As you may know, I am a fan of Machine Learning, a subfield of Artificial Intelligence (AI) that englobes computer programs that exhibit some kind of intelligent behavior. The first researchers on AI began analyzing how we (humans) did intelligent tasks in order to create programs that reproduced our behavior. So look at the irony of this HBR article”The self-Tuning Enterprise” where the authors Martin Reeves, Ming Zeng and Amin Venjara use the analogy of how machine learning programs do to transpose the behavior to enterprise strategy tuning:
[…] These enterprises [he’s talking about internet companies like Google, Netflix, Amazon, and Alibaba] have become extraordinarily good at automatically retooling their offerings for millions of individual customers, leveraging real-time data on their behavior. Those constant updates are, in fact, driven by algorithms, but the processes and technologies underlying the algorithms aren’t magic: It’s possible to pull them apart, see how they operate, and use that know-how in other settings. And that’s just what some of those same companies have started to do.
In this article we’ll look first at how self-tuning algorithms are able to learn and adjust so effectively in complex, dynamic environments. Then we’ll examine how some organizations are applying self-tuning across their enterprises, using the Chinese e-commerce giant Alibaba as a case example.”
You may have notice those new programs at work to recommend you books or other products each time you buy something on Internet (and in fact, even if you are just looking and didn’t buy anything ;-). Those programs are based on Machine Learning algorithms, and they improve over time with the new information of success (if you bought the proposed article) or failure (if you didn’t).
How do they work?
There is a ‘learning’ part that finds similarities between customers in order to propose you products that another customer similar to you bought. But it’s not so simple, these programs are coupled with other learning modules like the one that does some ‘experimentation’ not to get stuck with always the same kind of products. This module will propose you something different from time to time. Even if you like polar books, after the tenth one, you would like to read something else, isn’t it? So the trick is to find equilibrium between showing you books you have great chances to like and novelties to make you discover new horizons. You have to have the feeling that they know what they are doing when they propose you a book (so they fine-tune to be good at similarities) but you may like to change from time to time not to get bored, and also they are very interested in making you discover another bounty/category of literature, let’s say poems. If you don’t like it, you won’t accept so easily next recommendation, so here comes the next ‘tuning’ on how often to do it.
And that’s where self-tuning comes in. Self-tuning is related to the concepts of agility (rapid adjustment), adaptation (learning through trial and error), and ambidexterity (balancing exploration and exploitation). Self-tuning algorithms incorporate elements of all three—but in a self-directed fashion.
The ‘self-tuning’ process they are talking about adjusts the tool to the new information available to him without the need of reprogramming. The analogy the authors are doing is to do in organizations this same kind of automatics tunings that Machine Learning systems are doing: to ‘self-tune’ the companies without any top-down directive, to have agility, adaptation through trial and error and ambidexterity balancing exploration and exploitation.
To understand how this works, think of the enterprise as a nested set of strategic processes. At the highest level, the vision articulates the direction and ambition of the firm as a whole. As a means to achieving the vision, a company deploys business models and strategies that bring together capabilities and assets to create advantageous positions. And it uses organizational structure, information systems, and culture to facilitate the effective operation of those business models and strategies.
In the vast majority of organizations, the vision and the business model are fixed axes around which the entire enterprise revolves. They are often worked out by company founders and, once proven successful, rarely altered. Consequently, the structure, systems, processes, and culture that support them also remain static for long periods. Experimentation and innovation focus mostly on product or service offerings within the existing model, as the company leans on its established recipe for success in other areas.
The self-tuning enterprise, in contrast, takes an evolutionary approach at all levels. The vision, business model, and supporting components are regularly calibrated to the changing environment by applying the three learning loops. The organization is no longer viewed as a fixed means of transmitting intentions from above but, rather, as a network that shifts and develops in response to external feedback. To see what this means in practice, let’s look at Alibaba.[…]
Keep resetting the vision.
When Alibaba began operations, internet penetration in China was less than 1%. While most expected that figure to grow, it was difficult to predict the nature and shape of that growth. So Alibaba took an experimental approach: At any given time, its vision would be the best working assumption about the future. As the market evolved, the company’s leaders reevaluated the vision, checking their hypotheses against reality and revising them as appropriate.
In the early years, Alibaba’s goal was to be “an e-commerce company serving China’s small exporting companies.” This led to an initial focus on Alibaba.com, which created a platform for international sales. However, when the market changed, so did the vision. As Chinese domestic consumption exploded, Alibaba saw an opportunity to expand its offering to consumers. Accordingly, it launched the online marketplace Taobao in 2003. Soon Alibaba realized that Chinese consumers needed more than just a site for buying and selling goods. They needed greater confidence in internet business—for example, to be sure that online payments were safe. So in 2004, Alibaba created Alipay, an online payment service. […] Ultimately, this led Alibaba to change its vision again, in 2008, to fostering “the development of an e-commerce ecosystem in China.” It started to offer more infrastructure services, such as a cloud computing platform, microfinancing, and a smart logistics platform. More recently, Alibaba recalibrated that vision in response to the rapid convergence between digital and physical channels. Deliberately dropping the “e” from e-commerce, its current vision statement reads simply, “We aim to build the future infrastructure of commerce.”
Experiment with business models.
Alibaba could not have built a portfolio of companies that spanned virtually the entire digital spectrum without making a commitment to business model experimentation from very early on.
[…]At each juncture in its evolution, Alibaba continued to generate new business model options, letting them run as separate units. After testing them, it would scale up the most promising ones and close down or reabsorb those that were less promising.[…]
Again there was heated debate within the company about which direction to take and which model to build. Instead of relying on a top-down decision, Alibaba chose to place multiple bets and let the market pick the winners.[…]
Increasing experimentation at the height of success runs contrary to established managerial wisdom, but for Alibaba it was necessary to avoid rigidity and create options. Recalibrating how and how much to experiment was fundamental to its ability to capitalize on nascent market trends.
Focus on seizing and shaping strategic opportunities, not on executing plans.
In volatile environments, plans can quickly become out-of-date. In Alibaba’s case, rapid advances in technology, shifting consumer expectations in China and beyond, and regulatory uncertainty made it difficult to predict the future. […]
Alibaba does have a regular planning cycle, in which business unit leaders and the executive management team iterate on plans in the fourth quarter of each year. However, it’s understood that this is only a starting point. Whenever a unit leader sees a significant market change or a new opportunity, he or she can initiate a “co-creation” process, in which employees, including senior business leaders and lead implementers, develop new directions for the business directly with customers.
At Alibaba co-creation involves four steps. The first is establishing common ground: identifying signals of change (based on data from the market and insights from customers or staff) and ensuring that the right people are present and set up to work together. This typically happens at a full-day working session. The second step is getting to know the customer. Now participants explore directly with customers their evolving needs or pain points and brainstorm potential solutions. The third step entails developing an action plan based on the outcome of customer discussions. An action plan must identify a leader who can champion the opportunity, the supporting team (or teams) that will put the ideas into motion, and the mechanisms that will enable the work to get done. The final step is gathering regular customer feedback as the plan is implemented, which can, in turn, trigger further iterations.
So now you know how Alibaba does it, how is it in your company? What ideas from them would you adopt?
This report coordinated by Nesta and commissioned by the European Commission, DG CONNECT is the first systematic network analysis of the emerging digital social innovation (DSI) ecosystem in Europe
The study began 18 months ago with the research of the principal digital social projects. They used crowdmapping to map all the identified actors, who are entrepreneurs that use digital tools to tackle a particular social issue. There are initiatives in domains like health, creating websites to share information on particular diseases and to improve patients’ well-being by creating a sense of inclusiveness in a community, or like e-government to let citizens express their opinion or to suggest policies, to name a few.
The study has also focused on identifying the links between the organisations what allowed them to do a link analysis of the situation and come up with recommendations to improve the existing situation. The aim being to maximize the positive impact of digital social initiatives and at the same time, create awareness of the risks of misuses that could happen.
The study explores how emerging technologies in the digital economy can transform society by the mobilisation of collective action, enable a more collaborative economy, new ways of making, citizen participation, sustainability and social innovation. – See more at: http://www.nesta.org.uk/event/shaping-future-digital-social-innovation-europe#sthash.eJTDji6O.dpuf
The research shows that the identified initiatives are emerging from these 4 technological trends:
- Open Hardware: initiatives here create new tools for example for environmental measurements on a critical variable
- Open Networks: like connecting devices to collectively share a resource as Internet connection
- Open Knowledge: websites to collectively create and analyze information. There are great examples in health and in participatory democracy
- Open Data: facilitates awareness, participation and collaboration, creates opportunities for innovation
The identified organisations are involved in these 6 areas:
- Open democracy with publications of governmental spending for example
- Open access, here are the open standards, open licensing and others essential to guarantee an all-inclusive Internet
- Collaborative economy with crowdfunding and new socio-economic models like AirB&B
- Awareness networks, to help on crisis situations and to improve behaviours or services through sharing data
- New ways of making, with FABLabs and 3D printers but also designing personal configurations
- Funding acceleration and incubation
All the reported initiatives are for the social good, it makes good to read about them 🙂
For all of you who have an idea but are not sure it will work, or for the ones that are juggling with many ideas and never concretize any 🙂 here is a tool that could help you decide if to go further or not: the Pimento Map
[..] the Pimento Map methodology is a fast, easy and accurate way to evaluate the chances of success of your business model. It gives the opportunity to entrepreneurs, business angels or venture capital firms to build an objective opinion on a new business idea. It also points out in detail where the model can be improved.
The tool was presented on the Tech Startup Day last week, it’s easy of use, the system asks you to answer some questions around a factor, and then shows that slice of pie in Green, Yellow, Orange or Red. Guess what color you should wish to have? Yes, green or yellow are ok, if you get the other ones, you found the weak factor of your idea.
If you are afraid of letting Pimento know about it (yes, the question has been clearly asked), as they said: you are the one filling the description 🙂
So take action, test you Big Idea … and fine tune it if it needs it. I wish you a big success!
Last week I presented MOOCs (Massive Open Online Courses) at the Professional Women International association in Brussels, Belgium.
I had the pleasure of talking to the participants afterwards. They told me they were so pleased to learn they had such an easy way of taking good quality courses that they were going to check that same night for their preferred subjects 🙂
Happy to have contributed to spread the word about the availability of the MOOCs, putting all their encapsulated knowledge encapsulated at any user’s fingertips!
On the last slide, I just dropped words with the main implications of this trend; I encourage you to put a comment if any of the subjects I mention resonates with you:
He co-leads the World Economic Forum on Big Data and Personal Data initiatives. He was talking about all the digital bread crumbs we leave behind on our daily life (like gps and gsm info, or electronic payments) and what can be done with it.
With his students of the MIT Human Dynamics Laboratory, he is discovering mathematical patterns through data analytics that can predict human behaviour. ‘Bread crumbs record our behaviors as it really happens’ he says, it is more accurate than the information from social media, where we choose what we want to disclose from ourselves. Alex and his team are in particular interested in the patterns of idea flows.
Among the most surprising findings that my students and I have discovered is that patterns of idea flow (measured by purchasing behavior, physical mobility or communications) are directly related to productivity growth and creative output.
Analysing those flows, he uncovered 2 factors that have a positive pattern of healthy idea flow:
- engagement: connecting to others, usually in the same team or organisation, and
- exploration: going abroad to exchange ideas.
Both are needed for creativity and innovation to flourish. To find those factors, he based his research on graphs of different types of interactions, like person-to-person, emails, sms..
We may not have the tools he used (like an electronic badges for tracking person-to-person interactions) but intuitively this is something we know, a good communication is essential for the success of a team, but talking to an external person may provide a new insight. It’s always good to be proved right, isn’t it?
Check my next post, I’ll continue with his article, there are a lot of great concepts he is presenting as the ‘new deal on data’ for personal data protection.
Massive Open Online Courses (MOOC) are very recent, but are quickly gaining popularity. Coursera is one of the big platforms that offer those free courses, along with edX and Khanacademy just to mention a few. Last year I took a fantastic course offered by Coursera called ‘Model Thinking’ given by Prof. Scott E. Page, who’s the Director of the Center for the Study of Complex Systems at the University of Michigan ( I posted already about it here : – ).
In March this year, I was glad to receive a mail from Scott Page, giving us some feedback from his experience doing this course, and sending us also a link to a presentation he did about the making of the course.
To give you an idea of the popularity of this course, there were 60.000 students enrolled on the first run of Model Thinking, beginning of 2012. It grew to 100.000 for the fall run (by the way, if you are interested there will be a new run this fall 2013, and it may be the last one, says Prof. Page).
I would like to share with you Scott’s insights on his experience on making this online course contrasting it with the making of his online course ‘The hidden Factor’. This last one was professionally done in a studio and he called ‘Model Thinking’: my garage band online course : – )
In fact, it was really recorded in one unused room of his house, because he said that the starting and stopping of the heating system in the rest of the house was picked up by his mike, so sensible it was even though it was just a $100 one.
To prepare the course, he thought of making it more modular. So he cut it in small chunks, so that each video was independent, and treated a subject in no more than 15 minutes. But as he said, that was the easiest part because what took him much more time was the recording of each lecture. One big issue he had was that he was alone in this room to do the recordings, and trying to be smiling, engaging and enthusiastic is difficult without an audience. Not only that, but he had unforeseen events from time to time, like his dog wandering around, and he laughed and found himself doing funny movement to chase him.
The editing took a lot of time, each video had to be reviewed, and in case of errors, it was difficult to fix it. So at the end, some mistakes remained. On the other hand in the professional approach, they took care of each error, but they had better tools and a battery of technicians to look into them and find different alternatives to correct them. Sometimes he had to repeat one word they detected he had staggered with, and they told them even the intonation he had to use to repeat it; sometimes they just put a picture about the subject he was talking about, and he could rephrase one sentence.
In conclusion, here’s his comparison regarding costs to do the 2 videos:
So it is much more costly for a professional quality. Time-wise, it was surprisingly more or less equivalent:
The studio made video was undisputable better, being much easier to correct any mistakes:
But in the end, is the improvement in quality worth the cost? Not really he says; the best quality is not needed, a good enough approach is better, even more if the cost prohibits its making. So the best solution stands between those 2 options.
I found also very important his comment on how presenting this course changed his everyday work life. He has now 1 hour per day reading his mail, answering to diverse requests on his subject of expertise. He receives inquiries from technical advisors, deans, diverse influencial people that he cannot really discard. On the one hand it’s not strictly his job, for what he is paid for, but on the other hand, can these requests be ignored? Is it responsible if you know your intervention can have such an impact as to do better policies, to improve many people’s life?
As we already discussed on my Big Data presentations, being able to analyse the amount of data that traces all our actions and movements is a great opportunity to improve our lives, as much as to do business, but it can also be exploited for the worst. Now Edward Snowden has put a clear case under the spotlights, will this make us move? Will this lead to change?
It’s time to consider what ethical codes and regulations can be issued, so that this excellent opportunity that technology is putting in our hands, that is sharing, measuring and extracting knowledge from all aspects of our lives, is not misused.
Last week I presented this topic to professional women at PWI here in Brussels. It’s called ‘small talk’ because it is not a technical presentation but one for a broader audience, to create awareness on this Big Data trend. The main concept I wanted them to take away is the change in the business arena and in our society due to Big Data. If you are interested on this subject, just drop a line and let me know!
Prices of discs and storage devices have dropped a lot, so now basically any digital data is being stored. Cost is so low, that it is worth to save it ‘just in case, and we’ll see in the future what we can do with this data’. Technology has made also huge advances with massive parallel processing, and we can manage to jungle through thousands of servers to analyse a bunch of diverse data and extract information from it in a usable time-frame.
This allows business strategists to make smarter decisions based on facts, better than how it was done before, based on experience or intuition. So the message for all decision-makers is: go and check your data, you’ll find there valuable information to decide any business matter. Also, be aware that your competition is going into it too, it can out-smart you!
At the society level, there are many ethical issues to deal with, like privacy or equality and fairness. What to you think, is it fair to have a subsidy that is ‘personalised’, that may give more to someone than to others because of a particular factor, or allow access to a health treatment to someone and not to another based on his life expectancy for example? What about basing the decision on his ‘ROI’ like the capability of paying back for the given treatment? Or is it more fair to have instead equality on subsidies, same amount for everyone? Even for the ones that could pay it by themselves? Either we discuss them before-hand, or we will be at the mercy of any politician or entrepreneur taking a step deeper in an unethical direction.
And as a last twist, I would like to point out that the basic value of knowledge is challenged. We are already experiencing a change of values, knowledge is less and less valued as an asset anymore, but value remains in knowing how to get to the knowledge,where to find it and what to extract from data.
I loved this article from Vivak Wadhwa, from Stanford University about (the lack of) women in technology. He was saying that Sillicon Valley seemed to him a meritocracy, as a lot of nationalities where represented, but then his wife make him see the missing element…women! You can argue that for a real diversity, we should also look for the representation of other minorities, but women are not minority, we are basically half of the population! Thinking again on the article, more than Vivak, I love his wife :-))
Now the good news of his investigation:
This raised the question: are women less competent as entrepreneurs than men are? Are they not cut out for the rough-and-tumble world of entrepreneurship? The answer turned out to be none of this. An analysis performed by the Kauffman Foundation showed that women are more capital-efficient than men. Babson’s Global Entrepreneurship Monitor found that women-led high-tech startups have lower failure rates than those led by men. Other research has shown that venture-backed companies run by women have annual revenues 12 percent higher than those by men and that organizations that are the most inclusive of women in top management positions achieve a 35% higher return on equity and 34% higher total return to shareholders.
So men, find yourself a woman partner for your next business, you’ll have a competitive advantage from the start 😉