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Philip Russom

The Confluence of Big Data and Advanced Analytics

The term “big data” has arisen in recent years to describe multi-terabyte data sets. Big data certainly has its challenges, relative to scalability and data management. But it’s also useful for business intelligence purposes. In particular, the massive datasets of big data provide substantial data samples for various forms of analytics, especially advanced forms that are discovery oriented.

Date: February 9, 2012

Time: 9:00AM PT

Philip Russom


Seven Data Discovery Steps for Improving Information Delivery and Accuracy

Know your data: With today’s information-driven business projects, no maxim could be truer. Yet many organizations lack fundamental knowledge about their data – and the situation is getting tougher as “big data” sources grow in size and variety and manual documentation efforts can’t keep pace.

Date: March 1, 2012

Time: 9:00AM PT

David Stodder


David Loshin

Successful Strategic Planning in the World of Big Data

It is difficult to ignore the growing influence of broad, massive streams of data from unexpected sources, especially when these data sources can directly influence customer satisfaction, expose opportunities for revenue generation, lead to brand risk, or improve customer profiling.

Date: March 7, 2012

Time: 9:00AM PT

David Loshin


David Loshin

Is Your Approach to Modeling MDM Fixed or Flexible? How to evaluate the cost vs. benefits of Prepackaged vs. Model-driven MDM

Fundamentally, there are two different approaches to modeling MDM – (1) prepackaged, and (2) business model-driven. Each approach determines if the data model is fixed or flexible, web services are pre-generated or dynamically generated, and user interfaces are pre-defined or user-defined.

Date: March 20, 2012

Time: 9:00AM PT

David Loshin


Barry Devlin

Operational Analytics – Empowering business analytics and decisions in motion

"Look after the pennies and the pounds will look after themselves." Operational analytics works the same way. Analyze what’s happening historically and in the moment for all the day-to-day activities of your business.

Date: March 27, 2012

Time: 9:00AM PT

Barry Devlin


Philip Russom

Next Generation Master Data Management

Master data management (MDM) is one of the most widely adopted data management disciplines of recent years. That’s because the consensus-driven definitions of business entities and the consistent application of them across an enterprise are critical success factors for important cross-functional business activities.

Date: April 10, 2012

Time: 9:00AM PT

Philip Russom


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Channels by Topic

  • Agile BI »
    Includes:
    • Agile
    • Scoping
    • Principles
    • Iterations
    • Scrum
    • Testing
  • Big Data Analytics »
    Includes:
    • Advanced Analytics
    • Diverse Data Types
    • Massive Volumes
    • Real-time/Streaming
    • Hadoop
    • MapReduce
  • Business Analytics »
    Includes:
    • Advanced Analytics
    • Predictive
    • Customer
    • Spatial
    • Text Mining
    • Big Data
  • Business Intelligence »
    Includes:
    • Agile
    • In-memory
    • Search
    • Real-time
    • SaaS
    • Open source
  • Data Analysis and Design »
    Includes:
    • Business Requirements
    • Metrics
    • KPIs
    • Rules
    • Models
    • Dimensions
    • Testing
  • Data Management »
    Includes:
    • Data Quality
    • Integration
    • Governance
    • Profiling
    • Monitoring
    • ETL
    • CDI
    • Master Data Management
    • Analytic/Operational
  • Data Warehousing »
    Includes:
    • Platforms
    • Architectures
    • Appliances
    • Spreadmarts
    • Databases
    • Services
  • Performance Management »
    Includes:
    • Dashboards, Scorecards
    • Measures
    • Objectives
    • Compliance
    • Profitability
    • Cost Management
  • Program Management »
    Includes:
    • Leadership
    • Planning
    • Team-Building
    • Staffing
    • Scoping
    • Road Maps
    • BPM, CRM, SCM
  • Master Data Management »
    Includes:
    • Business Definitions
    • Sharing
    • Integration
    • ETL, EAI, EII
    • Replication
    • Data Governance

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