November 19, 2015

Data Architecture

Data Science and BI in the Business Landscape

There is confusion about how data science fits into the landscape of business…and IT. In this intriguing webinar with Bill Schmarzo, CTO of EMC’s Global Services Big Data Practice, we look at what data science is, what it hopes to accomplish, how it is related to business analytics, where it differs, and ultimately how organizations will benefit from using both data science and business analytics.

Viewers learn:

  • The difference between BI and data science and the complementary nature of both
  • How the data lake can empower data science teams and free up valuable data warehouse resources

Hadoop / big data, data warehouses


BI Report Authors, BI Report Consumers, BI Power Users (Developers, Support Staff), BI Managers, IT Managers, IT / BI Executives (CTO), Business Executives (CEO), Financial Planners, Financial Analysts, Marketing Analysts, Marketing Managers / Directors, Predictive: Quality Assurance, Risk & Fraud, Data Scientists


Bill Schmarzo 
EMC Global Services, Big Data Practice

Bill Schmarzo is the author of Big Data MBA: Driving Business Strategies with Data Science and Big Data: Understanding How Data Powers Big Business. Bill is responsible for setting the strategy and defining the big data service offerings and capabilities for EMC Global Services Big Data Practice. He has written several white papers, is an avid blogger, and is a frequent speaker on the use of big data and data science to power organizations’ business initiatives. He is a University of San Francisco School of Management (SOM) Fellow where he teaches the big data MBA course.


Poll Results
Where are you in the process of integrating Big Data with your existing data warehouse environment?

  • Actively working to replace data warehouse with data lake
  • Already using big data (Hadoop) to offload ETL work
  • In the testing phase
  • In early discussions
  • No plans/don’t know

Introducing Data Science

  • Data Science is about identifying variables and metrics that are better predictors of performance
  • Evolution of the analytics process – business intelligence vs. data science
  • Business intelligence engagement process
  • Transitioning the business questions
  • Data science engagement process
  • Power of analytic profiles (healthcare example)
  • Analytic profiles x use cases (healthcare example)

The Data Lake

  • Modern BI / analytics environment
  • Characteristics of a data lake – free up costly data warehouse and BI resources; enable your advanced analytics / data science environment
  • Why a data lake? Enable analytics! – analytics hub and spoke service architecture
  • #BigData reference architecture
  • Lean data governance lifecycle

How to Get Started

  • Prioritize your business initiatives

Additional Resources