Data Analysis: Definition, Overview and Methods

Table of Contents

A valuable ressouce: Data is known to be the oil of the 21st century. But just as oil must be refined before it becomes usable, data also gains its added value only through data analysis. The data information gained from data analysis is what we can work with and draw conclusions from.

Digitization is subjecting our everyday lives and, beyond that, the entire working world to a digital transformation. As a result, the volume of data is growing enormously fast worldwide. More and more of our work processes are running digitally and leaving their traces in the form of data in our systems, servers and hard drives.

According to projections, the global volume of data will increase to 163 zettabytes by 2025. To give you a better idea of this amount, here’s a comparison: The daily amount of data today is roughly equivalent to 350 times the weight of the Eiffel Tower. And to store the amount of data we produce every day, we would need more than 50 brains.

But no one could do anything with data in its raw form. They first have to be processed into information and then into knowledge by applying methods such as data analysis. Only in this way are you able to draw profitable insights from this knowledge that will take you further. We’ll tell you what data analysis is and how you can use it to get off to a vertical start in your digital future.

Definition: What is data analysis?

Data analysis is a process in which existing data is converted into a readable, evaluable form in order to obtain information and ultimately insights from it in the final step. You apply various statistical methods and procedures to identify trends and patterns, for example.

In order to gain real added value from your analysis, you will then use visualizations to present your findings in a clear way. So what’s in it for you? By using data analysis, you and your company can, for example, better understand your customers and offer personalized products and services. But you can also optimize internal processes by analyzing data and derive strategic decisions.

Figure 1: Data wealth: available data volumes grow exponentially

In the age of digitalization, companies produce huge amounts of data every day. Whether customer data in marketing, sensor data in industry or position data in logistics – companies have to extract information from all this available data in order to make the right decisions and remain competitive and improve their products.

Despite increasing digitization, many decision-making processes are prone to error, manipulation or bias. Therefore, the use of data analytics opens up tremendous opportunities for companies to make objective and informed decisions. As humans, we are all susceptible to cognitive biases and can be unconsciously influenced when making decisions.

We also tend to have a preference to re-make choices that have been successful in other contexts without re-evaluating the situation and context. For example, we always order the same thing in a restaurant or prefer to watch a movie we already know and like rather than try something new. In our private lives, this behavior does us no harm. In terms of business decisions, however, it’s a different story.

The analysis of data helps you to remain objective and to avoid misjudgements. The basis for your decisions are the insights you have gained, which you can clearly demonstrate in a report or dashboard in the form of figures and graphics.

Data analysis steps: How do you gain insights from data?

Data analysis process:

  1. Defining the problem
  2. Obtaining data 
  3. Preparing data 
  4. Analyzing data
  5. Communicating the results 

It all starts with a question

What would a win be without a challenge? The process of data analysis begins with a problem that you are asked to solve. As a data analyst, departments approach you and report a problem. This could be, for example, that a certain target group in marketing is less willing to buy. Or that more contracts are being cancelled.

From this problem, you have to derive a question that describes the problem exactly. You first have to find out which data can provide the right insights with regard to the problem. If you formulate the question precisely, this can save you a lot of work in the further course.

How do you gather data?

The next step is data gathering. For a data analysis you obviously need data. This data is available to you as raw data or must first be collected specifically. Companies usually have large amounts of data that can be made accessible for your analysis. Especially when data from different departments are combined, this creates great potential for deriving valuable or new insights.

Why do you need to clean data?

After the data is ready for your data analysis, it must be prepared in the data cleaning process. Your data can come from many different sources. This leads to a variety of structures and formats. Whether text, image or sensor data: You have to bring all data points into a uniform processing structure.

Missing data must also be added here and incorrect data removed. True to the motto “Garbage In, Garbage Out”, this step is the basis for the quality of your analysis results. If the data is not well cleaned, you cannot derive any reliable insights from it. Therefore, this step is often the most time-consuming in the work as a data analyst or data scientist. Data experts spend about 80 percent of their time cleaning and preparing data.

How do you analyze data?

Once your database has been cleaned and prepared, you then move on to the actual analytical part of your work. Depending on the type of question, you filter your data, group and aggregate it, or compare it using statistical ratios such as the mean, variance or standard error. Depending on the question, you adapt your approach.

Data analysis can help you answer the following questions:

  • What happened?
  • Why did something happen?
  • What will happen?

Descriptive data analysis describes data from the past and provides an answer to the question “What happened?

To uncover causes and relationships, you need to compare historical data. Here, a diagnostic data analysis can answer the question “Why did something happen?”.

If you want to answer the question “What will happen?”, predictive data analysis can predict future trends using machine learning and artificial intelligence.

Presenting results in a comprehensible and target group oriented way

Your collected information and the insights gained from it can only ever help you and your business if they are used and applied. Therefore, the last step of the data processing process is to communicate your results successfully and in a way that is appropriate for the target group. The key to this is data storytelling.

The focus of your presentation is on the question you answered and the problem you solved. Your storytelling is therefore always oriented to the target audience. Take into account the knowledge and skills of the audience and how they can best understand your results. Therefore, visualizations are also part of your convincing presentation. Depending on the question and the target group, different types of presentation such as bar charts or line diagrams are suitable.

Are you interested in how a data analysis works? In our article we explain the basics of data analysis and how to successfully extract knowledge from data. Figure 2: Data Storytelling: Communicating results convincingly

Do you want to uncover secret treasures in your company’s data and thus take your company further? Then check out our specialized, hands-on training offerings. You want to do further training, but need help with financing? No problem! As a state-certified training provider, we can offer training free of charge. Find out how that works for you here:

For companies: Apply for the Qualification Opportunity Act easily now with our free webinar.

For employees: Qualification Opportunity Act and education voucher.

For job seekers: Apply for the education voucher now and get free advice

We’d love to help you become a data analytics professional and successfully equip you and your business for the data-driven age.

Sources

Gartner (2022): „Data and analytics support business decisions“ [06.01.2022]

Rebecca Marzahn
Rebecca Marzahn
Rebecca is a StackFuel veteran. She has been on board for more than 2 years now, assisting our marketing and sales department. When she’s not writing social media or blog posts, she skillfully juggles between tasks for the two departments. As the real power woman that she is, she’s also in the final stages of her Master's degree. In her free time, Rebecca has a passion for dog sports and enters contests with her two dogs.

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