Data analytics and data science

Definition of similarities and differences

In a business context in particular, the process of digitization has established numerous terms relating to data analysis. On closer inspection, however, it is not always as easy to name their concrete meaning and delimitation as it seems at first glance.

Analytics engineering, big data, data science or data analytics? Many new terms require a lot of explanation. We will shed some light on this and explain the differences and similarities of a common pair of terms: data analytics and data science. With the emergence of new industries and new industrial sectors, there is a growing need to communicate processes, occupational fields and technologies with appropriate terms.

Understanding data science and data analytics as individual, independent areas is already the first misunderstanding that should be dispelled. This misconception is based on the fact that the term “data analysis” is used in German as a generalizing supercategory for the general investigation of data. However, data science is specifically a part of data analytics. And of course, in both areas, data is interviewed for correlations, causalities, patterns and insights 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. He visualizes, analyses and examines them for patterns, errors and special features. This is almost always about historical data. Which websites visited how many unique users during which period? Which products were bought by which demographic groups and when? In which time period were the most sensor values measured? Comprehensive statistics can be obtained from this data and visualizations can be created, for example to depict dependencies and relationships.

Data analysts often have excellent knowledge of mathematical statistics. Key areas of expertise and tools include databases and their management, SQL as part of them, and statistical programming languages such as R and SAS. In addition, there is profound expertise in handling large amounts of data, which is required for analyses of big data projects in order to understand and communicate data. It is a very application-oriented area 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 Data Science sub-discipline is more concerned with the scientific principles of pattern recognition and classification. Often, the underlying database is still indifferent and anything but well-defined. Data sets from various areas of investigation are included in the statistical evaluation. Data scientists use regression analyses and classification methods to make predictions for the future. These forecasts are usually 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 data processing on a large scale and the data scientist will strive to automate as much of it as possible so that he can concentrate on his results.

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 in particular is of great importance in the area of data science.

Congruences between data analytics and data science

The work areas of data analytics and data science often overlap. For both, the development of data sources, consolidation and cleansing, and integration with tools is essential in order to be able to work validly with the data sets. Like the data analyst, the data scientist uses visualization methods to represent statistical assumptions, for example. Both subject areas require comprehensive knowledge of the subject areas investigated in order to also identify recognizable connections. Both the data analyst and the data scientist will therefore look at the basics of the respective work area in order to get a better understanding of what the data says. Insofar as, of course, is also how they are to be interpreted. It is only through this expertise that the statements derived from the data can be correctly classified.

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 of 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. On the other hand, how reliable the results are and what they mean for decision makers is very important.

Balancing technology and consulting

Finding a 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 focusing on the technical focus. These tasks are often carried out by data analysts who provide reports and reports in direct contact with managers and customers, for example. The optimal team set-up in data analysis projects therefore combines data analysts and data scientists to set up customer projects in a goal-oriented manner, to ensure a valid analysis and to ensure successful customer communication.

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