Modelling: Creating the right structure and flow

Modelling is an integral part of analysis and provides actual results.
Stakeholders in this phase:
Data scientists, development team.
Building complicated models that don’t satisfy business needs (accuracy, legality, speed) or can’t be transferred into an integrated software solution (communication with the IT team about feasibility).
Situations where you have a nice model but it is not appli­cable or has no real value.
Engagement with stakeholders:
Data scientists must be aware that they need to share their thoughts with business and IT teams.

Evaluation: Present and visualise results, and assess benefits

What works best in this stage is great data visualisation and simple, clear results. You need to know how to present the results and communicate honestly no matter whether your goal was achieved. ERNI has developed evaluation frameworks that can be used in this stage.
Stakeholders in this phase:
Data scientists, visualisers/UI experts, business analysts, business end users.
(a) The communicated outcome is too complicated to un­derstand; (b) the result might not be satisfactory and data scientists may try to hide it.
What can go wrong:
(a) Results can’t be communicated appropriately, and the business does not see the benefit or success of the project and stops it; (b) the reverse might also happen: the failure to properly communicate that the experiment has pro­duced negative results with respect to the business goals and that another iteration is needed can lead to the imple­mentation of an unsuccessful application.
Difficult but correct decisions:
Communicate bad results and openly say when it is not worth it to investigate further.
Engagement with stakeholders:
Engage early, show results regularly and visualise in an appealing and simple way.
During the evaluation stage:
1. Present results in a clear, targeted and simple way.
2. Communicate failure.
3. Promote the benefits.
In this stage, your team makes a decision to operationalise a data product (or moves from an offline analysis to the pilot, or from the pilot to a large-scale solution), or even makes the hard decision to put an end to the project.

Make it work in real life

What makes a successful implementation? Good old project management, system integration, process integra­tion and a wisely chosen interdisciplinary team with broad technology know-how.
Stakeholders in this phase:
Data scientists, visualisers, UI experts, development team, data engineers.
(a) Poor integration of the data component into the existing software system; (b) a good model but a resulting integra­tion that is not user friendly; (c) poor visualisation can make even a good model and its results hard to understand; (d) you get stuck during the introduction and training of the new tool.
What can go wrong:
A project that, despite a successful start and promising results, must be stopped because of operational failures, or when the outcome is not accepted by the end users.
Engagement with stakeholders:
Show early pilots of the final solution to business stake­holders. Build one team out of data scientists and software developers.

News from ERNI

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

  • 06.12.2023.

    Streamlining software development: The journey from multiple to unified requirements management tools

    Productivity in software development is slowed down by managing specifications across various requirements management (RM) tools. Although moving to a single, updated RM tool involves an upfront investment, the long-term benefits are considerable. These include increased process efficiency, enhanced collaboration, superior traceability, improved software specification quality, cost reductions, scalability and better integration with other RM tools, among others.

  • 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.