Startup science: a scientific approach to build success.

The role of a startup/transformative entrepreneur consists of changing the world in a better way, making the world a better place.

We often confuse the sentence “changing for the better” with “creating something new”. We are used to ask the question “What does influence the success of a startup entrepreneur?” but I do believe that the right question should be “What a startup/transformative entrepreneur can do to influence the success of his own company?”.

The second one sounds more interesting, doesn’t it?

At 42Accelerator we have a couple of answers in mind. They are not ours, we borrowed them from the American entrepreneurial culture, then we filtered and validated them on a daily basis through our experience. Our job consists of updating and refining them every day, in order to be able to transfer them to teams we invest in. Here is what a startup entrepreneur can do to change the world. Going ahead with this article, you’ll realize why this topic is related to science and why I call it Startup Science instead of Startup Crystal Ball.

1. You can build models to represent the world, as it is and as it should be.

As scientists do. The transformative entrepreneur continuously produces models: business models, models of metrics, model of growth because he wants to own the power of changing things in a time-persistent way and with a scalable impact.

How can we produce models? Disfluency.

It starts from recognizing the pattern in the reality. A pattern is a recurring scheme, a repetitive structure of causes and effects, very complex.

The reality returns data and information in the form of events and being able to recognize the scheme is a skill that psychologists call disfluency. Not all the schemes can be reproduced (or the opposite, avoided), so recognizing the scheme doesn’t necessary imply that it can be used, but it’s an important starting point and the disfluency is a requirement for the entrepreneurial success. Too many people ignore it. Too often business ideas focus on what it’s possible to do with technology and not on the observation of the reality that drives you to what it takes to do that. After that, it’s necessary to identify the behavioural model of the events involved into the scheme, the reason why things happen in a certain way. Here comes the funny part of those who choose to do this job: the reality produces schemes, making hypothesis and validating the model in order to reproduce it in a very systematic way to look like a determinist model is up to you.

It’s not a coincidence that a startup team gains high credibility to the eyes of the investors once it proves to have a stable traction, that is being able to modeling the acquisition process and service in a way that is so accurate that it can be mechanically reproduce into the reality with no unexpected events.

Mark Zuckerberg has transformed Facebook into the biggest photo sharing platform because he obsessively observed the behavior of its early adopters and noticed a pattern: they often changed their profile picture, with apparently no reason. He identified what it’s called “unintended use of technology”.

 

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This is the first reason why the entrepreneurial success has a scientific root: “science” refers to that systematic process of building and organizing the knowledge in the form of objective descriptions and with a predictive trait of the reality and the laws that rule it.

The practical advice that we suggest to you is: train your disfluency. Train yourself to understand meanings from information that the rest of the world see as an inconsistent amount of data. This is your real competitive advantage: not the patents, not the solution idea, but the ability to read the real world through new lens. Be hungry of schemes.

An idea is nothing, an idea with an observation behind is the beginning of a change.

2. You can validate models.

There’s a difference between entrepreneur and startup/transformative entrepreneur. When talking about entrepreneurship, if we ignore this difference, it’s easy to misunderstand. A traditional entrepreneur, who follows a plan and who doesn’t innovate, he doesn’t necessary need to acquire the habits and the behavior of a startup entrepreneur that allow him to reach his goal of changing the world. To describe this difference I use the metaphor of poker. I’ve never played poker, but the way a poker champion described the difference between an intermediate player and a professional one inside this book really impressed me.
 

At an intermediate level, your goal is to know as many rules as you can, you are greedy of certainties. To become a Pro, you have to start to see the bets as a way to ask questions to the opponents. <<Wanna fold? Wanna raise? How often do I have to provoke you before you start acting impulsively?>>. Only when you get an answer, only in that moment, you are able to predict the future in a more accurate way. The art of poker consists of using your chips to gather unreleased information, before anyone else.

The art of starting a company consists of using your limited resources to gather useful information from the market before anyone else (so unreleased). The day-by-day of an entrepreneur consists of asking the right questions, at the right time, in the most frugal way possible, and being able to accept the answers.

Why frugal?

Because questions refer to hypothesis on the models that you formulated during the previous step. The process of constantly questioning the market with the aim of gathering useful information is called validation process and consists of verifying those hypothesis.

If the answer stultifies the hypothesis, you have to put yourself in a position of asking another question, and another one. That’s why I said frugal, because you have to make sure to always keep a few chips in your pocket.

This is called “execution”, and it’s so hard and difficult that startups fail because of “lack of execution” (that is “lack of validation”), not because the market of investments doesn’t work correctly. So, if this is the art, where is the science? The science adds the method. The method helps intermediate players developing the art of the choice in conditions of uncertainty, that only pro players have. When the method enters into the muscle memory, it becomes habit and mindset, and only at this point it becomes art. This is an aspect that the majority of entrepreneurs (those who end up by not excelling) ignores (because of lazyness, of course). The startup entrepreneurship, the innovation, is not a job for lazy and undisciplined people. The method is exactly the scientific and experimental one: as Charles Duhigg said:

The paradox of learning how to make better decisions, is that requires developing a comfort with doubt.

If it’s true that you are doing something new, that no one has done before, then it’s also true that you live into the uncertainty domain. Almost by definition, you start from assumptions. The experimental method teaches you to:

  • recognize the assumptions
  • isolate the most risky ones for your model
  • formulate an experiment that stultify them (using the entrepreneurship language, this is called MVE - minimum viable experiment)
  • formulate some goal metrics that help you to objectively compare the results
  • making experiments to gather results
  • decide if you need to stultify or validate the assumption and what this implies for the model, using an entrepreneurial language it’s called “Pivot or Persevere?”.

This is the process followed by biologists, physicists and inside the Google innovation laboratories, where everyone who has an idea accept the challenge of stultify it and prove that it’s wrong. Everyone involved in the innovation space should use the same approach. Following this process there’s no success or failure, just learning and the winner is the one who learns the most in the fastest way. As in the poker.

3. You can be coherent

The basic rule of the validation process is consistency.

This is related to science too, because the correlation between consistency and success has been scientifically validated by applying the finite state machine model of Markov to the life-cicle of over 600 startups (see: Startup Genome). The rule of consistency is extremely important from a practical perspective because it suggests to you which questions ask to the market, and when ask them.

In fact, it’s very important to have some kind of answers since the beginning. But there are other ones that it’s useless to get in the first stage because they just let you waste your time in finding them and they could be misleading.


The ones related to the market (who) and the problem (why), have to be got before the ones related to the offer (how) and the product (what) or the financial sustainability (how much).

The reason is apparently obvious: developing the product is useless if you don’t know the market very well and the important problem you aim to face for that market. I said “apparently” because focusing on the development of the product first and then try to put it on the market is the most human, arrogant and common thing that I see happening.

Innovating is neither a right nor an obligation. It’s a privilege, a permission that the market agrees with the entrepreneur, before he starts developing something new.

To make it easier for the entrepreneur to ask the right questions at the right time, different kinds of meta-models have been developed and they allow you to recognize - with more precision - in which stage you are, and let you to know what to do.


To mention a few of them: The Customer Development model (Steve Blank), the Startup Genome stages of growth (Max Marmer, Steve Blank et al.), The Disciplined Entrepreneurship model (Bill Aulet, MIT). Everyone offers a quite strict definition of thresholds and milestones that go from problem-solution fit, to product-market fit and beyond.

The experience and the science prove us that an entrepreneur moving organically from a step to the next one has way more chance of success compared to the one who acts incoherently against the development stage.

With all that said, inconsistency is still the main cause of failure.

To better understand what it means, let’s think about the startup as 5 dimension object.

  • team: the founder, the employees
  • product: the first tests, prototypes, the industrial version
  • finance: the personal one, pre-seed, seed, next rounds and IPO
  • market: early adopters, large majority
  • business model: you have to validate it starting from the right side to the left side (del cosiddetto business model canvas)

The symptoms of the incoherence can be different, these are the most frequent ones:

  • A team that is not balances against the market
  • A product that is too big against the market
  • Too much money (or too little) against the real validation of the business model.

Yes, too many, raised too early, are bad for your company.

The majority of the exclusions from our acceleration program are due to inconsistency, because our experience proves that is too hard to recover it.

Because inconsistency puts you in a loop of wrong decisions. It doesn’t mean that the decision is wrong, but the focus of your decision is and so the startup is completely out of focus.

The practical advice that we like to provide in these cases comes from an ancient Turkish proverb that says: “No matter how far you have gone on the wrong road, turn back.“.

Train yourself to come back, or even better, to frequently check if you are on the right way.

In a situation where you have to draw your own path, it’s objectively hard, and that’s why it’s very important for a founder to have the support of other people that know the nature of the journey you are going through and that are able to keep his focus on the right goals at the right time.

Education: The PhD Factory.

It is not unusual nowadays to encounter published papers from esteemed journals like Science or Nature, wondering about the current situation of Science PhD. Among all of those, “The PhD Factory” published by Nature in 2011 is particularly relevant and accurate in performing a comparison among systems throughout the whole world, like USA, Japan, Germany, Singapore and others.

We discover that often the desire for a larger population of highly educated scientist comes from political reasoning, aiming to a faster economic growth boosted by technological innovation. Unfortunately, it is more rarely found that governments act to create favorable conditions for such a supply of new PhD holders to enter the economic tissue and face the shrinking of academic positions.

Singapore: “I see a PhD not just as the mastery of a discipline, but also training of the mind,” says Ng. “If they later practise what they have mastered — excellent — otherwise, they can take their skill sets into a new domain and add value to it.”

USA: “It’s a waste of resources,” says Stephan. “We’re spending a lot of money training these students and then they go out and get jobs that they’re not well matched for.” [...] Some universities are now experimenting with PhD programmes that better prepare graduate students for careers outside academia.
 

Author of the article: Nature
Commented by: Daniele Conti

 

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From PhD to Entrepreneurs: here is the story of Hotblack Robotics.

The Hotblack Robotics’s story started in 2011 when TIM wanted to invest in Research and Development for new technological trends: Internet of Things and Cloud Robotics. Since the idea seemed to be interesting in 2012 TIM decided to strictly collaborate with top Italian universities. So they founded new laboratories inside the academic world. They were called Joint Open Lab and the idea was to have mixed team, both academics and TIM employees, working together in innovative projects. The lab where I carried out my Phd was JOL CRAB (Connected Robotics Application LaB). The vision of the laboratory was to realize a cloud robotics platform and find different markets where this idea could generate revenue. The lab had different test cases to show the potential of a cloud platform:

  • robots for energy monitoring into data centers
  • UAV management into smart cities
  • robots for education and entertainment
  • robots for agriculture

In addition one of the aims of the lab was also to create start-ups in order to speed up the go to market process. So the 23rd June 2015 me and my Phd colleague Ludovico Orlando Russo decided to found a start-up with the same vision of the lab and bring our scientific results to the market: we founded Hotblack Robotics. We decided to start from the most mature project that was a robot for energy monitoring into data centers. The result was that the shift from the scientific to the entrepreneur world was very painful and full of unexpected problems.

   
  
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  Fig 1. Robot for energy monitoring into data centers

Fig 1. Robot for energy monitoring into data centers

Data centers have a huge energy electrical consumption due to the fact that they need to cool down servers temperature continuously. There are many solutions for this and one of the best is to use a wireless sensor network to have a real time energy monitoring analysis. The problem of this solution is that in big data centers they need to install many different sensors so the network is very big and the maintenance very expensive. The idea was, why having many sensors into the environment while we could have only one moving around the entire area? So we realized a robot with sensors moving around autonomously and giving information in real time of the entire data center. It was working perfectly after 4 years of hard development with the state of the art algorithm to make the system working robustly. So we decided to bring it into the market since we had also some possible customers inside TIM. What was missing? It was a technology solution looking for a problem, thing that in business is very dangerous.

 

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Fortunately we apply the lean startup methodology in a very scientific way. So we interviewed 31 site manager and asked them how they were managing the infrastructure to have a better insight of the market. In particular we have learnt that it is composed as follow:

  • Housing service, is the service where customers are paying only for the physical space. They put their servers into this rented space and the service provider must guarantee that their servers stay within a certain safe temperature range.
  • Hosting service, the customer is paying only for remote data storage.
  • Cloud Service, it is very similar but the customer is paying only according to the space used day by day or hour per hour. So it is more flexible.
  • Internal data centers, are those data centers that are into a bigger company. The company doesn't want to deliver storage service to a different company so it decides to have everything internally.
  • Audit services, are those services to evaluate the energy efficiency and improve it.

Then we identified our competitive value and find the right market accordingly. Our solution was useful for physical monitoring of the servers in dynamic context and where the energy consumption is considered very big. We realized that customers that provide hosting and cloud services were not interested in our solution because they have no physical hardware to monitor.

Housing service seemed to be interesting because of its huge energy expense. Unfortunately we found out that most of them are designing new infrastructure in order to keep the efficiency always at the top. So we thought to internal data centers. Internal data centers are interesting because from the company perspective this infrastructure is always a cost. So we interview General Electrics CTO Fabio Borri. He told us that actually the energy problem is not the main reason of this cost but the infrastructure itself and all the IT team to support it. In fact they are shifting to a big centralized data center world wide that are designing with the highest possible energy efficiency. 

Only audit services was left as the latest chance. An interested potential customer was telling us that the main interesting feature was the dynamicity. So we thought that this would have been the right market. Unfortunately it ended that audit market for data center is too small for a business. In addition we needed to have audit skills for energy monitoring, thing that was not suitable to our vision as cloud robotics. We were basically out of any market with many prototypes and no people interested in using what we built!

We didn’t surrender but we took this experience as a good lesson learnt for creating something better.

So we thought about our key features. With a cloud robotics platform we can:

  1. Make robotics application development easy and accessible to everyone
  2. Through the internet we could seed a community of developers
  3. We could separate software from hardware and selling only the cloud platform itself instead of dealing with difficult services

So finally with the same strategy we found a market for makers and robotics enthusiasts! Now the cloud robotics platform is accessible at www.hotblackrobotics.com/cloud and helps everyone to use robots and write software in an easy way. It is an education and entertainment service on-line that makes everyone that want to use robots to make it working easily thanks to the power of the cloud. There are some cool functionalities such as voice recognition and face recognition that are already implemented into the platform and a growing community! And many and others things yet to come..  

  
 
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     Fig 2. Hotblack Robotics team: Gabriele Ermacora, CEO, and Ludovico Orlando Russo, CTO   

Fig 2. Hotblack Robotics team: Gabriele Ermacora, CEO, and Ludovico Orlando Russo, CTO

 

Research under the industry lens.

Most PhD students believe that academic post-doc is their only option, but this is the result of a lack of information on which non-academic career options are available to them and which of these positions fit their goals and lifestyle. It’s also very important for PhD students to pay close attention to changing trends and to make sure to note which job sectors are rising and which are falling.

As mentioned in our previous article PhD in Wonderland. Dream or Reality?, a valid reason to consider other options to the academic postdoc is the fact that - talking about the US, for example - there is an increasing amount of opening PhD positions that would never be absorbed in academia and - as emerged during a recent conference hosted by the UK Council for Graduate Education - 80% of PhD students are aware that it may be hard to get a job as a post-doc or junior research associate and secure a lifelong academic career.

The purpose of this blog post is to provide you with an overview on the world of research in industry, highlighting the benefits that this non-academic career option offers to you and the differences with the academia, in order to let you choose the right path for your career.

So, let’s go deeper and see what working in industry as a researcher actually means.

"In business, everything begins with the profit motive. ... Just the very idea of research is geared towards a product rather than knowledge itself. The most critical factor in determining whether a scientist is going to be successful in making the transition from the university to the private sector is the ability to buy into that point of view." said Michael A. Santoro, a business ethics professor at Rutgers Business School in New Jersey.

This statement introduces the first difference between academia and industry: in industry you have the chance to see the result of your research becoming a solution to a real-world problem and you can build something that the company can use and test in the real world.

But this is not the only benefit.

 

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Your progress is measured accurately and consistently based on performance.

Your performance on assigned work and shared goals is used to accurately measure your progress. Your performance is also used for determining promotions, salary increases, and bonuses. Not only this, but there are specific people designated for measuring your progress and they are actually held accountable to it.

As a research scientist in industry, you will report to a manager who will be responsible for advancing your team’s goals and the company’s vision overall.

Moreover, at the beginning of every fiscal year, you will write a Performance Development Plan (PDP), or similar, with clear business and personal goals.

These goals must be Specific, Measurable, Achievable, Result-Oriented and Time-Bound, also known as SMART goals.

You are rewarded for being a leader and showing initiative.

In industry, however, you will work with a very fast work pace and be expected to deliver accurate results quickly.

Working at a faster pace is enjoyable for different reasons, for example:

  • you are working with a team whose goals are aligned with yours
  • you are rewarded for finding better ways of doing things

If you take the initiative to improve the workflow of a system, improve a product, or make any part of the company better—you’re rewarded.

The more confidently you take initiative, the more you are rewarded.

You are part of a supportive and structured work environment.

Finally, as an industry research scientist, or as a professional in any of these non-academic careers, you are not alone and you know exactly where you’re going. In industry, every scientist has his own project.

At the same time, ever scientist is trained to support each other.

As the French chemist Christophe Eychenne said “Industry is not the dark side. Mostly, we can't find breakthroughs in the industry without the academy, and we can't find money for the academy without applications in the real life. Rather, it's just "another side" of the research endeavor.”

No jobs in academia? Consider becoming a scientist-entrepreneur.

An interesting article showing how the skillset you have as a researcher can be very useful for your career as a scientist-entrepreneur.

Take money from Business Angels or Venture Capitalist to develop your own idea can be a valid alternative to raise money from federal and no-profit organizations.

You have a solution to solve a problem (your research), you can write a business plan (like you write a grant application), you are able to pitch your idea (like you pitch during seminars)...so, you are probably a scientist-entrepreneur right now.

If academia represents intellectual freedom and independence, and industry represents job security and earning potential, then entrepreneurship is the best of both worlds.

In a biotech start-up, you are taking your own idea and framing it within the context of an applicable product. There is no more translational research than this, which epitomizes bench to bedside. Taking money from a private entity to get you started is not all that different from taking money from the federal government or not-for-profit organization (NPO); in fact, you will still need to do this as a private company.
 

Author of the article: University Affairs
Commented by: Enrico Cattaneo

 

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PhD in Wonderland. Dream or Reality?

Talking with other PhD Students and colleagues of mine, it emerges that the main drivers for choosing a PhD in science is passion. We love what we are doing, the project we took part into becomes a piece of us day after day. Most of us are not requested to stay into the lab such a large amount of hours, nevertheless who has never spent a night in there? It happens so frequently to see lights still turned on in department's labs late in the evening and, to whom is able to observe deeply, immediately a thought comes up in mind: “Research is going on there!”

That probably is the most meaningful definition of research: free-minded students' energies devoted to push knowledge ahead and to discovery.

That probably is the most meaningful definition of research: free-minded students' energies devoted to push knowledge ahead and to discovery.

On the same hand, everybody of us is aware of the whole set of difficulties that have to be faced every day in doing recognized quality research. Low salary per number of worked hours, lack of investments in infrastructures and materials, difficulties in publishing research in high ranked journals and most of all, the tremendous lack of opening positions in academia that would allow to proceed in a permanent career in university. It naturally makes arise a quest for positions all around the world that, although it allows a faster growth both from the professional and the personal side, it also demands a considerable amount of years of instability and precariousness...

The problem begins to be recognized worldwide. Recently a number of articles from respectable scientific journals, such as Nature and Science, addressed the problem of increasing amount of opening PhD positions in the US that would never be absorbed in academia [1]. It is the American Institute of Research (AIR) that in 2014 takes the task of quantifying the phenomenon of STEM (Science, Technology, Engineering, Math) PhD holders that rather than running for tenure track, exit the university to dive into the market [2].

Totally, 61% of PhD holders find a place in Industry, while the amount increases to a high 74% when only Engineering is considered. Interestingly, the 88% of graduates with doctoral degrees working in Industry at the survey moment comes from three major fields: Engineering, Biological Sciences and Physical Sciences (fig. 1) with an overall amount of 57% of PhD holders still working in Research and Development at different levels: Basic Research, Applied Research and Development; the remaining 43% is involved in management and other professional services. The combinations of these data make arise the need for soft skills to be acquired alongside with technical skills. Communications to general public, project management, basics in economic balance and market trends are missing competences to STEM PhD holders that may heavily affect the transition to industry into a tough job even for highly qualified researchers.

  Figure 1. Distribution of PhD degrees of workers in Industry at 2014 in the USA. Source AIR [2].

By the way, despite the impact that it can have on the researcher’s working habits, this kind of transition could provide benefits from a financial point of view. Indeed, OECD research states that higher education provides consistently higher salary with respect to secondary level education, to an amount that is about +150% higher for the US and about +100% in EU [3].

 

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Data definitely state that pursuing PhD is a profitable investment, but it is also as much evident how risky it could be running for an academic career. Indeed, focusing on the Italian scenario, statistics collected by the University and Research Ministry and elaborated by PhD and Researchers Association (ADI) make it clear that how only 8% of researchers will be absorbed into the academic path within 2020 [4]. Moving to the UK scenario, this percentage reduces to about 3.5% when Permanent Research staff is considered and drops to 0.45% for a Professor Position (fig. 2) [5].

  Figure 2. Diagram illustrating transition points encountered in typical career for scientific careers following a PhD. Source The Royal Society [5].

Talking to other PhD Students and colleagues of mine, it emerges that the longer they are into research, the more they become concerned about their future. Sometimes the will for staying into academia comes mainly from the lack of knowledge of what there is outside; Industry is often perceived as subject to money rules, as the killer of the free-spirit driving discovery.

Probably we will persist in being deaf to what data tell us, despite our analytical mindset, but sooner or later we will have to deal with the tough choice between academia and something else. It will be better to know more about the rivals.

 

References:

  1. Cyranoski D, Gilbert N, Ledford H, Nayar A, Yahia M, Education, the PhD Factory, Nature 472, 276-279 (2011)

  2. Turk-Bicakci L, Berger A, Haxton C, The Nonacademic Careers of STEM PhD Holders, Broadening Participation in STEM Graduate Education, STEM at American Institutes for Research (2014)

  3. OECD (2015), Education at a Glance 2015: OECD Indicators, OECD Publishing. http://dx.doi.org/10.1787/eag-2015-en

  4. V Indagine ADI - Il reclutamento di assegnisti ricercatori a tempo determinato di tipo a e b, ADI Associazione Dottorandi e Dottori di Ricerca Italiani. https://dottorato.it

  5. The Scientific Century, securing our future prosperity. The Royal Society. ISBN: 978-0-85403-818-3, Issued: March 2010 Report 02/10 DES1768