industry 4.0 data

Why breaking free from data silos is the key to success in Industry 4.0

The recent snowballing growth of decentralized data collection from IoT applications has profound implications on performance and scalability in industrial settings. Data needs to be aggregated, analyzed, contextualized, and visualized locally to meet regulatory requirements, network constraints, and to address the sheer volume and velocity of data generated. Companies within manufacturing also need to be able to receive data-driven business insights from a large amount of IT and OT sources to facilitate business decision making. Such data typically source from applications that solve operational issues or support business KPIs like time-to-customer, cost optimization, new revenue streams etc.

Key KPIs in Industry 4.0 often relate to: Product excellence, operational excellence, customer engagement, innovation, number of new products and services, operations and business models, compliance, sustainability.

The challenge of managing distributed digital infrastructure has historically been the lack of access to data trapped in silos, e.g. vendor-specific or self-built silos. Traditional industrial edge platforms have their background in single-vendor and vertically integrated IoT-platforms with no ability to export data. This approach has severely limited the ability to implement the real-time analytics and control at the edge needed in an Industry 4.0.

Furthermore, the lack of integration with modern tools for data management and analytics, and cloud-operations of edge applications has severely limited the ability for rapidly developing additional use cases with minimum additional investments.

A modern platform approach to industrial applications

By leveraging the edge topology it is possible to address data retention and availability requirements. Furthermore, you can minimize risks by extending governance capabilities to edge environments and providing deep visibility.

The distribution and complexity of edge environments bring greater challenges from a data and analytics governance perspective. It will be critical for teams to extend the reach of their governance practices to include edge-resident data workloads and to push controls on data quality, security, privacy, life cycle management and definitions/models into edge environments.

These challenges are not unique to the industrial vertical, but experiences can be taken from other industries like the retail, health care, and property tech. The solution design needs to take into account the varied nature of both the applications as well as enriching features from hardware platforms

  • By taking a “general computing”-approach – as opposed to proprietary and single-vendor solutions – it is possible to get a very high degree of compute node utilisation using common and well-known Linux features. This will allow existing industrial data applications and new applications to be consolidated on the same platform.
  • Applications with specific requirements on the performance, latency, and security profile can be tagged for placement only on sites that provide these requirements. Such application could for example include production line measurement application or video AI/ML applications. The solution must allow for these kinds of heterogeneous environments to make compute-level features explicitly available for the tooling and processes used to place and manage the lifecycle of applications.

By 2023, at least 75% of “greenfield” IoT projects will use containers for application life cycle management at the edge, up from 30% in 2021.

— Gartner, “Market Guide for Edge Computing Solutions for Industrial IoT”

Comprehensive infrastructure upgrade projects require a path to careful and controlled updates. Platform teams should easily be able to introduce in a small number of installations, and then further expand the footprint based on meeting and exceeding application-level SLAs.

One comprehensive platform for industrial application management

With a platform approach, your application and operations teams are enabled to build on their current application management tooling and processes. They simply extend their current practices and tooling with support for edge-specific features needed at the factory floor. Such features can include:

  • Precise and granular placement of containerized applications based on site-specific parameters driven by well understood infrastructure-as-code principles
  • Zero-touch lifecycle management of the infrastructure components in the edge-locations including the container runtime, event logging, distributed secrets management, container registry endpoints and many other features
  • A fully standards-compliant container orchestration system that easily integrates with public or private container registries and application release-orchestration providing the same build-to-deploy turnaround as public clouds

By thinking of the edge as a distributed platform as a service (PaaS) for multiple applications both in-house developed as well as commercial third-party applications, can efficiently contrast the thinking about the edge as a set of separate applications running on their own.

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