I was a computer science major in college. I won’t tell you my age, but I will say the hot programming language at the time was Pascal. If you knew Pascal, you could demand top dollar when you graduated. This made my computer science friends and me impatient and eager to learn this new technology.

As you can imagine, then, we were disappointed when the first few semesters came and went without any mention of Pascal. The languages we were using were BASIC, Fortran, and yes, even Assembly. (Assembly was pure torture, by the way. It was the class where the first day the professor said, “Look to your right, look to your left, only one of you will be here by the end of the semester.”)

Our professors emphasized programming concepts and principles, instead of learning the most popular new programming language. We spent lots of time on concepts including sequence; conditionals, such as IF-THEN statements; and iteratives, like WHILE loops. Our professors constantly stressed principles such as limiting procedures to one function, keeping code simple, indenting for readability, avoiding the dreaded GOTO statement, and testing and retesting.

When they finally taught Pascal in senior year, my friends and I were surprised by how quickly we picked up the language. It was so easy! In graduate school, I found the C programming language just as easy to master. Each time a shiny new programming language hit the marketplace, it was easy to learn because I knew the programming concepts and principles.

The moral of the story:

Avoid getting hypnotized by the job recruiter’s gold pocket watch. Ignore the songs from the vendor’s sirens. Do not walk into the light. Instead, learn the concepts and principles first, and then afterwards figure out how the new technology delivers.

We can apply this principle of learning the concepts before the technology to just about anything. A few years ago, I took sailing lessons. I just wanted to get the boat out in the water and start sailing, but my instructor spent hours in a classroom (without air conditioning) describing how to leverage the wind. For instance, one of his favorite concepts was picturing a clock, where the wind is at 12:00 and the bow of the sailboat is directed at a certain digit on the clock. We listened to him lecture for hours, using magnets on a whiteboard to represent the position of the sailboat with respect to the wind. We needed to understand the concept of wind before deploying the technology of the sailboat.

Sailing two months a year in New Jersey will never make me a great sailor, but I have become a skilled data modeler modeling data 12 months a year for the last 30 years.

A data modeler applies core concepts and principles to building databases. Just like in programming languages and sailing, the concepts and principles required for good database design must be mastered before deploying the technology of the database.

The data modeling process often starts off with talking to business professionals about their application requirements. Doing this can help the modeler understand project scope, and can clarify the meanings of core concepts such as customer, product, and account. Furthermore, good design principles must be followed; these include grouping similar properties together, planning for unknown requirements, and removing data redundancy.

The end result of all this data modeling work is a precise visual called the “data model.” It is described as “precise” because there is exactly one way to read a data model. Take this data model for example:

This model can be read exactly one way:

• Each Customer may own one or many Accounts.
• Each Account must be owned by one Customer.

This means, for example, that Bob can own Accounts 125 and 789. Each of those two accounts must be owned by one and only one customer such as Bob.

These precise diagrams solve many communication issues that could arise during development. The data model becomes a powerful communication tool for understanding the data, and is often used in building, supporting, and integrating software systems.

In short, data modelers are trained to transform ambiguity into precision. If you’re interested in learning more about data modeling, start with my book Data Modeling Made Simple.

We know that focusing first on the concepts and principles allows us to better leverage technology. Additionally, if these concepts and principles are conveyed to users very precisely, they will be more easily committed to memory. Therefore I—your trusted data modeler—will be your guide through the blockchain jungle. During your read, I will use my learned communication skills to illuminate the core concepts behind the world of blockchain.

This book will not teach you how to build a blockchain application. Instead, this book will teach you something much more valuable: the concepts and principles that provide the foundation for building any blockchain application.

This book comprises three parts:

1. Explanation. Part I will explain the principles underlying blockchain. A precise and concise definition is provided, distinguishing blockchain from blockchain architecture. Variations of blockchain are explored based upon the concepts of purpose and scope.
2. Usage. Now that you understand blockchain, where do you use it? The motivation behind building a blockchain application must include at least one of these five drivers: transparency, streamlining, privacy, permanence, or distribution. For each of these five drivers, we’ll show examples of blockchain use in the sectors of finance, insurance, government, manufacturing, retail, utilities, healthcare, nonprofit, and media. Process diagrams will illustrate each usage through inputs, guides, enablers, and outputs. Also examined are the risks of applying these usages, such as cooperation, incentives, and change.
3. Impact. Now that you know where to use blockchain, how will it impact the existing IT (Information Technology) environment? Part III explores how blockchain will affect data management. The Data Management Body of Knowledge 2nd Edition (DAMA-DMBOK2) is an amazing book that defines the data management field along with the complex relationships that exist between the various data management disciplines, such as between data governance and data architecture. Learn how blockchain will impact each of the 11 data management disciplines within DAMA-DMBOK2.

This book is for those who need and want to learn about blockchain. Once you—as a business or IT professional—understand the concepts and principles, you can position yourself, department, and organization to benefit and leverage blockchain technology.

As you begin to understand the immense power and potential of blockchain, you’ll come to recognize it as a truly disruptive technology—just like the wheel, printing press, computer, web, smartphone, or cloud. And just like these other groundbreaking technologies, once you understand the underlying principles and use them to build a solid foundation, the opportunities are endless.