Breaking Bad with 3D Enterprise Systems: Mobilizing Data with Keys, Models, and Governance, by Blair Kjenner and Kewal Dhariwal
Learn about a new paradigm in enterprise system development that is designed from the ground up to allow systems to easily exchange data.
Our current approach to systems
What solutions have we tried to address the problem
Critical success factors
Vision for the 3D ES
Five key principles
Impact of the new paradigm
Connecting to old paradigm systems
Examples of 3D ESs
Benefits of 3D ESs
Database and data dictionary
Design patterns to support data exchange
Core Data Models
Fixed asset registry
Searching For data
Duplicate detection and merging
Creating/maintaining automation parameters
Interface to the SQL database
Creating the vision
Creating the enterprise data dictionary
Creating the registry systems
Developing operational systems
Maintaining the 3D ES website
Governing the core data structures
Managing the master data exchange server
Application framework specifications
Managing specifications/test scripts
Training and certifications
In the early days of railways, each country decided the width of its railway track. This meant shipping freight across countries required unloading and reloading at each border. Unfortunately, this inefficiency from the lack of standards and principles is exactly where the IT industry is today. IT teams create systems using their own preferences, leading to difficulties moving data between systems, just like the railway system unloading and reloading freight at every border.
The railway system eventually standardized and so can we. In fact, IT is seemingly the only profession that has no principles. Accountants, engineers, and physicians all operate by well-defined principles that have excelled these professions.
We examine how we traditionally create systems and formulate a new approach to enterprise system development that addresses data integration issues and opens up possibilities for aggregating data between systems for Artificial Intelligence (AI) and analytics. The answer is NOT Software as a Service (SaaS), Electronic Data Interchange (EDI), Master Data Management (MDM), or microservices. Instead, five key principles drive this new paradigm to help our profession create enterprise systems that mobilize data securely and effortlessly within an integrated environment.
Technology doesn’t drive change—new models of organization, innovative people, and new business ideas drive change. Read this book if you are interested in building a future-oriented business and fostering innovative change to your business information systems.
Blair Kjenner has been architecting and developing enterprise software for over forty years. Recently, he had the opportunity to reverse engineer many different systems for an organization to help them find missed revenues. The project resulted in recovering millions of uncollected dollars. This inspired Blair to evaluate how systems get created and why we struggle with integration. Blair then formulated a new methodology for developing enterprise systems specifically to deal with the software development industry’s key issue in delivering fully integrated systems to organizations at a reasonable cost. Blair is passionate about contributing to an industry he has enjoyed so much.
Kewal Dhariwal is a dedicated researcher and developer committed to advancing the information technology industry through education, training, and certifications. Kewal has built many standalone and enterprise systems in the UK, Canada, and the United States and understands how we approach enterprise software today and the issues we face. He has worked closely with and presented with the leading thinkers in our industry, including John A. Zachman (Zachman Enterprise Framework), Peter Aiken (CDO, Data Strategy, Data Literacy), Bill Inmon (data warehousing to data lakehouse), and Len Silverston (Universal Data Models). Kewal is committed to advancing our industry by continually looking for new ways to improve our approach to systems development, data management, machine learning, and AI. Kewal was instrumental in creating this book because he immediately realized this approach was different due to his expansive knowledge of the industry and by engaging his broad network of experts to weigh in on the topic and affirm his perspective.
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