This document discusses the need for thought leadership and innovative thinking over a sole focus on technology and data. It argues that meta-ideas, rather than just metadata, are driving innovations today. Interdisciplinary thought across industries and non-traditional hires are needed to develop new perspectives and break from traditional views. As data grows exponentially, new approaches are required that combine different data techniques rather than relying on single technologies. Advanced data modeling is needed to capture human concepts and link data to real-world contexts and objectives.

1. © 2015 Modus Operandi, Inc. , All Rights Reserved. 1
META-IDEAS NOT METADATA: WHY THOUGHT LEADERSHIP
NOT TECHNOLOGY PROMOTES INNOVATION

2. • Innovation and entrepreneurship often go
hand in hand
• Do we really understand these concepts?
• Being innovative and entrepreneurial involve
advancing new ideas in Data Science
• This talk will examine some shifting
fundamentals in software development and
the need for new perspectives on Data
Science

3. © 2014 Modus Operandi, Inc. , All Rights Reserved. 3
• Thought is key in today’s industries
• Answers are not found in well-known text books,
manuals, algorithms, etc.
• Abstract thinking & problem solving is an increasing
commodity
• Meta-ideas are driving today’s innovations not just
data
• New paradigms for business & education are
needed that break down barriers between
disciplines
• Industries need to look to non-traditional hires
and resources for new skill sets
• Break out of traditional molds/views
INTERDISCIPLINARY THOUGHT IS KEY

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• The power of analytics is now just
beginning to be felt
• Moore’s Law for hardware may not be
entirely accurate
• What about software & analytics?
• From 1988-2003 Computer processing
speed grew by 1000x
• In the same period algorithm dev grew by
43,000x
• What does this tell you about the direction
in which we are headed?
• As data grows, so too will the need to
utilize it more effectively
THE SECOND HALF OF THE CHESSBOARD

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• Organization, structure, direction and
leadership amidst the chaos of the
mundane
• Seeing the potential of something
before it exists is an important quality
• Objectives should be driven by strategic
decisions as well as tactical ones
• Technology continues to rapidly change our
world – seeing the next big thing is
becoming more important
• Entrepreneurs must deal in the
abstraction of the possible, while
creating the actual.
WHAT IS THE VALUE OF THE ENTREPRENEUR?
“Out of every one hundred [people],
ten shouldn't even be there, eighty
are just targets, nine are the real
fighters, and we are lucky to have
them, for they make the battle. Ah,
but the one, one is a warrior, and he
will bring the others back.”
―Heraclitus

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• Innovation is stretching one’s conceptual boundaries
and embracing the art of the possible
• Innovation thus provides the underlying conceptual
capital necessary to be entrepreneurial.
• Innovation is about generating concepts and ideas that
at their very core are disruptive
• Provide an upheaval of commonly accepted norms
• Entrepreneurs take innovative concepts and use them to
produce new goods and services
INNOVATION AND THE ENTREPRENEUR

7. © 2015 Modus Operandi, Inc. , All Rights Reserved. 7
There is no single technology to rule all
your data – it has to combine different
approaches
TRADITIONAL RESPONSES ARE NOT WORKING
The following techniques are
not enough:
• Data Warehousing
• Data Lakes
• Over-reliance on Hadoop
• More hardware
• Front-end visualization tools

8. © 2014 Modus Operandi, Inc. , All Rights Reserved. 8
APPLICATIONS TO SOFTWARE DESIGN & DEVELOPMENT
• Data science is a growing field in academic
settings
• Can it actually be applied to real software?
What would this look like?
• Reasoning over big data is key
• Applying formal logic to capture context is
proving to be very valuable
• Growing need to link business drivers to
technology
• Too much emphasis is spent on Volume
and Velocity (for basic look-ups)
• Metadata is once again a hot topic –
especially with the rise in data Variety &
Veracity

9. © 2015 Modus Operandi, Inc. , All Rights Reserved. 9
LESSONS FROM THE INTELLIGENCE DOMAIN
Real-World
Data &
Knowledge
Capture
Flexible UIs and
Model Mgt.
Ontologies and
Data Models
Large Scale
Graph Stores
Users,
Collaborations,
Curations
The intelligence domain offers a glimpse
into Data Science complexities:
• Complex environments require
detailed analytics capture
• Large-scale connectivity is needed
between data and real world facts
• Reasoning is needed to infer missing
data and codify patterns
• Situational awareness needs context
- Spatial, Temporal, Cognitive and
Social Context all matter
• Users need to collaborate over the
same data simultaneously

10. © 2015 Modus Operandi, Inc. , All Rights Reserved. 10
Advanced Big Data Analytics tool that
integrates your data, automates processes and
provides alerts when necessary - breaking
down silos and barriers in your organization.
Captures real human concepts and
perspectives (in context) allowing your data to
behave more like you do, rather than force you
to behave like your data.

11. © 2015 Modus Operandi, Inc. , All Rights Reserved. 11
• Advanced metaphysics –
not just data models
• Layered models connect
data sources with higher-
order classifications
• Federation to scale
• Implied patterns become
explicit
• Codifies organizational
objectives
• Tracks complex items of
interest
• Bridges to reference
resources
SMART-LAYERED DATA MODELS ARE NEEDED
Data Source Models
Multi- & Single-Source Data
Integration Models
Domain Models (Objs, Attributes,
Process & Relations)
System Lvl Models (Business Rules)
Provenance,Trust
UserAccessModels
Upper-Lvl Models
Meta-data
Levels (Human
Concepts)
Data-centric
Levels
(Machine
Language)

12. © 2015 Modus Operandi, Inc. , All Rights Reserved. 12
ONTOLOGY
GRAPH
MAPPING
Ontologies should be
easily manipulated to
integrate all data
across an enterprise

13. © 2015 Modus Operandi, Inc. , All Rights Reserved. 13
Doc Repo
Provenance
ARCHITECTING SOFTWARE FOR THE 21ST CENTURY
Source
Handler
Artifact
Process
or
BASE
Predicate
Store
CSV
Source
Handler
Artifact
Process
or
MOVIA
Modeler Tweak model
Entity
Manager
Service
Repopulate
Refined Store
Reasoner
New Viz
New
Views
Add-change
Rules
Items adapted for
Specific Customer
Engagement
Statistical
Analytical
Processing
EnterpriseResources

14. 14
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