Digital Manifesto for Switzerland

The other day Digital Switzerland invited 50 digital movers and shakers to Bern to discuss digitalization and Switzerland and what we think a way forward is. The outcome is this Digital Manifesto (pdf).

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“We’ve tested 25 #AI engines & only @Squirro brought benefit”

As known, I work for Squirro. Our customer Evalueserve just released this short video:

“We’ve tested 25 #AI engines & only @Squirro brought benefit. This is why #robots won’t replace us”

@Evaluserve, 23 August 2016

I agree.

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New York New York, big city of dreams

New York Central Park

The big apple at 6am, just returning from my morning run in the Central Park. It’s already nearly 80 degrees Fahrenheit. And it was busy in Central Park, the center drive full of joggers and bickers.

I am here to join my team for a full week of customer, prospect and partner meetings. We’ve established our New York office last year and have with Peter a fantastic account executive and with Alex an amazing post-sales engineer on board.

Together with our team in Europe we work on a number of customers in the financial services world and beyond. And we broaden the reach daily. Just heading to a prospect together with our friends from Synpulse, our great consulting & integration partner here in the US.

The US is a great place to do business. The forward leaning approach, taking in new technologies to reap rewards early, helps a younger vendor such as us. The positive attitude to risk and risk taking (in considerate amounts) is a sharp difference from the risk resistive approach that I often see across Europe.

So over the next year or two we’ll expect to win quite a number of new customers & and add a few more colleagues to our team. And I expect to come many times more for an early morning moment to the Central Park.

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A year on

UntitledIt’s really the light. Coming into Charlotte two days ago there was this wonderful late afternoon light across the city, a kind of deep blue, combined with southern warmth. Later the sky turned warmer still. The reciprocal effect of light and clouds gave the skyline a mysterious and beautiful flavour.

Downtown Charlotte


I came here to Charlotte, North Carolina, the first time last year. We were visiting Wells Fargo, a US bank. Today, 12 months later, I am back to visit a visionary team, transforming the way the bank does more with data.

For the past decade any bank faced an ever-growing level of competition. As a reaction Wells Fargo puts long lasting client relationships in the center of its activities. With customer satisfaction being directly linked to competitiveness and profitability, the bank needs to leverage new technologies and do more with their (existing) data to create deeper and wider relationships.

The bank has chosen Squirro to deliver this vision. We’ll post in the near future a more complete customer success.

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Brexit: See EU later

The people in the United Kingdom voted for Brexit – My prediction was wrong. It’s just the opposite of what I expected in terms of percentage but correct in terms of the Scottish result.

Tough stuff.

As always we all will muddle through, but it’s unchartered territory.

At the risk of again being wrong about predictions: It’s possible that Cameron will go down as the guy who undid two unions.

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The Outcome of the Brexit Vote

Here’s my prediction for the Brexit outcome: It’s going to be ‘Remain’ by 51-52% overall, the English will vote out and the remain will only prevail because the Scots vote by about 60%+ for the remain side. The result will be increased tension within the United Kingdom.

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Make data human – Apply Deming’s principles to the industrialization of data analytics



It was a sunny spring day. I just received my coveted birthday present. A Sony Walkman. It was revolutionary. Small, lightweight, beautiful, it was music on the go. And it was the essence of Japanese ingenuity.

W. Edward Deming knew a few things about counting: He was the son of a chicken farmer in Iowa, USA. A trained mathematician, he worked as a census consultant to the post-war Japanese government.

While there, he was asked to hold a short seminar by the Japanese Union of Scientists and Engineers. He taught statistical process control and concepts of quality.

Deming called his system of thought “System of Profound Knowledge”. His message to Japan’s chief executives: Improving quality will reduce expenses while increasing productivity and market share.

Many more seminars followed, with one of the attendants being Akio Morita, the cofounder of Sony.


Industrialization of data analytics

Deming’s methods profoundly transformed the industrial processes in Japan. It’s time to apply these same concepts to data analytics.

74% of firms say they want to be “data-driven”, reports Forrester. Yet only 29% are actually successful at connecting analytics to action.

Rajeev Ronanki et al. of Deloitte Consulting pointed in a recent blog post to some of the reasons for this apparent contradiction. They outline:

“Advances in distributed data architecture, in-memory processing, machine learning, visualization, natural language processing, and cognitive analytics have unleashed powerful tools that can answer questions and identify valuable patterns and insights that would have seemed unimaginable only a few years ago.


Against this backdrop, it seems almost illogical that few companies are making the investments needed to harness data and analytics at scale. Where we should be seeing systemic capabilities, sustained programs, and focused innovation efforts, we see instead one-off studies, toe-in-the-water projects, and exploratory investments.”


Data Analytics Principles

It’s time to change and a good place to start are Deming’s methods. Deming advocated in his System of Profound Knowledge four key points:

  • Appreciation of a system: understanding the overall processes
  • Knowledge of variation: the range and causes of variation
  • Theory of knowledge: the concepts explaining knowledge and the limits of what can be known.
  • Knowledge of psychology: concepts of human nature.

Let’s apply these four points in turn to data analytics.

  • Appreciation of the system: any analytics initiative should be setup with the goal of improving products or services. This may include suppliers, producers, and customers (or recipients) of your goods and services. Any analytics initiative must have to goal to provide novel, timely and actionable insights in context, relevant to specific production process.
  • Knowledge of variation: Analytics today is correlation. Regardless of the level of sophistication any correlation has statistical sampling issues.
  • Theory of knowledge: Deming railed against blindly asserting opinion as fact, out of convenience or ignorance. At the start of any analytics initiative a company lacks the frame of reference to validate and assess results. A good way is exchange results between industry partners and providers (we’re ready to share ours) to learn what is necessary to improve the situation. Learning needs to be continual and organization-wide.
  • Knowledge of psychology: Deming understood the fundamental truth that people are different. Indeed one can create the best analytics system, know all about variation and still have a failing analytics initiative. The key is to understand people, and particularly what motivates them. The transformational effects of analytics are profound. The key to is make people not just part of such a journey but address intrinsic needs, including taking pride in workmanship and working with others to achieve common goals.

Example: One of our customers is rolling out our Service Insights solution. The key goal: optimize their call center response times by up to 30% (in fact deploying the pilot results across the entire call center). As part of the initial project setup we involved the call center agents in the actual design of the solution.

The effect: the agents were driving the project. It was no longer a management imposed efficiency initiative but a team effort to improve their workplace The team made use of data to transform their organization. In a way they made data human.


Image credit:

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The Future of Data: Autonomous Data


Self-Driving Cars are all the rage. Mary Barra, CEO of General Motors, said recently in the German Newspaper, Handelsblatt, that the automotive industry will change more radically in the next 5 years than in the past 50 years.

The key drivers she says are the e-cars and autonomous driving. Vehicles driven by data, that autonomously move you from your home to work and back again. With this technology comes an implicit shift in our expectations: Autonomous vehicles will have
little value unless they deliver us to the right destination at the right time.

What about if the same would hold for data?

What about if data gets to its required destination right on time – automatically?

Data’s Long and Winding Road

For the most part, this is not how it works these days. Today we we need to go to data, i.e. we need to search for information. The information doesn’t find us. For this we need search engines like Google. We need to know what we are looking for on Google, or else we wont find anything. We need to open countless files on a filer to find the document that we know is there… somewhere. It’s small wonder then, that most data is never used beyond its primary use. The fact is, it’s simply too complicated and too time consuming to get to that information.

This needs to change. Data, and our expectations of it, need to catch up to the world we’ve created with it. Imagine this: what if data ‘knew’ about our informational needs and could find its way to us? For business, this will be game changing.

Let’s say you’re about to call an important customer and their contact details are stored in a CRM system. The context of your call, and whether or not it’s even optimal or opportune timing to call at that moment, would be changed and challenged if you also had the data inform you about the last three product discussions between that customer and the company, or the breaking news of a critical new partnership; or even if that the customer was currently experiencing a major service incident. You’d no longer be one step behind your customer’s experience; instead you’d be travelling their real-time journey right alongside them.

Setting a new course for Data: Relevance, Timing and Destination

For this future to be the new norm, data needs to know about its own meaning and its contextual relevance. It needs to be enabled to find its way to its best possible destination – automatically. It needs to become a self-aware and self-operating piece of data that finds its way around the informational universe it lives in, and on the way, collate with other data to form new types of insights that are highly relevant to the end-user, which might be a human operator, or a machine driven by software requiring this input to make its next smart move.

While truly sentient data remains the stuff of science fiction, our data-driven world of today still demands the convenience and value such scenarios offer. Thankfully, there’s an intermediate step in the form of enabling software to add additional dimensions to existing data. By enriching the data in this way, it becomes computable. That’s the first condition.

The second condition is context: Within an existing enterprise software setup the context is driven by the user’s access control list and the work process within the current application. Add to this individual user activity parameters and you’ve got a got first approximation of the context.

Conclusion? By combining enriched data & user context we’re able to make data autonomous today: delivering the right information at the right moment in context.

PS: If you want a chance to find out more about the first such end-to-end context intelligence platform and how we’re already helping leading companies to elevate their data-driven enterprise, join us for one of our upcoming webinars focusing on Service Insights or Customer Insights.

PPS: Picture credits: Internet Archive ImagesCoast watch, ca. 1979.

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Opposition without a clue (about the future)

The Swiss political system starts to resemble the US system: A right wing party as look-similar to the irresponsible Republicans.

More in German.

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Start the right thing


My friend Patrick Stähler wrote an excellent book on starting the right thing. Unfortunately only in German – but hey Goethe’s language is on an ascend, what getting yourself fluent?! – it packs actionable advice with great insights from successful businesses like Yellowtail.

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