Creating superpowers
Research and development (R&D) include activities that companies undertake to innovate and introduce new products and services. In many cases, if a company is big enough R&D is separated from other units and in some cases R is separated from D as well. We could call this a separation of concerns – so every unit can 100% focus on their goals.
What separates R&D and Business units? Let’s first pause and think about what business is doing. A business unit is an organizational structure such as a department or team that produces revenues and is responsible for costs. Perfect so now we have company-wide functions (R&D, business) to support being innovative and produce revenue.
Hmmm, something is still missing – how to scale digital solutions in a cost-efficient way so we can have profit (row80) in good shape? Way back in 1978 information technology (IT) was used for the first time. The Merriam-Webster Dictionary defines information technology as “the technology involving the development, maintenance, and use of computer systems, software, and networks for the processing and distribution of data.” One of the IT functions is to provide services with cost efficiency on a global scale.
Combining superpowers business, R&D and IT: We should produce revenue, be innovative and have the latest IT systems up and running to support company goals – in real life this is much more complex, welcome to the era of data-driven products and services.
Understanding your organisation structure
To be data-driven, the first thing is to actually look around at which maturity level my team and company is. There are so many nice models to choose from: functional, divisional, matrix, team, and networking. Organisational structure can easily become a blocker in how to get new ideas to market quickly enough. Quite many times Conway’s law kicks in and software or automated systems end up “shaped like” the organizational structure they are designed in or designed for.
One example of Conway’s law in action, identified back in 1999 by UX expert Nigel Bevan, is corporate website design: Companies tend to create websites with structure and content that mirror the company’s internal concerns
When you look at your car dashboard, company websites or circuit board of embedded systems, quite many times you can see Conway’s law in action. Feature teams, tribes, platform teams, enabler teams or a component team – I am sure you have at least one of these to somehow try to tackle the problem of how an organization should be able to produce good enough products and services to market on time. Calling the same thing with Squad(s) will not solve the core issue. Neither to copy one top-down driven model from Netflix to your industrial landscape.
Why does data contextualisation matter?
Based on the facts mentioned above, creating industrial data-driven services is not easy. Imagine you push a product out to the market that is not able to gather data from usage. Another team is building a subscription-based service for the same customers. Maybe someone already started to sell that to customers. This solution won’t work because now we have a product out and aren’t able to invoice customers from usage. Refactoring of organisations, codes and platforms is needed to accomplish common goals together. A new data platform as such isn’t improving the speed of development automatically or making customers more engaged.
Contextualisation means adding related information to any data in order to make it more useful. That doesn’t mean data lake, our new CRM or MES. Industrial data isn’t just another data source on slides, creating contextual data enables to have the same language between different parties such as business and IT.
A great solution will help you understand better what we have or how things work, it’s like a car you have never driven and still you feel that this is exactly how it should be even if it’s not close to your old vehicle at all. Industrial data assets are modelled in a certain way and that will enable common data models from floor to cloud, enabling scalable machine learning without varying data schema changes.
Our industrial AWS SiteWise data models for example are 100% compatible with modern data warehousing platforms like Solita Agile Data Engine out of the box. General blueprints of data models have failed in this industry many times, so please always look at your use case also from the bottom up and not only the other way around.
Curiosity and open-minded
I have been working on data for the last 20 years and on the industrial landscape for half of that time. Now it’s great to see how Nordics companies are embracing company culture change, talking about competency-based organisation, asking from consultants more than just a pair of hands and creating teams of superpowers.
How to get started on data contextualisation?
- Gather your team and check how much time it will take to have one idea for the customer (production) – is our current organisation model supporting it?
- Look for models and approaches that you might find useful like an intro for data mesh or a deep dive – the new paradigm you might want to mess with (and remember that what works for someone else might not be perfect for you).
- We can help with AWS SiteWise for data contextualisation. That specific service is used to create virtual representations of your industrial operation with AWS IoT SiteWise assets.
Read more about our services and stay tuned for the next blog posts.