DELOITTE SUBJECT MATTER EXPERTS
DELOITTE SUBJECT MATTER EXPERTS
What is Data Science –
What is the Organisational Value of Data Science?
Two High-Value Use Cases
Deriving Value from Analytics
Analytic Stages and ROI
The Relationship Between Data Science and High ROI Analytics
Top Three Sources of High ROI
How is Data Science Different from Data Analytics?
A Short History of Analytics
Three Types of Analytics
Data Science Analytic Methods, the Same but Different
Comparison and Cautions of Data Science Analytics vs. Regular Analytics
What are the Skills Needed for Data Science?
What Does a Data Scientist Do All Day?
Data Scientist Fundamental Skills
Characteristics of Data Scientists
Data Science Core Concepts
Orientation to Big Data
The Official Definition
The Unofficial Definition
Some Executives’ Definitions
The “Real” Definition
A Strategic Definition
My Working Definition
Trends within the analytically competitive organisation
The advent of Data Science
The Arena: From business unit-based to IT department-based
The Professionals: From analyst to data scientist
The Analyses: From descriptive analyses / business intelligence to predictive analyses / data mining / machine learning
What is predictive analytics’ role in Big Data?
Big data needs advanced analytics …but does analytics need big data?
You will never have a perfect model
Market perceptions of big data
ROI of data science, big data and associated analytics
Retail use case
Guerrilla marketing use case
Medical or government use case
The future of data science, big data and advanced analytics
How to think like a Data Scientist
Stats 101 in ten minutes
A / B testing and experiments
BI vs predictive analytics
IT’s role in predictive analytics
Statistics and machine learning: complementary or competitive?
Primary project types
Predicting a value given specific conditions
Identifying a category given specific conditions
Predicting the next step in a sequence
Common analytic algorithms
Popular tools to manage large-scale analytics complexity
R and Python
Hadoop, MapReduce and Spark
Data Mining “workbenches”
Performing a data reconnaissance
Building the analytic sandbox
Preparing train / test / validation data
Defining data sufficiency and scope
The CAO’s Roadmap
The Modeling Practice Framework™
The elements of an organisational analytics assessment
Project Definition: The blueprint for prescriptive analytics
The critical combination: predictive insights & strategy
Establishing a supportive culture for goal-driven analytics
Defining performance metrics to evaluate the decision process
What is the behavior that impacts performance?
Do resources support stated objectives?
Leverage what you already have
Developing and approving the Modeling Plan
Selecting the most strategic option
Planning for deployment
What will the operational environment be?
Who or what is the end consumer?
How do results need to be purposed or presented?
Measuring finalist models against established benchmarks
Preparing a final Rollout Plan
Monitoring model performance for residual benefit
Building the goal-centered analytics operation
Attracting and hiring the right analytic talent
The roles and functions of the fully-formed analytic project team
Specialisation in analytic project teams
Analytic opportunity identification, qualification and prioritisation
Organisational resistance and developing a culture for change
Project failure is not the worst outcome
Staging the organisational mind shift to data-driven decisioning
Motivating adoption by domain experts, end users and leadership
Recording ongoing organisational changes
Monitoring and advancing organisational analytic performance
“Democratising” analytics: Advantages and risks of “self-service”
Establishing performance dashboards
Standing up an agile analytic modeling factory
Knowledge retention and skill reinforcement
The Future of Data Science and Advanced Analytics
From Rhetoric to Reality
Biggest Driver of Analytic Innovation
Continually Improving Productivity and Profitability
Predicting Problems Before They Happen Becomes the New Norm
Changing Ever More Operational Models
What’s Next in Data Science?
Defining Your Organisation’s Data Science Reality