Using The Past To Discover What The Customer Will Want Next

I loved the article What’s your best innovation bet? by Melissa Schilling in this summer issue of the Harvard Business Review, as it has always been very hard to guess the future:

Image from Magda Kochanowicz

Melissa Schilling says that “By mapping a technology’s past, you can predict what future customers will want.”  For that she explains her method:

  • 1 – Identify the key dimensions

What she means here is to examine/analyse/determine the different aspects in which the technology has evolved, like on processing speed or on precision just to mention some typical dimensions, and to relate them to the need of users: how much has the technology satisfied that need? She gives a clear example with the recording industry, where the basic dimension for many years was the audio fidelity:

By the mid-1990s, both industries were eager to introduce a next-generation audio format. In 1996 Toshiba, Hitachi, Time Warner, and others formed a consortium to back a new technology, called DVD-Audio, that offered superior fidelity and surround sound. They hoped to do an end run around Sony and Philips, which owned the compact disc standard and extracted a licensing fee for every CD and player sold.

Sony and Philips, however, were not going to go down without a fight. They counterattacked with a new format they had jointly developed, Super Audio CD. Those in the music industry gave a collective groan; manufacturers, distributors, and consumers all stood to lose big if they bet on the wrong format. Nonetheless, Sony launched the first Super Audio players in late 1999; DVD-Audio players hit the market in mid-2000. A costly format war seemed inevitable.

You may be scratching your head at this point, wondering why you’ve never heard about this format war. What happened? MP3 happened. While the consumer electronics giants were pursuing new heights in audio fidelity, an algorithm that slightly depressed fidelity in exchange for reduced audio file size was taking off. Soon after the file-sharing platform Napster launched in 1999, consumers were downloading free music files by the millions, and Napster-like services were sprouting up like weeds.

If you wonder: ”who could have predicted the disruptive arrival of MP3? How could the consumer electronics giants have known that a format on a trajectory of ever-increasing fidelity would be overtaken by a technology with less fidelity?” Well, that’s just the method she’s presenting in this article, which first step is identifying the different dimensions at play.

For example, computers became faster and smaller in tandem; speed was one dimension, size another. Developments in any dimension come with specific costs and benefits and have measurable and changing utility for customers. Identifying the key dimensions of a technology’s progression is the first step in predicting its future.

To determine these dimensions, trace the technology’s evolution to date, starting as far back as possible. Consider what need the technology originally fulfilled, and then for each major change in its form and function, think about what fundamental elements were affected.

Tracing its [the recording industry] history reveals six dimensions that have been central to its development: desynchronization, cost, fidelity, music selection, portability, and customizability. Before the invention of the phonograph, people could hear music or a speech only when and where it was performed. When Thomas Edison and Alexander Graham Bell began working on their phonographs in the late 1800s, their primary objective was to desynchronize the time and place of a performance so that it could be heard anytime, anywhere. Edison’s device—a rotating cylinder covered in foil—was a remarkable achievement, but it was cumbersome, and making copies was difficult. Bell’s wax-covered cardboard cylinders, followed by Emile Berliner’s flat, disc-shaped records and, later, the development of magnetic tape, made it significantly easier to mass-produce recordings, lowering their cost while increasing the fidelity and selection of music available.

For decades, however, players were bulky and not particularly portable. It was not until the 1960s that eight-track tape cartridges dramatically increased the portability of recorded music, as players became common in automobiles. Cassette tapes rose to dominance in the 1970s, further enhancing portability but also offering, for the first time, customizability—the ability to create personalized playlists. Then, in 1982, Sony and Philips introduced the compact disc standard, which offered greater fidelity than cassette tapes and rapidly became the dominant format.

[…] I usually ask teams to agree on three to six key dimensions for their technology.

The recurring dimensions across industries are: ease of use, durability and cost.  To foresee the future, it is worth also to imagine new  dimensions worth exploring. A good tip to come up with those new aspects is to think big, no constraints, what could the customer want in an ideal world.

Folklore has it that Henry Ford once said, “If I had asked people what they wanted, they would have said faster horses.” If any car maker at the time had really probed people about exactly what their dream conveyance would provide, they probably would have said “instantaneous transportation.” Both consumer responses highlight that speed is a high-level dimension valued in transportation, but the latter helps us think more broadly about how it can be achieved. There are only limited ways to make horses go faster—but there are many ways to speed up transportation

  • 2 – Locate your position

For each dimension, examine the value consumers are receiving for actual technology

This will help reveal where the greatest opportunity for improvement lies.

[..] For example, the history of audio formats suggests that the selection of music available has a concave parabolic utility curve: Utility increases as selection expands, but at a decreasing rate, and not indefinitely. When there’s little music to choose from, even a small increase in selection significantly enhances utility. Consider that when the first phonographs appeared, there were few recordings to play on them. As more became available, customers eagerly bought them, and the appeal of owning a player grew. Increasing selection even a little had a powerful impact on utility. Over the ensuing decades, selection grew exponentially, and the utility curve ultimately began to flatten; people still valued new releases, but each new recording added less additional value. Today digital music services like iTunes, Amazon Prime Music, and Spotify offer tens of millions of songs. With this virtually unlimited selection, most customers’ appetites are sated—and we are probably approaching the top of the curve.

Many dimensions have S-shaped curves: Below some threshold of performance there is no utility, but utility increases quickly above that threshold and then maxes out somewhere beyond that.

  • 3 – Determine your focus

Once you know the dimensions along which your firm’s technology has (or can be) improved and where you are on the utility curves for those dimensions, it should be straightforward to identify where the most room for improvement exists. But it’s not enough to know that performance on a given dimension can be enhanced; you need to decide whether it should be. So first assess which of the dimensions you’ve identified are most important to customers. Then assess the cost and difficulty of addressing each dimension.

For example, of the four dimensions that have been central to automobile development—speed, cost, comfort, and safety—which do customers value most, and which are easiest or most cost-effective to address?

[..] Tata Motors’ experience with the Nano is instructive. The Nano was designed as an affordable car for drivers in India, so it needed to be cheap enough to compete with two-wheeled scooters. The manufacturer cut costs in several ways: The Nano had only a two-cylinder engine and few amenities—no radio, electric windows or locks, antilock brakes, power steering, or airbags. Its seats had a simple three-position recline, the windshield had a single wiper, and there was only one rearview mirror. In 2014, after the Nano received zero stars for safety in crash tests, analysts pointed out that adding airbags and making simple adjustments to the frame could significantly improve the car’s safety for less than $100 per vehicle. Tata took this under advisement—and placed its bets on comfort. All 2017 models include air-conditioning and power steering but not airbags.

Once you have identified the dimensions, the author suggests scoring these criteria to help you prioritize where to put the effort of innovation: how much users care about the dimension, room for improvement of the technology, and the cost involved in developing a new product on that dimension.  See this example for blood-sugar monitoring devices:

DIMENSION IMPORTANCE TO
CUSTOMERS (1–5 SCALE)
ROOM FOR
IMPROVEMENT (1–5 SCALE)
EASE OF
IMPROVEMENT (1–5 SCALE)
TOTAL
SCORE
RELIABILITY 5 1 1 7
COMFORT 4 4 3 11
COST 4 2 2 8
EASE OF USE 3 2 3 8

This matrix is very helpful to explicit the need to change a company’s traditional strategy:

It can also help overcome the bias and inertia that tend to keep an organization’s attention locked on technology dimensions that are less important to consumers than they once were.

Depending on your company’s situation (lack of cash, strong market position,..) you can weight some of the scoring to get your ‘personalised score. You can also use this method to analyse your competitors positioning and expected future products.  Knowing their actual market strength and their potential future directions will make you see the best ‘bet’ for your company in an ever evolving industry.

The technology assessment exercise can help companies anticipate competitors’ moves. Because competitors may differ in their capabilities (making particular technology dimensions harder or easier for them to address), or because they may focus on different segments (influencing which dimensions seem most important or have the most room for improvement), they are likely to come up with different rankings for a given set of dimensions.

The great insight of the method presented in this article is not on getting the innovation idea, but more at a strategic level, on where it will be better to put the effort for Your company considering its Actual circumstances at this Present market (evolution of the industry and existing competence).

Perhaps more valuable is the big-picture perspective it can give managers—shedding new light on market dynamics and the larger-scale or longer-term opportunities before them. Only then will they be able to lead innovation in their industries rather than scramble to respond to it.

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Big Data and Ethics

BIG Data and Ethics was held a few weeks ago in the new premises of the DigitYser, downtown Brussels.

It was a great Meetup, with interesting speakers and an interested public 😉 It’s always a pleasure when the public can contribute and presentations raise great discussions, and it is more important here on this gathering on ethics, as people still have to position themselves on the different aspects of this topic.

I was particularly surprised when Michael Ekstrand from Boise State University mentioned a use of the recommendations systems that I hadn’t think of: using it as a tool to tackle the intention behaviour gap: ‘I don’t do what I want to do’ (for example not eating while on a diet). Recommenders can be used to help you change your behaviour, giving you nudges as incentive.

Jochanan Eynikel also mentioned the use of technology as a morality enforcer.

Still, there are possible drawbacks:

Another area that was discussed was the ethical fact that Personalisation has a direct negative impact on Insurance as it goes against Risk mitigation (mutualising it among customers). There are sensible domains where a ‘human’ approach should be taken.
How to ensure ethical and moral concerns are taken into account? One approach is through participatory design, that is a framework to get users voices on the subject during the design phase. MIT is strongly pushing participatory design to tackle many basic dilemmas.

Solving and clarifying our human position on these kind of dilemmas is more than relevant when we are talking here about autonomous technology, that is when technology is teachings itself, as driving cars learning from users.
Can we imagine not having human supervision in all domains? How to introduce Ethics so that the system itself can choose the ‘good’ decision and discard the others?

Pierre-Nicolas Schwab presented us the General Data Protection Regulation as “the only thing that the EC can do to force companies to take data privacy into account: fine them if they don’t”:

At the end of the meeting, this question has been raised: “Do data scientist and programmers need an Hippocratic oath?” Like ACM that has a code of conduct, something like ‘don’t harm with your code’.
What’s your opinion on this?

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Kill your dragons to be creative

Walter Vandervelde did a presentation at Professional Women International on creativity this month. He taught us how to kill our internal dragons to be more creative 😉

  • NONO, the dragon of the criticism, prejudices and conservatism:
    Change your automatic reply from ‘yes but’ to a ‘yes and’. That will stop criticism and you’ll feel the energy rising as you build up collectively a solution and your ideas get wider and wilder.
  • HOHO, the dragon of fear of failure, lack of courage and uncertainty:
    There is a quick solution to this dragon: just do it! “Doing is the new Thinking”. To begin things rolling, use gamification -that is using techniques of games for serious stuff.  As example, Walter suggests to put 2 teams to compete, giving them basic instructions and restrictions to begin with, so that they are not stopped by uncertainty. Be sure to tell everybody that it’s ok to fail.
  • GOGO, the dragon of the stress, time constraints and lack of reflection:
    To kill this dragon do your working place more attractive, an enjoyable experience and less stressful.
  • DODO, the dragon of resignation, habit and lack of curiosity:
    To ovecome this, foster the creative thinking mind, the one that, in front of a question, tries to come up with many other questions instead of just a straight answer. In fact the creative thinker tries to find the best question to describe the problem.

When trying to come up with creative ideas, know how our mind works: after a while it becomes lazy and you cannot find more ideas, but if you allow it to rest just a few minutes and come back to your problem at hand, you ‘ll get more ideas and usually those will be the more creative ones, the first ones being the obvious ones. During the resting time your unconscious mind continues working, incubating your thoughts, finding new relations to the problem.

Some techniques Walter mentioned to open your mind is reverting a question or rephrasing it. You’ll be verbalising other ideas behind your problem : Ask “An examples of a car is…” and people will tend to name brands: “a Mercedes, a Ford, …”. Ask “A car is…” and you’ll get other definitions like the function ” a driving device”, the uses “a device for transportation” and other relations.
give examples of things, imagine new uses, different ways of doing the same thing

Thanks Walter  for an entertaining event.  I learned interesting tips and tricks to be creative, and even some swear words in different languages that I swear not to repeat 😉

 

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Visual tool for Systems Thinking


Thanks to @PascalMestdach for presenting the visual systems modelling method at the #LeanCampBxl Unconference.

This is a very easy management technique to help visualising a complex organisation, and reflect on the dynamics at work when aiming for a particular goal.  A full model of an organisation with all the different perspectives is very hard to do, and anyway, any model is only an approximation to reality and never perfect. But you can construct a model focusing on a particular sector or aspect of your business: it will help you grasp what’s going on, how things impact one another, and share that same information with your team.

A model allows you to reflect on it: you get a deeper understanding and that can provoke insight. You can see how external changes could influence your system, and you can use it to test new ideas, simulating the impact of a particular change.

Also, understanding how the system works and what is our role in it let us be more effective and proactive.

Here is how to begin: you will need many post-its, a whiteboard and a marker.

  1. Prepare beforehand some basic post-its, writing already on them an element at play for the part of the business you want to analyse. You can mention inputs, activities, events, stakeholders, processes, behaviours, business objectives, personal goals, external influences…
  2. Then, present the process to construct the model of your business system: the idea is to identify the relationships between the presented elements that are at work on your company.To begin with, focus on 2 types of relationships: either it is a positive one where one activity goes on the same direction of another one (the more defects in a product, the more time spent fixing defects), or a negative relationship where it goes the opposite way (e.g. the more defects in a product, the less happy is the customer).  Here is how to represent those links:
    You see there mentioned a third notation to indicate ‘Delay’. This reflects the dynamic aspect on the two types of relationships (positive or negative) that is the delayed effect on time of an action. When the effect of an action is almost immediate, we see the relationship (like if you hear a horn waiting at a red light, you know the person has lost patience, and he horned because of a long timing of the red light), but the longer the delay between cause and effect, the increased complexity of the model because it’s more difficult to see its influence (like the returns of a marketing campaign).
  3. Don’t forget to give indications on the mindset needed to accomplish this!

    The objective is to construct a model where participants agree that it reflects their reality. That will allow them to clearly understand the issues at play. Managers may be surprised to discover how some activities are perceived by the team, the knowledge (or lack of) of objectives or of parts of the process, and even by what motivates the different participants (if they label a relationship as positive or negative).As every organisation needs everybody to work together for the whole to function successfully, this gained insight is very valuable. New post-its can be added if the participants feel there is an element to be captured.
  4. Validating the Model: once there is a consensus on the resulting model, put it to test using the technique of an ‘Ideal world’.Imagine our goal is: ‘Customer satisfaction’, write then ‘100%’ on top of that post-it. Then propagate this value to any post-it that is linked with a positive link.  So all of them have 100%.  Then follow the opposite links, and instead of labeling them ‘100%’ you write ‘0%’.  Are there any conflicts? Could you propagate the values and keep the model consistent?  Well done then!If not, open a discussion on possible changes to make this model work ?

You may notice that if you maximise a particular goal, like for example ‘customer satisfaction’, you may end up minimizing another potential goal (like ‘employee satisfaction’, or ‘minimal investment’). Nothing is 100% or 0% in our real world, but this ‘Ideal world’ propagation strategy makes you see what are the important factors to achieve a particular goal, and where are its negative impacts.

With this new insight, you are better prepared to decide on how to realign your goals, and you also have a visual representation of your business to communicate the objectives to the team.

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Managing techniques to improve employee’s engagement: build a culture of trust

In a very interesting article in last Harvard Business Review “The neuroscience of trust” by Paul J. Zak. describes how trust works, and its relationship with employee engagement. Then presents eight management behaviors that create a culture of trust as the base to improve productivity through employee engagement.

Gallup’s meta-analysis […] shows that high engagement—defined largely as having a strong connection with one’s work and colleagues, feeling like a real contributor, and enjoying ample chances to learn—consistently leads to positive outcomes for both individuals and organizations. The rewards include higher productivity, better-quality products, and increased profitability.

[…]In my research I’ve found that building a culture of trust is what makes a meaningful difference. Employees in high-trust organizations are more productive, have more energy at work, collaborate better with their colleagues, and stay with their employers longer than people working at low-trust companies. They also suffer less chronic stress and are happier with their lives, and these factors fuel stronger performance.

Leaders understand the stakes[… but] they aren’t sure where to start. In this article I provide a science-based framework that will help them.

Paul Zak is a professor of economics, psychology  and management, and founder of the Center for Neuroeconomics Studies. He knew that a brain chemical called oxytocin was responsible in rodents to signal that another animal was safe to approach, and he wonder if it was the same for humans. He initiated a long term research to verify if the same neurological signal was also indicating us that we can trust someone. His  experiments proved that:

Oxytocin appeared to do just one thing—reduce the fear of trusting a stranger.

My group then spent the next 10 years running additional experiments to identify the promoters and inhibitors of oxytocin. This research told us why trust varies across individuals and situations. For example, high stress is a potent oxytocin inhibitor. (Most people intuitively know this: When they are stressed out, they do not interact with others effectively.) We also discovered that oxytocin increases a person’s empathy, a useful trait for social creatures trying to work together. We were starting to develop insights that could be used to design high-trust cultures, but to confirm them, we had to get out of the lab.

So he developed safe experiments to measure oxytoxin and stress levels of employees, and also measured their productivity and creativity.

Through the experiments and the surveys, I identified eight management behaviors that foster trust. These behaviors are measurable and can be managed to improve performance.

  1. Recognize excellence.
    The neuroscience shows that recognition has the largest effect on trust when it occurs immediately after a goal has been met, when it comes from peers, and when it’s tangible, unexpected, personal, and public. Public recognition not only uses the power of the crowd to celebrate successes, but also inspires others to aim for excellence. And it gives top performers a forum for sharing best practices, so others can learn from them.
  2. Induce “challenge stress.”
    When a manager assigns a team a difficult but achievable job, the moderate stress of the task releases neurochemicals, including oxytocin and adrenocorticotropin, that intensify people’s focus and strengthen social connections. When team members need to work together to reach a goal, brain activity coordinates their behaviors efficiently. But this works only if challenges are attainable and have a concrete end point; vague or impossible goals cause people to give up before they even start. Leaders should check in frequently to assess progress and adjust goals that are too easy or out of reach.
    76% of people reported that their best days involved making progress toward goals.
  3. Give people discretion in how they do their work.
    Once employees have been trained, allow them, whenever possible, to manage people and execute projects in their own way. […]
    Autonomy also promotes innovation, because different people try different approaches. Oversight and risk management procedures can help minimize negative deviations while people experiment. And postproject debriefs allow teams to share how positive deviations came about so that others can build on their success.
    […]
  4. Enable job crafting.
    When companies trust employees to choose which projects they’ll work on, people focus their energies on what they care about most.
    […]
  5. Share information broadly.
    Only 40% of employees report that they are well informed about their company’s goals, strategies, and tactics. This uncertainty about the company’s direction leads to chronic stress, which inhibits the release of oxytocin and undermines teamwork. […] Ongoing communication is key: A 2015 study of 2.5 million manager-led teams in 195 countries found that workforce engagement improved when supervisors had some form of daily communication with direct reports.
    […]
  6. Intentionally build relationships.
    […] at work we often get the message that we should focus on completing tasks, not on making friends. Neuroscience experiments by my lab show that when people intentionally build social ties at work, their performance improves. A Google study similarly found that managers who “express interest in and concern for team members’ success and personal well-being” outperform others in the quality and quantity of their work.Yes, even engineers need to socialize. A study of software engineers in Silicon Valley found that those who connected with others and helped them with their projects not only earned the respect and trust of their peers but were also more productive themselves. You can help people build social connections by sponsoring lunches, after-work parties, and team-building activities. It may sound like forced fun, but when people care about one another, they perform better because they don’t want to let their teammates down.
    […]
  7. Facilitate whole-person growth.
    High-trust workplaces help people develop personally as well as professionally. Numerous studies show that acquiring new work skills isn’t enough; if you’re not growing as a human being, your performance will suffer. […]
    Investing in the whole person has a powerful effect on engagement and retention.
  8. Show vulnerability.
    Leaders in high-trust workplaces ask for help from colleagues instead of just telling them to do things. My research team has found that this stimulates oxytocin production in others, increasing their trust and cooperation. Asking for help is a sign of a secure leader—one who engages everyone to reach goals. Jim Whitehurst, CEO of open-source software maker Red Hat, has said, “I found that being very open about the things I did not know actually had the opposite effect than I would have thought. It helped me build credibility.” Asking for help is effective because it taps into the natural human impulse to cooperate with others.

The effect of trust on self-reported work performance was powerful. Respondents whose companies were in the top quartile indicated they had 106% more energy and were 76% more engaged at work than respondents whose firms were in the bottom quartile. They also reported being 50% more productive—which is consistent with our objective measures of productivity from studies we have done with employees at work. Trust had a major impact on employee loyalty as well: Compared with employees at low-trust companies, 50% more of those working at high-trust organizations planned to stay with their employer over the next year, and 88% more said they would recommend their company to family and friends as a place to work.

My team also found that those working in high-trust companies enjoyed their jobs 60% more, were 70% more aligned with their companies’ purpose, and felt 66% closer to their colleagues.

Looking at his conclusions, I see the same values that we foster on Agile/SCRUM management methodology.

[…] you cultivate trust by setting a clear direction, giving people what they need to see it through, and getting out of their way.

It’s not about being easy on your employees or expecting less from them. High-trust companies hold people accountable but without micromanaging them. They treat people like responsible adults.

His research proved that these techniques work, and also that a culture of trust accounts for more joy: “joy on the job comes from doing purpose-driven work with a trusted team”.

Embrace the movement for a happier society raising awareness on the benefits of stopping micro-management and  of trusting people to do their jobs.

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Elections warn about ethical issues in algorithms

I tweeted recently on this article about how Big Data has been used on the last American Presidential campaign.

Concordia Summit, New York 2016

“At Cambridge,” he said, “we were able to form a model to predict the personality of every single adult in the United States of America.” The hall is captivated. According to Nix, the success of Cambridge Analytica’s marketing is based on a combination of three elements: behavioral science using the OCEAN Model, Big Data analysis, and ad targeting. Ad targeting is personalized advertising, aligned as accurately as possible to the personality of an individual consumer.

Nix candidly explains how his company does this. First, Cambridge Analytica buys personal data from a range of different sources, like land registries, automotive data, shopping data, bonus cards, club memberships, what magazines you read, what churches you attend. Nix displays the logos of globally active data brokers like Acxiom and Experian—in the US, almost all personal data is for sale. […] Now Cambridge Analytica aggregates this data with the electoral rolls of the Republican party and online data and calculates a Big Five personality profile. Digital footprints suddenly become real people with fears, needs, interests, and residential addresses.
[…]

Nix shows how psychographically categorized voters can be differently addressed, based on the example of gun rights, the 2nd Amendment: “For a highly neurotic and conscientious audience the threat of a burglary—and the insurance policy of a gun.” An image on the left shows the hand of an intruder smashing a window. The right side shows a man and a child standing in a field at sunset, both holding guns, clearly shooting ducks: “Conversely, for a closed and agreeable audience. People who care about tradition, and habits, and family.”

Now I came across this other article by Peter Diamandis, featuring what we can expect in 4 year’s time for the next future elections’ campaign.

5 Big Tech Trends That Will Make This Election Look Tame

5 Big Tech Trends That Will Make This Election Look Tame

If you think this election is insane, wait until 2020.

I want you to imagine how, in four years’ time, technologies like AI, machine learning, sensors and networks will accelerate.

Political campaigns are about to get hyper-personalized thanks to advances in a few exponential technologies.

Imagine a candidate who now knows everything about you, who can reach you wherever you happen to be looking, and who can use info scraped from social media (and intuited by machine learning algorithms) to speak directly to you and your interests.

[…] For example, imagine I’m walking down the street to my local coffee shop and a photorealistic avatar of the presidential candidate on the bus stop advertisement I pass turns to me and says:

“Hi Peter, I’m running for president. I know you have two five-year-old boys going to kindergarten at XYZ school. Do you know that my policy means that we’ll be cutting tuition in half for you? That means you’ll immediately save $10,000 if you vote for me…”

If you pause and listen, the candidate’s avatar may continue: […] “I’d really appreciate your vote. Every vote and every dollar counts. Do you mind flicking me a $1 sticker to show your support?”

I know, this last article is from the SingularityHub, but even though they tend to be alarming, knowing how fast technology advances, the predictions they advance are not too exaggerated…

In any way, that reminds me how important it is to ACT on the ethical issues of algorithms. Please notice the capital letters to stress on the movement, which is to take action.  There are many issues that need to be identify, to be discussed, to raise awareness upon, to regulate, and on some of them we can already act on at company level.

I talked in May last year at the Data Innovation Summit about the biases that can be (and usually are) replicated by the new algorithms based on data.  Since then I began working on a training program to help identify and correct those bias when designing and using algorithms, and I’m reminded with the above mentioned articles that this cannot be delayed, it’s needed right now.

So if you are interested on getting your people and organization be aware of biases (human biases and digital ones), and be trained to fix these issues, contact me!

EmojiOne

We are creating our future, let’s don’t close our eyes, we can take control and assume our responsibility setting the railings that will guide the path to our future society.

 

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New year’s resolution: Apply the 8-Day Data Detox Kit

https://theglassroomnyc.org/data-detox/

theglassroomnyc.org/data-detox/

We are approaching the end of the year. For most of us this is the time to Last Year’s introspection and New Year’s big resolutions…(and if you don’t usually do it I recommend it to you: time flies (!) and taking the wheel of your life brings you a lovely sense of realisation 🙂

Have you given a thought about what you accomplished this year? How do that match your good intentions from the previous new year? Yes, I know, that’s a low blow… who can remember that far? And even if you do, we all tend to be so optimistic about our capabilities 😉

But if you don’t remember what you did this year, or what you were doing that didn’t allowed you to reach your goals…well, you can always check the web to remind you about that (or as we say nowadays: just google it!)

There was recently an exhibition in New York City called The glass room: Looking into your online life about our online data imprinting and the many tools that track our online behavior.

After checking it, you will be more convinced than ever to begin 2017 with the proposed Data Detox Plan.

So here I am proposing you to put, next to your diet to recover after the gastronomic excesses of New Year’s Eve, the 8-Day Data Detox Plan.  It will help you see how you look like to others online, and adjust the level of traces you leave behind, taking back control of your public image, of your ‘persona’.

Happy New Year 2017!  let me pass along a great message from my friend Marie-Noëlle (do I have to mention that she has a communication agency? ;-): ‘Welcome the 365 new opportunities to convert your goals into success

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AI and Machine Learning in business: use it everywhere!

How One Clothing Company Blends AI and Human Expertise, HBR nov-16

How One Clothing Company Blends AI and Human Expertise, HBR nov-16

Last week Bev from PWI’s group in Linkedin pointed me to a great HBR article: “How One Clothing Company Blends AI and Human Expertise”, by H. James Wilson, Paul Daugherty and Prashant Shukla.

It describes how the company Stitch Fix works, using machine learning insights to assist their designers, and as you will see, they use machine learning at many levels throughout the company.

The company offers a subscription clothing and styling service that delivers apparel to its customers’ doors. But users of the service don’t actually shop for clothes; in fact, Stitch Fix doesn’t even have an online store. Instead, customers fill out style surveys, provide measurements, offer up Pinterest boards, and send in personal notes. Machine learning algorithms digest all of this eclectic and unstructured information. An interface communicates the algorithms’ results along with more-nuanced data, such as the personal notes, to the company’s fashion stylists, who then select five items from a variety of brands to send to the customer. Customers keep what they like and return anything that doesn’t suit them.

The Key factor of success for the company is to be good at recommending clothes that not only will fit the customer and that they’ll like enough to keep them, but better than just ‘like them’, that they like them enough to be happy with their subscription.

Stitch Fix, which lives and dies by the quality of its suggestions, has no choice but to do better [than Amazon and Netflix].

Unlike Amazon and Netflix that recommend directly products to the customers, here they use machine learning methods to provide digested information to their human stylists and designers.

[…] companies can use machines to supercharge the productivity and effectiveness of workers in unprecedented ways […]

Algorithms are for example analysing the measurements to find other clients with same body shape, so they can use the knowledge of what items fitted those other clients: the clothes that those other clients kept. Algorithms are also used to extract information of clients’ taste on styles, from brands preferences and their comments on collections.  Human stylists, using the results of that data analysis and reading the client’s notes, are better equipped to choose clothes that will suit the customers.

Next, it’s time to pick the actual [item of clothe] to be shipped. This is up to the stylist, who takes into account a client’s notes or the occasion for which the client is shopping. In addition, the stylist can include a personal note with the shipment, fostering a relationship, which Stitch Fix hopes will encourage even more useful feedback.

This human-in-the-loop recommendation system uses multiple information streams to help it improve.

See how stylists maintain a human dialog with their clients through the included note. This personalised contact is usually well appreciated by customers and it has a positive effect for the company because it opens the door to receive their feedback to better tailor their next delivery.

The company is testing natural language processing for reading and categorizing notes from clients — whether it received positive or negative feedback, for instance, or whether a client wants a new outfit for a baby shower or for an important business meeting. Stylists help to identify and summarize textual information from clients and catch mistakes in categorization.

The machine learning systems arelearning through experience’ (=adapting with the feedback) as usual, but in a humanly ‘supervised’ way. This supervision allows them to try new algorithms without the risk of losing clients if results are not as good as expected.

Stitch Fix employs more than 2,800 stylists, dispersed across the country, all of them working from home and setting their own hours. In this distributed workforce, stylists are measured by a variety of metrics, including the amount of money a client spends, client satisfaction, and the number of items a client keeps per delivery. But one of the most important factors is the rate at which a stylist puts together a collection of clothes for a client.

Speed is an important factor to satisfy their customers’ demands, and machine learning gives them the needed insight so much quicker than if stylists had to go through all the raw data!

This is where the work interface comes into effect. To enable fast decision making, the screen on which a stylist views recommendations shows the relevant information the company keeps about a client, including apparel and feedback history, measurements, and tolerance for fashion risks — it’s all readily accessible

The interface itself, which shows the information to the stylist, is also adapting through feedback, being tested for better performance.  And you could go again one step further and check for bias on the stylists:

Stitch Fix’s system can vary the information a stylist sees to test for bias. For instance, how might a picture of a client affect a stylist’s choices? Or knowledge about a client’s age? Does it help or hinder to know where a client lives?

By measuring the impact of modified information in the stylist interface, the company is developing a systematic way to measure improvements in human judgment

And there are many other machine learning algorithms throughout the company:

[…]the company has hundreds of algorithms, like a styling algorithm that matches products to clients; an algorithm that matches stylists with clients; an algorithm that calculates how happy a customer is with the service; and one that figures out how much and what kind of inventory the company should buy.

The company is also using the information of the kept and returned items to find fashion trends:

From this seemingly simple data, the team has been able to uncover which trends change with the seasons and which fashions are going out of style.

The data they are collecting is also helping advance research on computer vision systems:

[…] system that can interpret style and extract a kind of style measurement from images of clothes. The system itself would undergo unsupervised learning, taking in a huge number of images and then extracting patterns or features and deciding what kinds of styles are similar to each other. This “auto-styler” could be used to automatically sort inventory and improve selections for customers.

In addition to developing an algorithmic trend-spotter and an auto-styler, Stitch Fix is developing brand new styles — fashions born entirely from data. The company calls them frankenstyles. These new styles are created from a “genetic algorithm,” modeled after the process of natural selection in biological evolution. The company’s genetic algorithm starts with existing styles that are randomly modified over the course of many simulated “generations.” Over time, a sleeve style from one garment and a color or pattern from another, for instance, “evolve” into a whole new shirt.

How does a company using so many machine learning systems look like at employee level? How is it perceived by the employees? This is what they say:

Even with the constant monitoring and algorithms that guide decision making, according to internal surveys, Stitch Fix stylists are mostly satisfied with the work. And this type of work, built around augmented creativity and flexible schedules, will play an important role in the workforce of the future.

Machine learning and AI (artificial intelligence) systems are changing the way companies do business.  They are providing an insight that either could not be grasped before, or that it could, but not at that speed, nor being accessible as a tool to assist each and every employee.

The least that can be said is that this will improve productivity in all sectors and, as today almost everyone has access to the Internet to verify a word, look for a translation, a recipe, check the  weather and countless other uses, the new generation of employees will be assisted by tons of algorithms that will analyse data and deduce, induce or summarize information to assist them in their work and in their decision-making.

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Negotiating contracts, getting the deal.

This post is about THE basic stuff in business: how to get the contract, and how to make it be a good deal.

Talking about Money, by www.vco-global.com

Image from Talking about Money, www.vco-global.com

Lately, I’ve participated in 2 great talks for women entrepreneurs from PWI and PWN Munich. One was about about discussing money and remunerations and the other about sales.  The key basic principles both speakers mentioned are that you have to gain the trust of your customer before entering in the deal negotiation, and that you have to involve the customer in the construction of the deal.  Become his partner, not his servant.

Here are my gained insights on the process:

  • Open up the conversation simple but get to have your customer curious by what you can provide. This can be done with a case study, a blunt (but realistic 😉 statement like “my previous customer gained a 50%  ROI” or “solved his problem in x months”, something that will put him in an attentive mode.
    Once there, he will want to hear more.  So now, you have gained the right to ask questions.  He’ll accept to give information in order to go further and hear your solution.
  • That’s your opportunity to ask questions to learn about his problem and adapt the proposal to his needs. This part of asking questions is crucial, use it for contextual questionning: the more insight you have on the situation of the customer the better you’ll be to evaluate the work involved.In the first questions you will be learning about the customers’ situation, ask factual and context questions.  But remember that you are entitled to just a few questions before he gets bored: in this phase he’s not learning anything.  So at some point, you move to next phase, where you have to challenge his description of the situation using your previous experience, and give away some insight of the ‘solution’ you could provide, but don’t go into much detail.
  • It’s during this second ‘challenging’ questioning phase that you are gaining his trust.  Because you are proving that you understand his situation, that you had previous experiences with the same challenges.  You are rising questions that prove that you know what issues are in stake, making him think about them, giving him insight he may not have on the challenges ahead.Be ‘Columbus’ guiding the customer to see the solution. You are also gaining insight on the level of understanding of your customer on the problematic at hand.  And you’re setting the value you are bringing to the table with your proposal by the same way: the less he knows, the more you are bringing to the table.Here are some examples of great contextual questions:

    – about the stakeholders: “Who else is involved in the decision process? ”

    – about their previous experiences, good and bad: the work will be harder if there is a reluctant stakeholder in the game, or you may add value with your experience if they had a bad previous experience already.

    – about time constraints: “Why is it important to solve this issue NOW?” A question like “What would happen in a year if we don’t do this?” makes them realize the value of your proposal.

    – remind him of his PAIN: “What could happen if you don’t do ..? It’s better than you saying: “If you don’t do .. then …”

    By the end, you should have learned about the context of your work, the available or expected budget, you may have learned about your competitors (if any) and about the decision making process.
    Think of this process as Diagnose before you prescribe.

  • Co-create the solution with the customer. Don’t push the sale, make the customer wanting the purchase. Come up with him with different options, like 3 proposals with different levels of scope and price, so he can choose the budget (and content) he’s willing to sign.  The great advantage to co-create the proposal, is that you don’t have to convince him of the proposal, he did it with you.  You have his ‘buy in’ from the beginning.
    Let the customer be the HERO that comes to ask you for the solution. Better than saying “The benefits of my solution are…” is when the customer says “Your solution could help us with …”
  • Negotiate money issues at the end, when he’s convinced of the value added of the deal.
  • To end the conversation: “How would you like to proceed?”  opens the line to “I could send you a letter of understanding”: that’s the HAPPY ENDing you are looking for!
    Who will say that first sentence?  With a big SILENCE you could make him ask for it 😉

Great HAPPY ENDing to all your deals!!!

Many thanks to Jack Vincent and John Niland for their insights and entertaining presentations.

 

 

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Sexism spotted with Maths!

cc-restore2

I did a talk in May this year called ‘Restore the balance of data’ at the Data Innovation Summit.  It was about sexism and other biases that are implicit in our existing electronic traces (actual and historical data) and my concern because we are using that data as baseline information to create the new prediction algorithms.

I’ve discussed this many times at home when preparing the talk.  We had vivid discussions with my husband and lovely sons over our family Sunday lunches. That’s how it didn’t surprise me that my eldest son, Alex, thought of me when reading  this article of the MIT Technology Review about sexism in our language.

The article is about a dataset of texts that researchers are using to “better understand everything from machine translation to intelligent Web searching.”  They are transforming words in the text into vectors, and then applying mathematical properties to derive meaning:

It turned out that words with similar meanings occupied similar parts of this vector space. And the relationships between words could be captured by simple vector algebra. For example, “man is to king as woman is to queen” or, using the common notation, “man : king :: woman : queen.” Other relationships quickly emerged too such as  “sister : woman :: brother : man,” and so on. These relationships are known as word embeddings.

The article is about the problem that researchers have identified on this data set, they say “: it is blatantly sexist.”  Here are some examples they provide:

But ask the database “father : doctor :: mother : x” and it will say x = nurse. And the query “man : computer programmer :: woman : x” gives x = homemaker.

Thinking about it, isn’t it obvious that if we have biases on our behavior, the writings about our world would be biased too?  And anything derived from our biased writing traces will reflect our views with all our biases too.

So we learned to extrapolate from our old behavior to predict our future behaviour… just to discover that we don’t like what we are getting out of it!  Our old behavior, amplified by the algorithm, doesn’t seem so good isn’t it? It’s clearer than ever that we don’t want to continue behaving like that in the future… Well, that’s a positive point, it’s good that this uncovers our blind spots, isn’t it?

Now the good news: it can be fixed!

The Boston team has a solution. Since a vector space is a mathematical object, it can be manipulated with standard mathematical tools.

The solution is obvious. Sexism can be thought of as a kind of warping of this vector space. Indeed, the gender bias itself is a property that the team can search for in the vector space. So fixing it is just a question of applying the opposite warp in a way that preserves the overall structure of the space.

Oh, seems so easy…for mathematicians anyway 😉  But no, even for mathematicians it is difficult to find and to measure the distortions:

That’s the theory. In practice, the tricky part is measuring the nature of this warping. The team does this by searching the vector space for word pairs that produce a similar vector to “she: he.” This reveals a huge list of gender analogies. For example, she;he::midwife:doctor; sewing:carpentry; registered_nurse:physician; whore:coward; hairdresser:barber; nude:shirtless; boobs:ass; giggling:grinning; nanny:chauffeur, and so on.

Having compiled a comprehensive list of gender biased pairs, the team used this data to work out how it is reflected in the shape of the vector space and how the space can be transformed to remove this warping. They call this process  “hard de-biasing.”

Finally, they use the transformed vector space to produce a new list of gender analogies[…]

Read the full article if you are interested on their process to de-biased.  Their conclusion, with which I completely agree is:

“One perspective on bias in word embeddings is that it merely reflects bias in society, and therefore one should attempt to debias society rather than word embeddings,” say Bolukbasi and co. “However, by reducing the bias in today’s computer systems (or at least not amplifying the bias), which is increasingly reliant on word embeddings, in a small way debiased word embeddings can hopefully contribute to reducing gender bias in society.”

That seems a worthy goal. As the Boston team concludes: “At the very least, machine learning should not be used to inadvertently amplify these biases.”

 

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