goals you’re aiming to achieve.

But by understanding these core techniques, you’ll be well on your way to becoming a data analysis whiz, able to transform raw data into actionable insights that drive real-world results.

You might like: 23 Free Public Datasets Sites Every Data Analyst Must Know

Data Analysis Soft Skills

While technical skills are crucial, success in data analysis hinges on a surprising secret weapon: soft skills.

Soft skills encompass the interpersonal and communication abilities that enable you to navigate the human side of data. They are the glue that binds your technical expertise with effective communication, collaboration, and problem-solving, transforming you from a data translator into a trusted partner who can influence decisions and drive results.

Here’s a toolbox of essential soft skills for data analysts:

• Communication: Data analysis is all about translating insights from complex data into clear, concise, and actionable stories. You need to communicate effectively with both technical and non-technical audiences, tailoring your message to resonate with their level of understanding. Strong writing and presentation skills are key to getting stakeholders invested in your findings.

• Collaboration: Data analysis is rarely a solo endeavor. You’ll often collaborate with subject matter experts from different departments, data engineers who maintain the infrastructure, and business leaders who make strategic decisions based on your insights. The ability to work effectively as part of a team, actively listen to diverse perspectives, and foster a collaborative environment is essential.

• Critical Thinking: Data can be messy and misleading. Critical thinking empowers you to analyze data objectively, identify patterns and trends, and separate signal from noise. You’ll need to ask the right questions, challenge assumptions, and draw sound conclusions based on evidence.

• Curiosity: The best data analysts are inherently curious, with a thirst for knowledge and a desire to understand the why behind the numbers. They never stop asking questions, exploring new approaches, and staying up-to-date on the latest data analysis trends and technologies.

• Problem-Solving: Data is often used to identify problems and develop solutions. Strong problem-solving skills are essential for dissecting complex issues, identifying root causes, and leveraging data to formulate effective solutions.

• Storytelling: Data visualizations and reports are powerful tools for conveying insights. But true impact comes from weaving data into a compelling story that captures the audience’s attention and ignites action. Hone your storytelling skills to make your data analysis resonate and inspire data-driven decisions.

By cultivating these soft skills, you’ll transform from a data technician into a data analyst who can truly make a difference. So, don’t underestimate the power of soft skills; they are the secret weapon that will unlock your full potential in the exciting world of data analysis.

Data Analytics Jobs

Data Analytics is one of most demanded jobs today specially remotely. The demand for data analysts is higher than the number of qualified data analysts. According to Lightcast™ US Job Postings, the median US Salary for data analytics jobs is $92,000 with more than 480,000 us job openings.

You should note that there is often a lot of jobs and roles that seems similar to data analysis and might even have some overlap in skillset and tasks. In medium size business they often combine different roles in one position.

Below is a list of similar yet different roles that focus mainly on specific tasks other than data analytics. You should always read the full job description to align the job requirement with your list of skillsets and specialties.

• Business analyst — analyzes data to help businesses improve processes, products, or services. They often focus on the business side and work closely with data engineers and data analysts.
• Data analytics consultant — analyzes the systems and models for using data.
• Data engineer — prepares and integrates data from different sources for analytical use.
• Data scientist — uses expert skills in technology and social science to find trends through data analysis and develop models and AI to predict future results.
• Data specialist — organizes or converts data for use in databases or software systems.
• Operations analyst — analyzes data to assess the performance of business operations and workflows.

Technically a data analyst can work in any industry, however their are other industry-specific specialist positions that you might come across in your data analyst job search which requires a knowledge in specific domain, those include:

• Marketing analyst — analyzes market conditions to assess the potential sales of products and services.
• HR/payroll analyst — analyzes payroll data for inefficiencies and errors.
• Financial analyst — analyzes financial status by collecting, monitoring, and reviewing data.
• Risk analyst — analyzes financial documents, economic conditions, and client data to help companies determine the level of risk involved in making a particular business decision.
• Healthcare analyst — analyzes medical data to improve the business aspect of hospitals and medical facilities.

Data Analyst Responsibilities

• Collecting data from various data sources.
• Creating queries to extract data from relational databases.
• Filtering, cleaning, standardizing, transforming and reorganizing data in preparation for data analysis.
• Assessing data quality.
• Using statistical tools to interpret data sets. 
• Using statistical techniques to identify patterns and correlations in data.
• Analyzing patterns in complex data sets and interpreting trends.
• Preparing reports and charts that effectively communicate trends and patterns.
• Creating appropriate documentation to define and demonstrate the steps of the data analysis process.

Recommended Data Analytics Courses

There are many great data analytics courses available online, but some of the most highly recommended courses comes from technology leaders in data analytics as following:

• Google Data Analytics Professional Certificate: available on Coursera with Financial aid support. This course is offered by Google and covers the basics of data analytics, including data cleaning, data wrangling, and data visualization. It’s a great option for beginners who want to learn the fundamentals of data analysis. Get ready to learn about spreadsheets, SQL, BigQuery, R programming and Tableau.

• IBM Data Analytics with Excel and R Professional Certificate: available on Coursera with Financial aid support. This course is offered by IBM and covers the basics of data analytics, including data cleaning, data wrangling, and data visualization with introductory into Data Science and building models. It’s a great option for beginners who want to learn the fundamentals of data analysis and data science. Get ready to learn about spreadsheets, SQL, DB2, R programming and IBM Cognos Analytics.

• Microsoft Power BI Data Analyst Professional Certificate: available on Coursera with Financial aid support. This course is offered by Microsoft and covers the basics of data analytics, including data cleaning, data wrangling, data modelling and data visualization. Although it provide the basics about Excel however it is build extensively around Power BI and is definitely the best resource if you want to learn Microsoft Power BI.

• Tableau Business Intelligence Analyst Professional Certificate: available on Coursera with Financial aid support. This course is offered by Tableau. Although the course is intendent for Business intelligence analyst, however it overlaps a lot with data analytics and is the best resource to learn the ins and outs of Tableau desktop for data visualization.

The best course for you will depend on your experience level and learning goals. If you’re a beginner, then a courses like the Google Data Analytics Professional Certificate or IBM Data Analytics with Excel and R Professional Certificate is a good place to start. If you have some experience with data analysis, then you may want to consider a more specialized course towards a specific tool, such as the Excel, SQL, python or R.

The Bottom Line

Data analysis is the key to unlocking the hidden potential within your data. By analyzing data effectively, you can make data-driven decisions, improve efficiency, gain a competitive edge, and solve problems. There’s a vast array of data analysis tools to empower you, including spreadsheets, programming languages, business intelligence tools, statistical analysis software, query languages, and data cleaning software.