Data Science

Extract hidden, valuable information from Big Data with Data Science to make data-driven decisions in the future.
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What is Data Science?

Data Science has for some time been considered the supreme discipline in the discovery of valuable information in larger data sets. It promises to extract hidden, valuable information from data of any structure – i.e. not only numerical values such as measured values and key figures, which are often referred to as "structured", but also texts, images, videos and even sounds ("unstructured data"). 

  • Hidden, because this information is very difficult/long to reveal or, due to the limited capacity of the human brain, cannot be revealed by just looking at it.
  • Valuable, because information may be hidden, but knowledge of it could add value or lead to action to achieve a desired effect.

Learn all about the benefits of using it, the different disciplines that make up Data Science, and the factors that play a role. Together we will find the optimal way to find valuable information in your data with Data Science. We will be happy to advise you.


Recognize data potential

Every company has vast amounts of data. Through Data Science and the respective processes, the potential of insights from this data becomes visible

Data-Driven Enterprise

Linking enterprise data, automated analytical processing, supported by Artificial Intelligence, enables the "data-driven enterprise"

Reliability & Transparency

In every Data Science project, data quality and data origin become visible – the prerequisite for comprehensible, transparent decisions

New business models

The consistent use of all the company's data not only enables existing processes to be improved, but also creates the basis for complete new business models

Disciplines of Data Science

Here you learn about the different disciplines according to which the necessary data was extracted from the previous systems and prepared for the analytical use case:

 

Artificial Intelligence

Today, the term "Artificial Intelligence" (AI) is often used as an umbrella term for systems that emulate or simulate human thinking. Technologies such as Machine Learning (ML) or Deep Learning with special algorithms play a special role here.

In the context of Data Science, AI is often mentioned when decision support systems are developed for specific use cases. As can be seen on the left chart, Data Science does not exclusively cover the creation of AI, but rather the combination of AI, computer science and expertise. Informatics includes, among other things, obtaining the data, and putting it with the necessary expertise ("domain knowledge") into a format necessary for the AI.

Machine Learning

In Machine Learning, "experiences", i.e. already known results, are processed in a structured manner and a system learns the relationships between input and output variables. Using a test data set with likewise known results, the learning result (= the recognized mathematical model) is checked and, if necessary, sharpened. Subsequently, the model can be applied to unknown data and predict a result with a certain quality.

Deep Learning

Deep Learning is a sub-discipline of Machine Learning in which neural networks are used. In most cases, large amounts of data are processed without human intervention during the actual learning process (see also Supervised vs. Unsupervised Learning). Neural networks imitate the functioning of the human brain: they make decisions, question them and, if necessary, learn again. Large neural networks require enormous computing power, which is often provided by GPUs because they are internally capable of performing matrix calculations very quickly. Deep Learning is often used for automatic image or speech recognition.

Operationalization of Data Science (MLOps)

Data and Artificial Intelligence (AI) can support almost any business process based on facts. Many companies are in the middle of a phase of professional assessment of the algorithms and technical testing of the corresponding technologies. MLOps describes the integration into the business process to exploit the full added value of each algorithm.

More on MLOps

Factors that play a role in Data Science:

  • Stock of data

    The amount of data available has grown enormously. In production, sensors send thousands of measurements per second; in logistics, goods can be tracked by GPS; and when surfing the web, potential buyers consciously or unconsciously leave traces that can be used to draw conclusions about their shopping behavior. 

  • Availability of powerful computing capacity

    It has never been easier or cheaper to process the data supply with mathematical methods. Performance on demand (including in the Cloud) allows capacities to be increased even at short notice, so that in total many use cases become economical more quickly. In addition, there are new parallel computer architectures (including GPUs) that can recognize unexpected combinations and patterns through native processing of mathematical models. 

  • New mathematical methods

    New versions of well-known methods (see parallel processing and GPU), new methods that are rapidly being shared worldwide due to the prevailing "sharing economy", or Artificial Intelligence or Machine Learning methods, make it much easier to model and solve solutions today. 

  • Quality and traceability of the data

    And despite or because of the outstanding possibilities, it is also true for Data Science that the preparation of data from different sources is time-consuming and error-prone. At the same time, the requirements for quality and traceability of the data are increasing in order to substantiate findings or to be able to justify them retrospectively. 


Our services in Data Science:

The combination of requirements and challenges results in a decision matrix for the use of Data Science, AI or ML in the company. Together, we find the right way to make optimal use of the information.

Expertise

With our experts we cover all necessary qualifications for successful Data Science projects. No matter if the connection of data sources, the preparation, assessment, modeling, quantification or operationalization.

Workshops

Together, we identify Data Science potential and discuss the approaches that are valuable for your company and their feasibility. We then determine the best technology for this, in order to then use the insights gained for your business processes.

The right vendor for every project

Our experts rely on various Open Source tools such as R, Python, Jupyter, but also on commercial tools and solutions from IBM and Microsoft for the implementation of Data Science projects.

IBM

IBM offers its customers a comprehensive portfolio of solutions and services under the "Watson" brand. IBM differentiates between solutions that comprehensively support the development and operation of AI solutions, predefined AI applications for the analysis of large data volumes, AI APIs for embedding in applications, and ready-made, AI-supported industry solutions. With the "Cloud Pak for Data" IBM provides a technical platform for the operation of the above solutions, supplemented by data integration, data governance, databases and analysis tools.

Microsoft

Microsoft has invested heavily in AI over the past few years, especially in its Azure platform, and offers a robust, comprehensive framework for developing AI solutions in many areas. Ready-to-use services, dedicated infrastructure and tools provide extensive functionality and massively facilitate the deployment of AI applications.

Our Success Stories:

Contact us now!

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Marc Bastien
TIMETOACT Software & Consulting GmbHcontactpersonhelper.linkProfile.title