Siemens Data Hub & Data Science Services

The newly developed data lake bundles company-wide data centrally and enables extensive analyses.

Merkle Reference Siemens Data Hub Image

At a glance

  1. 01The challenge Company-wide distributed, asynchronous communication data and evaluations
  2. 02Our approach Single source of truth as a smart, integrated data platform that provides the basis for complex performance evaluations in a timely manner
  3. 03The result Efficient, data-driven decisioning in communications through real-time data analytics
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  • Sector

    • Industry
  • Disciplines

    • Data Strategy & Consulting
    • Conception
    • Technical implementation
  • Year

    • 2020 - 2021
  • Tech Stack

    • Custom Application
    • Apache Hadoop
    • Apache Spark (Data Processing)
    • Apache Livy
    • Cloud Services
  • Services

    • Data Platform Engineering
    • Data Science Consulting
    • Data Visualization
    • Data Dashboard

The challenge

Global corporation, decentralized data, distributed systems, different standards

Data-driven and real-time decisions are becoming increasingly important when it comes to using communications budgets efficiently. Due to increasing digitalization, the number of data and sources is growing - keeping an eye on all of them at the same time is becoming more and more complex. In addition, a strong distribution and decentralization of data, as well as data duplications and diversity of data structures make a comprehensive view difficult. As a result, evaluations become costly, complex, erroneous, incomplete, and inefficient.  

The goal was therefore to create a central data platform in which all communication information is globally converged, cleansed, merged, and normalized. Reliable, reconciled, cleansed and available data should make corporate communications more efficient and decisioning more data-based.

Our approach

Central data hub as single source of truth

A central "data hub" integrates all available data sources, enabling comprehensive, data-driven insights and decisions.

Big Data architecture

The Big Data architecture makes it possible to collect, cleanse, process and make available data and data sources in a very flexible, high-performance, and comprehensive manner.

Integrated and flexible

All relevant systems are connected to the Data Hub. The result is a continuous and optimized flow of information. Flexible interfaces, adaptations and ongoing expansion with new sources included.

Close partnership

The Siemens Data Science Team gets a central working tool for data work. Together with Siemens, we created a technical proof-of-concept in the joint lab and then launched the overall solution.

Efficiency for communications and budgets

Thanks to a central data hub, data-based decisions are possible, and the potential of data can be harnessed.

The result

Specific solution for global group

For the development of the Data Hub, no standard solution was used but a Data Pool Solution specifically tailored to the customer's requirements was developed. In this way, it is possible to react quickly to the special requirements of many sources and to better meet the needs of the global group. 

Data pool of communication โ€“ completed, real-time, integrated

Data-driven communication in a new way: The Data Hub creates the basis for processing large volumes of data - and for using machine learning and artificial intelligence. Each data source only needs to be connected once. Data evaluation is more efficient and faster. Thanks to data-based decisions, there is efficiency potential for communication as well as for the associated budgets. 

  • data sources converge in one place - and growing

    10.0+
  • more effective data analysis

    12.0%
  • to create a global dashboard for a brand campaign

    14.0days

More success stories

Do you want to centralize your data and make data-based decisions? We are your partner for everything around data.

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Your contacts at Merkle: Tobias Hollritt for Switzerland, David Haunold for Austria and Benjamin Quitter for Germany