What is meant by a data platform?

Data platforms are comprehensive end-to-end solutions that are able to collect, store, process and analyse data from various sources. They can manage structured, semi-structured and unstructured data. The main purpose of a data platform is to efficiently collect, organise, analyse and manage data. Essentially, the objective is to efficiently manage data on a large scale and use advanced analysis methods such as artificial intelligence (AI) or machine learning (ML) to gain insights from this data to make data-based decisions.

How do data platforms work?

Data platforms consist of several components that can be divided into layers, each of which has a specific function:

Datenplattform Informatec


  • Data Sources Layer

    The data sources layer includes structured, unstructured and/or semi-structured data from various sources such as ERP or CRM systems, Excel files, text files, images, audio and video streaming sources such as IOT devices or web services.

  • Ingestion Layer

    This is where the data are extracted from the data sources, the data quality is checked and the data are saved in the landing or staging area of the data platform.

    Passende Lösungen:

    • Azure
    • SSIS
    • Microsoft Fabric
    • Qlik
    • Talend Data Integration
    • <span> iVIEW </span> Dataflow
  • Storage Layer

    The data are stored in this layer. The most common storage technologies are relational database management systems (RDBMS), NoSQL (Not Only SQL) databases, in-memory databases, Hadoop, and cloud storage.

    Passende Lösungen:

    • Azure
    • Microsoft Fabric
    • <span> iVIEW </span> Dataflow
  • Processing Layer 

    In the processing layer, the collected data are prepared, cleaned, enriched, transformed and aggregated to make them usable for analytics and business applications. Processing can occur in batches (at a specific time/day) or in real time, depending on the type of data source and data availability requirements.

    Passende Lösungen:

    • Azure
    • SSIS
    • Microsoft Fabric
    • Qlik
    • Talend Data Integration
    • <span> iVIEW </span> Dataflow
  • Analytics Layer

    After processing, the data are analysed using analytics software and methods to gain insights from the data. This can include the use of modern analytics tools, data visualisation, machine learning and predictive analytics. The results are presented to users in the form of reports, dashboards, self-service BI tools or live streams.

    Passende Lösungen:

    • Azure
    • Microsoft Fabric
    • Power BI
    • Qlik
    • Power BI Report Builder
    • Qlik NPrinting
    • Mail & Deploy

Throughout the entire process, the data platform is also responsible for the security and governance of the data. This includes access control, encryption and data protection compliance.

What are typical use cases for data platforms?

Data platforms are used across numerous industries and companies to gain insights and make data-driven decisions:

  • 360° view of customers


    The collection of cross-channel data on customer data platforms enables the use of numerous applications that can strengthen customer relationships through better customer understanding and subsequently increase sales. For example, customer journey analyses help companies to better adapt marketing strategies and sales activities. By analysing onsite, engagement and transaction data, future purchasing behavior and market requirements can be predicted (= predictive analytics) and personalised product recommendations can be generated. Digital intelligence can also be used to determine whether there are any upsell or cross-sell opportunities, and what these would be. Further examples of use include customer segmentation as well as customer service and support.

  • Real-time production monitoring


    Data platforms create the basic prerequisites for increasing production efficiency. By analysing IoT data, for example, bottlenecks caused by technical malfunctions can be quickly identified and automatically reported to the responsible people by means of appropriate alerting functions. Likewise, measures such as temperature control of a machine can be started automatically when a limit value is exceeded – without employees having to intervene.

  • AI-supported predictive maintenance

    KI Maintenance

    By evaluating historical and real-time sensor data, companies can predict when a machine failure or safety risk is likely, and proactively plan maintenance work. Based on the determined failure rates of individual parts or items depending on factors such as hours of use or kilometres driven, customers can, for example, be proactively notified when their vehicles need to be serviced and which parts should be repaired or replaced to avoid a failure or defect from occurring.

  • Intelligent / Smart Warehouses


    By recording and managing important information about the entire warehouse and logistical processes on a data platform, a digital view of all physical processes is created. This gives companies full transparency about inventory levels. Items can be found quickly and easily. Analysing factors such as demand forecasts, lead times, and real-time inventory levels helps determine optimal inventory levels and safety stock requirements. This allows companies to balance their inventories to avoid overstocks or shortages. If any shortages arise, a warning message is immediately triggered so that the responsible employees can take appropriate countermeasures. Compliance with supply chain regulations (chain-of-custody) is also simplified. RFID and RTLS solutions combined with temperature sensors enable warehouse operations to monitor the temperature of sensitive goods in real time. If temperature limit values are reached, a warning message is sent to those responsible or the temperature is automatically readjusted. Warehouses become even more smart in conjunction with AR (Augmented Reality): Employees in the warehouse are guided to the location of the goods as quickly as possible via a heads-up display with an AR application. When the location is reached, the area where the goods are to be picked lights up. The respective quantity to be picked and the place where it is to be placed on the trolley are also displayed.

  • Supply chain optimisation


    Efficiency improvements in end-to-end supply chain processes arise by integrating and analysing data from various sources, such as from suppliers, manufacturers, distributors, retailers and customers, on a single data platform. Analysing data from these sources allows companies to gain comprehensive insights into their supply chain processes to identify inefficiencies, bottlenecks and areas for improvement in procurement, production, inventory management, transportation and distribution. For example, the best suppliers can be identified based on factors such as lead time, quality and cost. By analysing factors such as shipping costs, transit times and route efficiency, logistics operations can be optimised.

  • Personnel management


    Employee-related data on factors such as performance, demographics and engagement can be used within workforce analytics to understand workforce productivity, satisfaction and retention patterns. Important information about talent acquisition strategies can be derived from these patterns. Additionally, the most suitable candidates can be identified based on their skills, qualifications and experience. This improves hiring outcomes, resulting in decreased time and costs spent on talent acquisition.

  • Fraud Detection

    Fraud detection

    Fraud detection is another important use case for data platforms, especially in the financial and e-commerce sectors. By analysing transaction data and user behavior, data platforms can identify patterns and anomalies that may indicate fraudulent activity. Modern analytical techniques such as machine learning and artificial intelligence can be used to improve the accuracy of fraud detection systems. Implementing a robust fraud detection solution can protect companies and customers from financial loss and maintain trust in the company.


How do you choose the right data platforms?

There is a mixed bag of type names on the market: cross-company and special use case platforms such as customer data platforms (CDP), enterprise data platforms and cloud data platforms are just a few examples. Companies should not be guided by terminology, but by the goal of building a data platform that enables all employees to easily gain insights from all data in the company – regardless of the source, format, time frame or type of questions which require answering.

The following are key aspects that companies should consider when selecting a data platform:

  • Hosting

    The important decision is whether the platform should be hosted locally or operated in a cloud/SaaS environment.

  • Scalability

    Consideration should be given to the ability of the platform to keep pace with the growth of data needs.

  • Flexibility

    The platform should be flexible enough to handle different data sources, formats and requirements.

  • User friendliness

    Attention should also be paid to user-friendliness and the ability to support different users.

  • Security and compliance

    The security of the data in accordance with our own compliance requirements and compliance with legal regulations are of great importance.

  • Intelligence and automation

    The platform should offer intelligent functions and automation capabilities (AI/ML) to generate data-driven insights.

As a modern intelligence expert for holistic end-to-end data intelligence with many years of project experience, we support companies in all process steps – from requirements analysis to architecture design to tool selection and licensing advice to solution implementation and employee training. That's why we also know that there is no such thing as "one solution fits all", but that every company needs a data platform suited to its individual conditions and requirements.

We support you in finding the right platform for your current and future requirements: Get in touch!