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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? 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.

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