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:
- Positive relationships with caring adults;
- Strong and tailored instruction;
- Opportunities to engage in learning experiences that connect school to careers and life beyond; and
- The support and resources to help them figure out what they want to do once they have earned their diploma.
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:
- 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;
- Tackling the issue of big city concentration: Most “dropout factory” schools with < 60% graduation rates are found in urban areas;
- Making special education students part of the solution: Their average graduation rate is 20% below the national average;
- Focusing on California: Home to 20% of the nation’s low income students; and
- Accelerating graduation rates for young men of color: This is currently only in the upper 50’s and low 60’s.
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
Latest posts by Harold Morris (see all)
- Cognitive Computing Systems: Want to build a great MVP? Pick your ingredients carefully - September 26, 2016
- Cognitive Computing Systems: Want to build a great MVP? It’s all about the prescription… - September 9, 2016
- Cognitive Computing Systems: Want to build a great MVP? Define your Product Vision - August 19, 2016