Smart Data Management

An edge through knowledge

Data are the raw material of the future because they contain valuable information which is not necessarily obvious at first glance. Due to the large volume of product, customers, and other (often unstructured) data, the underlying patterns easily get drowned out in the sheer mass. However, the right analysis mechanisms make it possible to detect them and use them in a targeted manner in order to achieve a decisive competitive advantage. This is exactly what Big Data is about.

Looking for the needle in the haystack

In many ways, Big Data projects look like the famous search for the needle in the haystack. Only that it is not always clear what the needle even looks like. Not to speak of the haystack. Would you know off the top of your head what information is hidden in all of your data? Where you could start the search for it and what analytical methods you could use? These are precisely the questions that must be answered when starting a Big Data project - and this also pays off for you!



Your benefits

Together with you we determine what information could be important to you and where to find it. Our experts have the necessary know-how: in addition to many years of industry experience, we also bring analytical skill and experience in cooperation with a leading research institute in terms of Big Data to the table.

We can find your "needle" – and you can use the edge this gives you for your Company.

  • Big Data project start
  • Big Data project information
  • Big Data methods and models

How do I start a Big Data project? Current studies show: the majority of companies considers a Big Data project to be possible in the future. However, many companies hesitate in starting a specific project. The reason for the hesitation is assumed implementation barriers and obstacles which many expect. We help you in four steps to set up your Big Data project. more on this less

Step 1: Workshop – what can Big Data do and what not?
We will hold an initial workshop with your employees to explain the basics of Big Data. We develop the first scenarios from your environment and thus create the foundation for a successful Big Data Project.

Step 2: Analysis of data and flows of Information
We look through and analyze your data inventory for potential patterns and select the necessary data sources. We verify whether external data sources need to be used. In this process, we particularly pay attention to data protection concerns.

Step 3: Proof of concept
As a proof of concept we create a model of a specific scenario and thus show how Big Data can create added value - and thus competitive advantages. The implementation barriers and obstacles have thus been removed. You now have all information in order to make a serious decision of what your Big Data project will look like. 

Step 4: Implementation
Based on a suitable infrastructure, we implement your Big Data project. You employees will be further trained during the project in order to be able to recognize new scenarios in the future and gain valuable information from data independently.

These four steps enable you to use the Big Data concept in your Company.

What data are relevant for my Big Data project? Data sources with valuable information A large variety of data is available in every company. This data often contains valuable information in the shape of patterns which exist but are not immediately visible and recognizable. In particular a combination with "public data" e.g. from forums, networks, communities, or other sources can result in combinations and patterns which provide valuable new information. more on this less

Relevant data sources could be:

  • Data bases
    Classical data bases contain so-called structured data of the company, e.g. data concerning products, customers, or transactions.
  • Time sequence data
    The variation of values over time. This can be helpful in creating prognoses for the future.
  • Images and unstructured text
    Faxes, scanned documents, reports from news agencies,
    letters, e-mails, press releases, support requests etc.
  • Machine data
    Many machines – e.g. cars, devices, sensors, and computer systems in various areas of deployment – generate data which may contain patterns.
  • Social networks
    Social networks but also forums, boards, communities, or similar media contain data which – analyzed correctly – may provide valuable connections and thus valuable information.
  • The internet in its widest sense
    In general, all data the internet can deliver may contain valuable information, such as geological data, weather data.

What analytical methods and models are there? Methods and models for pattern recognition Data mining turns your data into usable information. We help you select suitable methods. more on this less

  • Finding striking patterns
    Clustering methods search through data inventories and group similar entries into so-called clusters. They find patterns and connections from which data scientists can quickly gain valuable insights.
  • Recognizing connections
    Graph-mining methods analyze relationships between data. This helps find patterns which remain hidden when looking at individual elements in isolation.
  • Making predictions
    Predictive methods make predictions based on input data. They provide support for routine tasks by making sensible suggestions and indicate opportunities and risks. Automatic learning improves the models on a continuous basis.
  • Plausibility
    It must be possible to make predictions and analyses that were made plausible. Corresponding methods and visualization tools make the used models transparent.

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