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.

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.

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.

Big Data workshop at the First European Celebration of Women in Computing

ECWCThis last Tuesday, I lead the ‘Discover Big Data’ workshop at the First European Celebration of Women in Computing.  There were many parallel sessions that morning and I received some questions about my presentation from the participants that couldn’t divide themselves to attend this workshop 😉

Welcome to the Big Data workshop, we need women in Big Data!

This workshop is called ‘Discover Big Data’ because Big Data is a hyped word. It is being used for anything where data is involved, but it still remains confusing as what it means.

  • You are also in Big Data  if you are dealing with data that has to be processed at great velocity, as is the case for GPS or for mobile phones.
  • You are in Big Data if you cross information that come on a variety of formats, like your customer’s transactions and your customer’s emails, or if you go to the social networks, like Facebook or  Twitter.  You can discover what are the topics being discussed, what is being said about your company or  who is talking about your product.
  • You are in Big Data  if you’re exploiting one of the many big available datasets like weather information, official administration records like property records or  financial information, economic indicators…

What can be done with Big Data?

It is mainly used for customer intimacy, discovering your customer profiles and target them on a one to one base. Finding their preferences and the hidden patterns to predict customer churn.

It can be used for optimisation, finding patterns of systematic problems hidden in your historical data. It can help for organising your maintenance, or to improve the supply-chain, finding better logistic solutions, optimise processes.

It is also used for innovation: It can help you create your new product. Looking at your competitors and finding the white-spaces or uncovering market trends.

And more generally, with all the available data you can create models forecasting future events and behaviors. Through what-if analysis to predict the outcomes of potential changes, you can direct your business strategy. It helps anticipating previously unforeseen opportunities, as well as avoiding costly situations, finding new revenue opportunities or identifying more effective business models.

As you see, there are great business opportunities!

How can we do all that?

There are many techniques like statistical analysis, data mining, text analysis, sentiment analysis, graph analysis, machine learning, predictive analysis, neural networks, conceptual clustering…

You may have heard already some of those words that sound promising but that also sound very complicated. And even so, the Big Data field is growing exponentially as men are running for it.  There are only 10% of women, don’t you want to be part of it? Companies that took this wave are thriving, well ahead of classical business. They are proposing you the right product at the right time, with the features you are looking for, for the price you are willing to pay. They are  increasing their profits while shaping our future with the products and business strategies they are creating.

I hear you saying: This is great but I don’t know a thing about this and it sounds so complicated. I’m here to tell you that not all of it is that difficult.

YOU could be in Big Data.

If you are in computing you have a leg up. And if you like mathematics you’ll enjoy being a data scientist. But you could be in Big Data even if you are not a techy person.  If you are in HR, in marketing, if you are a manager or a decision-maker with the right mindset open to data, you can exploit the Big Data wave.

Even if you see the potential, women tend to think ‘it’s not for me, I don’t have the competencies’.

Let me use some feminine stereotypes to illustrate we have the basic skills:

  • We have a tradition of getting together and talking too much.  And we have a tendency to be matchmakers.  We can put those skills of information gathering and making connections to good use finding relationships between data.
  • Who recognises herself in this? We are control freaks and plan everything, even the time of our loved ones.  Don’t you have a TODO list for your partner on Saturdays?  I do: Love, since you are driving Alex to the scouts, could you please pass by and drop the trousers at the dry cleaner?  What if you knew what your GPS knows already, that a road is blocked?  You could have asked him to bring some bread back as he’s going to pass near the bakery.  Don’t you feel satisfaction when doing things efficiently, optimising the Saturday time? So imagine tapping into all the available information and using it to improve the processes, it’s a rewarding job.
  • And if you have artistic skills, visualisation is your field. This is a new branch of data science, they are creating new techniques very interesting to show more than 3 dimensions of data, so you can see easily relationships graphically.
  • Generally speaking, I think we women have a natural talent to be data analysts: the ‘What if’ comes natural to us, we always investigate all possibilities before deciding for one, isn’t it?

Summarising, we saw there is business in here, and that we have the basic skills to be in the Data business.

Moreover, it is important that more women move into this field, not only because of the many business opportunities, but also because there are ethical issues involved in Big Data. We can mention data privacy and price gauging as some of these issues, but there are other business models that can be controversial.

The rules of what can be done with the data and what is off-limits, are being defined right now.  Let’s not miss the opportunity to give our view on this.

As an example, there is a great initiative from the Data2X program of the UN, who’s doing a research on women’s freedom of movements through satellite images and their phone geolocation.  Are they limited in their movements in some countries, do they have access to education, to health care? Great initiative, but what about the same at a private level: is following the movement of your partner with her/his phone geolocation ethical? What about tracking the movement of your children, as it’s done already in some countries?

It’s important to have our saying in the ethical uses of all those lakes of data and be represented in the decisions that will define our future society. We, women, have a natural tendency of looking after our loved ones, taking their needs in consideration. That’s what Big Data is needing, people that set the rules for using the incredible amounts of data, taking into account the different perspectives and with a long term view in mind. It’s the moment to use our feminine voice to shape a better society for all of us, participating also in the creation of the new business models.

In this workshop you will hear success stories to show you the opportunities to be included in this field. I hope you’ll join the Big Data movement.

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?

Testing tool for Big Ideas

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

 

Pimentochart

[..] 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!

MOOCs: the new learning style

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:

Alex Pentland’s article on Data-Driven Society

I recently got the new issue from Scientific American (October 2013), and in the front page was announced the article ‘The Data-Driven Society’ by Alex Pentland.  I just had to read it 🙂

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.