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Companies and organizations attach more and more value to data. Almost every company that matters in the 21st century takes data seriously. This does not matter what industry the company is in or what products or services the company offers. By collecting data, you are able to improve almost every aspect of the company. If a company or organization does not invest in data, chances are that they will lag behind competitors that do collect data.
You probably know the saying: 'knowledge is power'. There is some truth to this. Just think of some of the most powerful companies or organizations such as Google, Facebook, Amazon, or the government. All these companies and organizations have so much power because of their knowledge. How do they get this knowledge? Of course, that won't just come to her. They have so much knowledge because they collect a lot of data and then use it to their advantage. Data is worth billions to companies like Facebook and Google. For example, Facebook and Google earn money with personal data by allowing companies to advertise specifically based on this data.
Within the government, data is not only used to analyze cyber threats, but also, for example, to monitor the water level and therefore know when dikes need to be reinforced.
Data analysts ensure that companies have access to the data they find so important. This is why so many companies are currently looking for data analysts.
Data analysts deal with data on a daily basis. Every day, they are busy with different phases of data analysis, where they process data (data) and convert it into useful information. Analyzing data isn't the only thing data analysts are concerned with. This is just one part of the entire data analysis. This is because data analysis includes the entire process of extracting insights from data to make better business decisions.
The data analysis process usually consists of the following six iterative phases:
As you have read, data analysts are involved in the various iterative phases of data analysis. We are now going to tell you what each phase stands for and what activities go with it.
Each data analysis is carried out with a specific goal in mind. As a first step, we consider what you want to achieve. Next, it is identified what needs to be done for it and finally, it is determined what data is needed.
If the effort does not contribute to achieving the goals that the company has in mind, then the entire data analysis is pointless. So, before you start collecting data, always ask yourself the following: what are the motives for starting the research and what does the organization want to achieve with it in both the long and short term?
Once the purpose has been determined, data can be collected. After all, without good data, you can't make analyses. That is why, as a data analyst, you will have to set up a data infrastructure.
The data you collect can occur in various forms and come from multiple sources. When it comes to the different forms, think not only of numbers and texts, but also, for example, of photos, videos and audio fragments. The sources where the data comes from can also be very diverse. Examples include physiological measurements, advice panels, eye tracking, research or sales figures.
Imagine having to collect all available data manually, that is of course not possible. That is why it is essential that, as a data analyst, you can automate certain data collection routines. This makes data collection perhaps one of the data analyst's most technical activities.
Routine tasks are easy to automate with a programming language. One of the most popular languages in the field is Python. Take web scraping, for example. This is one of the most important skills of data analysts. It is a technique that can automatically extract relevant data from external websites. It is an indispensable skill because it allows data analysts to work faster, more efficiently and less error-prone.
Another indispensable skill that is separate from automation but no less important for data collection is mastering the Structured Query Language (SQL) programming language. This language is used to retrieve data from databases.
Once all raw data has been collected by the data analyst, he or she is far from ready to analyze the data. First, a lot will have to be cleaned up. After all, data is never immediately suitable for analysis. There is almost always incorrect or missing data. For example, data that has been entered twice.
Cleaning data is a very important step that can often take up to half the time of data analysts. A data analysis based on incorrect data is more than useless. The further you are in the analysis process, the harder it becomes to fix the errors. In addition, it can lead to making wrong decisions and errors in process execution. And this is, of course, with all its consequences.
As a data analyst, you always strive for the most optimal data quality. To do this, take into account the following:
“more data beats clever algorithms but better data beats more data” - Peter Norvig
Once you have found the right data to solve the problem and the data is completely clean, the data analyst can start analyzing the data. In this phase, the data analyst performs various analyses of a certain type.
There are 6 types of data analysis
The data analyst uses tools such as Python, Tableau, Google Sheets and Excel to perform these analyses.
Would you like to learn more about the different types of data analyses? Then download this white paper. Here we tell you in detail what the differences between these analyses are.
After the data analyst has analyzed the data, he or she will interpret the results. If you're going to interpret your analysis, keep in mind that you can't always confirm your hypothesis.
When the data analyst interprets data, he/she always asks himself the following important questions:
If the interpretation of the data holds up under all these questions and considerations, you've most likely come to a good conclusion. Being able to draw the right conclusions after all the hard work will create a euphoric feeling. Almost every data analyst recognizes this feeling.
Presenting the data is the final step of data analysis. Analyzing the data is not an end in itself but a means. The data analyst always does his utmost to make beautiful visualizations so that he or she can best convey the results of the research to all stakeholders. Because if stakeholders don't understand anything, they won't be convinced to do anything with the results of the research. And of course, this would be a waste of all the time that went into it.
Most data analysts use Python programs such as Matplotlib and Sea Born to create powerful visualizations. With these data visualization programs, you can easily see trends and patterns and make the most beautiful visualizations.
