A data analyst will retrieve and gather data, organize it and use it to reach meaningful conclusions. But what does a data analyst actually do?
Companies and organizations are attaching more and more value to data. Virtually every company that matters in the 21st century takes data seriously. It 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 business. If a company or organization does not invest in data, there is a good chance that they will lag behind the competitors that do collect data.
You probably know the saying: ' knowledge is power '. There is a kernel of truth in this. Just think of some of the most powerful companies or organizations like 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, she doesn't just fly by. 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 because they allow 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 to 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 deal with different phases of data analysis, where they process data (data) and convert it into useful information. Analyzing data is not the only thing data analysts are concerned with. This is only one part of the entire data analysis. Data analysis encompasses 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 deal with the different iterative phases of data analysis. We are now going to tell you what each phase stands for and what work is involved.
Every data analysis is performed with a specific goal in mind. The first step is to think about what you want to achieve. It is then mapped out what needs to be done and finally it is determined which data is needed.
If the effort does not contribute to achieving the objectives that the company has in mind, then the entire data analysis is of little use. So always ask yourself the following before you start collecting data: 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, you cannot make analyzes without good data. That is why as a data analyst you will have to set up a data infrastructure.
The data you collect can come in different forms and come from multiple sources. With the different shapes, think not only of numbers and texts, but also, for example, photos, videos and audio fragments. The sources from which the data comes can also be very diverse. Think, for example, of physiological measurements, advice panels, eye tracking, research or sales figures.
Imagine having to manually collect all the available data, which is obviously impossible. That is why it is essential that as a data analyst you can automate certain routines of the data collection. This makes data collection perhaps one of the most technical tasks of the data analyst.
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 with which relevant data can be automatically retrieved from external websites. It is an indispensable skill because it allows data analysts to work faster, more efficiently and less prone to errors.
Another indispensable skill that is separate from automation, but is no less important for data collection, is mastering the Structured Query Language (SQL) programming language. This language is used to retrieve data from databases.
When all raw data has been collected by the data analyst, he or she is far from ready to analyze the data. A lot will have to be cleaned up first. Data is never immediately suitable for analysis. There is almost always incorrect or missing data. Consider, 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 worthless. The further you are in the analysis process, the more difficult it becomes to fix the errors. It can also lead to 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, keep the following in mind:
"More data beats clever algorithms but better data beats more data" - Peter
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 analyzes of a certain type.
are 6 types of types of data analyzes,
The data analyst uses tools such as Python, Tableau, Google Sheets and Excel to perform these analyses.
After the data analyst has analyzed the data he or she will interpret the results. When interpreting your analysis, keep in mind that you cannot always validate your hypothesis.
When interpreting data, the data analyst always asks himself the following important questions:
If the interpretation of the data holds up under all these questions and considerations, you have 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 last step of the data analysis. Analyzing the data is not an end in itself, but a means. The data analyst always does his best to create beautiful visualizations so that he or she can convey the results of the research to all stakeholders in the best possible way. Because if the stakeholders do not understand anything, they will not be convinced to do anything with the results of the research. And of course this would be a waste of all the time that was put into it.
Most data analysts use Python programs like: Matplotlib and Sea Born to create powerful visualizations. With these data visualization programs you can easily see trends and patterns and create the most beautiful visualizations.