Great visualisation tips

I would like to share with you this article on the Harvard Business Review.  They give excellent advice to ‘make extreme numbers resonate’.  They give 3 examples to illustrate their tips:

  1. Challenge: Green Mountain sold 18 billion coffee pods in two years. How can you give people a concrete sense of just how many objects that is?
    HBR-Visual Huge numbers- R1601Z_VS_CUPS_B-1024x774

 

  1. Challenge: Only three in 10,000 high school basketball players ever make it to the NBA. How can you give someone a deep understanding of the rarity of that feat?
    HBR -Visual small numbers-R1601Z_VS_BASKETBALL-1024x584

 

  1. Challenge: Every year tens of thousands of people leave one U.S. city for another. How can you show changes on this scale when it’s so hard to keep track of complex movement? […]

 

HBR -Visual complexity -R1601Z_VS_MOVEMENT-1024x568

In the first example, they give tips to visualise huge numbers, the second one is for small numbers, but the the third one is really interesting, as it shows an extremely clear way to picture complexity.

 

 

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Good resolution for 2016: let’s improve our communications skills

Dr Travis Bradberry wrote this post in Linkedin some days ago about “Why We Struggle to Communicate”.

Communication is the real work of leadership; you simply can’t become a great leader until you are a great communicator.”

communication-importanceYes, communication is critical in leadership, inspiring people and taking into account every member of the team. For an entrepreneur, it allows you to transmit your thoughts and ideas better, improving the chance of convincing investors and make ‘it’ happen.  For intrapreneurs, it helps aligning people towards the same goal. But in the end, it is an essential skill for everyone because understanding each other is the basis for better collaboration with your professional and personal relations. 

So join me on this New Year’s resolution for 2016:  let’s improve our communications skills following the strategies to take action that the author states in his article:

Speak to groups as individuals.[…] You want to be emotionally genuine and exude the same feelings, energy, and attention you would one-on-one.[…]
Talk so people will listen. […] means you adjust your message on the fly to stay with your audience […].
Listen so people will talk. […] you must give people ample opportunity to speak their minds.[…]
Connect emotionally.[…] Show them what drives you, what you care about […].
Read body language. Your authority makes it hard for people to say what’s really on their minds.[…] Pay as much attention to what isn’t said as what is said […].
Prepare your intent.  Don’t prepare a speech; develop an understanding of what the focus of a conversation needs to be […].
Skip the jargon. […]

And the last advice:

Practice active listening. Active listening is a simple technique that ensures people feel heard, an essential component of good communication. To practice active listening:

  • Spend more time listening than you do talking.
  • Do not answer questions with questions.
  • Avoid finishing other people’s sentences.
  • Focus more on the other person than you do on yourself.
  • Focus on what people are saying right now, not on what their interests are.
  • Reframe what the other person has said to make sure you understand him or her correctly (“So you’re telling me that this budget needs further consideration, right?”)
  • Think about what you’re going to say after someone has finished speaking, not while he or she is speaking.
  • Ask plenty of questions.
  • Never interrupt.
  • Don’t take notes.

Happy 2016!

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The Value of Emotional Connection

HBR-Emotions MAGIDS_value_v4-small

Scott Magids, Alan Zorfas and Daniel Leemon tell us that research on motivational values is paying off:

Our research across hundreds of brands in dozens of categories shows that it’s possible to rigorously measure and strategically target the feelings that drive customers’ behavior. We call them “emotional motivators.” They provide a better gauge of customers’ future value to a firm than any other metric, including brand awareness and customer satisfaction, and can be an important new source of growth and profitability.

The article guides you through a detailed process to find out your customers’ motivators, that begins with:

Online surveys can help you quantify the relevance of individual motivators. Are your customers more driven by life in the moment or by future goals? Do they place greater value on social acceptance or on individuality? Don’t assume you know what motivates customers just because you know who they are. Young parents may be motivated by a desire to provide security for their families—or by an urge to escape and have some fun (you will probably find both types in your customer base). And don’t undermine your understanding of customers’ emotions by focusing on how people feel about your brand or how they say it makes them feel. You need to understand their underlying motivations separate from your brand.

Check here the full Harvard Business Review’s article for the full description. What is surprising is this finding:

To increase revenue and market share, many companies focus on turning dissatisfied customers into satisfied ones. However, our analysis shows that moving customers from highly satisfied to fully connected can have three times the return of moving them from unconnected to highly satisfied. And the highest returns we’ve seen have come from focusing on customers who are already fully connected to the category—from maximizing their value and attracting more of them to your brand.

It is analogous to the different strategies used on education:

  • In secondary school you have to get a minimum knowledge from all the courses you have.  It is frequent that students must focus on the ones for which they are not naturally talented.
  • In higher studies, it pays to focus on your strengths, on your best skills, and to improve them until you are really good at them.

It’s not frequent to get youngsters very motivated by the courses they don’t really like, even if they finish the year managing them enough to pass. It is no surprise that it is easier to motivate the second group, and as a result, seems reasonable that the acquired knowledge or skill may be more astonishing on the second group than on the first one. Surprising not have had this intuition and need a research to show it with data.

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The European Data Innovation Hub

What began as a community of like-minded people, with nice meetups around data science and get-together’s, is now taking the form of the European Data Innovation Hub.  Its mission is to be an active actor in the data innovation ecosystem and to support data professionals throughout Belgium and Europe with networking activities, events, training and meeting facilities, learning platforms, co-working space and mentorship. It will foster grassroots community initiatives and take the burden out of realising and organising them. The idea is to set the conditions where people with the right skills and organisations in the right positions can have the option to move forward.

Here are some of the activities of the Hub:

  • To organise data innovation events
  • To provide co-working space for data professionals
  • To support the education and training of the data workforce, from academic to data scientists to managers to data end-users

I’m very happy to be part of this eco-system, participating not only in the trainings in Big Data and Machine Learning, but hopefully opening as many opportunities as I can to women in this domain.

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The rise of the Self-Tuning Enterprise

Alibaba

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?

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New computer interface using radar technology

Thanks to Otticamedia.com

Thanks to Otticamedia.com

Have you seen this article?  It’s about the project Soli from the Google’s Advanced Technologies and Projects (ATAP) group.  They have implemented a new way to comunicate with a computer: through radar.  The radar captures the slight movements of the hand like in this picture, where just moving your fingers in the air makes you move a ‘virtual’ slider.

Fantastic, can’t wait to try it!

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How to lie with charts

I hope you didn’t miss the article on visualization from the Harvard Business Review.  It is called ‘Vision statement: How to lie with charts‘, and it’s full of clear stated examples.

http://en.wikipedia.org/wiki/United_States_presidential_election,_2008

Source: Wikipedia

This color-coded map is one of the examples they show where coloring a county with the political color of the majority vote in that state is misleading.  The map represents the 2008 election (Obama versus McCain) and we can see 80% of the US colored in red (the Republican color), and in fact the Republican candidate John McCain received only 40% of the votes.  The mismatch of the (natural) election’s expectation after looking at this map and the real outcome comes from representing in a map information that is not related to geography.  The number of votes in a county or a state is not proportional with its geographical size.
[..] you could call it the New York City problem -0,01% of the area but 2,7% of the population.

A suggested better representation is using bubbles with sizes proportional of the number of votes, ending with this map showing more correctly a majority of blue instead.

Source: hbr.org

Source: hbr.org

Visualization is growing in importance nowadays that we have so much data all around.  Visualization can help to identify trends, to find patterns, to show relations between data.  It can show what the data represents, putting it in an intuitive way.

But as this article shows, used in a wrong way, visualization can mislead you just as well.

To be on the safe side, it’s better to check the numbers or data behind the representation in order to confirm what the image is showing you … or if somebody is not tricking you!

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Correlation and Causation in Big Data

Big data began as a term used when you have extremely large data sets, These big data sets cannot  be managed nor analyzed with conventional database programs not only because  of the size exceeding the capacities of standard data management , but also because of the variety and unstructured nature of the data (it comes from different sources as the sales department, customer contact center, social media, mobile devices and so on) and because of the velocity at which it moves (imagine what it entails for a GPS to recalculate continually the next move to stay on the best route and avoid traffic jams: looking at all traffic information coming from official instances as well as from other drivers on real time, and transmitting all the details before the car reaches a crossroad).

The term ‘Big Data’ is also used to identify the new technology needed to compute the data and reveal patterns, trends, and associations.  Furthermore, this term is now synonym of big data’s analytical power and its business potential that will help companies and organizations improve operations and make faster, more intelligent decisions.

What is big data used for?

First and the more evident part is to do statistics: how many chocolates have we sold? What are the global sales around the world, splitted per country? Where do the customers come from?

Then correlation comes to play:  things that have the same tendency, that go together or that move together: countries that are strong on chocolate sells also have  a lot of PhDs.

Thanks to http://tylervigen.com

Thanks to http://tylervigen.com

Correlation is not causality. It’s not because you eat chocolate that you become a PhD (nor the other way around, having a PhD doesn’t mean you are more likely of loving chocolate).  Analyzing correlations is still a big deal.  It can be a conjunction, like with thunder and lightning. It can be a causality relation, and even when there is causality, it is hard to say the direction of the relationship, what is the cause and what its effect.  Nevertheless, big data predictive behaviour analysis is doing a great job, even when the ‘why’s behind it, the underlying causes, are still hidden, not explained.

The great potential in Big data is that it helps us discover correlations, patterns and trends where we couldn’t see them before, but it’s up to us to create theories and models that can explain the relations behind the correlations.

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