Modus Operandi

14
META - IDEAS NOT METADATA: WHY THOUGHT LEADERSHIP NOT TECHNOLOGY PROMOTES INNOVATION

Transcript of Modus Operandi

© 2015 Modus Operandi, Inc. , All Rights Reserved. 1

META-IDEAS NOT METADATA: WHY THOUGHT LEADERSHIP

NOT TECHNOLOGY PROMOTES INNOVATION

• 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

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• 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

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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

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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

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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

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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.

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• 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)

Pro

ven

ance

, Tru

st

Use

r A

cces

s M

od

els

Upper-Lvl Models

Meta-dataLevels (Human

Concepts)

Data-centric Levels

(Machine Language)

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ONTOLOGY

GRAPH

MAPPING

Ontologies should be easily manipulated to integrate all data across an enterprise

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Doc Repo

Provenance

ARCHITECTING SOFTWARE FOR THE 21ST

CENTURY

Source Handler

Artifact Process

or

BASEPredicate

Store

CSV Source

Handler

Artifact Process

or

MOVIA Modeler Tweak model

Entity Manager Service

Repopulate Refined Store

Reasoner

New VizNew

Views

Add-change Rules

Items adapted for Specific Customer Engagement

Statistical Analytical Processing

Ente

rpri

se R

eso

urc

es

14

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