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 phase requires different skills and knowledge. However, to gain meaningful insights, it is 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 will explore the key steps in the data analysis process. Here you will learn how to define your goal, collect data and conduct an analysis. Where appropriate, we will also use examples and highlight some tools to make the path easier. When you're done, you'll have a much better grasp of the basics. This will help you customize the process to suit your own needs.

Here are the steps we'll walk you through:

1. defining the question
2. collecting the data
3. cleaning the data
4. analysis of the data
5. Share your results
6. embrace failure
7. Summary

By popular request, we have also developed a video for this article. Keep scrolling down this article to see that.

Ready? Let's start with step one.

1. Step one: define the question

The first step in any data analysis process is to define your goal. In data analysis jargon, this is sometimes referred to as a “problem statement.”

Defining your goal means developing a hypothesis and figuring out how to test it. Start with the question: What business problem am I trying to solve? While this may sound easy, it can be trickier than it seems. For example, your company's senior management might raise an issue such as: E.g.: "Why are we losing customers?" However, this may not get to the heart of the problem. A data analyst's job is to understand the business and its goals well enough to properly frame the problem.

Suppose you work for a fictional company called TopNotch Learning. TopNotch creates custom training software for its customers. While it's excellent at attracting new customers, it has far less repeat business. So your question might not be “why are we losing customers?” but rather “what factors negatively impact customer experience?” or even better, “how can we increase customer retention while minimizing costs?”

After you define a problem, you need to determine which data sources will best help you solve it. This is where your business acumen comes into play again. For example, you may have noticed that the new customer sales process is very smooth, but the production team is inefficient. Knowing this, you could hypothesize that the sales process brings in many new customers, but the subsequent customer experience is lacking. Could that be the reason customers aren't coming back? What data sources will help you answer this question?

Tools to define your goal

The definition of goals is primarily about soft skills, business knowledge and lateral thinking. But you also need to keep an eye on business metrics and key performance indicators (KPIs). Monthly reports allow you to track trouble spots in the business. Some KPI dashboards are paid, e.gDatenboxAndDashThis. However, you can also find open source software likeGrafana,freeboard, AndDashbuilder. These are great for creating simple dashboards, both at the beginning and at the end of the data analysis process.

2. Step two: collect the data

Once you've set your goal, you need to develop a strategy for collecting and merging the appropriate data. An important part of this is determining what data you need. This can be quantitative (numerical) data, e.g. Sales figures or qualitative (descriptive) data such as customer ratings. All data falls into one of three categories: first-party, second-party, and third-party data. Let's examine each one.

What is First Party Data?

First-party data is data that you or your company have collected directly from customers. This may take the form of transaction tracking data or information from your company's Customer Relationship Management (CRM) system. Regardless of its source, first-party data is typically structured and organized in a well-defined manner. Other sources of first-party data may include customer satisfaction surveys, focus groups, interviews, or direct observation.

What is Second Party Data?

To enrich your analysis, you may want to back up a secondary data source. Second-party data is the first-party data of other organizations. This can be available directly from the company or through a private marketplace. The main benefit of second-party data is that it's typically structured, and while less relevant than first-party data, it also tends to be fairly reliable. Examples of third-party data are website, app or social media activity such as online purchase histories or shipping data.

What is third party data?

Third Party Data is data that has been collected and aggregated by a third party organization from multiple sources. Often (but not always) third-party data contains a large amount of unstructured data points (big data). Many organizations collect big data to create industry reports or conduct market research. The research and consulting firm Gartner is a good real-world example of an organization that collects big data and sells it 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 developed a data strategy (meaning you've identified what data you need and how best to collect it), there are many tools you can use to help you. One thing you need, regardless of your industry or specialty, is a data management platform (DMP). A DMP is software that allows you to identify and aggregate data from numerous sources before editing, segmenting, and so on. There are many DMPs available. Some well-known enterprise DMPs includeSalesforce-DMP,SAS, and the data integration platform,Much. If you want to play around, you can also use some open source platforms such asPimcoreorD: crush.

Would you like to learn more about what data analysis is and the process a data analyst undertakes?We cover this topic (and more) in our free introductory short course for beginners. CashTutorial one: An introduction to data analysis.

3. Step three: cleaning the data

Once you have collected your data, the next step is to prepare it for analysis. That means cleaning or "scrubbing" it, and is crucial to making sure you're working with ithigh quality data. Key data cleansing tasks include:

• Remove fatal errors, duplicates and outliers– all of these are inevitable problems when aggregating data from numerous sources.
• Remove unwanted data points– Extracting irrelevant observations that do not affect your intended analysis.
• Bring structure to your data—General “housekeeping”, i. H. fixing typos or layout issues that help you map and manipulate your data more easily.
• Fill in large gaps– While cleaning up, you may find that important data is missing. Once you've identified gaps, you can fill them in.

A good data analyst spends about 70-90% of their time cleaning their data. That may sound like an exaggeration. But focusing on the wrong data points (or analyzing bad data) will severely hurt your results. It might even send you back to the beginning... so don't rush it! you will find itHere is a step-by-step guide to data cleansing.
You might be interested in this introductory data cleaning tutorial created by Dr. Humera Noor Minhas is held.

Conducting an exploratory analysis

Another thing many data analysts do (besides cleaning data) is performing exploratory analysis. This helps to spot early trends and characteristics and can even refine your hypothesis. Let's take our fictional learning company again as an example. If you conduct an exploratory analysis, you might notice a correlation between the amount of money TopNotch Learning's customers pay and the speed at which they switch to new providers. This could indicate that substandard customer experience (the assumption in your initial hypothesis) is actually less of an issue than cost. You can therefore take this into account.

Tools to help you clean your data

Manually cleaning up datasets—especially large ones—can be daunting. Fortunately, there are many tools available to streamline the process. Open source tools such asOpenRefine, are great 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. Of course you have to be familiar with the languages. Alternatively, enterprise tools are also available. For example,data conductor, one of the top rated data matching tools in the industry. There are many more. Why not check out some free data cleaning tools you can play around with?

4. Step Four: Analyze the Data

Finally you cleaned your data. Now comes the fun part - the analysis! The type of data analysis you do 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 may have heard of. More important than the different types, however, is how you use them. That depends on what insights you're hoping for. Broadly speaking, all types of data analysis fall into one of the following four categories.

Descriptive Analysis

Descriptive Analysis recognizes what has already happened. This is a common first step companies take before proceeding with deeper exploration. Let's go back to our fictitious learning provider as an example. TopNotch Learning may use descriptive analytics to analyze course completion rates for its clients. Or they determine how many users access their products in a certain period of time. Maybe they use it to measure the sales figures for the last five years. While the organization may not draw firm conclusions from these findings, summarizing and describing the data helps determine how to proceed.

Learn more:What is descriptive analytics?

Diagnostic Analysis

Diagnostic analytics focuses on understanding why something happened. It is literally diagnosing a problem, just as a doctor uses a patient's symptoms to diagnose an illness. Do you remember the TopNotch Learning business problem? “What factors negatively impact customer experience?” A diagnostic analysis would help answer this. For example, it could help the company draw correlations between the problem (fighting for repeat business) and factors that might be causing it (e.g. project cost, delivery speed, customer sector, etc.). Let's imagine that with the help of diagnostic analytics, TopNotch is aware that its retail customers are leaving faster than other customers. This could indicate that they are losing customers because they lack expertise in this area. And that's a useful insight!

Predictive Analysis

Predictive analytics enables you Identify future trends based on historical data. For example, in economics, predictive analytics is commonly used to forecast future growth. But it doesn't stop there. Predictive analytics has become increasingly sophisticated in recent years. The rapid development of machine learning is enabling companies to create surprisingly accurate forecasts. Take the insurance industry. Insurance providers often use historical data to predict which customer groups are more likely to be involved in accidents. As a result, they increase customer insurance premiums for these groups. Likewise, the retail industry often uses transactional 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 analytics is pretty compelling.

Prescriptive Analysis

With prescriptive analysis, you can make recommendations for the future.This is the last step in the analysis part of the process. It is also the most complex. This is because it includes aspects of all of the other analyzes we've described. A great example of prescriptive analytics is the algorithms that power Google's self-driving cars. Every second, these algorithms make countless decisions based on past and current data to ensure a smooth and safe ride. Prescriptive analytics also helps companies decide on new products or business areas to invest in.

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

5. Step five: Share your results

You have completed your analyses. You have your insights. The final step in the data analysis process is to share these insights with the world at large (or at least your company's stakeholders!). This is more complex than simply sharing the raw results of your work — it involves interpreting the results and presenting them in a way that's digestible for all types of audiences. Because you often present information to decision makers, it is very important that the insights you present are 100% clear and unambiguous. Because of this, data analysts often use reports, dashboards, and interactive visualizations to support their findings.

How you interpret and present results often influences the direction of a company. Depending on what you share, your company might decide to restructure, launch a risky product, or even shut down an entire department. For this reason, it is very important to provide all the evidence collected and not to single out data. Make sure you cover everything clearly and concisely to prove your conclusions are scientifically sound and factual. On the other hand, it is important to highlight gaps in the data or flag insights that may be open to interpretation. Honest communication is the most important part of the process. It will help the business while also helping you excel at your job!

Tools to interpret and share your results

There are tons ofData visualization toolsavailable, suitable for different experience levels. Popular tools that require little or no programming knowledge are e.gGoogle-Diagram,Tableau,Datawrapper, AndInfogramm. If you are familiar with Python and R, there are also many data visualization libraries and packages available. For example, look at the Python librariesPlotzlich,Born of the sea, AndMatplotlib. Regardless of what data visualization tools you use, make sure you brush up on your presentation skills as well. Remember: visualization is great, but communication is key!

You canLearn more about storytelling with data with this free, hands-on tutorial.We'll show you how to create a compelling narrative for a real dataset, resulting in a presentation you can share with key stakeholders. This is an excellent insight into the work of a data analyst!

6. Step six: Accept your mistakes

The last "step" in the data analysis process is to accept your errors. The path described above is more of an iterative process than a one-way street. Data analysis is inherently messy, and the process you follow will be different for each project. For example, when you clean data, you might discover patterns that raise a whole new set of questions. This might take you back to step one (to redefine your goal). Likewise, exploratory analysis can highlight a number of data points that you never thought of using before. Or maybe you find that your core analysis results are misleading or flawed. This can be caused by errors in the data or human error earlier in the process.

While these pitfalls can feel like failures, don't be discouraged when they happen. Data analysis is inherently messy and errors do occur. What is important is that you improve your ability to identify and correct mistakes. If data analysis were easy, it might be easier, but certainly not as interesting. Use the steps we've outlined as a framework, stay open-minded and creative. If you get lost, you can refer back to the process to keep yourself informed.

7. Summary

In this post, we have covered the main steps of the data analysis process. These core steps can be modified, reordered, and reused at will, but they underpin any data analyst's work:

• Define the question—What business problem are you trying to solve? Frame it as a question so you can focus on finding a clear answer.
• collect data—Devise a strategy for collecting data. Which data sources are most likely to help you solve your business problem?
• Clean the data- Search, clean, clean up, deduplicate and structure your data as needed. Do what you have to do! But don't rush... take your time!
• Analyze the data—Perform various analyzes to gain insights. Focus on the four types of data analysis: descriptive, diagnostic, predictive, and prescriptive.
• Share your results—How best to share your insights and recommendations? A combination of visualization tools and communication is key.
• accept your mistakes- Mistakes happen. learn from them. That's what makes a good data analyst 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 basic principles we have outlined, you can create a custom technique that works for you.

To learn more, visit ourfree 5 day data analysis short course. You might also be interested in:

• These are the top 9 data analysis tools
• 10 Great Places to Find Free Datasets for Your Next Project
• How to build a data analysis portfolio