Data-centric to data-driven: the real transformation behind industry 4.0

Data-centric to data-driven: the real transformation behind industry 4.0

data-driven industry

In a previous article we discussed about how to generalise the usage of data in the plant. In this article we will explore how it is necessary to change the way data is managed in the organisation to become a data-driven industry.

Performance achievements are linked to scale of deployment

Let’s go back to the benefits of using data in the organisation:

  • Employees using data to build insights, regardless of their activity, make better decisions, directly impacting operational performance.

  • Teams sharing the same data and information repository cooperate harmoniously, eliminating silos within the organisation.

  • All uses built on data are a way of formalising the organisation’s know-how, securing it and allowing greater organisational agility and resilience.

All organisations have realised the importance of Industry 4.0 and the digitalisation of their operational / manufacturing activities. Looking at what has been done so far, we can identify some trends that can help to think about how to intensify and scale-up digitalisation in industrial organisations.

Most of digitisation approaches were data-centric

Many digitisation projects in the industry have been data-centric in nature, meaning that they have focused primarily on the collection and analysis by data experts, rather than on the needs and challenges of the operational teams who will be using the data. This approach can be problematic for several reasons:

  • Technology-driven projects are more likely to be driven by the capabilities of the technology or tools being used, rather than by the specific needs and challenges of the organisation. This can lead to solutions that are not well-suited to the needs of the operational teams, and may be difficult to use or maintain in the long term.

  • Projects that are driven by central teams, rather than by the operational teams who will be using the data, may not fully understand the context in which the data will be used. This can result in solutions that are not well-aligned with the needs and challenges of the operational teams, and may not be fully adopted or used effectively.

  • Data-centric projects may not fully consider the broader organisational context in which the data will be used. This can lead to solutions that are not well-integrated with existing systems and processes, and may not fully support the needs of the organization.

Why another approach is necessary?

We often hear the term “PoC graveyard” who refers to millions of euros and thousands of man-days spent on PoCs that come to nothing. What we can learn from these experiences:

  • Technology is not enough to drive a digital transformation project. It must be driven by real business cases from management and operational teams.

  • Transformation is the key word: it involves technology, tools but also a lot of management, organisational evolution, facilitation and methods.

  • If we focus on technology and tools, they must be a catalyst for the operational organisation. If these tools offer a nice UX/UI but don’t give the user the autonomy to build and evolve their business case, adoption in the medium-long term will be at stake.

Industry 4.0, and the accompanying digital transformation, is really about extending the capabilities, autonomy and agility of manufacturing organisations by making data an additional string to their bow.

How to scale up digitisation in industrial organisations?

Scaling up digitisation in industrial organisations requires a comprehensive approach that takes into account the needs and capabilities of the operational teams, as well as the technology and tools available. Some key factors to consider when scaling up digitisation include:

  • Identifying business cases that can benefit from the use of data and information.

  • Establishing a data governance framework to ensure that data is being collected, managed, and used in a consistent and transparent manner.

  • Providing training and support to operational teams to help them become more proficient in using data and information to make better decisions.

  • Ensuring that the technology and tools being used are scalable and enables operational teams to become autonomous in their use of data.

By addressing these factors, organisations can successfully scale up their digitisation efforts and achieve better operational performance, increased collaboration and knowledge sharing, and higher levels of agility and adaptability.

Becoming a data-driven industrial organisation

Becoming a data-driven industrial organisation requires a focus on using data and information to drive decision making and operational improvement. A data-driven organisation is one where data (indeed information derived from data) is used in the right place, at the right time by the right person. That said, an additional characteristic is the ability of the actors in the operational organisation to be true actors in the system by being able to build the uses (they know their needs best) and to make them evolve (they are the ones who have to adapt the tools and uses of the data according to their evolving challenges).

This approach offers many advantages:

  • Increased capabilities and agility of operational teams: by providing operational teams with the data and tools they need to make informed decisions, organisations can help them become more agile and responsive to changing market conditions and operational challenges.

  • Diffusion of a fact-based and data-driven culture in the plant: by making data and information widely available and encouraging its use in decision making, organisations can foster a culture of data-driven decision making.

  • Better recognition of business experts work, reduction of non added value task around data: by providing operational teams with the tools and data they need to build and evolve their own data-driven solutions, organisations can better recognize the expertise and value of their business experts.

  • Valorisation of data experts: by focusing data experts on more complex subjects that require specialised analytics or data science expertise, organisations can better utilize their skills and expertise and make more effective use of their time.

Overall, by becoming a data-driven industry, organisations can better tackle performance and efficiency challenges, while also increasing their resilience and adaptability in the face of changing market conditions.

Mathieu CURA

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