DATA, ANALYTICS & AI USE CASE

Breaking down Data Silos

Respond to the fast-changing factors using real and trustworthy data. Get the best possible decision foundation to make informed decisions with confidence and facts.

AI and machine learning in data strategy

In numerous manufacturing and logistics firms, it’s common to encounter systems that have been devised as standalone solutions. This is because over time, the system landscape has grown situationally and without coordination over the years, the systems are relatively old, or in the case of company acquisitions, it did not make sense to fully integrate the systems. In itself, this is not bad as long as the systems operate independently. However, challenges arise when the systems need to communicate with each other, and data from various systems within a company must be utilized collectively. These challenges may include manually loading data from the system, data structures that are not immediately combinable due to different technologies, data structures that cannot be connected to other systems due to missing keys, or systems that have outdated and maintenance-intensive interfaces for delivering data in a clean and automated manner. In some situations, it gets even worse, where paper documents are printed and manually entered into another system to connect the data.

Due to the standalone solutions, the quality of data transmission is somewhat uncertain and prone to errors. Usually, there is no comprehensive standard defined, and each interface is handled differently. Establishing process documentation, process monitoring, and process harmonization seems challenging and requires significant effort.

Challenges of standalone solutions and data silos include:

    • Standalone solutions and data silos that are not interconnected
    • Diverse and non-standardized interfaces for data provision
    • Manual exports, which are error-prone and executed differently by different individuals (e.g., exporting different data, different columns)
    • Lack of standardization in data processes
    • Lack of a data infrastructure to organize data cleanly and make it accessible to the business

    Establishing a data intermediary layer that connects diverse data sources, consolidates them, and provides or forwards them with minimal quality is crucial for success in this context. Whether it is called a Data Lake, Lakehouse, Data Mesh, or data platform is not central. All follow a similar principle, the principle of the data intermediary layer with multiple stages, to process data from various sources in a more structured and straightforward manner and to provide new stakeholders relatively easily with minimal quality.

    This construct of a data intermediary layer enables easy and quick retrieval of data from various systems, thus generating business value in the shortest possible time. Whether it’s analyzing material consumption in production machines, including warnings just before running out of raw materials, transparency issues that provide the current status of various warehouses every 15 minutes for the Sales Manager to work with up-to-date figures and know where each material is in stock, or a central Supply Chain Visibility Cockpit that everyone in the supply chain accesses to see the current data, alarms, and specifics directly.

    When preparing and presenting data for business purposes, there are generally no limits. The boundary is often defined by the “80/20 Pareto principle”. During the development of data visualization, it’s essential to question whether further iterations of visual representation of data are purposeful, add business value, and make sense.

    With a data platform, as we call it, data can be interconnected easily, quickly, and with high quality fully automatically. This enables companies to respond to the fast-changing factors using real and trustworthy data. This provides the best possible decision foundation to make informed decisions with confidence and facts.

    Contact us

    Sandro Breandle, Chief data and analytics officer in Log-hub AG

    Chief Data and Analytics Officer

    Sandro Brändle

    Have a question about our Data, Analytics & AI Consulting Services? You can contact our CDAO directly.

    Schwandweg 5, Schindellegi

    Switzerland 8834

    Tel: +381 60 358 52 83

    support@log-hub.com

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