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 action 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 customers 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 graduating 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 answers 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? Start with the data

A key first step in the customer discovery process is building a solid Minimum Viable Product (MVP).

“An MVP is that product which has just those features and no more that allows you to ship a product that early adopters see and, at least some of whom resonate with, pay you money for, and start to give you feedback on.”

In this article I look at how Keith, our fictional startup founder, begins his journey to deliver a disruptive solution to market with the help of an interactive cognitive computing system called “Herbie”. Step one begins with data acquisition and analysis.

As discussed previously, Keith is looking for a solution to increase the graduation rate in America from 80% in 2014 to 90%+ by 2020. He and his team will accomplish this by focusing their efforts on three primary demographics: minorities, impoverished students and the handicapped. They also want to discover if there is a scalable business model that will provide funding for long-term research and development.

Bringing disruptive high-tech solutions to market? Partner with Big Data and a Cognitive System Click To Tweet

Keith begins this particular journey by asking a simple question: What is the nature of the “graduation” problem and how can a cognitive computing system like Herbie help us solve it?

According to Gradnation: America’s Promise Alliance, students who have fallen off track to graduation have been found to be lacking four critical elements:

  1. Positive relationships with caring adults;
  2. Strong and tailored instruction;
  3. Opportunities to engage in learning experiences that connect school to careers and life beyond; and
  4. The support and resources to help them figure out what they want to do once they have earned their diploma.

school dropout cognitive system solution

Addressing these elements will be at the core of any solution that Keith and his team pursues. It also provides some guidance as to the potential benefits and or outcomes that we should expect from the business.

Positive adult relationships plus tailored education equals a higher graduation rate Click To Tweet

Achieving a 90% graduation rate by 2020 will require a thorough understanding of several key challenges:

  1. Closing the opportunity gap: Graduation rates for low-income students ranges from 58% to 85% compared to the national average of 80% for all students;
  2. Tackling the issue of big city concentration: Most “dropout factory” schools with < 60% graduation rates are found in urban areas;
  3. Making special education students part of the solution: Their average graduation rate is 20% below the national average;
  4. Focusing on California: Home to 20% of the nation’s low income students; and
  5. Accelerating graduation rates for young men of color: This is currently only in the upper 50’s and low 60’s.
Urban areas proliferate “dropout factories in America. How do we stop the cycle? Click To Tweet

Getting an objective understanding of each of these is one of the biggest challenges facing Keith and his team. Herbie’s focus is to access, organize and make sense of various forms of big data that will add clarity to the current state of the problem. He will use that as a foundation for ongoing business model strategy analysis.

Herbie begins by reviewing and organizing a shortlist of all data sources relevant to the four critical elements listed above and, under the direction of Keith’s data scientist, he will determine the optimal Enterprise Data Architecture (EDA) framework for this project. This will not only establish a baseline of what we currently know about this problem but also set the stage for the next step in the customer discovery process: Hypothesis Development and Testing.

An Enterprise Data Architecture provides fertile ground for rapid growth. Click To Tweet

In general, an EDA model is comprised of different layers that provide a strong foundation to develop 3 key strategic initiatives, such as:

  • Defining a data strategy that outlines the objectives of the business. This improves data collection and how the data is used in the business process;
  • Facilitating decisions on the potential future of new and modified solutions; and
  • Executing data warehousing, integration and reporting initiatives.

An enterprise data architecture exists on four different levels:

  • High-Level Data Model (HLDM): Constitutes a collection of HLDMs that describe business data through a conceptual viewpoint independent of any present realization by real systems. The HLDM consists of a standard UML class model of the primary data items and their relationships; a superset of business features, such as semantics, universal constraints and syntax;
  • Realization overviews: Describes the relationships between the real vital data objects of the present or planned systems and the conceptual units of the HLDM. This shows the way in which conceptual units are realized by actual units;
  • Source and consumer models: Demonstrates the correlation between various realizations of the same data items, diverse organizational custodians of data elements and the way in which modifications are circulated around different systems; and
  • Transportation and transformation models: Explains the way in which data in the implementation systems changes when moved between systems. They include attribute structure and physical class of system interfaces. This model also depicts the realization of the HLDM within the interface mechanisms, including a backbone or an enterprise application integration (EAI) hub.

Implementing an EDA at such an early stage has clear competitive advantages for Keith:

  • Helps the team accelerate the process of gaining strategic insights from the data;
  • Increases speed to market for new product initiatives;
  • Develops and implements a governance structure that supports an overall data strategy; and
  • Guides developments across systems, such as common reporting, EAI and data warehousing initiatives.

Once the EDA has been developed and the process of data acquisition has begun, Keith and Herbie are now ready to execute the all-important task of hypothesis development and testing. We will discus the nature and value of this process in my next article.

Why do you believe dropout rates are so high for minority, impoverished and handicapped students? As a founder, do you believe that a cognitive computing system will accelerate the problem solving process? Do you agree that designing and deploying an EDA is something that should be done as early as possible?

I look forward to reading your responses

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.

Isn’t It Time For Student Relationship Management?

Customer relationship management

Imagine that someone enters your place of business and offers you a way to reduce costs, increase profitability, solidify customer satisfaction, build loyalty, and improve advocacy. After pointing to the “no solicitation” sign on the front door you might decide to give him two minutes to tell you about this brand new form of magic.

This certain someone would mention the miracle of customer relationship management (CRM*). Ok, so maybe miracle is a bit of an overreach, but CRM is the #1 buzzword in Corporate America and, on most accounts, has been very effective in delivering on these points. After getting a promotion for taking the advice of this complete stranger, your next thought might be, “How could this marvelous tool be leveraged to increase critical stakeholder relationships within the K-12 and higher education community?” In reality, CRM as applied to the education industry (student relationship management or SRM) has an almost unlimited opportunity for practical everyday use.

Support_Help1-300x199With the goal of tightening relationships between students and their educational mentors, SRM can be a powerful resource to ensure any number of positive outcomes(e.g., increased enrollments, retention, and graduation) for our students. For the sake of brevity I will only focus on three areas that I believe will drive the creation of long-term, mutually beneficial relationships between all those with a stake in improving our educational system.


These three focus areas include:
• Increasing levels K-12 college preparedness
• Positively affecting high school dropout rates
• Increasing alumni participation and donation

Educators1-105x150A successful SRM program helps educators at all levels collaborate to instill and promote a college-going culture focused on arduous coursework and high expectations very early in a student’s educational experience. This holistic approach translates to better lesson plans, a greater sense of shared mission, and the development of students better prepared to cope with the rigors of college.


It is often difficult to inspire a child/young adult to see beyond the moment. As a rule, teenagers aren’t very good at following the advice of their parents. Peer pressure often has the most lasting effect on their level of motivation. Finding and cultivating long-term mentoring relationships between students at all levels has been a proven path toward inspiring students to excel throughout their secondary school experience. It is much harder to drop out of school when your peer group is cheering you on and showing you the real-life benefits of an education.


Alumni empathy represents the culmination of every personal and educational experience a student has up to the moment of graduation and beyond. More specifically, if there is not a strong connection to a particular person, place, or thing related to their post secondary educational experience, engaging alumni would be a very difficult if not impossible task. SRM provides higher education institutions with a path toward developing deep and lasting relationships with its most important constituents. These relationships endure from the moment students express interest in attending an institution to the first time they receive requests from their chosen school and, ultimately, when they gain acceptance.Corporate America leverages CRM to develop and grow important revenue-generating business relationships. The education industry version, SRM, seeks to do the same with a laser focus on developing and growing life-long learning professionals. Do you think implementing an institution-wide SRM program will ensure students are better prepared for college, lower high school dropout rates, and produce a growing and highly-engaged alumni association?Tell me what do you think….

* Customer relationship management (CRM) is a company-wide business strategy designed to reduce costs and increase profitability by solidifying customer satisfaction, building loyalty, and improving advocacy. True CRM brings together information from all data sources within an organization (and where appropriate, from outside the organization) to provide a holistic view of each customer in real-time. This allows customer-facing employees in such areas as sales, customer support, and marketing to make quick yet informed decisions on everything from cross-selling and upselling opportunities to target marketing strategies to competitive positioning tactics.


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3 Business Development Lessons that the NFL can Teach Higher Education

As an avid (or should I say rabid) fan of the National Football League, I pay a lot of attention to the business moves my favorite teams make during the offseason. Their primary mission is to expand their markets and gain competitive advantage in advance of the upcoming season. A similar planning process takes place every year in the executive offices of every higher learning institution in the country. Both organizations compete internationally to have transparent access to the best and brightest prospects available. Although they seek similar outcomes, the NFL has been much more successful in developing and marketing to its stakeholders.

There are three business development lessons that higher education institutions can learn from the NFL that can help them increase the number of quality professionals being delivered to the marketplace.

1. Spread Your Business Support Network Far and Wide

No single organization can provide all of the products, services, and support necessary to establish and grow a multi-national organization. Since its formation in 1920 with eleven teams, the NFL has grown to thirty-two teams that are worth roughly a billion dollars each. A primary driver of this growth has been a focus on systemic workforce development and building an international network of business partners.

Not–for-profit iconic partners like Pop Warner’s Little Scholars provides youth football and cheer & dance programs for over 425,000 members from ages five to sixteen across forty-two states and several countries around the world. The National Football League Players Association (NFLPA) estimates that between 60% and 70% of all NFL players began their careers playing Pop Warner Football. The quality and depth of your network will ultimately determine the long-term success of your organization.

2. Sell a Long-Term Vision . . . Not Short-Term Tasks

As children, we all had visions of what we wanted to be when we grew up. These dreams usually came either from the influences of people that came into our lives or from exposure to images in movies or on TV.  No one did a better job of marketing the dream of becoming a professional football player better than the NFL, which effectively connected being selected to play in the NFL with achieving “supreme manhood.” Most every man-child since the dawn of time has been drawn to “feats” that convey strength and courage.

Higher education institutions, in general, have always done a good job of marketing the value of a college education for its own sake and, until recently, this has been a very successful albeit passive strategy. The current economic realities suggest that the age of the academic generalist is over and that the future will belong to the specialist. There will always be that 20% of the population that will automatically be motivated to excel in the college environment. The remaining 80% will be motivated by seeing a clear path between earning a college degree and having a successful career. College professors become dream enablers vs. taskmasters.

3. Always Provide Ample Opportunities to Showcase Skills

Making difficult choices among many qualified prospects that appear to have similar credentials is a classic recruiting dilemma. We all know that anyone can look great “on paper,” but the real challenge is knowing whether a key prospect will perform as well in the real world. The NFL has been very effective at not only developing superior prospects but also “culling” the herd to identify the best of the best. In late winter of every year ESPN broadcasts an in-person, invitation-only event that offers NFL prospects the opportunity to perform and compete in six key performance measures (the 40-yard dash, the bench press, the vertical jump, the broad jump, the 3-cone drill, and the shuttle run) in the presence of all NFL scouting and team decision-makers. Because this event is so well publicized and the requirements seem so achievable, it fosters the belief that the dream is within reach of just about anyone.


  1. “NFL Regional Combines Are Working” by Jack Bechta, National Football Post, May 16, 2012:–nfl.html
  2. NFL Scouting Combine:
  3. Pop Warner Little Scholars, Inc:

Looking to Negotiate Discounts on Your College Tuition…Start Here

Everybody loves a discount, especially when big-ticket items are involved. There are fewer items bigger than those attached to funding a four-year college education. Most savvy retail shoppers know that regardless of the list price in the store or online, it is always a good idea to ask for a discount. Surprisingly enough, the same concept applies when approaching your college institution of choice. Beyond the standard in-state vs. out-of-state tuition discounts, there are many more levels of institutional aid available that will help parents/students make better higher education fiscal choices, as well as help the average university to level the competitive student recruitment playing field. College costs, student costs, and available subsidies are the three primary elements that will determine the level of financial assistance you can reasonably expect from the school of your choice.

College costs include the amount of time and money allocated to providing educational services to students. If a student can be considered a raw material, the amount of money it takes to bring this product to market (i.e., graduate) would be the total cost of goods sold. In 2009, community colleges educated over 6.5 million students—the single biggest sector nationwide, serving over a third of all students—yet spent about $10,000 per FTE student annually, an amount less than any other type of college or university. Nationally, state and local spending per college student, adjusted for inflation, reached a 25-year low in 2011, jeopardizing the long-held conviction that state-subsidized higher education is an affordable steppingstone for the lower and middle classes.  “I readily admit it,” said E. Gordon Gee, the president of Ohio State University, who has also served as president of Vanderbilt and Brown, among others. “I didn’t think a lot about costs. I do not think we have given significant thought to the impact of college costs on families.”

You should think of college cost as wholesale, student prices as retail, and subsidies as the coupons in your Sunday newspaper. As in any retail transaction, knowing what an item costs is the first step in determining what you should be paying for it.

Price is synonymous with “cost of attendance” and usually refers to the university list price paid by students. It typically includes tuition, room/board, books and supplies, plus similar costs for personal expenses and transportation. In the retail world an increase in price would normally lead to an increase in net profit, but things work a little differently for the average educational institution. Increases in price do not always translate into increases in student spending. Most public institutions increase prices to cover revenue lost from state and local budget shortfalls. However, with the exception of for-profit institutions, these increases often cover less than half of the revenue lost.

Among public community colleges, revenues from state and local appropriations declined an average of $488 per student between 2008 and 2009, whereas tuition increases generated new net tuition revenues of only $113 per student.  There is an obvious gap between the costs colleges bear and the price students pay that must be closed. This brings us to the subject of subsidies.

Subsidies are institutional financial aid offered to students to help defray the cost of attendance by leveraging financial sources such as state appropriations, gifts, and endowments. The cost/price/subsidy relationship is the major financial difference between public/nonprofit and profit-making institutions. If profit is the goal, an institution charges more than it costs to provide a service or deliver a product, and the difference is profit. In a public or nonprofit institution, price is less than cost. In public higher education, prices are increasing, costs are remaining fairly steady, and subsidies are declining.

All of this brings us to the central point of this article. The more informed consumers (students) are about how retailers (institutions) go about setting prices, the easier it will be for everyone to achieve their desired outcomes. Students/parents will be able to take a more fiscal approach to making higher education school choices, and will be better positioned to negotiate desirable financial aid packages. College institutions will be able to increase their competitiveness by offering compelling and competitive recruitment packages to prospective students.

How well do you think students/parents are prepared to effectively negotiate their financial aid packages? As an institution, are colleges doing enough to close the gaps between costs and price? Tell me what you think.


  1. Tuition Discounting: Not Just a Private College Practice, by Sandy Baum of Skidmore College and The College Board, and education consultant Lucie Lapovsky of Mercy College, The College Board, New York, 2006.
  2. The College Board’s Annual Survey of Colleges.
  3. The New York Times: “A Generation Hobbled by the Soaring Cost of College,” by Andrew Martin and Andrew W. Lehren, May 12, 2012.

How Saying the “R” Word Can Cost Students $18,200 in High Interest Loans

The average cost of remediation for incoming freshmen attending a four-year private institution could be as little as $9,100 or as much as $18,200 for incoming freshman. Eliminating the need for remedial classes would go a long way to significantly reduce the cost of higher education and by extension the amount of student loan post graduation debt burden.

According to a 2011 study sponsored by Minnesota State Colleges and Universities and the University of Minnesota, more than 55% of 13,000 recent high school graduates were required to take at least two and sometimes more than four remedial courses during their freshman year. Remedial classes are usually given in the areas of math, science, or language. Colleges offer an average of three to five credits for each accredited course. The College Board reports that the average per credit cost of attending a private four-year college is $910.1

Compounding the problem is the fact that these developmental courses represent “empty calories.” Although it is debatable whether they actually prepare a student to succeed, the fact is that most, if not all, colleges DO NOT count these courses toward the completion of a degree. Both students and colleges are burning money on classes that don’t satisfy any of their degree requirements.

“Teachers, parents and students should understand that developmental courses do not count toward a certificate, diploma or degree,” said Scott Olson, the system’s interim vice chancellor for academic and student affairs.

One strategy often suggested to students in this position is to attend a two-year community college prior to full enrollment in a four-year institution. Although it may solve the “preparation” problem, it will inherently result in additional student loan debt due to the extended period of time in school if the student proceeds to attend a four-year college or university.

Nationally, four-year colleges graduated an average of just 53% of entering students within six years, and “rates below 50%, 40%, and even 30% are distressingly easy to find,” says the report by the American Enterprise Institute,2a conservative think tank. It’s based on data reported to the Education Department by nearly 1,400 schools about full-time first-time students who entered in fall 2001.

Furthermore, according to an analysis by the Federal Reserve Bank3 of New York, in 2011 the average debt for student borrowers was about $23,300, while 10% owed more than $54,000 and 3% owed more than $100,000.

It is clear to me that the process of college remediation can lead to a significant increase in student debt. There have been many initiatives put in place to address the issue of remediation in higher education. The National Governors Association (NGA) has identified four metrics that can be used to measure our effectiveness at addressing this problem:

  1. Percentage of completion rates of remedial and core courses
  2. Number of students that advance from remedial to credit-bearing courses
  3. Number of transfers from a two-year to a four-year institution
  4. Remedial student graduation rates

Assuming that we had access to the information above, how could high school officials, parents, students, state and local organizations, and colleges use it to make appropriate decisions regarding the need for remediation?

At the end of the day the only real question seems to be, “Is there really a GOOD reason why a student would have to take remedial classes after high school when there are so many cost-effective remedies that can be put in play before they arrive on campus?”


  1. Bright Hub:
  2. USA Today:
  3. The New York Times: