What is Dats Science

Data science as the key to digital transformation

Companies generate and collect enormous amounts of data. Every company that wants to have long-term success in a rapidly changing and accelerating economy must use this data profitably. To start digital transformation and turn data into valuable knowledge, you need data science.

Data science is an extremely diverse field that requires expertise in the areas of IT, statistics, mathematics and big data as well as knowledge of business and macroeconomic processes. The ways and opportunities to become a data scientist - one of the most sought-after professions of the 21st century - are correspondingly diverse.

What is data science?

Data Science stands for data science, which is a interdisciplinary science to extract knowledge from data acts. Here, large amounts of information are obtained from data in order to obtain a statement on optimal management in the company on this basis. This makes it possible to improve the quality of your own decisions and to increase the efficiency with regard to the already active work processes.

The data science approach dates back to 1960, when the term “data science” was used as a synonym for “computer science”. It was not until 2001 that data science became one through the US computer scientist William S. Cleveland independent specialist discipline, on the basis of which new models and scientific methods for the analysis and use of data were developed.

In today's phase, data science has been able to evolve increasingly. It includes focal points of science-based mathematics as well as modern informationk. In conjunction with industry-specific expertise, it can be applied to any industry in order to increase sales potential and provide greater added value in management.

Data Science vs. IT

The goals of data science differ significantly from conventional IT tasks. Data science projects are at the interface between different types of corporate data and related issues that - concrete and potential - can affect future scenarios, trends or events.

The central goals of data science are:

  1. Create a better basis for business decisions
  2. Control, optimize or automate processes
  3. Achieve competitive advantage
  4. To create reliable forecasts about future events as part of predictive analytics

In this respect, data science can be regarded as a synonym fora new way of perception Understand: Data analyzes make it possible to gain new insights into areas that were previously hidden from our perception. This creates new perspectives for companies to assert themselves in the competition of a digital and global economy.

The difference between big data and data science

In the last few years, research and industry have become very much aware of the topic of big data and data science. In this context, big data is a powerful tool that is often used accordingly important component of data science solutions is.

First of all, big data is a collective term that, like data sciences, encompasses diverse aspects. Big data can include the following sub-areas:

  • The extensive collection and collection of data
  • Secure and mass storage, for example in a data lake
  • The simultaneous, parallel processing of large amounts of data
  • The analysis of data using special methods
  • The meaningful connection with business issues

The insights that can be drawn from data analyzes make it possible to better understand business processes, to optimize them, and to develop new business models or a comprehensive data strategy. Due to the potential of big data to open up new branches of business, data science is increasingly becoming an entrepreneurial success factor.

The hype topic big data is often at the center of digital transformation today. In addition, it is important to emphasize that big data and data science are more than just IT topics.

Areas of activity in data science

There are numerous areas of activity within data science. These include, for example, computer scientists, programmers, specialists in the field of software development, database experts and many other specialists. The specialist knowledge must include mathematics and the computer science referred to as computer science in almost all areas. Knowledge of the specific industry of the application is also elementary, depending on the position, and indispensable for success.

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 technical colleges and universities have their own data science courses that can be completed with a bachelor's or master's degree in science or engineering. The classic bachelor's degree usually comprises six semesters, the subsequent master’s degree a further four semesters. After successfully completing your degree, you will be able to work as a data scientist in numerous industries and acquire specific know-how. However, practical relevance is often neglected in the course of study. This is why some companies offer trainee programs for data science and trainee programs for data engineers for young professionals.

Technical 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 few and far between. Without specific technical knowledge too business processes and the respective industry, it is difficult to develop meaningful questions.

The name Big Data comes from huge amounts of datathat often need to be processed in data science projects. With our solutions, there are sometimes millions of individual measured values ​​every day, which corresponds to many hundreds of gigabytes of data.

The technical requirements of big data are still great today, even if the costs for it have been falling for many years. In order to store and process large amounts of data, large data centers and in some cases many hundreds of processors working in parallel are necessary. As an alternative to on-premise storage and processing, data can now be outsourced in the cloud.

In which industries is data science used?

The use of data science is particularly important in larger companies. But more and more medium-sized companies are also using data science solutions. Examples of the application of data science are retail and trading companies, logistics companies, companies in the health sector, banks, insurance companies and industrial companies.

The defining characteristics of data science

Over the past few years it has become common practice to use a varying set of V-terms - such as V.olume, V.ariety or V.elocity - to be defined. The exact number of necessary terms of this kind can be argued for a long time. A small note for insiders: Of course, it has to be exactly 42 in the end. We limit ourselves to that here five essential characteristics of big data:

1. The amount of data

As the word suggests, big data is initially a “large” amount of data (“volume”). Since data represent a small excerpt from reality, the following applies as a rule: the more data are available, the more complete the picture that we can use to form of reality.

2. The data variety

In most cases, big data consists of a wide variety of data types and extremely complex data sets (“variety”) - this makes connections and patterns recognizable. The challenge is therefore often to bring the data into a meaningful relationship with one another.

3. The processing speed

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

4. Data must be changeable

In some cases, data is generated extremely quickly - the turbine of a wind power plant or an airplane, which is monitored by sensors, supplies 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 changeable or have to be collected again and again in order to continue to be relevant.

5. The data visualization

At the end of the day, data has to be interpreted and translated into meaningful action plans. An appealing, clear and comprehensible data visualization is a key success factor for big data projects.

The last example also shows why big data is based on the Interaction of the various sub-aspects is based. If, for example, a malfunction is imminent, which regularly indicates 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 it can be compared with other, older databases. To do this, a model for recognizing and evaluating the data must be available at the same time, and results in real time, if possible.

The decision as to which action follows from the result of data analyzes is not made by the data scientist. That is why data must be presented in a form that is understandable for decision-makers. Only then does aTime and knowledge advantage, from which there is room for maneuver: The operator knows at an early stage of impending damage and can take countermeasures before the actual failure.

Business Intelligence vs. Data Science

With the Analysis of business data So far, classic business intelligence (BI) has been concerned. This concentrated on the evaluation of company data with the aim of better understanding processes and optimizing processes. With data science, this concept is being modernized significantly.

At (Advanced) data analytics and predictive analytics is no longer just about analyzing existing data and processes in order to better understand the past, but about looking to the future. On the basis of data about customers, portfolios, sales and marketing processes, service, risks, compliance, price development and formation and from financial accounting, statements can be derived that sustainably improve future-oriented decisions.

The decisive change that data science brings with it compared to BI through its orientation towards the future isDynamization. Instead of reacting to the past and drawing conclusions for the present, one can look directly at future scenarios or events.

Conclusion

Data science enables proactive action and is precisely because of this a driver for innovation. The change triggered by digitization can be mastered through data science and puts companies in the position to actively shape the future.

Michaela Tiedemann

Michaela Tiedemann has been part of the team at Alexander Thamm GmbH since the start-up days. She has actively shaped the development from a fast-paced, spontaneous startup to a successful company. With the establishment of her own family, a whole new chapter began for Michaela Tiedemann. Quitting the job was out of the question for the new mother. Instead, she devised a strategy for balancing her role as chief marketing officer with her role as a mother.