What Is the Data Analysis Process? 6 Key Steps to Follow

The data analysis process, like any other scientific discipline, follows a strict step-by-step process. Each stage requires different skills. Understanding the entire process is essential to gaining meaningful insight. A framework can help you produce results that are strong and durable.

This post will cover the key steps involved in the data analysis process. This post will explain how to set your goal, collect data, and conduct an analysis. We’ll use examples where applicable and point out some tools that can help you make your journey more manageable. You’ll be able to understand the basics better once you’re done. This will allow you to tailor the process to your needs.

What Is the Data Analysis Process? 6 Key Steps to Follow

1. Defining the question

The first step in any data analysis process is to identify your objective. This is often referred to as the “problem statement” in data analytics.

Determining your objective requires you to create a hypothesis and then figure out how to test it. Ask yourself: What business problem do I want to solve? This may sound simple, but it can be more complicated than you think. Your organization’s top management may be a problem, asking “Why are customers losing money?”. However, it is possible that this doesn’t address the root cause of the problem. Data analysts are expected to have a deep understanding of the business and its goals so that they can correctly frame the problem.

Let’s suppose you work at TopNotch Learning. TopNotch develops customized training software for clients. It is great at securing new customers, but it has lower repeat business. So, the question is not “Why aren’t we losing customers?” But “What factors are negatively impacting customer experience?” Or “How can I increase customer retention and minimize costs?”

Also read: 5 Best Data Quality Issues and How to Fix Them

Once you have identified the problem, it is time to identify which data sources will be most helpful in solving it. Here is where your business acumen shines again. Perhaps you have noticed that sales processes for new clients are very smooth, but the production team is slow. This could lead to the conclusion that while the sales process is successful in securing new clients, the customer experience is poor. Could this be the reason customers stop coming back? What data sources will you use to answer this question?

Tools to help define your objective

Determining your goal is mainly about soft skills, business knowledge, and lateral thinking. You’ll need to monitor key performance indicators (KPIs) and business metrics. Monthly reports are a great way to identify and fix problems in your business. Some KPI dashboards, such as Databox or DashThis, come with a monthly fee. Open-source software such as Grafana and Freeboard is also available. These tools are excellent for creating simple dashboards at both the beginning and end of data analysis.

2. Collecting the data

Once you have established your goal, you will need to develop a strategy for collecting and aggregating data. This involves determining the data you require. It could be numerical (numeric), data such as sales figures. This could be quantitative (numeric) data such as sales figures or qualitative (descriptive), data such as customer reviews. All data can be classified into one of the following three categories: first-party or second-party data, or third-party data. Let’s look at each.

What is first-party data?

First-party data is data you or your company have collected directly from customers. It could be transactional tracking data or information from your company’s customer relationship management system (CRM). First-party data, regardless of its source, is typically structured and organized in clear, defined ways. Customer satisfaction surveys, focus groups, and interviews are all possible sources of first-party data.

What is second-party data?

You might be able to enrich your analysis by obtaining secondary data sources. The second-party data refers to the first-party information of other organizations. These data may be obtained directly from the company, or via a private market. Second-party data has the advantage of being structured. Although they may not be as relevant as first-party data they tend to be more reliable and trustworthy. Website, app, and social media activity are all examples of second-party information. These include online purchase histories or shipping data.

What are third-party data?

Data that is third-party data has been gathered from multiple sources and compiled by a third-party organization. Third-party data often contains large amounts of unstructured data points (big data), but this is not always the case. Many companies collect big data in order to create market research reports and industry reports. Gartner, a research and advisory firm, is an example of a real-world organization that gathers large amounts of data and then sells it to other companies. Open data repositories, government portals, and other data sources can also be used to obtain third-party information.

Data collection tools

Once you have created a data strategy (i.e. Once you have a data strategy (i.e., a plan for how you will collect the data), there are many tools that you can use. A data management platform (DMP) is something you will need regardless of your industry or expertise.

A DMP allows you to find and combine data from multiple sources before segmenting, manipulating, and so forth. There are many DMPs. There are many DMPs for enterprise, including Salesforce, SAS, and the data integration platform Xplenty. You can also play with open-source platforms such as Pimcore and D.Swarm.

Also read: Data Conversion vs. Data Migration – What’s the Difference

3. Clean the data

After you have collected your data, it is time to prepare it for analysis. This involves cleaning or scrubbing it. It is essential to ensure that you are working with high-quality data. The following are key data cleaning tasks:

• Removing major errors and duplicates — all of which are common problems when aggregating data across multiple sources.
• Remove unwanted data points–extracting non-relevant observations that are not relevant to your analysis.
• Adding structure to your data — is a general ‘housekeeping’ task, i.e. Fixing typos and layout problems will make it easier to map and manipulate your data.
• Filling major gaps– As you clean up, you may notice that some important data is missing. After identifying gaps, you can begin filling them.

An expert data analyst will spend between 70-90% of their time cleaning data. This may sound excessive. This may sound excessive. However, focusing on the wrong data points or analyzing incorrect data will have a severe impact on your results. You might be sent back to square one.

Carrying out an exploratory analysis

An exploratory analysis is another thing that data analysts often do, along with cleaning data. This allows you to identify potential trends and characteristics and refine your hypothesis. Let’s take our hypothetical learning company as an example. Perhaps you find a correlation between the amount TopNotch Learning clients pay and the speed at which they switch suppliers. This could suggest that customer service quality (as assumed in your initial hypothesis), is less important than cost. This might be something you should consider.

Tools to help you clean your data

It can be difficult to clean large datasets manually, especially if they are complex. There are many tools that can help you streamline this process. Open-source tools like OpenRefine are great for data cleaning and high-level exploration. For very large datasets, however, the functionality of free tools is limited.

Python libraries (e.g. R packages and Python libraries (e.g. The languages will need to be known. Enterprise tools are also available. Data Ladder is an example of one of the most highly-rated data-matching tools. There are many others. You can also find many other data cleaning tools.

Also read: Top 20 Data Analytics Tools Used By Experts

4. Analyzing the data

Now you have cleaned up your data. The fun part is now–analyzing it! The goal of your data analysis will determine the type of data analysis that you do. There are many options. Analysis of univariate and bivariate data time-series analysis and regression analysis are two examples you may have heard of. The way you use them is more important than their types. It all depends on the insights you are trying to gain. All types of data analysis fall into one of these four categories.

Descriptive analysis

A descriptive analysis determines what has happened. This is the first step companies take before moving on to deeper explorations. Let’s take, for example, our fictional learning provider. TopNotch Learning may use descriptive analytics to determine the completion rates of their customers’ courses.

They might also track how many people access their products in a given time period. They might use it to determine sales figures for the past five years. Although the company may not be able to draw any firm conclusions, it can summarize and describe the data to help determine the best way to move forward.

Diagnostic analysis

Diagnostic analytics is about understanding why something happened. It’s the actual diagnosis of a problem. A doctor will use symptoms to diagnose a condition. What is TopNotch Learning’s business problem, you ask? What factors are negatively impacting customer experience? A diagnostic analysis could help answer that question.

It could be used to help companies draw connections between the problem (failing to win repeat business) as well as factors that may be contributing to it (e.g. Project costs, delivery speed, customer sector, etc. Let’s say TopNotch discovers that its retail clients are leaving at a faster pace than the rest of its clients by using diagnostic analytics. This could indicate that their clients are losing customers due to a lack of expertise in the sector. This is a valuable insight!

Predictive analysis

Predictive analytics allows you to identify future trends using historical data. Predictive analysis is used in business to predict future growth. It doesn’t end there. In recent years, predictive analysis has become more sophisticated. Machine learning has advanced rapidly, allowing organizations to produce remarkably accurate forecasts. Consider the insurance industry.

Insurance companies often use historical data to predict which customer segments are most likely to be involved in accidents. They will increase customer insurance premiums for these groups. The retail industry also uses transaction data to predict future trends or determine seasonal buying patterns to help them plan their operations. These are just a few examples of the potential for predictive analysis, but there is much more.

Prescriptive analysis

Prescriptive analytics allows you to make future recommendations. This step is also the most difficult. Because it includes aspects of all the analyses we have already described, this is also why it is the most complex. Prescriptive analytics can be seen in the algorithms that Google uses to guide its self-driving cars.

These algorithms make thousands of decisions every second based on past data and present data. This ensures a smooth and safe ride. Companies can also use predictive analytics to help them decide which products or business areas they want to invest in.

5. Sharing your results

Your analyses are complete. Your insights are now complete. The last step in the data analytics process involves sharing these insights with the world or at least your organization’s stakeholders! It’s more than sharing the results of your work.

This involves interpretation and presentation in a way that is understandable for all audiences. It is important to present clear and unambiguous information to decision-makers because you will be presenting it often. Data analysts often use dashboards, reports, and interactive visualizations to back up their findings.

The direction of a company will often be influenced by how you interpret and present results. Your organization may decide to restructure or launch a high-risk product or close a division based on the information you provide. It is important that you provide all evidence and not just a few.

It will show that your conclusions are scientifically sound, based on facts, if you present everything clearly and concisely. It’s also important to point out any inconsistencies or open questions. Honest communication is key to the success of any project. Honest communication will benefit the business and help you excel in your job.

Tools to interpret and share your findings

There are many data visualization tools that can be used to visualize data. They are available for different levels of experience. Google Charts and Tableau are some of the most popular tools that require little to no programming skills.

There are many packages and libraries for data visualization if you are familiar with R and Python. You can check out the Seaborn and Matplotlib Python libraries. No matter what data visualization tool you choose, you should also improve your presentation skills. Visualization is great but communication is the key!

Also read: Top 10 Biggest Data Center Companies in World

6. Embrace your failures

Accepting your mistakes is the last step in the data analytics process. This is a more iterative than a one-way process. Data analytics is messy by nature, so the process that you use will vary for each project. You might find patterns in your data that lead to new questions while you are cleaning it.

This could lead you to go back to step 1 (to redefine your goal). An exploratory analysis could also reveal data points that you hadn’t considered before. Perhaps you discover that your core analyses have produced misleading or incorrect results. This could be due to errors in the data or human error at an earlier stage of the process.

These pitfalls may seem like failures but don’t let them discourage you. Data analysis is chaotic by nature, so mistakes are bound to happen. It is important to improve your ability to spot and correct errors. It might seem simpler to do data analytics, but it wouldn’t be nearly as fascinating. As a guideline, keep your mind open and creative. To keep you on track if you get lost, you can always refer to the process.

Conclusion

This post will cover the key steps in data analytics. These core steps are flexible enough to be re-ordered or re-used however they form the foundation of every data analyst’s work.

• Define your question — What business problem are we trying to solve? To help you find a clear answer, frame it as a question.
• Collect data — Create a strategy to collect data. What data sources will you use to solve your business problem most effectively?
• Clean up the data — Explore and scrub your data –Tie, organize, de-dupe, and arrange your data as necessary. Do what you need to! Take your time and don’t rush!
• Analyze data — Create insights by performing various analyses. You should focus on the following types of data analysis: predictive, predictive, descriptive, and prescriptive.
• Share your results –– How can you best share your thoughts and suggestions? Communication and visualization tools are key.

Learn from your mistakes Take the lessons learned. This is how you can transform a good data analyst into a great one.

Next, what? We encourage you to continue exploring the topic. Explore the steps involved in data analysis and discover the tools that you can use. You can design a customized technique that works for your needs as long as you adhere to the core principles described.