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Introduction to Data Analysis: Overview and Methods

If data is the oil of the 21st century, information is the new gold. Digitalization is not only changing our everyday lives but is digitally transforming our professional lives. As a result, the volume of data worldwide is growing at an unprecedented pace. More and more of our work processes are taking place digitally and leaving their traces in our systems, servers and hard disks. By 2025, the global volume of data will increase to 163 zettabytes. For comparison: If one gigabyte weighed one kilogram, the daily volume of data would be about 350 times the weight of the Eiffel Tower. And to store our own daily data volume, humans would need more than 50 brains.

But data alone is not usually very useful. Your data first has to be processed into information and into knowledge afterwards by applying methods such as data analysisThis allows you to draw profitable conclusions from the knowledge that will help you to move forward. We will explain to you what data analysis is and how you can use it to pave your way into a digital future.

What is data analysis?

In the age of digitalization, companies produce huge amounts of data every day. Whether it’s 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 be competitive.

Data analysis is a processYou use existing data to gain information and insights from it. You use various statistical methods and procedures to identify trends or patterns, for example.

In order to gain real added value from your analysis, you present the insights you have gained with visualizationsHow does that benefit you? By using data analysis, you and your company can better understand your customers and offer personalized products and services, for example. But you can also optimize internal processes by analyzing data and derive strategic decisions. 

Figure 1: Data wealth: available data volumes grow exponentially

Despite increasing digitalization, many decision-making processes are prone to errors, manipulation or distortion. Using data analysis opens up enormous opportunities to make objective and data-driven decisions. People are susceptible to cognitive biases and are unconsciously guided by irrational effects when making decisions. When making purchasing decisions, we are quick to just follow other people and choose products that many others have bought. In other cases, we tend to make decisions again that were successful in other contexts without reassessing the situation. Analyzing data helps you to remain objective and avoid misjudgments. The basis for your decisions is the knowledge you have gained, which you make available in the form of facts and figures in a report or dashboard solution. 

Data analysis steps: How do you get from data to insights?

Data analysis takes place in several steps:

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

The process of data analysis starts with a problem you need to solve. Starting with the problem, you have to derive a question which describes it exactly. Make it clear what added value the question’s solution provides in the context of the problem. If you formulate the question precisely, you can save a lot of work in the further analysis steps

In the next step, you deal with data gathering. For a data analysis you need raw data, which you either already have stored somewhere, or you need to collect. Companies usually have large amounts of data which can be made available for your analysis. There is great potential to derive valuable or new insights, especially when data from different business areas are combined. 

Once the data is available for your analysis, you have to prepare it in the data cleaning step. The multitude of data sources leads to a variety of structures and formats. This could be text, image or sensor data - you have to bring all the data points into a uniform processing structure. Missing data is added, and incorrect data is removed. True to the motto "Garbage In, Garbage Out" this step forms the basis for the quality of your analysis results.  Don't underestimate data cleaning: data experts spend around 80 percent of their time cleaning and preparing the data.

Once your data has been cleaned up, the next step is the actual data analysis. Depending on the type of question, you typically filter, group and aggregate the data or describe it using statistical indicators such as the mean, variance or standard error.

For example, descriptive data analysis describes data from the past and provides an answer to the question "What happened?. However, to uncover causes and relationships, you need to compare historical data. A diagnostic data analysis can answer the question "Why did something happen? On the other hand, if you need to answer the question "What will happen?"predictive data analysis using machine learning and artificial intelligence can predict future trends.

However, the information you collect and the insights you gain from it can only help you and your company if you use and apply it. Therefore, the final step of the data analysis process is the challenge of communicating your results successfully and in a way that is appropriate for your target audience. In other words: data storytelling.

So, the focus of the presentation is on the question you answered and the problem you solved. Your storytelling is always aimed at the target audience. Take the knowledge and expertise of the target audience and the thought patterns they react to into account. A convincing presentation should therefore include vivid visualizations. Depending on the problem and the target group, different kinds of visualizations such as bar or line charts are suitable.

Figure 2: Data Storytelling: Communicating results convincingly

Would you like to uncover hidden treasures in your company’s data volumes and gain a wealth of information and insights? Feel free to contact us here. We want to help you become a data analysis professional and successfully equip you and your company for the data-driven age.

Rebecca Marzahn

Rebecca Marzahn

Rebecca gehört bei StackFuel fast schon zum alten Eisen. Mehr als 2 Jahre ist sie jetzt an Bord und Assistentin für Marketing und Vertrieb. Wenn Sie nicht gerade Beiträge für Social Media oder den Blog schreibt, jongliert sie geschickt zwischen den beiden Abteilungen. Als echte Powerfrau ist sie nebenbei noch in den letzten Zügen ihres Masterstudiums. In ihrer Freizeit widmet sich Rebecca leidenschaftlich dem Hundesport und nimmt mit ihren beiden Hunden an Wettkämpfen teil.

Introduction to Data Analysis: Overview and Methods

The amount of data is increasing at an enormous rate. But data alone is of no use; it first has to be processed into information. We will explain to you what data analysis is and how you can use it to pave your way into a digital future.

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