Definition of similarities and differences
Numerous terms relating to data analysis have become established as a result of the process of digitalization, particularly in the corporate context. On closer inspection, however, it is not always as easy as it might seem at first glance to define their specific meaning and boundaries.
Analytics engineering, big data, data science or data analytics? Many new terms require a lot of explanation. We shed some light on the subject and explain the differences and similarities between a frequently used pair of terms: data analytics and data science. With the emergence of new sectors and new branches of industry, there is a growing need to explain processes, professional fields and technologies using appropriate terms.
Understanding data science and data analytics as separate, independent areas is the first misunderstanding that needs to be dispelled. This misconception is based on the fact that the term "data analysis" is used in German as a generalizing umbrella category for the general investigation of data. However, data science is specifically a sub-area of data analytics. And of course, in both areas, data is analyzed for correlations, causalities and patterns and the findings that can be derived from them.
What is data analytics and how does a data analyst work?
A data analyst deals with well-defined and therefore dedicated data sets. They visualize and analyse them and examine them for patterns, errors and special features. This almost always involves historical data. Which websites were visited by how many unique users in which period? Which products were bought by which demographic groups and when? In which period were the most sensor values measured? Extensive statistics can be obtained from this data and visualizations can be created, for example to illustrate dependencies and relationships.
Data analysts often have a strong knowledge of mathematical statistics. The most important areas of expertise and tools include databases and their management, SQL as part of this, and statistical programming languages such as R and SAS. In addition, there is in-depth expertise in dealing with large amounts of data, which is required for analyzing big data projects in order to understand data and make it communicable. This is a very application-oriented field of work, which is largely similar to working as a consultant.
What is data science and what does a data scientist do?
In contrast, the sub-discipline of data science is more concerned with the scientific principles of pattern recognition and classification. Here, the underlying database is often still indifferent and anything but well-defined. Data sets from different areas of investigation are included in the statistical analysis. Data scientists use regression analyses and classification methods to make predictions for the future. These predictions are generally not based on analytical methods, but rather on the statistical evaluation of large amounts of data. Data scientists combine scientific principles with experience in development and programming. This is really about large-scale data processing and the data scientist will strive to automate as much of this as possible in order to focus on their findings.
The aim is to draw conclusions for the future from past data. This only makes sense if the data is properly prepared, filtered, structured and understood. Data science projects are implemented on a mathematical basis in the form of algorithms. In addition to various other programming languages, Python is particularly important in the field of data science.
Congruences between data analytics and data science
The fields of data analytics and data science often overlap. For both, the development of data sources, consolidation and cleansing as well as integration into tools is essential in order to be able to work validly with the data sets. Just like the data analyst, the data scientist uses visualization methods to map statistical assumptions, for example. Both specialist areas require comprehensive knowledge of the subject areas under investigation in order to identify recognizable correlations. Both the data analyst and the data scientist will therefore deal with the basics of the respective field of work in order to gain a better understanding of what the data says. And, of course, how to interpret it. This specialist knowledge is the only way to correctly classify the statements derived from the data.
An occasionally overlooked but very important area of work in both areas is communication within the team and with stakeholders. Data science takes place at the interface between technology and management and must communicate with both levels. Very few managers really want to understand what a support vector machine or a neural network is and how exactly it works. How reliable the results are and what they mean for the decision-makers, on the other hand, is very important.
Balance between technology and consulting
Finding the right balance between technical basis and consulting services is often a challenge, especially for data scientists due to their mostly technical background. The results must be presented or published in an argumentative manner without placing the technical focus in the foreground. These tasks are often performed by data analysts, who communicate, for example, reports in direct exchange with managers and customers. The optimal team set-up in data analysis projects therefore combines data analysts and data scientists in order to set up customer projects in a target-oriented manner, ensure a valid analysis and guarantee successful customer communication.
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