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Cognitive Computing Systems: Want to build a great MVP? Pick your ingredients carefully

Over the last five articles I have hopefully presented a very strong business case for the value that a cognitive computing system like Herbie can bring to startup founder like Keith and his team. They have successfully collaborated up to this point and have developed a very strong product vision:

We believe that we can build a cognitive computing system that acts both as an early warning system for students “at-risk” of dropping out of high school and as a virtual education “doctor.” The system would be capable of diagnosing in real-time the reasons why a student is likely to drop out and then be able to specifically tell all relevant stakeholders what actions they could and should take to help the student qualify for graduation. Student Lifecycle Management is a key component to solving the graduation problem and it will be powered by a national cognitive computing system.

Now that we know what we want for dinner we need to talk about the recipe we want to use. The recipe I’m referring to in this case is the specific product features and benefits that will bring our solution to life. By leveraging all of the complex analytics completed up to this point and including the lessons learned from our direct customer interaction, Herbie is ready to help Keith develop and finalize one the last important steps before delivering the Minimum Viable Product (MVP): Documenting the product features list.

The recipe for your MVP is a list of the specific product features and benefits to your solution Click To Tweet

The product features list is a one-page document consisting of one or two sentence summaries of the top 10 (or fewer) features of the complete product vision. Essentially, this is the product development team’s contract with the rest of the company. The biggest challenge will be deciding what features will ship in what order. Developing the MVP will start the prioritization process.

The product features list is a 1 page document of the top features in your product vision Click To Tweet

Customers will be critical in guiding the process as they begin interacting with the earliest version of the MVP. You can think of features as the things that the engineering team is building, and the benefits as the problem you are solving for the customer. Your goal is to describe the specific product benefits as seen through the customer eyes (Something new? Better? Faster? Cheaper?) to then develop a “user story.”

A user story is a short narrative that explains what job the product will do. How will it solve a problem that customers are eager to fix, or fulfil a need they have? Ideally, the product solves a mission-critical problem, delivers a compelling, exciting customer benefit, or addresses an unspoken need:

  • Unfortunately, by the time a high-risk student reaches high school as a freshman, many lack both the academic and social skills necessary to see the long-term value of staying in school;
  • We lose as many as 55% by the time their class becomes seniors;
  • Educated workers are the basis of economic growth. They are especially critical as sources of innovation and productivity given the pace and nature of technological progress;
  • Studies show that the typical high school graduate will obtain higher employment and earnings — an astonishing 50% to 100% increase in lifetime income — and will be less likely to draw on public money for health care and welfare. They will also be less likely to be involved in the criminal justice system. Further, because of their increased income potential, the typical graduate will contribute more in tax revenue over their lifetime than if they had dropped out;
  • When the costs of investment to produce a new graduate are taken into account, there is a return of $1.45 to $3.55 for every dollar of investment, depending upon the educational intervention strategy. Under this estimate, each new graduate confers a net benefit to taxpayers of about $127,000 over their lifetime. This is a benefit to the public of nearly $90 billion for each year of success in reducing the number of high school dropouts by 700,000 — or something close to $1 trillion after 11 years;
  • All stakeholders who significantly impact an individual student’s decision to stay in school until graduation will receive real-time advice on how to effectively collaborate to minimize or eliminate the negative environmental elements that influence many to dropout prior to graduation; and
  • This transaction based omni-channel student lifecycle management cognitive computing system will be the tip of the spear that drives behaviors that will enable the US to achieve a national graduation rate of 90%+ by the year 2020.
A user story is a short narrative that explains what job the product will do Click To Tweet

Remember that at this early stage we are still in the process of developing hypotheses that we must test and validate with real customers. All that is required is to follow the customer discovery process with ideas that are reasonable educated guesses. The goal is to quickly move into building an effective MVP that you can put in front of your prospective market for immediate feedback and iteration.

Creating an MVP can increase development productivity between 14 – 95% while reducing costs by 7 – 29%. Being able to quickly bring a product to market, gather crucial feedback, and iterate on it in real-time is a formidable competitive advantage.

Creating an MVP can increase development productivity between 14-95% and reduce costs by 7-29% Click To Tweet

Do you feel comfortable with the process of defining your product features and benefits? Do you believe that Keith and Herbie have collaborated well up this point and come up with a great product vision? With the help of a cognitive computing system like Herbie, do you think that it is possible to achieve a national graduation rate of 90%+ by 2020?

Let me know what you think.

Cognitive Computing Systems: Want to build a great MVP? It’s all about the prescription…

As I discussed in my last article, with Herbie’s assistance Keith and his team determined that being able to proactively predict the likelihood that an at-risk student is going to dropout of high school would be a key component of their product vision. The other half of the equation involves the actions that an educator and the relevant stakeholders should take once a specific problem with an at-risk student has been identified. This involves a brief discussion about the science of prescriptive analytics and how Keith and his team plan to leverage this capability to help complete their product vision.

As a cognitive computing system, Herbie was instrumental in developing the analytical models (called the High School Dropout Propensity Score) needed for early identification of at-risk students. He also took the next step by leveraging prescriptive analytics to provide educators and relevant stakeholders an interactive voice that would offer them iterative advice on how to set the student back on track. This article will define prescriptive analytics, as well as the role it will play as part of our Minimum Viable Product.

Prescriptive analytics builds upon descriptive analytics and predictive analytics; it predicts what will happen, when it will happen, why it will happen and what the best course of action is to optimize outcomes and reduce risk. These solutions combine predictive models, deployment options, localized rules, scoring and optimization techniques to form a powerful foundation for decision management.

Prescriptive analytics predicts what, when & why something will happen and the best course of… Click To Tweet

As a follow-on to the extensive hypothesis development and testing being continually performed by Herbie, he will begin to use those simulated outcomes to develop a prescriptive list of actions that should be taken when those “conditions” occur again in the real world. Machine learning will allow Herbie to build a comprehensive library of real-time actionable “prescriptions” that will directly impact how effectively and efficiently educators and relevant stakeholders serve their at-risk students.

Machine learning can build a comprehensive library of real-time actionable “prescriptions” Click To Tweet

In the process of building this prescriptive library, Herbie and the team will focus on learning the characteristics, attributes and behaviors of the following demographics: 

  1. Low income students: The link between low income and low academic performance is strong, but research shows it is solvable. Among non-low income students, 40 states are above the national average graduation rate of 80%. However, among low-income students, 41 states are below the national average. The good news is states with narrow achievement gaps between low-income and non-low income students appear to be those with the most robust interventions in place to counteract the effects of poverty;
  2. Big city dwellers: While there are nearly 200 fewer dropout factories in urban areas in 2012 than in 2002, more than half of those remaining are located in large urban areas. Most big cities with high concentrations of low-income students still have graduation rates in the 60s, with a few even in the 50s;
  3. Students with disabilities: The national average graduation rate for students with disabilities is 20 percentage points lower than the overall national average. While graduation rates for these students varies greatly by state, these students represent 13% of all students. Without gains nationwide, a 90% graduation rate cannot be reached;
  4. The state of California: As the most populous and most diverse state, California needs to be the focus of national attention and work. With the highest poverty rate in the country, a median household income 20% higher than the nation’s, and a population that is 61% non-Anglo, California is key to reaching the 90% graduation rate nationally, but it also remains a laboratory of innovation in education reform. California has 14% of the nation’s total students, and 20% of the country’s low-income student cohort. The school age population is 52% Latino and 12% Asian/Pacific Islander, with a poverty rate among school age children of 63%; and
  5. Young men of color: Despite gains made by all students of color over the past six years, young men of color continue to lag behind other subgroups of students. In a sub-set of Midwestern and Southern states, which educate a large percentage of African American students, graduation rates for African American males remain in the upper 50s and low 60s.

It is important to think of prescriptive analytics as one of the many successful outcomes possible with a well-designed cognitive computing system. I believe that the real value that Herbie offers the founding team is a single-minded focus on finding better approaches to solving the designated problem based on real data received from real-life attempts to solve the problem with real customers. The only way to reduce bias is to minimize the programmer’s pre-conceived notions of the problem from the equation.

Cognitive systems offer founders a way to solve their problem based on real data from real… Click To Tweet

The ideal process should look like this:

  • The founding team presents Herbie a problem in the form of a question;
  • Herbie understands the context of the question and performs the necessary analytics to formulate an answer;
  • The founding team considers the answer and makes a decision as a team;
  • The founding team tells Herbie their decision. Herbie then tracks and records the results and impact of that decision; and
  • Based on this data, Herbie learns lessons and adjusts his approach to thinking so that the team will achieve better results and outcomes next time.

With Herbie’s help, Keith and his team have enough information to put forward their first product vision. Their first draft sounds something like this:

  • The United States used to be number one for high school graduation, but times have changed. In 2009, the U.S. ranked 21st out of 26 OECD countries when it came to high school graduation rate according to Andreas Schleicher, Deputy Director for Education for the Organization for Economic Co-operation and Development (OECD);
  • Portugal and Slovenia were tied for first in the rankings, Japan and Finland hold the number two spot, and the Czech Republic ranks 17th;
  • We believe that we can build a cognitive computing system that acts both as an early warning system for students at-risk of dropping out of high school and as a virtual education “doctor” capable of diagnosing in real-time the reasons why a student is likely to dropout, and then be able to tell all relevant stakeholders what specific actions they could and should take to help them qualify for graduation;
  • Millions of students, parents, educators and third party stakeholders will be able to collaborate at a level that was not possible before; and
  • By proactively communicating and executing best practice strategies for reducing dropout rates through the medium of their choice in real-time, we can leverage the entire global community to make sure that every child has a 100% chance of graduating.

This is just an example of the many different ways that Keith and his team could have drafted their product vision, but the documentation process is a crucial first step. It is important during these early stages that everyone agrees on a specific product vision so that the product development team has as clear a mandate as possible before building the MVP.

We can leverage the entire global community to make sure that every child has a 100% chance of… Click To Tweet

Now that the team has established a certain level of clarity on the product vision, it is now time to move forward with formally defining the product features and benefits. We will cover that in my next article.

Do you understand the power of prescriptive analytics and how it might be utilized to make sure that the right action is taken at the right time? Do you agree that focusing on the five demographic areas listed above will have the biggest impact on increasing our graduation rates?

Thanks in advance for your continuing feedback.

Cognitive Computing Systems: Want to build a great MVP? Define your Product Vision

In my last post I discussed why it was important for Keith and his team to focus on establishing a repeatable and scientifically sound method of hypothesis development and testing, and reasons why this was a critical step along the path of building an effective Minimum Viable Product (“MVP”). Now it’s time for Keith and his team to formally agree and declare their product vision for solving the graduation problem in America. This step is focused on developing a solid shortlist of product features that will be shared with the product development team.

Fictional startup founder Keith and Herbie, his interactive cognitive computing system, will now dig even deeper to investigate specific aspects of the graduation problem. GradNation Research has shown that there are four factors known to predict or exacerbate dropping out. They are as follows:

  1. Chronic Absenteeism. Missing more than 10% of the school year for any reason is an early indicator of potential dropout. Often associated with lower academic performance, this can be seen as early as first grade;
  2. Lack of Early Intervention. Middle grades are pivotal years that either set a student on the path to high school, college and a career, or a path to disengagement and low achievement in key subjects;
  3. Lack of System Visibility. There are more than six million people between the ages of 18 and 24 who currently are not in school, in possession of a high school diploma or working. These young people cannot be forgotten, and need access to pathways to education, employment and opportunities to take on the jobs of the future; and
  4. Lack of role models. Success in life cannot just come from a classroom education. Students need to develop additional skills, such as self-awareness, self-control, collaboration and conflict resolution. Young people need public, private and nonprofit agencies to work together to provide them with access to positive role models, not just adults, but also by giving them the opportunity to learn from their peers.
Missing more than 10% of a school year as early as 1st grade is likely to lead to dropping out Click To Tweet

On the surface, it is reasonable to assume that the effects of these challenges can be minimized or negated with a proactive and preventative approach. Educators should be able to predict and have sight of the small problems at-risk students are having long before they blossom into big problems.

Keith is exploring the development of iterative predictive models that will give schools the ability to identify these at-risk students very early in their school experience so that proactive actions can be taken to put them back on course. Herbie will leverage his cognitive computing infrastructure to create an effective attribute management program (this defines the data that will be used for analysis). This program will support the construction of industry standard predictive models. These models will feed an executive level scorecard that educators will use to actively and proactively manage the progress of at-risk students.

When it comes to predicting when a student is at-risk of dropping out, the sooner the better Click To Tweet

If having access to effective predictive analytics to an educator represents a major part of Keith’s product vision, then being able to actively provide that same educator with industry best practice advice as to what they should do next to address an identified problem is equally important. What specific actions should an educator take once an at-risk student has been identified?

Being notified of a student problem is not enough. What should an educator do? Click To Tweet

For example, assume that Herbie has built a predictive model that informs an educator that an individual student is falling behind in math skills. Knowing that the next math class will be even more challenging, Herbie executes a predictive model, (the high school dropout propensity score), that calculates the likelihood that the identified student will succeed in their next grade level. Depending on the results, Herbie will proactively send out a threat level notification to the proper educational stakeholder (i.e. principal, teacher or parent).

A realtime high school dropout propensity score would transform education Click To Tweet

Once the proper notifications have been seen, Herbie moves from predictive mode to prescriptive. The ability to offer real-time prescriptive advice in the context of the problem at hand is the biggest value that a cognitive computing system like Herbie can provide to an educator. Prescriptive analytics along with natural language processing is an integral part of the solution and as such will play an equally important role in the makeup of the MVP.

Real-time advice to an educator via prescriptive analytics is a key success factor Click To Tweet

Chronic absenteeism, the lack of early intervention, poor system visibility and lack of good role models are factors that are highly predictive of high school student dropout rates. I believe that a cognitive computing system working in tandem with educators is a key element to solving the graduation problem. Do you share my vision regarding the role that cognitive computing systems can play in significantly speeding up the process of identifying at-risk students, as well as offering consistent real-time advice to all stakeholders involved?

Let me know what you think.

Lean Market Development: How to turn an educated guess into a scalable business model

In my previous article, our fictional startup founder Keith continued his quest to discover a solution that will increase our national high school graduation rate from just over 80% in 2014 to 90%+ by the year 2020. He narrowed the team’s focus to four key elements of the problem and with the assistance of his cognitive computing partner Herbie, the team was able to establish a strong Enterprise Data Architecture (EDA) for the project. All of this puts them in a great position to take the next step towards bringing a solid Minimum Viable Product (MVP) to market: Hypothesis Development & Testing.

An MVP is a concise summary of the smallest possible features that will work as a stand-alone product while still solving the core problem and demonstrating the product’s value. As this is our final destination for this phase of customer discovery we have a few questions we need to answer in the context of the graduation problem before we get there:

  1. What is the long-term product vision for the solution?; and
  2. What are the initial product features & benefits? (Why should people use or buy it?)
Your long-term product vision for your MVP must clearly solve the graduation problem Click To Tweet

According to GradNation, the high school graduation rate, as measured by the Averaged Freshman Graduation Rate, increased from 71.7 % in 2001 to 81 % in 2012. While we are making great progress, minorities, low-income and the handicapped continue to lag behind. Our ability to reach our goal of 90%+ by 2020 will depend almost entirely on our ability to increase our performance in these three demographics. Under the new criteria set by the Every Student Succeeds Act (ESSA), low-graduation-rate high schools (dropout factories) are defined as schools that enrol 100 or more students and have graduation rates of 67 % or less.

The Every Student Succeeds Act addresses the problem of high school dropout factories Click To Tweet

ESSA mandates that these schools use evidence-based reforms that correlate with measurable improvements. There is an endless combination of factors that could account for why these students are dropping out. The only way to be sure is to scientifically test each and every possible theory and to record each and every outcome. This is an extremely time-consuming and resource intensive activity that is often skipped due to either lack of resources or lack of manpower. Herbie can bring his entire cognitive computing infrastructure to bear for Keith in order to alleviate the need he would normally have for these resources. Lower costs and higher profits will be a direct result.

Herbie utilizes some of the latest machine learning algorithms available (i.e. Linear Regression, Logistic Regression, k-Means, Support Vector Machines, Random Forests, Matrix Factorization/ SVD, Gradient Boosted Decision Trees / Machine Naïve Bayes, Artificial Neural Networks, Expectation Maximization etc.) to develop and test the veracity of hundreds and thousands of hypotheses. There are 3 types of predictions typically sought from using these algorithms and they include:

  1. Binary classification which is used to predict the answer to a Yes/No question;
  2. Multiclass classification predicts the correct category from a list; and
  3. Regression predicts the value of a numeric variable.

The standard scientific method of putting forward a testable hypothesis that the team will use is as follows:

“If _____[I do this] _____, then _____[this]_____ will happen.”

For example, “If we assign an adult tutor to an at-risk student the very first time they score below average in a core ‘subject’ test area, then we expect that student to experience a ‘letter grade’ increase over the course of a school term”. This is one of many educated guesses that will need to be made in order to perform the necessary experiments to turn these guesses into verified facts so that real-life decisions can be made in real-time.

Asking the “right” questions is critical if we want cognitive computing to give us the right… Click To Tweet

The goal of standardizing a specific scientific hypothesis testing methodology is to ensure both data and results integrity throughout the analytical model construction process. Herbie will leverage these models to simulate and test countless variations on Keith’s hypotheses until acceptable patterns of causation begin to emerge. This will allow the team to quickly identify complex business problems that he and his team can profitably address.

Achieving a 90% graduation rate by 2020 will require focus on five key areas:

  1.  Closing the opportunity gap for low-income students;
  2.  Tackling big city ‘dropout factory’ challenges;
  3.  Making handicapped students part of the solution;
  4. Focusing on ‘urban’ California; and
  5. Accelerating the lagging graduation rates for young men of color.

My next article will discuss how Herbie will help Keith the best approach to gaining unique insight into these challenges and how that insight will affect their product vision.

Cognitive Computing can help close opportunity gaps for impoverished students Click To Tweet

 

Do you see how extensive hypothesis testing very early in the startup development process can drive long-term success? Can you see the potential time and cost savings that a cognitive computing system could offer a founder?

I look forward to hearing your feedback. Stayed tuned for more on this story.

 

Cognitive Computing Systems: Want to build a great MVP? You need the right partner

For the first time in U.S. history the nation’s high school graduation rate rose above 80 percent, according to the 2014 Building a GradNation: Progress and Challenge in Ending the High School Dropout Epidemic (a report released April 28 by Civic Enterprises, the Everyone Graduates Center, America’s Promise Alliance and the Alliance for Excellent Education.) This is obviously great news. However, when you look deeper there are some disturbing facts hidden just below the surface.

The stated goal is to “achieve a national high school graduation rate of 90% by 2020.” Data analysis indicates that the only path to achieving that goal is by improving the graduation rates for minorities, low-income students and the handicapped. This is an enormous challenge that has global implications. It’s a complex problem that will require a complex solution.

Achieve a 90% #graduation rate by 2020? Focus on #minorities, #poverty & the #handicapped. Click To Tweet

Boys in caps and gowns holding diplomasUsing the “graduation problem” as a use case for this article, and those that follow, will showcase how a startup founder can partner with a cognitive computing system to solve problems that have the potential to evolve into a scalable, self-sustaining business enterprise. We will be borrowing heavily from the customer development methodology as put forward by Steve Blank and Bob Dorf in their seminal work, “The Startup Owner’s Manuel: The Step-by-Step Guide to Building a Great Company. We will begin with the Customer Discovery process and the first phase of that process: Building a solid Minimum Viable Product (“MVP”).

Can we solve the high school “#dropout” problem AND create a scalable, self-sustaining business? Click To Tweet

In the foreseeable future, the ability of a startup founder to bring a scalable and self-sustaining business model to market will be a general function of how well we as human beings can successfully “collaborate” with a “machine” (represented here as a fully functional cognitive computing system). As defined by the American Psychological Association (APA) a cognitive system is:

“A mental system consisting of interrelated items of assumptions, beliefs, ideas, and knowledge that an individual holds about anything concrete (person, group, object, etc.) or abstract (thoughts, theory, information, etc.). It comprises an individual’s worldview and determines how he or she abstracts, filters, and structures information received from the world around. A Cognitive Computing system is our attempt to replicate this function digitally.”

Using self-learning algorithms that use data mining, pattern recognition and natural language processing, the computer can mimic the way the human brain works.

Trying to mimic the brain? Try #MachineLearning, #DataMining & #NaturalLanguageProcessing. Click To Tweet

There are multiple phases that our fictional high tech startup founder “Keith” must move through in order to become a scalable self-sustaining business enterprise. I will walk you through each phase as I describe how Keith and his team seek to solve the “graduation” problem. We will also discuss how partnering with a cognitive computing system like “Herbie”, Keith will significantly enhance the speed to market, the product quality and the customer perception of the market-facing product.

In general each phase will involve a process that answers some very important questions such as:

  • How do I build an effective MVP?
  • How can I tell if the target market for my big idea is big enough?
  • Who do I think is going to pay money for my big idea and how much?
  • How will my distribution network get my product from here to my customer?
  • What is my chosen market and is there room for one more?
  • How do I compete in my chosen market?
  • How do I find and acquire new customers?
  • How do I keep the customers that I already have?
  • How do I get my existing customers to spend more money with me?
  • What external resources do I need to succeed?
  • What value-added partner relationships do I need to have?
  • Exactly how much money can we make?

All of these questions point to complex challenges for startup founders that will take more than a few articles to explore.

#Founders & #AI teamup to enhance #Speed-to-Market, #CustomerValue and #ProductQuality. Click To Tweet

What process or methodology do you use as a founder to develop a strong go to market strategy? What has been your experience with cognitive computing systems and can you see how they could add value to an early stage startup? Do you believe that lagging graduation rates is a big problem in America? How would you fix it?

I thank you in advance for coming on this journey with me. I expect to learn as much as you do along the way.

Cognitive Systems: Moving Your Big Startup Idea from ‘Educated Guess’ to ‘Proven Fact’

As we learned in my previous posts, the purpose of a high-tech “cognitive system” is to not only offer real-time expert assistance to the founder with regards to the market viability of their concept, but to also answer the most critical question they face on a daily basis; “What should I do next?” Previously we painted the big picture of how a cognitive system will build the next startup and then dug a little bit deeper into how to execute the first phase of the customer development model “Customer Discovery”. This article kicks off a series that will explain why you, as a founder, should follow and execute the Customer Discovery process to find your business model. It will also delve into how having access to assistance from a cognitive system like “Herbie” will significantly multiply your chances of building an extremely valuable and highly scalable enterprise.

Herbie will significantly increase your chances of building a scalable global enterprise Click To Tweet

Customer Discovery is part science/part art-form designed to capture your vision and turn it into a series of business model hypotheses. These hypotheses are then tested with customers in order to turn these educated guesses into firm actionable facts. Because of the copious amounts of research, the need for complex analytics, the constant cycles of testing and iteration, and the high level of team collaboration involved, a cognitive system like Herbie can add immediate value to the founding team of an early stage startup searching for the right business model. Borrowing inspiration from “The Startup Owners Manual” authored by Steve Blank and Bob Dorf and leveraging business insight acquired via my 20+ years of cross-industry global market development experience, I will share with you my future vision of how Herbie can and will guide the intrepid entrepreneur through “The Matrix” of building a very valuable startup (while avoiding and/or overcoming all “agents”).

A hypothesis is an “educated guess…Herbie can help you turn them into actionable facts Click To Tweet

Whether you are building a physical product or one that only exists online, there are nine business model hypotheses that must be formulated and tested during the Customer Discovery process. They involve: Is my market big enough? Will the customer pay for it? How will I distribute it? What market am I in? What is my competitive edge? How do I acquire new customers? How do I keep existing customers? How do I grow “product share” with existing customers? What type of external support do I need? What partnerships are good for my business? Exactly how much money can I make?

Startup direction

As you can see, there is no shortage of subject areas that the next Zuckerburg must continuously navigate through in order to bring their idea to profitable life. While I don’t know how Zuck did it, he surely would have appreciated having access to the virtually unlimited informational and analytical resources of a cognitive system like Herbie. Even if you are fortunate enough to have a dream team of subject matter experts as co-founders, there are five areas that Herbie can assist in driving bottom-line results to your enterprise:

1 Enhanced speed to market – speed kills the competition and penetrates markets;

Speed to market kills the competition and Herbie is your lethal weapon Click To Tweet

2 Unlimited access to historical subject matter expertise – constantly leveraging past business lessons to help establish current business model states;

Analyzing past success/failures drives tomorrow's winning strategy Click To Tweet

3 Unlimited access to current market conditions – constantly scanning current data/recent events to determine real-time strategy shifts/pivots;

Constantly scanning for global market business shifts drives adaptability Click To Tweet

4 Enhanced collaboration – constantly blending, reassessing and communicating the impact of business model changes across all roles/functions; and

5 360 Degree learning – the ability to constantly learn and adapt to the unpredictable nature of an early stage startup by offering prescriptive feedback based on the results of hypothesis testing and real-time inputs.

Prescriptive analytics offers founders objective actionable insights Click To Tweet

The ability to rapidly plan, act, and react in real-time to the vagaries of the startup experience is the clear dividing line between success and failure. The competitive advantage afforded by having a cognitive system like Herbie at the disposal of the founding team has been demonstrated in recent days via the exploits of IBM Watson and other highly publicized use cases (ie.“Connie” the robotic concierge at the Hilton) that highlight current advancements in AI.

The ability to plan & react in real-time is the difference between success and failure Click To Tweet

From a technical infrastructure perspective, there are a mix of several key technologies that make Herbie such a dynamic offering. The starting lineup includes BIG DATA acquisition and storage, Machine Learning Algorithms, RDBMS/NoSql Databases, Hadoop Ecosystem, Enterprise Service Buses (ESB), Content Management Systems, Descriptive/ Predictive/ Prescriptive Analytics, Artificial Intelligence and Natural Language Systems. They will all work in concert to enable the next generation of cognitive systems, which in turn will drive the next generation of highly valued technology startups.

The future is exciting and I look forward to sharing my vision on the critical role that I believe cognitive systems will play in making it happen. My next series of articles will serve to build my case for how I believe this technology will help founders identify, define and test whether the payoff from their new venture is worth rolling the dice and going all in for or if you are about to come up snake eyes and you should really go cash out.

Thanks for reading and thanks in advance for following me on the journey.

Building the ‘perfect’ tech startup: The ‘customer discovery’ process

Imagine that you are out one night, having your favorite beverage, when inspiration hits (as it sometimes does at these institutions) and you say out loud “It would be great if only people could do “xxxx” which would give them the benefit of “yyyy”. You have just told the universe your big plan for changing the world. Unfortunately you couldn’t find a pen in time to write it down and the moment and the thought passes.

Fortunate for you, the cognitive system (let’s call them Herbie), listening through your Apple watch, heard everything you said.

“Sounds good to me. Do you also want to know if anyone else also feels that this is a problem?” asks Herbie.

“Sure, and while you are at it, can you also let me know if there is anything that exists in the market today that solves this particular problem,” you respond.

“Cool, just give me a few minutes and I’ll let you know what I find,” and Herbie is off to research your great idea.

Herbie begins to systematically search every social media application from Facebook, Twitter, and Instagram to slideshare decks, YouTube videos, chat rooms and forums. It’s listening in on what potential customers and competitors are saying, analyzing the context of those conversations, drawing out insights about what problems they are experiencing. It’s trying to determine whether one of those challenges being discussed “sounds” like your problem, and the level of success they are having in trying to resolve it.

A few minutes later Herbie calls your watch and tells you that he has compiled a list of customer problems that your idea might solve, along with potential markets, target customers, preferred distribution channels and even preferred pricing models. You tell Herbie that you’d prefer the information was collated into your preferred cloud project management tool so you can review it, and Herbie begins to execute your request. Because Herbie has already taken the liberty of ranking each problem and solution based on criteria that you helped create, all you have to do now is review the information and tell Herbie which theoretical or hypothetical problem you would like help testing first. This is the future of collaboration enabled by collaborative systems

This is also the future of the customer discovery process. As the first phase of the Customer Development Process, it involves taking a founders’ vision and turning it into a series of business model hypothesis. These are then used to develop a plan to test customer reactions, with the ultimate goal of turning the theory into facts. When you consider that 9 out of 10 startups fail, it makes sense to take the time to do some hypothesis testing before you go too far down the track.

9 out of 10 startups fail so it makes sense to do some hypothesis testing before you go too far… Click To Tweet

After all, it’s at the beginning of the startup process, where many founders make their biggest mistakes. Some fall so in love with their big idea that they rush to build something hoping that customers will come in the future. According to CB Insights, the number one reason startups fail, a whopping 42%, is because there’s no market need for their solution. There are still others who wrongly assume who their target customers will be, and develop a business model to suit the wrong market. A spear used for fishing in streams doesn’t work so well in the open sea. As a founder, you owe it to yourself and your future investors to constantly test your assumptions about your target market. Unfortunately, most startups skip this crucial step.

42% of startups fail because there’s no market need for their solution Click To Tweet

This is where having access to a cognitive system like Herbie can add tremendous value. With real-time access to virtually unlimited amounts of information, combined with the capability to contextualize, understand and learn from it. Herbie will help you experiment with new concepts that were previously either too expensive and or time consuming to pursue

Sound like the plot of next summer’s science fiction movie blockbuster? The answer is yes (I’ll be buying my advance tickets next week), but there’s more than a little bit of reality in these concepts.

Scientists have already created hypothesis generation software that finds new flavor combinations (pork and strawberries work a treat together, who knew?). And software like BrainSCANr is helping neuroscientists select and hypothesize research projects that may solve serious medical problems based on a range of seemingly disconnected information. These innovators have used the technology to sift through millions of recipes and research papers, to identify trends and clues about what flavor profiles work well together or what research gaps exist.

Cognitive systems can now filter information from much broader data sources, including videos, sentiment in social media and verbal conversations. With 80% of the world’s data now unstructured, and with technology become smarter everyday, it’s only a matter of time before they can bring all of this together to create more complex hypotheses.

It’s only a matter of time before #cognitive systems can create more complex hypotheses Click To Tweet

Once you have a hypothesis, the next step is to test it, refine it, and iterate on it until it’s ready for the next phase, customer validation. This involves getting out of the office and conducting hands on market research with your target customers. Herbie can help you execute a highly coordinated outbound marketing campaign in the most optimal time and cost efficient manner possible. Herbie will work with you to provide a list of probing questions to ask potential customers to help test the hypothesis. And if requested, Herbie can actually make the calls for you, ask the questions, record the responses, analyze the results and give you an opinion on what was and what wasn’t a good hypothesis. But I don’t know if I’m ready to advocate for the latter, as there’s no replacement for direct customer interaction (at least not yet).

When you meet with your customers, tell Herbie to listen and record the interview, and help guide you through the interview process. This might include putting together a list of questions like these; Do they feel the same way you do about the nature of the problem? What do they believe is the source of the problem? How much pain is this problem causing them both professionally and personally? And most importantly, how much would they be willing to pay to fix it?

During the course of the interview, Herbie sends a vibration alert through your phone with a new question to ask, based on the previously received responses. Herbie has been constantly evaluating your prospects responses, checking them against previously received responses, and relevant data sources in order to assist you in refining your hypothesis in real-time. As a result, a bigger picture begins to form on an alternative or more refined market application for your idea. It will suddenly be possible to have multiple “Ah-Ha” moments during this phase of the process. And what entrepreneur wouldn’t want to have multiple “big ideas” to choose from?

Consider a cognitive system like Herbie more like a business conscience than a market prophet. It can’t predict the future, but it can tell you the success potential of your ideas, based on the past and supported by the present. Herbie analyzes massive amounts of information, identifies patterns hidden within, weighs all available options, and offers plausible ideas. Herbie’s ultimate goal is to give you both the insight and the time to convert the best hypothesis into an actionable set of facts. This is the best way to begin your startup journey.

Cognitive systems can tell you the success potential of your ideas Click To Tweet

Of course, as much as we like the cognitive computing power Herbie bring us, it does have a few “kinks” in its innovative armor:

  1. It’s not easy to obtain or validate the petabytes of data required to properly feed this system. After all, not everyone will have a “data lake” in their backyard. As you may not have your own customers yet, you’ll need to access to a diverse set of publicly available data sources. Although I see a future where raw, validated market data may become generally available, I see challenges in the near-term. As a founder, it is also important to step back and look for information that may be relevant in new places. The Internet is awash with data, as over-sharing has become both a burden and a blessing. The challenge may be just finding where potential customers are voicing their problems and discussing potential solutions; and
  1. The challenge of active listening. Listening and responding to another person in a way that improves mutual understanding is difficult enough between humans. The communication between a passionate founder and Herbie may involve working through some trust issues, as natural human bias may color receptivity to “machine” feedback. It may take some time for Herbie to acquire both the data and the “experience” before a founder trusts their opinions. Until that day comes, it may be hard for the typical “control freak” founder to loosen their grip on the current reality and see things rationally.

By leveraging big data assets, machine learning algorithms and natural language processing, cognitive systems can quickly guide you through the first and most important phase of the customer development process. An effective Customer Discovery process builds a strong foundation that will allow you to build the strongest business plan and enterprise possible. After all, the most successful founders are 78% more likely to have created a formal business plan first. Don’t be the founder that builds their “dream” application first, only to then try and find no-one besides themselves actually wants it. Cutting this process down from months to a matter of days, allows an aggressive founder to quickly pivot, either forward or backwards, before betting “against the house” and begin their startup journey.

Successful founders are 78% more likely to have created a formal business plan first Click To Tweet

Now that Herbie has gotten you through the first phase of the Customer Development process, it’s time to take it to the next level. The level where you validate whether your business model is capable of being both repeatable and scalable. This will represent the first true pivotal moment for your startup.

Next stop? Customer validation.

I’d love to hear at least two valuable pieces of feedback from you. Do you think the “customer discovery” process is an effective way to begin your startup journey? Can you imagine the value of having a cognitive computing system like Herbie guiding you through this critical phase of the start-up process?

Welcome to the era of cognitive systems….

References

Blank & Dorf, “The Startup Owners’ Manual: The Step-by-Step Guide for Building a Great Company.”

http://www.economist.com/news/science-and-technology/21621704-new-type-software-helps-researchers-decide-what-they-should-be-looking

http://dupress.com/articles/2014-tech-trends-cognitive-analytics/

http://www.research.ibm.com/software/IBMResearch/multimedia/Computing_Cognition_WhitePaper.pdf

https://hbr.org/2015/07/what-every-manager-should-know-about-machine-learning

https://www.cbinsights.com/research-reports/The-20-Reasons-Startups-Fail.pdf

https://www.sage.com/na/~/media/site/sagena/responsive/docs/startup/report

http://www.forbes.com/sites/neilpatel/2015/01/16/90-of-startups-will-fail-heres-what-you-need-to-know-

Can a cognitive system build the ‘perfect’ tech startup?

At this very second, somewhere in the world,  there are 3 new high tech startups opening up for business . At that very next second, there are 3 that are waving goodbye.  Every day 3 new high tech startups open for business and another 3 wave goodbye Click To Tweet To a tech insider this is hardly breaking news but to the casual observer it’s scary business. Everyone knows that “starting-up” is extremely risky, so imagine what it would be like if everyone suddenly had the ability to bring their “big idea” for changing the world to market in the shortest possible time, with the least amount of staff and at low cost. Even better, what if you could speak to an “advisor” that could definitively tell you within a few short weeks/months of being in business whether you were riding a “unicorn” or you were feeding a potential “money pit”. Imagine if an advisor could tell you whether you're riding a unicorn or feeding a money pit Click To Tweet This is the promise of the cognitive era, and that future is right around the corner.

In the story of Pinocchio, Jiminy Cricket acted as both his spiritual advisor and sage. Although it can’t mimic his personality (at least not yet), a cognitive computing system will be able to come close. It has the ability to listen to your questions, quickly sift through massive amounts of information to give you relevant answers, and continuously learn from the decisions you make so that its next answer is better. Imagine the power of having a “partner” that has access to every lesson learned by all the startups that ever existed. Lessons that they used to give you insightful advice at every stage of your building process. Your partner will help you evaluate your big idea, validate the need in the market, let you know if your idea can scale, and finally help you graduate to a fully functional enterprise.

Sound like a Tony Stark invention from the last Iron Man movie? The fantasy is not so far from becoming a reality. Very soon now, a well-designed enterprise cognitive system will be able to accomplish this and more.

It is common for most startups to stumble for months or even years trying to build the perfect product, identify their target audience and find the ideal business model, while simultaneously burning through mountains of cash and time as they go. There are many published “how to” manuals advocating a structured approach to creating a viable startup. One of the best is put forward by startup strategy specialists Steve Blank and Bob Dorf in the their tremendous reference guide “The Startup Owners’ Manual: The Step-by-Step Guide for Building a Great Company,” These guides offer an excellent methodology and terrific insights, but it’s extremely difficult to both absorb and leverage 1000’s of pages of detailed and actionable content while also executing a business in real-time. Consider the sheer volume of information involved, the expertise required to determine the veracity of the information, the variety of actions you could potentially pursue, and the velocity of change you would be required to manage, This is a key area where cognitive systems will clearly add value.

There are four phases that every startup must manage and navigate through in order to achieve success:

Customer Discovery: In this phase the business is not only trying to test the founder’s vision but also identify markets, customers, channels and pricing. Is there a market?

Customer Validation: A business model is identified and a startup sees whether it can actually sell its product or service. Is it repeatable and scalable?

Customer Creation: Once the business model has been tested, it’s time to scale. This involves building end-user demand and converting sales. What is the potential?

Company Building: Once a valid business model is found it’s time to graduate into a fully-fledged company. How can the operation scale?

The goal of a cognitive system is to proactively gather massive amount of information, offer active business insights and offer a clear path for a “founder” looking to quickly build a successful company.

A cognitive system can provide a clear path for a founder looking to build a successful company… Click To Tweet

As defined by Sue Feldman, founder and CEO of Synthexis, cognitive computing “allows people and machines to work together in easy, intuitive ways.” Cognitive systems learn at scale, reason with purpose, and interact with humans naturally. With massive amounts of information available at our disposable in structured and unstructured formats, cognitive computing enables people to effectively interact with and leverage that data.

In order to be effective in the startup world, a cognitive system must exhibit and offer four key capabilities:

It must be Adaptive: Being able to pivot your strategy as necessary to respond to rapidly changing market conditions is key to a startup. A cognitive solution will be able to monitor and predict these external changes and offer real-time advice on how to adapt.

It must enhance Interaction: There are many stakeholders (both people and technology) that must interact seamlessly in order for the business to grow. Being able to absorb and translate feedback in real-time is a key capability.

It must offer Iterative and Stateful feedback: By helping to identify operational business problems, proactively seeking supportive data to determine impact, and applying lessons learned from past actions to recommend “tweaks” in business strategy, it will significantly increase the chances of success for your enterprise.

It must provide Contextual Information: Being able to able to intuitively judge how complex elements could impact your business. For example, being able to understand how the use of certain words by internal and external stakeholders may affect your operations or how a seemingly simple change in government regulations could derail your expansion plans. These are the real actionable insights a startup needs to survive and thrive

The end goal is to translate these “capabilities” into an active support system for a founder seeking to build a solid and scalable organization. It must be able to act as an insightful “expert advisor” capable of successfully shepherding them though all four phases of the Blank and Dorf startup life cycle.

In the future I see cognitive systems capable of either producing the next “unicorn” or, even better, letting a founder know when it’s time to “turn out the lights” and say goodbye as soon as possible.

Over the course of the next four posts, I will expand on my vision on how cognitive systems will transform the high tech startup industry beginning with how to determine if there is a market for your idea.

Welcome to the cognitive era. Are you ready?

References

Blank & Dorf, “The Startup Owners’ Manual: The Step-by-Step Guide for Building a Great Company.”

http://www.cityam.com/220819/graphic-shows-just-how-many-startups-are-launched-worldwide-every-second

http://www.research.ibm.com/software/IBMResearch/multimedia/Computing_Cognition_WhitePaper.pdf

http://www.customermatrix.com/news-and-press-releases/news/159-wikipedia-definitions-what-is-cognitive-computing

Starting Up in Mexico: Do the opportunities outweigh the dangers?

On the surface, Mexico appears to be a great place to both found and grow a high-tech startup. Positioned at #57 on the 2015 Global Innovation Index, ahead of South Africa, Brazil, Argentina and even India, it’s predicted to be the world’s 8th largest economy by 2050. Before it can become the “Silicon Valley of LATAM” there are a few rather large internal speed bumps that they will need to show the world it can successfully navigate. The first and biggest one is well known: Personal security and widespread corruption

There is a perception of danger and a fear of loss

Nothing puts the brakes on big business like the fear of personal safety and the threat of losing trade secrets. Unfortunately, Mexico has major struggles with both. Tales of source code appropriation, intellectual property rights violations and executive kidnapping schemes continue to put a damper on innovation and foreign investment. Even though copyright and patent laws offer some level of protection, the problems have literally reached “urban legend” status globally. Fear can be an amazing motivator both in moving a sale forward and as a “wet blanket” when angel investors and founders are thinking about maximizing their resources.

If Mexico is really serious about elevating it’s position as a “startup hub” in the global community, it will need to present its “Startup Manifesto” in a very clear and convincing manner. The Mexican government, Mexican business leadership and foreign allies will need to stand together to make this future state a reality.

The Mexican government needs to assert its leadership

While sidestepping some issues, the Mexican government has certainly made significant efforts to encourage its startup ecosystem. In 2014 alone, it distributed $658 million to fund 6,000 new startups, and is expected to invest $18 billion in entrepreneurial projects by 2018.

The Instituto Nacional del Emprededor (INADEM) is a world-class program established to encourage innovation and promote the country’s startup credentials. INADEM has five frameworks – regional development, business development, high-impact financial and entrepreneurial culture, medium and small business projects, and technology access for micro business.

“The talent exists, there’s population that is a force for change and dares to create options rather than follow them,” stresses the General Manager of High-Impact Entrepreneurship Programs at INADEM, Adriana Tortajada, adding that Entrepreneurs in Mexico need to be prepared, be sophisticated, be the best at what they love to do Click To Tweet

The Mexican government is also seeking to encourage more foreign investment by growing its international network with free trade agreements. The impact of this effort is not yet clear, especially when you consider that the tariff-free technology benefits of NAFTA have been in place for over 20 years.

That being said,cities like Guadalajara, based in the central region of Jalisco, are pushing ahead with their own agenda. Governor Jorge Aristoteles Sandoval has established an Innovation Department and is building a business hub to attract more hi-tech business to the city and bring together its growing innovation community. Global high-tech firms such as IBM and Intel are investing heavily in the city. Intel sank more than $177 million into the Guadalajara Design Center which offers vital, co-working space to fledgling start-up enterprises. With all of this support, attracting entrepreneurs should be easy.

Building an entrepreneur friendly environment is still a work in progress

In a normal world, with the support of the INADEM framework and a burgeoning startup ecosystem, creating and supporting innovative entrepreneurs is becoming much easier. Startup hubs, like 500 startups and Startup Mexico, represent some of the over 100 incubators operating in Mexico. They serve to bring together advisers, investors and entrepreneurs to accelerate ideas. Coupled with co-working spaces and crowdfunding platforms, Mexico is well on its way to becoming one of the world’s greatest innovation centres Click To Tweet

Angel investors are not yet “feeling the love”

While there is certainly considerable growth at the incubation stage, investors are gun-shy when it comes to funding ambitious projects. In 2014, only $38 million was invested in 54 startup deals, which is less than the startup community in Nebraska. It’s not just the overall amount of investment that’s an issue, but also the level of funding for specific deals. Seed funding of over $500,000 is rare, leading to low-level investments that just don’t provide startups with the runway they need to fly. In the US, it is normal for a startup to take years to develop a solid customer base and several years beyond that to prove to investors the real potential for market/revenue scalability. Without the proven success of a “Google” in their past, angel investors don’t yet have the confidence needed to fully commit to supporting Mexican startups. This reality makes the possibility of a “Unicorn” coming out of Mexico more “blue sky” than reality.

FinTech may finally get investment dollars flowing in the right direction

Banking in Mexico has never been easy, so it’s no surprise that fintech startups are flooding the market with solutions for everything from micro-finance to credit scoring. While disruption in countries like Hong Kong are restricted by regulatory issues, Mexico has fostered innovation out of sheer desperation. While investors prevaricate over whether they should involve themselves in the country’s growing scene, some startups may just solve some of the pervasive issues hindering financing or payments in many developing countries.

While Mexico is flush with quality talent it needs more “cowboys”

One thing Mexico is not short of is talent. Its universities produce 130,000 high quality engineers a year giving it a potential pool greater than Canada, Brazil or Germany. The issue is keeping the talent in-country rather than just supplementing the US’s STEM pool. Even though brain drain is a problem, there’s a real advantage to the US (and other countries) to bring their opportunities to Mexico rather than import the talent; they’re cheaper if they stay home. This is a big bonus for companies that do shift their technology programs to Mexico, providing experience to budding entrepreneurs who can then reinvest that into the startup world.

Like India, it’s also attracting expatriate entrepreneurs, like Andy Kiefer at Agave Labs, back into the fold. He brings practical experience, a cowboy mentality and US dollars; three things badly needed to drive quality tech startup development.

Good business requires good Infrastructure in order to grow

A good tech hub needs sound telecommunications infrastructure, and Mexico has done a great job in making that happen. About 78.2 million people have mobile phones, and foreign investment is making connectivity faster and better. AT&T is planning on adding 3,000 microwave stations, something that other networks will be able to leverage. This will not only ensure that business can function without interruption, but also fuel internal demand as an attractive cherry on the top.

English is great but knowing Spanish is necessary

Even as foreign investment grows in Mexico, most professionals prefer to communicate in Spanish. Fortunately, technology translators like SpanishDict have advanced to the point of helping you communicate “well enough” to allow you to move your venture forward without having to spend six months in class.

There are many legitimate reasons why you should and shouldn’t consider building your start-up in Mexico. While security and safety are valid negatives, tremendous talent and global community support are obvious positives. If you perform an extra level of due diligence and then focus on developing/funding a value-added business idea that can scale well beyond Mexican borders, I think the opportunities presented in Mexico significantly outweigh the risks,

The real question is “Do you have both the vision and the heart to succeed in Mexico?” The answer to that question trumps any argument made in this article. Clint Eastwood said it best. “Do you feel lucky today? Well do ya?”

Let me know!

 

References

https://www.globalinnovationindex.org/userfiles/file/reportpdf/GII-2015-v5.pdf

http://www.nearshoreamericas.com/mexicos-startup-ecosystem-drives-talent-development-innovation/

http://www.bloomberg.com/news/articles/2015-06-23/mexico-to-overtake-russia-by-2050-as-u-s-slides

http://www.usatoday.com/story/money/2016/02/02/mexico-startup-economy-tech/79679364

http://www.wired.com/2016/02/startups-can-escape-their-cash-crunch-by-going-to-mexico/

http://www.panamericanworld.com/en/article/mexicos-location-benefiting-tech-firms-and-startups-new-ways

http://www.ibtimes.com/mexico-city-next-silicon-valley-how-mexicans-became-obsessed-unicorns-2317659

www.orange-business.com/en/magazine/business/mexico-the-new-start-up-frontier

http://stories.newco.co/2016/02/03/startup-mexico-incubates-homegrown-innovation/

http://www.centrodeinnovacionbbva.com/en/news/new-opportunity-us-startups-mexico

http://tech.co/5-damn-good-reasons-startup-mexico-2015-02

http://www.ft.com/cms/s/2/ec7461a8-ffb3-11e4-bc30-00144feabdc0.html#axzz46CZts82g

http://techcrunch.com/2015/03/26/beyond-the-maquiladora-a-look-at-mexicos-startup-scene/

http://techcrunch.com/2015/08/01/the-next-great-startup-will-be-a-unicornio/

 

Starting up in India?..Things are good, working on great

In my last post I shared my thoughts on building a tech startup in Hong Kong. Now, let’s turn our attention to India. Often called the Silicon Valley of Asia, India is now the third largest tech startup hub in the world (behind only the US and UK). Its startup ecosystem (which extends well beyond high techis estimated to almost quadruple, from 3,100 startups in 2014 to 11,500, by 2020.  

High tech startups in India cover almost every industry from healthcare to education. Click To Tweet While consumer and e-commerce encompass the lion’s share of investment, health insurance and payment platforms are emerging as key areas of growth. As per usual in my posts, there are as many positives regarding doing high tech business in India as there are concerns about whether India has the environment to accommodate such rapid growth. While I generally believe India represents a great growth opportunity for tech startups there are a few challenges you should keep in mind.

Government and legal support is outstanding

In August 2015, Prime Minister Narendra Modi announced the Startup India Movement. Focused on supporting startups and growing ecommerce , it provided a Rs 2,000 crore (over $300m) funding boost. There are 3 key initiatives it puts forward:

  • Creating a Credit Guarantee Fund that provides funding support at a regional level. This is aimed at encouraging innovation and supporting communities who would not normally have access.
  • Establishing up to 75 new technology startup hubs (including innovation parks and research hubs) with the intention of supporting new businesses in getting off the ground, and encouraging job creation.
  • Launching the Atal Innovation Mission which is focussed on establishing new incubators and sources of seed funding. It also promotes innovation through industry recognition, state councils, trade workshops and conferences.

This initiative also includes regulatory measures that make “starting-up” easier as well as clearing barriers to entry. The 5 key measures worth noting include:

  • Establishing a business portal designed to reduce government bureaucracy by enabling 14 regulatory permissions at one time.
  • Introducing capital gains tax exemptions for people investing their own wealth.
  • Exempting startups from income tax for the first 3 years.
  • Reducing royalty tax to entrepreneurs from 25% to 10%.
  • Offering greater protection to patent holders, and improving the speed of patent filings.

The initiative has been met with a positive response from both entrepreneurs and business leaders as it also demonstrates Prime Minister Modi’s dedication to innovation and growth in the market.

Funding / Venture capital is plentiful

Venture capital funding, private equity and angel investment is booming in India. Funding for startups grew from $2.2b in 2014 to $4.9b just a year later, with the number of startups being funded growing rapidly. As the charts below demonstrate, VCs, Private Equity, angel investors and incubators are also growing exponentially. Seed funding is also growing, while small in value, has increased from $15m to $99m in the space of one year.  

Nasscom

Image courtesy of Nasscom, Start-up India – Momentous Rise of the Indian Start-up Ecosystem 

The money is coming from both local and international sources, with many serial entrepreneurs such as Ratan Tata of Tata Group and Kunal Bahl of Snapdeal getting in on the action. VC companies are also throwing cash into the ecosystem, including several international firms such as TGM, Tiger, DST Global, SoftBank, Sequoia Capital and Accel Partners.

While initial valuations in India are lower than their US counterparts ($2.3m compared to $4.2m), there have been some major investments in companies such as Flipkart and Ola that show the potential for large D, E and F funding rounds.  

Startup enablers are many

Business has also jumped on the startup bandwagon, with incubators, shared workspaces and advisors creating their own booming industries.  There are now over 100 incubators and accelerators supporting startups with resources, mentoring and seed funding. Chief among them is NASSCOM’s 10,000 startups which is providing direct support. This initiative is supported by the likes of Google, Microsoft and Intel.

Language is not an issue

English is one of the official languages in India and the prevailing language of business. While there are challenges for businesses targeting customers in regional India, due to the wide variety of dialects, this is an operational issue rather than one of strategic impact.

Talent is readily available (with a few caveats)

Skilled talent is one of India’s strengths, but it may soon be a weakness. Click To Tweet The majority of startup founders are young, with 73% under the age of 36.  This is a reflection of the general population. By 2020 India’s average age will be only 29 years. When coupled with a burgeoning middle class that is educated and ambitious, it sets the stage for the country to become a global talent powerhouse. Unlike their parents and grandparent, the new generation will be more willing to take risks, seek out new challenges and relish the blue sky opportunities offered by startups.

While brain drain was once a major sore point for India, talent is now returning home. With many returning with valuable experiences in some of the world’s top blue chip technology companies (i.e. Peeyush Ranjan left Google to take a position at Flipkart, Namita Gupta swapped Facebook for Zomato, and Rushil Goel left Boston Consulting to join Ola).

These ex-expatriates can now assume a mentor role to support their younger counterparts and foster an exciting community of innovation and growth.

The legal environment is daunting

While the Startup India Movement contains several initiatives to reduce regulatory hindrances, as outlined above, many doubt that that the new policy will work as planned. Progress in the sub-continent has been historically plagued by both the slow wheels of bureaucracy and high levels of regulation and corruption. While there have been moves to change this, many businesses are still reporting obstacles.

Last year, approximately 65% of tech startups that had raised Series A funding were looking to move out of India due to legal and regulatory issues, with many opting to relocate to the US, Singapore and the UK. Issues such as capital gains tax, conducting IPOs and facilitating international payments are slowing these businesses down. Navigating the maze of paperwork and red tape would be challenging enough for large businesses, for startups it’s a distraction they don’t need.

Infrastructure continues to be a significant barrier

Infrastructure in India still has a long way to go before it can meet the needs of a developed nation. Digital infrastructure is just one area that requires support, with major cities still experiencing daily electricity outages. While growth is occurring at an exponential rate, it is unclear whether India will be able to keep up with the growing demands of its burgeoning technology ecosystem. This is a risk factor that would be hard for any founder to ignore.

While startups are booming across the sub-continent, I believe that there are many reasons to be equally optimistic about the long-term potential of the India marketplace. If you can go in armed with the knowledge regarding  the challenges I discussed, I have every confidence that you will be able to build a successful tech startup in India.

Arm yourself with knowledge of the challenges in India before you build your tech startup Click To Tweet

Now that I’ve spoken my mind, I’d love to hear your thoughts and experiences on tech investing and working in India. Do you believe it can someday surpass the US as the startup hub of the world?

References:

http://www.nasscom.in/startup-india-%E2%80%93-momentous-rise-indian-startup-ecosystem

http://thenextweb.com/in/2015/07/05/india-the-worlds-fastest-growing-startup-ecosystem/#gref

http://techstory.in/startup-india/

http://yourstory.com/2015/10/disruptive-technology-company/

http://www.firstpost.com/business/startup-india-the-ecosystem-has-taken-off-we-are-the-youngest-startup-country-now-2586266.html

https://e27.co/can-india-prevent-exodus-tech-startups-us-singapore-20150528/

http://indianexpress.com/article/technology/tech-news-technology/start-up-india-what-india-inc-has-to-say-about-modis-pet-scheme/

http://www.thehindu.com/business/corruption-delays-hamper-startups-in-india-survey/article8069465.ece