New kids on the block, IoT, Siemens, AI in clinical care, MSc big data
Hello, I am Louis-David Benyayer and you are reading Datanomics and strategy, my weekly newsletter on business strategy in a datanomics era.
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For this edition, I‘ll discuss how data and analytics change competition landscape using the example of IoT, share the results of a study by Data and Society and give an update on my students’ work (spoiler: they are pretty good this year).
Data and analytics change the competitive landscape: New kids on the block
The multiplication of sensors capturing data every second has opened opportunities for new value propositions in BotB product or equipment markets. With Internet of Things technologies, manufacturing companies like Bosch, Siemens or Schneider Electric can move from a product-based business model to a service-based business model. Of course, the efforts and investments required are huge: building the technology stack, changing the operations, transform the culture, … The reward can be significant: more steady revenues, higher margins, increased loyalty of clients, …
I will concentrate here on one specific aspect triggered by the generalisation of IoT technologies and offers: the competitive landscape evolution.
Before, in these markets, the battle was usually structured around 2 fights:
a fight with direct competitors. They are pretty much organised the same way, face the same tradeoffs and opportunities but each implements distinctive strategies to defend a position in the market
a fight with suppliers on components cost and with distributors on their margins. These organisations are different but they need each other. The evolution of the bargaining power shaped the ability to capture value at one part of the value chain.
With Internet of Things technologies, as the value proposition change, the key resources and capabilities to master change:
Solution and services
To deliver the promises of IoT to the clients, a company needs to secure all this stack. Which means that new battles emerge and new competitors challenge incumbent positions.
To illustrate this, I will use the example of Siemens, as presented by the deputy CEO of the company in a recent video during the MIT Platform Summit. Dr. Roland Busch identifies 3 groups of companies competing with Siemens:
the traditional players (formerly “direct competitors”, ABB, Schlumberger, Schneider Electric, …). The competition is particularly fierce to master the IoT resources and capabilities either by internal development, acquisitions or partnerships.
the tech companies (e.g. Amazon Web services). These companies are organised differently, they do not leverage the same type of assets and they try to position as an intermediary to capture a fraction of the value provided by IoT to the end-users. They offer cloud solutions and platform technologies.
consulting firms and system integrators (e.g. Accenture). They offer integration services and technologies. In some cases, they compete directly with Siemens offer, in other cases, they act as a supplier of technology or service.
You can easily relate the types of companies competing with the technology stack required. The entry point differs but they share the same objective: capturing a fraction of the price paid by the end-users of IoT technologies.
What does it mean for strategy making:
IoT technologies change the nature of the value proposition in BtoB environment. From product quality and performance to service and global performance (from selling electric equipment to selling energy consumption reduction).
The competition intensifies: in addition to the direct competition layer, new layers of competition emerge (technology, solution, infrastructure, …). The companies competing do not leverage the same distinctive assets and do not face the same tradeoffs. Previous manoeuvres are less effective to control these new competitors. New entrants enter the market leveraging capabilities the incumbent do not master (service design, community building, platform architecture and management).
New resources and capabilities are required for the incumbents to thrive. Hence, the ability to build and maintain a distinctive stack positioning is key. Balancing cost and independence is one major question, make-buy-partner decisions are key.
Every company wants to position as an industry platform, but the platform gameplay is hard to learn. Platform business model is attractive but the code has not been cracked yet. A lot of incumbent express their difficulty to engage in this business model.
The jury is still out on which companies will capture the biggest part of the value: incumbents, technology providers, industry platforms or new entrants leveraging technology.
Leveraging non-digital distinctive assets and capabilities is crucial for incumbents. The strategy for incumbents consisting of mimicking the digital companies playbook requires to invest as much as they do in pure digital capabilities, which is in most cases impossible. Synchronizing non-digital assets and capabilities (brand, distribution network, client base, technical expertise, …) with digital capabilities is a more viable option as it builds a more distinctive position.
From the think tanks: AI in clinical care
Data & Society published A Study of Integrating AI in Clinical Care. The authors reveal the hybrid sociotechnical nature of repair work required to integrate an AI system into clinical health care. “Technological systems don’t exist in a bubble. They require a complex interaction of humans, infrastructure, and organizational structure to work effectively.”
As explained in an article on Wired covering the study: “Some challenges came from disrupting the usual workflow of a busy hospital—many doctors aren’t used to taking direction from nurses. Others were specific to AI, like the times Sarro faced demands to know why the algorithm had raised the alarm. The team behind the software hadn’t built in an explanation function, because as with many machine learning algorithms, it’s not possible to pinpoint why it made a particular call.”
What does it mean for strategy making:
capturing the value of data and analytics requires to support and accompany the implementation of such techniques by organisation changes and training
the use of algorithmic decisions can be accepted only if the specialist understands how the algorithm works
Update on MSc in Big Data
As I told you in a previous edition, students enrolled in the MSc in Big data at ESCP have worked for 4 weeks to solve real-life business problems with data and analytics. They had to come up with a solution prototype for a business problem using data and analytics. This solution should have been tested and backed with value creation and costs hypotheses.
They presented their results two days ago and I was impressed by the quality of their propositions. Even more given the constraints they had: short lead time, limited time to allocate to the project (they have a lot of other courses), very specific and context-dependent questions, no team experience (they didn’t choose their teammates and didn’t know each other before).
Each of the 16 teams succeeded to meet the expectations, a lot did a very good job and a couple of them produced outstanding solutions.
Congratulations to all students (I will not name them here, 83 are enrolled)! One other proof (if needed) of what can accomplish a tiny group of motivated and talented individuals.
Huge thank you to Nicolas Janicaud and Estelle Lorant from Bouygues Construction, Hatim Belyasmine from La Redoute, François Nguyen from L’Oréal and Simon Chignard from Etalab. They offered the topics and guided the groups in the process.
Just hit reply if you want to know more about the topics and results.
I would appreciate your feedback, just hit the reply button to email me.
Have a good week!
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