Data science as the key to digital transformation

from | 20 May 2018 | Basics

Companies generate and collect enormous amounts of data. Any company that wants to achieve lasting success in a rapidly changing and accelerating economy must use this data profitably. To start digital transformation and turn data into valuable knowledge, they need data science.

Data science is an extremely diverse field that requires expertise in IT, statistics, mathematics and big data, but also knowledge of business and macroeconomic processes. Accordingly, there are many ways and possibilities to Data Scientist one of the most sought-after professions of the 21st century.

What is Data Science?

Data science stands for data science, whereby it is a Interdisciplinary science for gaining knowledge from data is involved. Here, large amounts of information are extracted from data in order to obtain a statement on the optimal management in the company on this basis. This makes it possible to improve the quality of one's own decisions and to increase efficiency with regard to the already active work processes. 

The approach of data science dates back to 1960, when the term "data science" was used as a synonym for "computer science". It was not until 2001 that the US computer scientist William S. Cleveland turned data science into an independent discipline, on the basis of which new models and scientific methods for the analysis and utilisation of data have been developed. 

In its current phase, data science has been able to develop increasingly. It includes focal points of science-based mathematics as well as modern informatik. Combined with industry-specific expertise, it can be applied to any industry to increase revenue potential and provide greater added value to management. 


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Data Science vs. IT

In this context, the goals of data science differ significantly from conventional IT tasks. Data science projects are at the interface between entrepreneurial data of various kinds and related questions that may concern - concrete and potential - future scenarios, trends or events.

The central goals of Data Science are:

  1. Provide a better basis for business decisions
  2. Control, optimise or automate processes
  3. To achieve competitive advantages
  4. Within the framework of Predictive analytics to make reliable forecasts about future events

In this respect, data science can be used as a synonym for a new way of perceiving understand: Data analyses make it possible to gain new insights in areas that have so far eluded perception. This creates new perspectives for companies to compete in a digital and global economy.

Difference between Big Data and Data Science

In recent years, the attention paid to the issue of the Big Data and data science on the part of research and industry has risen very sharply. In this context, Big Data is a powerful tool that is correspondingly often a Important component of data science solutions is.

First of all, big data is a collective term that, like data sciences, covers many different aspects. Big data can encompass the following sub-areas:

  • The extensive collection and collation of data
  • The secure and mass storage, for example in a data lake
  • The simultaneous, parallel processing of large amounts of data
  • The analysis of data with special methods
  • The meaningful link with entrepreneurial issues

The insights that can be gained from data analyses make it possible to better understand business processes, optimise them, and develop new business models or a comprehensive data strategy. Due to the potential of big data to open up new business sectors, data science as a whole is becoming more and more of an entrepreneurial success factor.

Today, the hype topic of Big Data is often at the centre of the digital transformation. Furthermore, it is important to emphasise that Big Data and Data Science are more than just IT topics.

Areas of activity in Data Science

There are numerous fields of activity within data science. These include, for example, computer scientists, programmers, software development professionals, database experts and many other professionals. The expertise must include mathematics and the computer science known as computer science in almost all fields. Knowledge of the specific industry of the application is also elementary and indispensable for success, depending on the employment. 

In addition to personal requirements such as active problem-solving skills and creativity, a degree is often a prerequisite for working as a Data Scientist. In this regard, many universities of applied sciences and universities have independent Data science degree programmeswhich can be completed with a Bachelor's or Master's degree in the fields of science or engineering. The classic Bachelor's programme usually takes six semesters, the subsequent Master's programme another four semesters. After successfully completing the degree, it is possible to work as a data scientist in numerous industries and acquire the specific know-how. However, the degree programme often lacks practical relevance. For this reason, some companies offer career starters Trainee programmes for Data Science and Trainee programmes for data engineers an. 

Professional and technological requirements

Accordingly, data science projects cannot be understood as purely technological projects, although many aspects of them are data-based. Technical know-how alone is not enough to develop profitable data science solutions. This is one of the main reasons why data science experts are so rare. Without specific technical knowledge of economic processes and the respective industry, it is difficult to develop meaningful questions.

The name Big Data comes from huge amounts of datawhich often have to be processed in the context of data science projects. In some cases, our solutions generate millions of individual measurement values every day, which corresponds to many hundreds of gigabytes of data.

The technical requirements of Big Data are still large today, although the costs have been falling for many years. In order to store and process large amounts of data, large data centres and in some cases many hundreds of processors working in parallel are necessary. As an alternative to storing and processing data on premise, it is now often possible to outsource data in the Cloud an.

In which industries is data science used?

The use of data science is particularly important for larger companies. But more and more medium-sized companies are also using data science solutions. Examples for the application of data science are retail and trade companies, logistics companies and companies in the health sector, Banks, Insurances and industrial enterprises.  

The characteristic features of Data Science

Over the last few years, it has become customary to refer to Big Data in terms of a varying set of V-terms - such as "data". Volume, Variety or Velocity - to define. The exact number of necessary terms of this kind can be debated for a long time. A small hint for insiders: In the end, there must of course be exactly 42. We limit ourselves here to the Five essential, characteristic features of Big Data:

1. the amount of data

As the word suggests, big data is initially a "large" amount of data ("volume"). Since data represents a small section of reality, the following generally applies: the more data available, the more complete the picture of reality we can form with it.

2. the data variety

In most cases, Big Data consists of very different types of data and extremely complex data sets ("Variety") - this makes connections and patterns recognisable. The challenge is therefore often to bring the data into a meaningful relationship with each other.

3. the processing speed

In addition to the amount and variety of data, the fast availability of results is becoming increasingly important. With a corresponding processing speed ("velocity"), which is guaranteed by many hundreds of processors working in parallel, results are sometimes available in real time. If only conventional computers were at work, it would take days or even weeks until the results of analyses were available. The findings would then be largely useless.

4. data must be changeable

Data is sometimes generated extremely quickly - the turbine of a wind power plant or an aircraft monitored by sensors delivers up to 15 terabytes of raw and sensor data per hour. However, the relevance of the information that can be derived from this data deteriorates over time ("variability"). Data must therefore be variable or collected again and again in order to remain relevant.

5. the data visualisation

At the end of the day, data must be interpreted and translated into meaningful concepts for action. An appealing, clear and comprehension-promoting Data visualisation is a key success factor for Big Data projects.

The last example also shows why Big Data is on the Interaction of the various sub-aspects is based on. If, for example, a malfunction is in the offing, which is regularly indicated by increased temperatures of a component, this information is only helpful if the data basis is, on the one hand, as accurate as possible and, on the other hand, can be compared with other, older data sets. For this, a model for recognising and evaluating the data must be available at the same time, and the results must be available in real time if possible.

Deciding which action to take from the result of Data analyses is not made by the data scientist. That is why data must be presented in a form that is understandable to the decision-makers. Only then does a Time and knowledge advantagefrom which there is room for manoeuvre: The operator knows at an early stage of an impending damage and can take countermeasures even before the actual failure.

Business Intelligence vs. Data Science

With the Business data analysis The classical Business Intelligence (BI) has been concerned with this until now. This concentrated on the evaluation of company data with the aim of better understanding procedures and optimising processes. With data science, this concept has been significantly modernised.

At (Advanced) Data Analytics and predictive analytics, it is no longer just about analysing existing data and processes to better understand the past, but about looking into the future. Based on data about customers, portfolios, sales and marketing processes, service, risks, compliance, price development and pricing and from financial accounting, statements can be derived that sustainably improve future-oriented decisions.

The decisive change that Data Science brings in comparison to BI through its focus on the future is Dynamisation. Instead of reactively drawing consequences for the present from looking into the past, the gaze can be directed directly towards future scenarios or events.


Data science enables proactive action and thus becomes a driver for innovation. The change triggered by digitalisation becomes controllable through data science and puts companies in a position to actively shape the future.


Michaela Tiedemann

Michaela Tiedemann has been part of the Alexander Thamm GmbH team since the early start-up days. She has actively shaped the development from a fast-moving, spontaneous start-up to a successful company. With the founding of her own family, a whole new chapter began for Michaela Tiedemann at the same time. Hanging up her job, however, was out of the question for the new mother. Instead, she developed a strategy to reconcile her job as Chief Marketing Officer with her role as a mother.

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