Data Analytics2019-08-29T12:45:41+01:00
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Data Analytics Services

The use of statistics increases the success of your organization. Success in the form of increasing conversion usually comes very quickly and sustainably. Our Data Analytics Consulting as well as our Big Data Analytics Consulting brings you, depending on the maturity level of your company, fast measurable success.


  We offer Data Science Knowhow in the following industries:

  • Research
  • Automotive
  • Banks
  • Health Care and Pharmacy
  • Insurances
  • Travel

Examples for the use of Data Analytics

Data analytics and data analysis in combination with software development and high-performance database systems are indispensable in today’s progressive companies. Reliable statements are statements with a high probability of occurrence. Such reliable statements are not only a competitive advantage, but also decisive for the business. Below we would like to list a few Data Analytics examples that we have developed and implemented for our enterprise customers.

Data Analytics for Dynamic Pricing

One of our most challenging data analytics projects was the introduction of Dynamic Pricing. The complexities were not only the mathematical weighting factors or the stochastic analysis methods.  The challenges were the high amount of data and data sources and their different qualities. More information about Data Analytics for Dynamic Pricing.

Data Analytics for the analysis of historical data – research project with English elite university

The analysis of historical data for the identification of past sociological and economic changes in entire societies requires clever analytical methods. But especially reliable data sources are required. Information from ages long before the introduction of computers. Interesting questions were, for example, whether religions were necessary for the formation of civilizations. Or whether religions were a hindrance to the development of civilizations. Do religions, beliefs, gods and spirituality prevent the economic development of societies? This Big Data Analytics project provided surprising insights. Here you can find more information about Data Analytics of historical data.

Review data sources for Data Analytics

Data analytics in digital marketing

For some years now, the marketing departments have been constantly overrun by new requirements. The increasing digitalization in marketing brings with it vast amounts of new data sources and data volumes.

Data Analytics in Marketing not only requires experience in the application and development of complex statistical methods and their combination with each other. Data Analytics also requires knowledge and intuition. For example, in the definition or selection of target groups (B2B target groups and B2C target groups). Our data scientists are more than just experienced statisticians; they are also database specialists with expertise in the programming languages R and SQL.

Our Big Data Analysts and Data Scientists can thus create both simple descriptive and complicated stochastic analyses and evaluations for you.

The analysis results are, for example:

  • The marketing channels, campaign types and time points that are particularly valuable for your products or services
  • Alignment of your marketing budget to optimize advertising channels as well as internal and external marketing resources
  • Identification of those information and marketing channels that are successful for real-time campaigns (newsletter or programmatic advertising)
  • Time Series Analyses for your Omnichannel Marketing Strategy
  • Creation of automated reports and reporting systems
  • Target group analyses and creation of target group product marketing channel bundles
  • Digital Transformation of Marketing Processes and Workflows
  • Construction of trend analysis systems
  • Continual improvement and automation of marketing processes
  • Predictions of campaign successes and conversions
  • Cost-benefit analysis of data providers and third-party data sources
Service provider offers Data Scientists for Data Analytics

Trial & Error versus Stochastic

Many companies are trying to use Trial & Error methods to find answers to the above questions. The trial and error method is popular and is one of the simplest heuristic ways to gain insight. But this method costs a lot of time and money. It is also often a reason why many companies are simply outpaced by competitors.

Nowadays successful companies need intelligent statistical methods but they also need machine learning.

Therefore it is important to answer crucial questions:

E.g. questions like how much data with which statistical procedures are to be analyzed, in order to be able to apply results in a reliable way? Statistically speaking, the question here is which methods can determine a minimum sample size. On the other hand, the use of machine learning components requires less statistics, but more data.

The skills in the application of stochastic methods such as Kolmogorow, Laplace or various Bavarian methods, e.g. for the analysis of customer benevolence expectations and the associated combinatorics, have been lost. Also even simple regression analyses, e.g. via Likely-Hood methods. As well as the skills in the application of geometric probabilities (e.g. with infinite customer data) or the handling of ARIMA processes.

Our data scientists provide transparency through meaningful data analysis and help you make the right decisions. Before making a decision, it is important to ask the relevant questions and evaluate the statements.

Increasing digitization in all organizational areas brings with it vast amounts of new data sources and data volumes.

Which data sources should be trusted?

  • What data is important?
  • How do the data have to be related to each other in order to increase their significance or to gain new insights?
  • Which data is relevant when?
  • What information is significant but not relevant?
  • What data is real-time relevant?
  • How long is the stored data of use?
  • How do I calculate the utility value of data? Is it worthwhile to start a Data Analytics project?
Data Analyst makes statistically calculations

Data Analytics – Data origin and information abstractions

The credibility of data sources is just as important to Data Analytics as the way in which data is collected. It is irrelevant whether it is internal company data or third-party data. For company-owned data, the data collection and processing method is generally more transparent than for partners.

We use special stochastic methods, standard applications and in-house created programs for the analysis of data reliability in complex data analytics projects.

Our Data Scientists support you in the data analysis of classical data sets stored in SQL databases as well as in the investigation of big data data rooms (NoSQL or hybrid datarooms). We are able to support you regardless of which Data Analytics programming languages or Data Analytics query languages you use – and of course we can also do this with R and SQL.

We will be happy to help you in all areas related to Data Analytics. Call us or write us an informal message. We look forward to hearing from you.