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The IoT impact

The IoT impact

IoT get smarter, faster & stronger

The effect technology has on our lives is something beyond words. We are smarter, growing faster, learning in innovative ways and experiencing things every day, all because of technology and science. We might sit and contemplate on how forward and advanced the technology has become today, but those thoughts will hardly be able to match the levels to current IoT advancement.

Since the first generation iPhone was released on June 29th, 2007, sci-fi world came true: your mobile allow you to call someone face to face like Steve Austin did in The Six Million Dollar Man. This small connected object help you a little more in your daily tasks. This creates a new opportunity to analyze users daily behaviors and evolve mobile phones into truly intelligent personal devices, which provide accurate context-adaptive and individualized services.

Vitality: a real IoT example for insurance

This is what Generali achieved with it brand new product: Vitality. More than insurance, Vitality want to "sell an holistic health management program". In addition to diagnosing and treating medical issues it aims to keep customers healthy by offering them rewards and discounts with health and leisure brands and retailers.

Vitality has arranged deals with partner organisations that are available to all policyholders. These include half-price Virgin Active gym memberships, 75% off Champneys breaks, cheaper holidays and cash back on bicycle and sportswear. If you do a certain amount of exercise a month you will receive rewards such as free cinema tickets and coffee. This new kind of product use to be a healthy balance of the mind, body and spirit that results in an overall feeling of well-being.

Vitality app users data collected and submitted to provide health scores and progress toward goals. Is based on key IoT benefits and use cases. As a reminder, use cases are a list of actions or event steps, typically defining the interactions between a role and a system, to achieve a goal. The product owner knows how use cases are indispensable!

What should we learn from this example?

Alistair Cockburn defined a more detailed structure for a use case in Writing Effective Use Cases, but permits it to be simplified when less detail is needed:

  1. Title: "an active-verb goal phrase that names the goal of the primary actor"
  2. Primary Actor
  3. Goal in Context
  4. Scope
  5. Level
  6. Stakeholders and Interests
  7. Precondition
  8. Minimal Guarantees
  9. Success Guarantees
  10. Trigger
  11. Main Success Scenario
  12. Extensions
  13. Technology & Data Variations List

Our organizations should be able to determine customers need and appetite for IoT with use cases. So far, the right question could be: How does our current value proposition fit into our connected customers smart, data-driven manufacturing environments?

Perhaps, it is time for us to rebuild our value proposition depending on the industries, products and services we offer to our customers. Maybe, we should recalibrate our value propositions according to the lifecycle stage in which our customers reside. It could be based upon risk prevention & self-service. After all, Vitaly could be considered as life coach to prevent medical issues with dashboards and gamification in it.

Educate your business partners on IoT

In the end, IoT is just another new technologie which needs to be understood by our business partners. IT environment become more and more specific, and IoT requires orchestration of an extraordinarily diverse set of technologies, capabilities and scenarios.

So our main goal is to explain and explain again the miscellaneous way to do it. It's time to go back to the drawing board. This isn't a marketing or sales exercise, either. It is a cross-functional, collaborative one. Those IT, technical and engineering folks? You know, the folks residing in those departmental silos whom you tend to marginalize? They are the best interpreters of what is happening in manufacturing and engineering as a result of the IoT. Keys elements are Tactical & Established:

  1. Review current systems and create a plan to ready them for IoT
  2. Work with your business partners to appoint an IoT leader and establish a cross-functional team
  3. Identify specific use cases to pilot and/or move to production
  4. Implement a sourcing model to fill capability and value-chain gaps

Be Agile: Use The Iterative Way

This kind of project have to be design & develop in iterative way in order to architecting & implementing. An iterative process is one that makes progress through successive refinement. A development team takes a first cut at a system, knowing it is incomplete or weak in some (perhaps many) areas. The team then iteratively refines those areas until the product is satisfactory. With each iteration, the software is improved through the addition of greater detail.

An agile approach is essential to produce high-quality services that both meet user needs and are of value to the user. Working agile helps the team to adapt quickly to user feedback, accurately estimate its speed and output, and encourages a culture that fails and recovers fast. That is, experimenting often and learning quickly from failures. In a way, the most difficult thing we have to change is culture. Because after all this work done, we have to:

  1. Establish KPIs aligned to the business benefits expected from IoT
  2. Look to employ IoT platforms to be part of the game
  3. Use your first-mover advance to establish partnerships with key industry players
  4. Promote innovation and surface opportunities through hackathons and engagement inside our teams
  5. Encourage our business partners to use IoT to explore new products, services and business models

And, it's just the begining…

So what's the deal with M2M?

Technology moving so fast now that by the time the business partners becomes interested and the buzzwords become trendy. Machine To Machine (M2M) communications is the latest buzzword they are getting excited about.

IoT is this notion that everything around us is connected and a source of valuable data. Remember, when you're running your Garmin watch get data for your iPhone Vitality app. But what if your Garmin watch embedded a SIM card and was able to talk directly with Vitality data Server? That's the basic idea behind M2M. In the same behavioural pattern, other terms are rising: Telematics or Mesh Network.

Telematics refers to connected vehicles that talk to the cloud by using cellular networks. Mesh Networking is more like your multi-room Sonos System. You buy Sonos connected loudspeaker to create a "mesh" of musical network all around your home place. You understand, it's all about network and the way for IoT devices to communicate with the main data server who received them.

What can we do with this amount of data?

You must be telling yourself, this guy will talk about Big Data & Deep Learning. Bingo! You are right!
While the techies can debate among themselves the difference between "machine learning" and "deep learning", we're going to consider the two terms synonymous and henceforth just talk about "deep learning".

When you use Google Images to search for a "sailing ship", Google Images shows you sailing ship. Is this because Google can recognize what a sailing ship looks like? Off course not. This is simply because Google searches text to find sailing ship images. So how can we train Google to identify sailing ship by only looking at images? Here's how we do it.

Let's start with a sample of 10 million random pictures teach Google Images how to learn. Let's say the pictures were composed of circles, triangles and squares. You could quite easily imagine an algorithm that could first map sharp differences that would denote shapes. Now let's tell the algorithm to try and find similar objects in this "big data" set and then group them. These groups are displayed to a developer who can then label them.
After many many iterations, the algorithm then starts to recognize patterns in the mistakes that it makes. It can now recognize the difference between a sailing ship & a boat.

Now, let's imagine business partners' use cases who drive an algorithm upon Vitality data. You got it!
You're able to identify patterns and behavioural, and design behavioral components for your marketing tools or sales exercises. All these technologies are pretty unripe and remains of best to come. But the very first and hardest step we have to accomplish to succeed is the cultural one.

about me

Laurent Raboteau

Music lover, assiduous reader, surfer & snowboarder, I live staring at the Atlantic Ocean and below Pyrénées with my lovely wife and childrens. During week, I work for Salesforce

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