The cultural issues related to IoT are in some ways similar to those encountered during the adoption of enterprise-based Cloud services, as the Cloud isn‘t really a technology as much as a new way of working. Getting the benefits of the Cloud requires a shift towards a self-service or IT-as-a-service mentality. Such changes are often met with strong organisational resistance.

Maximising the benefits of IoT data might present similar challenges. A self-service analytic insight creation mindset (for example correlating data from within disparate organisational silos) requires the managers to be willing to tear down territorial walls.

  • The organisation should have competence in the field of technology and a good business model

The first step to a successful IoT journey is to have a goal and an ROI model, and to understand the market. The best way to initiate IoT is by employing Agile development and systems engineering. Systems engineering practices are vital for managing the complexity and creating the best solutions for the devices. This is connected with system architecture (the overview of the whole system and its components), requirement engineering and system testing. With the Agile methodologies, KANBAN and sprint planning, the teams are able to collaborate better and stay on the right path throughout the whole process.

  • The companies should know what the system requirements are and choose a suitable strategy accordingly.

For example, companies will need a cloud solution that will connect their apps with devices. They can either go with proven cloud service vendors or develop their own cloud-based solution from scratch.

  • Try out the use cases in a real-life setting and also assess the feasibility.

The best practice is to work on the IoT solution and use “security by design.” A pilot product should be launched to test the product on a small scale.

  • Identify meaningful data

There is nothing to be gained from simply collecting data itself; there must be some business value in the insights. In the industrial world, downtime is a major cause of lost productivity and cash. As such, it represents one of the key areas where IoT can help.

  • And finally, after successful evaluation of the use cases and pilot product, the solution should be scaled up so that it can be rolled out for a larger number of consumers or regions

News from ERNI

In our newsroom, you find all our articles, blogs and series entries in one place.

  • 22.11.2023.

    Recognising trends: An insight into regression analysis

    Data plays a very important role in every area of a company. When it comes to data, a distinction is made primarily between operational data and dispositive data. Operational data play an important role, especially in day-to-day business. However, they are not nearly as relevant as dispositive data. This is because these data are collected over a longer period of time and provide an initial insight into the history or the past.

  • 08.11.2023.

    Why do we need digital transformation for medical devices?

    For hospitals, it is not up for discussion as to whether they want to digitalise. The increasing age of the population in western countries and the progressive shortage of medical professionals mean that without digitalisation, the healthcare system will not be able to provide the quality that patients want in the future.

  • 25.10.2023.

    Mastering the challenges of mobile app testing: Strategies for efficient quality assurance

    Discover the unique challenges faced in testing mobile applications and learn how to overcome them effectively. From selecting suitable devices and operating systems to leveraging cloud-based test platforms, test automation and emulators, this article provides seven essential strategies for optimising your mobile app testing process.

  • 11.10.2023.

    Incorporating classical requirements engineering methods in agile software development for a laboratory automation system

    Traditional agile methodologies can sometimes struggle to accommodate the complexity and regulatory requirements of laboratory automation systems, leading to misalignment with stakeholder needs, scope creep, and potential delays. The lack of comprehensive requirements documentation can result in ambiguous expectations and hinder effective communication among cross-functional teams.

  • 27.09.2023.

    Unveiling the power of data: Part III – Navigating challenges and harnessing insights in data-driven projects

    Transforming an idea into a successful machine learning (ML)-based product involves navigating various challenges. In this final part of our series, we delve into two crucial aspects: ensuring 24/7 operation of the product and prioritising user experience (UX).

  • 13.09.2023.

    Exploring Language Models: An overview of LLMs and their practical implementation

    Generative AI models have recently amazed with unprecedented outputs, such as hyper-realistic images, diverse music, coherent texts, and synthetic videos, sparking excitement. Despite this progress, addressing ethical and societal concerns is crucial for responsible and beneficial utilization, guarding against issues like misinformation and manipulation in this AI-powered creative era.

  • 01.09.2023.

    Peter Zuber becomes the new Managing Director of ERNI Switzerland

    ERNI is setting an agenda for growth and innovation with the appointment of Peter Zuber as Managing Director of the Swiss business unit. With his previous experience and expertise, he will further expand the positioning of ERNI Switzerland, as a leading consulting firm for software development and digital innovation.

  • data230.08.2023.

    Unveiling the power of data: Part II – Navigating challenges and harnessing insights in data-driven projects

    The second article from the series on data-driven projects, explores common challenges that arise during their execution. To illustrate these concepts, we will focus on one of ERNI’s latest project called GeoML. This second article focuses on the second part of the GeoML project: Idea2Proof.