3-product developmentthought leadershippost-modernism and the age of big data_public_022217
TRANSCRIPT
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!