Home Page Icon
Home Page
Table of Contents for
cover
Close
cover
by Dave McComb
The Data-Centric Revolution: Restoring Sanity to Enterprise Information Systems
CHAPTER 1 The Data-Centric Movement
This movement requires executive sponsorship
If you are not an executive
Chapter Summary
CHAPTER 2 What is Data-Centric?
Data-centric vs. Data-driven
We need our applications to be ephemeral
Data-centric approaches are designed with data sharing in mind
The Data-Centric vision
Evolve-able
Specialize-able
Single but federated
Enterprise app store
Includes all types of data
The economics of the end game
Chapter Summary
CHAPTER 3 Getting There
What it requires
Inertial resistance
Overt and covert resistance
What it doesn’t require
This is a program, not a project
The transition to a Data-Centric approach requires discipline and consistency
The IT fashion industry
Is the Data-Centric approach a fad?
Can Data-Centric methods benefit from other fads?
From Fad Surfing to New Discipline
New modeling discipline
New delivery architecture
Chapter Summary
CHAPTER 4 Why We Need This Now
The status quo is getting exponentially worse
Code creates maintenance
Complexity creates high priests
Application-centricity creates silos
Silos create the need for integration
Legacy creates entrenchment
Inflexibility creates shadow IT
Mega projects create mega failures
Where application complexity comes from
A case example in complexity
Separation and isolation
Humans in the loop
The negative network effect
Complexity math and the way out of the quagmire
Chapter Summary
CHAPTER 5 A Deeper Look at Data-Centric Approaches
It’s the data, stupid
Task-centric is a trap
It’s the stupid data
The “what if” view on Data-Centric methods
Fewer models
Simpler models
Integration almost for free
More flexibility
Chapter Summary
CHAPTER 6 A Paradigm Shift
Paradigm shift
The original paradigm shift
How new ideas take hold
Round earth
Heavier than air flight
Scurvy
Hand washing before the germ theory
Non-linear change
Who is not going to help you with your transformation?
Digital transformation
The herd
Social proof
Incentives
Chapter Summary
CHAPTER 7 Case Studies
S&P market intelligence
Sokil
Chapter Summary
CHAPTER 8 Linked Data
When Linked Data and Semantic Technology become Data-Centric
Separating meaning from structure
A single structure for expressing all data
Graph databases (triple stores) for structures
RDF Resource Description Framework
Global identifiers
Dealing with non-unique but unambiguous IDs
Self-assembling data
Resolvable IDs
Follow your nose
Querying a triple store
Linked data
Chapter Summary
CHAPTER 9 Ontologies, Knowledge Graphs, and Semantic Technology
Metadata is triples as well
Formal definitions
Self-describing data
Schema later
Open world
Local constraints
Curated and uncurated data
Ontologies
Modularity and reuse
Self-policing data
Computable models
Integration with relational
Integration with big data
Natural language processing
Semantic standards stack
Chapter Summary
CHAPTER 10 Case Studies with Semantic Technology
Garlik
Montefiore
Chapter Summary
CHAPTER 11 Application Software is the Problem
Isn’t software a good thing?
How much code do we have?
How much do we need?
Where does it all come from?
Chapter Summary
CHAPTER 12 Data-Centric Means Massive Code Reduction
Reducing schema complexity
Reducing schema variety
Making possible massive reuse
Writing to a subset of the schema
Code reduction through integration elimination
Chapter Summary
CHAPTER 13 Model-Driven Everything
Model-driven development
Low-code and No-code
Declarative code
Model-driven constraints and validation
Model-driven Constraints
Model-driven UI
Model-driven identity management
Model-driven security
Chapter Summary
CHAPTER 14 Data-Centric and other Emerging Technology
Big data
Data lakes
Cloud
NLP
Rule-based systems
Machine learning
Microservices
Kafka
Internet of things
Smart contracts
Chapter Summary
CHAPTER 15 Assess Your Starting Point
Accessing your current situation
A small core
Getting to self-funding
Chapter Summary
CHAPTER 16 Executing Your Initial Projects
Think big and start small
Enterprise ontology
Gist as a starting point for your ontology
Pilots, not POCs
True contingencies
Corporate antibodies
Federated development
An enterprise knowledge graph
Chapter Summary
CHAPTER 17 Governance and the New Normal
The new approach becomes “hot”
The executive’s role in piloting the change
A kinder/ gentler voluntary governance structure
Good, better, best
TBox, CBox, ABox
Share the learning
Data-centric maturity
Chapter Summary
CHAPTER 18 Wrapping Up
About Semantic Arts
About the author
Index
Search in book...
Toggle Font Controls
Playlists
Add To
Create new playlist
Name your new playlist
Playlist description (optional)
Cancel
Create playlist
Sign In
Email address
Password
Forgot Password?
Create account
Login
or
Continue with Facebook
Continue with Google
Sign Up
Full Name
Email address
Confirm Email Address
Password
Login
Create account
or
Continue with Facebook
Continue with Google
Next
Next Chapter
FrontMatter
Add Highlight
No Comment
..................Content has been hidden....................
You can't read the all page of ebook, please click
here
login for view all page.
Day Mode
Cloud Mode
Night Mode
Reset