A Step-by-Step Guide to the Data Analysis Process [2023] (2023)

Like any scientific discipline, data analysis follows a rigorous step-by-step process. Each stage requires different skills and know-how. To get meaningful insights, though, it’s important to understand the process as a whole. An underlying framework is invaluable for producing results that stand up to scrutiny.

In this post, we’ll explore the main steps in the data analysis process. This will cover how to define your goal, collect data, and carry out an analysis. Where applicable, we’ll also use examples and highlight a few tools to make the journey easier. When you’re done, you’ll have a much better understanding of the basics. This will help you tweak the process to fit your own needs.

Here are the steps we’ll take you through:

  1. Defining the question
  2. Collecting the data
  3. Cleaning the data
  4. Analyzing the data
  5. Sharing your results
  6. Embracing failure
  7. Summary

On popular request, we’ve also developed a video based on this article. Scroll further along this article to watch that.

A Step-by-Step Guide to the Data Analysis Process [2023] (1)

Ready? Let’s get started with step one.

1. Step one: Defining the question

The first step in any data analysis process is to define your objective. In data analytics jargon, this is sometimes called the ‘problem statement’.

Defining your objective means coming up with a hypothesis and figuring how to test it. Start by asking: What business problem am I trying to solve? While this might sound straightforward, it can be trickier than it seems. For instance, your organization’s senior management might pose an issue, such as: “Why are we losing customers?” It’s possible, though, that this doesn’t get to the core of the problem. A data analyst’s job is to understand the business and its goals in enough depth that they can frame the problem the right way.

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Let’s say you work for a fictional company called TopNotch Learning. TopNotch creates custom training software for its clients. While it is excellent at securing new clients, it has much lower repeat business. As such, your question might not be, “Why are we losing customers?” but, “Which factors are negatively impacting the customer experience?” or better yet: “How can we boost customer retention while minimizing costs?”

Now you’ve defined a problem, you need to determine which sources of data will best help you solve it. This is where your business acumen comes in again. For instance, perhaps you’ve noticed that the sales process for new clients is very slick, but that the production team is inefficient. Knowing this, you could hypothesize that the sales process wins lots of new clients, but the subsequent customer experience is lacking. Could this be why customers don’t come back? Which sources of data will help you answer this question?

Tools to help define your objective

Defining your objective is mostly about soft skills, business knowledge, and lateral thinking. But you’ll also need to keep track of business metrics and key performance indicators (KPIs). Monthly reports can allow you to track problem points in the business. Some KPI dashboards come with a fee, like Databox and DashThis. However, you’ll also find open-source software like Grafana, Freeboard, and Dashbuilder. These are great for producing simple dashboards, both at the beginning and the end of the data analysis process.

2. Step two: Collecting the data

Once you’ve established your objective, you’ll need to create a strategy for collecting and aggregating the appropriate data. A key part of this is determining which data you need. This might be quantitative (numeric) data, e.g. sales figures, or qualitative (descriptive) data, such as customer reviews. All data fit into one of three categories: first-party, second-party, and third-party data. Let’s explore each one.

What is first-party data?

First-party data are data that you, or your company, have directly collected from customers. It might come in the form of transactional tracking data or information from your company’s customer relationship management (CRM) system. Whatever its source, first-party data is usually structured and organized in a clear, defined way. Other sources of first-party data might include customer satisfaction surveys, focus groups, interviews, or direct observation.

What is second-party data?

To enrich your analysis, you might want to secure a secondary data source. Second-party data is the first-party data of other organizations. This might be available directly from the company or through a private marketplace. The main benefit of second-party data is that they are usually structured, and although they will be less relevant than first-party data, they also tend to be quite reliable. Examples of second-party data include website, app or social media activity, like online purchase histories, or shipping data.

What is third-party data?

Third-party data is data that has been collected and aggregated from numerous sources by a third-party organization. Often (though not always) third-party data contains a vast amount of unstructured data points (big data). Many organizations collect big data to create industry reports or to conduct market research. The research and advisory firm Gartner is a good real-world example of an organization that collects big data and sells it on to other companies. Open data repositories and government portals are also sources of third-party data.

Tools to help you collect data

Once you’ve devised a data strategy (i.e. you’ve identified which data you need, and how best to go about collecting them) there are many tools you can use to help you. One thing you’ll need, regardless of industry or area of expertise, is a data management platform (DMP). A DMP is a piece of software that allows you to identify and aggregate data from numerous sources, before manipulating them, segmenting them, and so on. There are many DMPs available. Some well-known enterprise DMPs include Salesforce DMP, SAS, and the data integration platform, Xplenty. If you want to play around, you can also try some open-source platforms like Pimcore or D:Swarm.

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Want to learn more about what data analytics is and the process a data analyst follows? We cover this topic (and more) in our free introductory short course for beginners. Check out tutorial one: An introduction to data analytics.

3. Step three: Cleaning the data

Once you’ve collected your data, the next step is to get it ready for analysis. This means cleaning, or ‘scrubbing’ it, and is crucial in making sure that you’re working with high-quality data. Key data cleaning tasks include:

  • Removing major errors, duplicates, and outliers—all of which are inevitable problems when aggregating data from numerous sources.
  • Removing unwanted data points—extracting irrelevant observations that have no bearing on your intended analysis.
  • Bringing structure to your data—general ‘housekeeping’, i.e. fixing typos or layout issues, which will help you map and manipulate your data more easily.
  • Filling in major gaps—as you’re tidying up, you might notice that important data are missing. Once you’ve identified gaps, you can go about filling them.

A good data analyst will spend around 70-90% of their time cleaning their data. This might sound excessive. But focusing on the wrong data points (or analyzing erroneous data) will severely impact your results. It might even send you back to square one…so don’t rush it! You’ll find a step-by-step guide to data cleaning here.
You may be interested in this introductory tutorial to data cleaning, hosted by Dr. Humera Noor Minhas.

Related reading: What is data transformation?

Carrying out an exploratory analysis

Another thing many data analysts do (alongside cleaning data) is to carry out an exploratory analysis. This helps identify initial trends and characteristics, and can even refine your hypothesis. Let’s use our fictional learning company as an example again. Carrying out an exploratory analysis, perhaps you notice a correlation between how much TopNotch Learning’s clients pay and how quickly they move on to new suppliers. This might suggest that a low-quality customer experience (the assumption in your initial hypothesis) is actually less of an issue than cost. You might, therefore, take this into account.

Tools to help you clean your data

Cleaning datasets manually—especially large ones—can be daunting. Luckily, there are many tools available to streamline the process. Open-source tools, such as OpenRefine, are excellent for basic data cleaning, as well as high-level exploration. However, free tools offer limited functionality for very large datasets. Python libraries (e.g. Pandas) and some R packages are better suited for heavy data scrubbing. You will, of course, need to be familiar with the languages. Alternatively, enterprise tools are also available. For example, Data Ladder, which is one of the highest-rated data-matching tools in the industry. There are many more. Why not see which free data cleaning tools you can find to play around with?

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4. Step four: Analyzing the data

Finally, you’ve cleaned your data. Now comes the fun bit—analyzing it! The type of data analysis you carry out largely depends on what your goal is. But there are many techniques available. Univariate or bivariate analysis, time-series analysis, and regression analysis are just a few you might have heard of. More important than the different types, though, is how you apply them. This depends on what insights you’re hoping to gain. Broadly speaking, all types of data analysis fit into one of the following four categories.

Descriptive analysis

Descriptive analysis identifies what has already happened. It is a common first step that companies carry out before proceeding with deeper explorations. As an example, let’s refer back to our fictional learning provider once more. TopNotch Learning might use descriptive analytics to analyze course completion rates for their customers. Or they might identify how many users access their products during a particular period. Perhaps they’ll use it to measure sales figures over the last five years. While the company might not draw firm conclusions from any of these insights, summarizing and describing the data will help them to determine how to proceed.

Learn more: What is descriptive analytics?

Diagnostic analysis

Diagnostic analytics focuses on understanding why something has happened. It is literally the diagnosis of a problem, just as a doctor uses a patient’s symptoms to diagnose a disease. Remember TopNotch Learning’s business problem? ‘Which factors are negatively impacting the customer experience?’ A diagnostic analysis would help answer this. For instance, it could help the company draw correlations between the issue (struggling to gain repeat business) and factors that might be causing it (e.g. project costs, speed of delivery, customer sector, etc.) Let’s imagine that, using diagnostic analytics, TopNotch realizes its clients in the retail sector are departing at a faster rate than other clients. This might suggest that they’re losing customers because they lack expertise in this sector. And that’s a useful insight!

Predictive analysis

Predictive analysis allows you to identify future trends based on historical data. In business, predictive analysis is commonly used to forecast future growth, for example. But it doesn’t stop there. Predictive analysis has grown increasingly sophisticated in recent years. The speedy evolution of machine learning allows organizations to make surprisingly accurate forecasts. Take the insurance industry. Insurance providers commonly use past data to predict which customer groups are more likely to get into accidents. As a result, they’ll hike up customer insurance premiums for those groups. Likewise, the retail industry often uses transaction data to predict where future trends lie, or to determine seasonal buying habits to inform their strategies. These are just a few simple examples, but the untapped potential of predictive analysis is pretty compelling.

Prescriptive analysis

Prescriptive analysis allows you to make recommendations for the future. This is the final step in the analytics part of the process. It’s also the most complex. This is because it incorporates aspects of all the other analyses we’ve described. A great example of prescriptive analytics is the algorithms that guide Google’s self-driving cars. Every second, these algorithms make countless decisions based on past and present data, ensuring a smooth, safe ride. Prescriptive analytics also helps companies decide on new products or areas of business to invest in.

Learn more:What are the different types of data analysis?

5. Step five: Sharing your results

You’ve finished carrying out your analyses. You have your insights. The final step of the data analytics process is to share these insights with the wider world (or at least with your organization’s stakeholders!) This is more complex than simply sharing the raw results of your work—it involves interpreting the outcomes, and presenting them in a manner that’s digestible for all types of audiences. Since you’ll often present information to decision-makers, it’s very important that the insights you present are 100% clear and unambiguous. For this reason, data analysts commonly use reports, dashboards, and interactive visualizations to support their findings.

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How you interpret and present results will often influence the direction of a business. Depending on what you share, your organization might decide to restructure, to launch a high-risk product, or even to close an entire division. That’s why it’s very important to provide all the evidence that you’ve gathered, and not to cherry-pick data. Ensuring that you cover everything in a clear, concise way will prove that your conclusions are scientifically sound and based on the facts. On the flip side, it’s important to highlight any gaps in the data or to flag any insights that might be open to interpretation. Honest communication is the most important part of the process. It will help the business, while also helping you to excel at your job!

Tools for interpreting and sharing your findings

There are tons of data visualization tools available, suited to different experience levels. Popular tools requiring little or no coding skills include Google Charts, Tableau, Datawrapper, and Infogram. If you’re familiar with Python and R, there are also many data visualization libraries and packages available. For instance, check out the Python libraries Plotly, Seaborn, and Matplotlib. Whichever data visualization tools you use, make sure you polish up your presentation skills, too. Remember: Visualization is great, but communication is key!

You can learn more about storytelling with data in this free, hands-on tutorial.We show you how to craft a compelling narrative for a real dataset, resulting in a presentation to share with key stakeholders. This is an excellent insight into what it’s really like to work as a data analyst!

6. Step six: Embrace your failures

The last ‘step’ in the data analytics process is to embrace your failures. The path we’ve described above is more of an iterative process than a one-way street. Data analytics is inherently messy, and the process you follow will be different for every project. For instance, while cleaning data, you might spot patterns that spark a whole new set of questions. This could send you back to step one (to redefine your objective). Equally, an exploratory analysis might highlight a set of data points you’d never considered using before. Or maybe you find that the results of your core analyses are misleading or erroneous. This might be caused by mistakes in the data, or human error earlier in the process.

While these pitfalls can feel like failures, don’t be disheartened if they happen. Data analysis is inherently chaotic, and mistakes occur. What’s important is to hone your ability to spot and rectify errors. If data analytics was straightforward, it might be easier, but it certainly wouldn’t be as interesting. Use the steps we’ve outlined as a framework, stay open-minded, and be creative. If you lose your way, you can refer back to the process to keep yourself on track.

7. Summary

In this post, we’ve covered the main steps of the data analytics process. These core steps can be amended, re-ordered and re-used as you deem fit, but they underpin every data analyst’s work:

  • Define the question—What business problem are you trying to solve? Frame it as a question to help you focus on finding a clear answer.
  • Collect data—Create a strategy for collecting data. Which data sources are most likely to help you solve your business problem?
  • Clean the data—Explore, scrub, tidy, de-dupe, and structure your data as needed. Do whatever you have to! But don’t rush…take your time!
  • Analyze the data—Carry out various analyses to obtain insights. Focus on the four types of data analysis: descriptive, diagnostic, predictive, and prescriptive.
  • Share your results—How best can you share your insights and recommendations? A combination of visualization tools and communication is key.
  • Embrace your mistakes—Mistakes happen. Learn from them. This is what transforms a good data analyst into a great one.

What next? From here, we strongly encourage you to explore the topic on your own. Get creative with the steps in the data analysis process, and see what tools you can find. As long as you stick to the core principles we’ve described, you can create a tailored technique that works for you.

To learn more, check out our free, 5-day data analytics short course. You might also be interested in the following:

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  • These are the top 9 data analytics tools
  • 10 great places to find free datasets for your next project
  • How to build a data analytics portfolio

FAQs

A Step-by-Step Guide to the Data Analysis Process [2023]? ›

In addition, data analyst education and skills open the door to several other career paths, including information security, market research, management, computer systems, and financial analytics—all among the top 25 careers according to the 2023 U.S. News's best jobs ranking.

What are the 5 steps to the data analysis process? ›

In this post we'll explain five steps to get you started with data analysis.
  • STEP 1: DEFINE QUESTIONS & GOALS.
  • STEP 2: COLLECT DATA.
  • STEP 3: DATA WRANGLING.
  • STEP 4: DETERMINE ANALYSIS.
  • STEP 5: INTERPRET RESULTS.

How to be a data analyst in 2023? ›

To fulfill the responsibilities a data analyst must possess a vast and rich skillset:
  1. Degree and Domain Expertise.
  2. Knowledge of Programming.
  3. Knowledge of Data Analysis Tools.
  4. Understanding of Statistics and Machine Learning Algorithms.
  5. Knowledge of Data Visualization Tools.
Apr 20, 2023

Is data analysis a good career in 2023? ›

In addition, data analyst education and skills open the door to several other career paths, including information security, market research, management, computer systems, and financial analytics—all among the top 25 careers according to the 2023 U.S. News's best jobs ranking.

What are the five C's of data analysis? ›

The five C's pertaining to data analytics soft skills—many of which are interrelated—are communication, collaboration, critical thinking, curiosity and creativity.

What are the 8 stages of data analysis? ›

data analysis process follows certain phases such as business problem statement, understanding and acquiring the data, extract data from various sources, applying data quality for data cleaning, feature selection by doing exploratory data analysis, outliers identification and removal, transforming the data, creating ...

Is 40 too old to become a data analyst? ›

As long as you've got the right skills, you can become a data scientist at any age.

Is 50 too old to become a data analyst? ›

No, 50 is not too old to become a data scientist.

It's never too late to become a data scientist - as long as you've got the right skills and determination, you can become a data scientist at any age. Assuming you have the skillset, there isn't an age limit - even if you're starting from scratch with a degree.

How much do entry level data analysts make? ›

The average Entry Data Analyst salary in the United States is $67,601 as of May 01, 2023, but the range typically falls between $60,801 and $76,101.

What are the 10 steps in analyzing data? ›

  • Define the question.
  • Define the ideal data set.
  • Obtain data.
  • Clean the data.
  • Exploratory Data Analysis.
  • Statistical Prediction/modeling.
  • Interpret results.
  • Challenge results.

What are the 6 phases of data analysis? ›

These six phases will help you grow as a data analyst.

You must have read the conventional data analysis process definition, which is to clean, transform, process, visualize, and model data to get insights.

What are the 6 steps of the data analysis process? ›

6 Steps of Data Analysis
  • Determine your goal. ...
  • Understand the context around business and data. ...
  • Acquire an overview of the data. ...
  • Formulate specific hypothesis. ...
  • Test the hypothesis. ...
  • Validate results.
Feb 28, 2023

Is data analyst a high paying job? ›

Highest salary that a Data Analyst can earn is ₹11.7 Lakhs per year (₹97.5k per month). How does Data Analyst Salary in India change with experience? An Entry Level Data Analyst with less than three years of experience earns an average salary of ₹4.3 Lakhs per year.

Is data analyst a stressful job? ›

Several data professionals have defined data analytics as a stressful career. So, if you are someone planning on taking up data analytics and science as a career, it is high time that you rethink and make an informed decision.

What are the data collection trends for 2023? ›

Advanced analytics and AI/ML continue to be hot data trends in 2023. According to a recent IDC study, “executives openly articulate the need for their organizations to be more data-driven, to be 'data companies,' and to increase their enterprise intelligence.”

What are top 4 data analysis techniques? ›

The four types of data analysis are: Descriptive Analysis. Diagnostic Analysis. Predictive Analysis. Prescriptive Analysis.

What are the 5 A's of data? ›

5 A's to Big Data Success (Agility, Automation, Accessible, Accuracy, Adoption)

What are the 4 areas of data analysis? ›

Modern analytics tend to fall in four distinct categories: descriptive, diagnostic, predictive, and prescriptive.

What are the 10 types of data analysis? ›

10 Top Types of Data Analysis Methods and Techniques
  • Descriptive Analysis. Descriptive analysis is an insight into the past. ...
  • Regression analysis. ...
  • Factor analysis. ...
  • Dispersion Analysis. ...
  • Discriminant Analysis. ...
  • Time Series Analysis.

What are the first three rules of data analysis? ›

These steps and many others fall into three stages of the data analysis process: evaluate, clean, and summarize.

What are the three 3 kinds of data analysis? ›

Descriptive, predictive and prescriptive analytics.

Is data analysis a hard job? ›

Becoming a data analyst isn't hard per se, though it does require certain technical skills that might be more challenging for some than others. Additionally, because of continuing advancements in the field, data analysis is a career path that requires ongoing education.

Is it hard to become a data analyst without a degree? ›

You don't need a full-blown degree to become a data analyst, but you do need a structured and formal approach to learning the necessary skills. The best (and most flexible) way to do so is through a project-based course.

Can you be a data analyst with no experience? ›

Can you get data analyst jobs with no experience? It's possible for you to become a data analyst even without experience in the field. The data industry is expansive, so you're likely to find open jobs even without prior experience.

Does data analyst require a lot of math? ›

While data analysts need to be good with numbers, and a foundational knowledge of Math and Statistics can be helpful, much of data analysis is just following a set of logical steps. As such, people can succeed in this domain without much mathematical knowledge.

Do you have to be smart to be a data analyst? ›

Yes and no. While data analysts should have a foundational knowledge of statistics and mathematics, much of their work can be done without complex mathematics. Generally, though, data analysts should have a grasp of statistics, linear algebra, and calculus.

Can I become a data analyst at 60? ›

You can become a data scientist at any age if you're willing to put in the work.

Who pays data analysts the most? ›

Technology
CompanyAverage (In USD)Range (In USD)
Google99,50065,700-120,000
Apple95,80069,000-190,000
Meta123,00096,000-150,000
Microsoft93,06011,000-226,000
1 more row
May 19, 2023

What is the highest paid data analyst? ›

According to the U.S. Bureau of Labor Statistics, operations research analysts tend to have some of the highest salaries among data analysts, with an average salary of $82,360 per year recorded in May 2021. Common skills used by operations research analysts include: Analytical and critical thinking.

Can I make 100k as a data analyst? ›

Candidates with advanced skills or at least three years of work experience on their resume can earn an average salary of over $100,000 per year.

What is the easiest way to analyze data? ›

Data analysis tools, like Excel, Google Sheets, and Airtable, and business intelligence tools, like Tableau and Google Data Studio, are excellent for crunching numbers. They allow you to plug in your quantitative data and create comprehensive visualizations, charts, and graphs.

How do you manually analyze data? ›

Qualitative data analysis requires a 5-step process:
  1. Prepare and organize your data. Print out your transcripts, gather your notes, documents, or other materials. ...
  2. Review and explore the data. ...
  3. Create initial codes. ...
  4. Review those codes and revise or combine into themes. ...
  5. Present themes in a cohesive manner.

How do you analyze data in Excel? ›

Simply select a cell in a data range > select the Analyze Data button on the Home tab. Analyze Data in Excel will analyze your data, and return interesting visuals about it in a task pane.

What are the four 4 steps in data analysis? ›

All four levels create the puzzle of analytics: describe, diagnose, predict, prescribe. When all four work together, you can truly succeed with a data and analytical strategy.

What is the cycle of data analysis? ›

The Data Analytics Lifecycle is a cyclic process which explains, in six stages, how information in made, collected, processed, implemented, and analyzed for different objectives.

What are two important first steps in data analysis statistics? ›

The first step is to collect the data through primary or secondary research. The next step is to make an inference about the collected data. The third step in this case will involve SWOT Analysis. SWOT Analysis stands for Strength, Weakness, Opportunity and Threat of the data under study.

What are 3 basic steps of analysis process? ›

The three basic steps in the data analysis process are: assess the quality and reliability of the data, sort and classify data, and perform statistical tests and analyze the results.

What are the stages of data analysis? ›

These steps and many others fall into three stages of the data analysis process: evaluate, clean, and summarize.

What is the basic process of data analysis? ›

Data Analysis is a process of collecting, transforming, cleaning, and modeling data with the goal of discovering the required information. The results so obtained are communicated, suggesting conclusions, and supporting decision-making.

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