POST-MODERNISM AND THE AGE OF BIG DATA

data and analytics in a changing world

Tony Cosentino

CONTENTS

The big picture – Post modern

Historical context and opportunity space

Analytic trends today and tomorrow

Four Best Practices for organizational success in data and analytics

Why cloud based data and analytics

THE BIG PICTURE Post-Modern

POSTMODERNISM

postmodernism is typically defined by an

attitude of skepticism or distrust toward grand

narratives, ideologies, and various tenets

of Enlightenment rationality, including the

existence of objective reality and absolute

truth

CONTEXTUAL NARRATIVES

Move from top down singular narratives to contextual narratives

In politics, in society, and in business

Choice frame, relative choices, and delivery channel

Traditional Business Intelligence versus modern systems

HISTORICAL CONTEXT AND OPPORTUNITY SPACE

Business context

HISTORICAL CONTEXT

Legacy business intelligence assumes high priced storage, and structured data

Google cannot afford it, builds its own system

Distributed systems and their impact

The challenge of Hadoop

Moving from a technology context to a business context

OPPORTUNITY CONTEXT

Earnings growth of an S&P company last year was 5%, while revenue was only up 1%

In the 1958, the average tenure of an S&P company was 61 years. Today, it is 15 years

OPPORTUNITY SPACE

Product innovation New models focus on services (asset light)- DaaS, AaaS, SaaS

Recurring revenue models and cloud re-platforming

Customer innovation Metrics that matter

The promise of big data: Context specific and omni-channel

Operational Eliminate inefficiencies and/or automate

Governance, risk, compliance

ANALYTIC TRENDS Today and Emerging

TRENDS IMPACTING TODAYCloud based data and analytics

Data gravity and perception of security challenges are receding

New business models- DaaS, AaaS, SaaS emerging

Real-time analytics and unstructured data

From ETL batch to R/T data pipelines, Internet of Things (IoT), NoSQL

Need for integration, data management, security

Third generation of analytics emerging in Business Intelligence

From “observer effect” to prescriptive analytics

Embedded predictive capabilities

Emergence of new organizational roles such as the CDO/CAO

Data governance is a big challenge in organizations

How to govern data, manage risk and monetize the data are goals

EMERGING TRENDS

Block-Chain

Distributed ledger system

Impact on ERP and data governance systems

Conversational Commerce

Still need to perfect natural language processing

Full integration with commerce systems will be hard

Artificial Intelligence

See an image and write a sentence

Still needs a lot of data to perfect

Virtual Reality/Augmented reality

Niche applications in industries

May be the future of how we interact

Robotics

Some niche applications in industries

What is easy for a human is hard for a machine, and vice versa

FOUR BEST PRACTICES For Organizational Success in Data and Analytics

START WITH THE OUTCOME

Understand the “4 W’s”

The “What”: all available data

The “So What”: data inferences and analytics

The “Now What”: decisions to be made

The “Then What”: actions and closed loop learning

Source: Into the River, Tony Cosentino

UNDERSTAND ANALYTIC CONTEXTStrategic Ad Hoc Operational

Business Goal Long Range Planning and Analysis (e.g. balance sheet, loyalty and brand value, product, distribution)

Near Range planningand analysis (e.g. product, root cause analytics, marketing analytics)

Execution oriented goals (e.g. call center, sales, dynamic pricing, “moment of truth”)

User Executive Managers Consumers, line workers, M2M

Time Horizon Longer Medium Now

Source: Into the River, Tony Cosentino

Centralized /Cross Functional Decentralized/ Departmental

SEGMENT ANALYTIC PERSONAS

Information consumer (e.g. sales rep or consumer)

Knowledge worker (e.g. division head or doctor)

Analyst (e.g. data janitor or marketing scientist)

Designer (e.g. user experience professional or digital ethnographer)

Data geek (e.g. Chief Data Scientist or solution architecture lead)

Source: The Personas that Matter the Most in Business Analytics, Information-management.com, Tony Cosentino

ADDRESS THESE CHALLENGES NOW

Communication and knowledge sharing

Technology and analytics prioritization

The skills gap

Governance of data

TAKEAWAYS

TAKE AWAYS

Data and analytic technology is enabling context specific choice sets to be offered at the point of decision.

Data and analytic services are the enabler of an entirely new and disruptive opportunity space.

A multitude of trends are impacting different categories and companies at different rates.

Today’s best practices focus on people and process first, then on technology.

Cloud based analytics have evolved significantly in just the last few years.

THANK YOU!