4 Day Seminar Encompassing Four One-day Courses

TDWI Business Analytics
Building the Foundation for Business Analytics in Your Organisation


Demand for business analytics is reaching new levels of intensity. The economy, politics, and the regulatory environment are all adding pressure to already difficult decisions about markets, customers, and the workforce. Today's business climate is fragile and tomorrow's is uncertain. Analytics makes available the tools for business managers to plan for, predict, and solve problems in a comprehensive way. But you also need to optimize business performance with the right analytics for your audience.  In this four day seminar encompassing four one-day courses, you will discover the many practical ways business analytics can work for you. You will return to the office with ideas, strategies, techniques, and connections you can put to use immediately to make analytics work for you and your organisation.  The four one – day courses include:

Design Techniques for Dashboards and Scorecards
Chris Adamson


Dashboards and scorecards are among the most popular ways to deliver today’s business intelligence. A top-quality dashboard or scorecard looks deceptively simple. But creating simple and effective interfaces is surprisingly difficult. A powerful dashboard or scorecard involves the right indicators and metrics, the right visual elements, attention to relationships among visual elements, and the right kinds of click-through and user interaction. Further complexity arises when you work with groups of related scorecards and dashboards that must fit together to form an integrated performance management system.

Learning Objectives

  • How to define and design performance management architecture
  • The role and use of a performance management portal
  • When to use scorecards and when to use dashboards
  • How to integrate dashboards and scorecards, including cascading and drill-in
  • How to choose the right indicators and metrics for dashboards and scorecards
  • How to choose the right visual elements and the best visual design
  • Data management techniques for scorecards and dashboards

Seminar Outline

Dashboards and Scorecards – What and Why

  • Definitions
    • Performance Dashboard
    • Dashboard and Scorecard
    • Performance Management
  • Using Dashboards
    • Who, When, and Why?
    • Example: Cisco Systems
    • Example: Navistar
  • Using Scorecards
    • Who, When, and Why?
    • Example: Cisco Balanced Scorecard
    • Example: Rohm and Haas

Performance Management Architecture

  • PM Architecture Overview
    • What and Why of PM Architecture
    • Components of PM Architecture
  • Business Architecture
    • Balanced Scorecard
    • Strategy Mapping
    • Performance Indicators
  • Services Architecture
    • Portal Services
    • Dashboard Services
    • Scorecard Services
    • Analysis Services
    • Reporting Services
    • Measurement Services
  • Technical Architecture
    • Data Architecture
    • Technology Architecture

Performance Dashboards

  • Implementing Dashboards
    • From Planning to Production
    • The Design Phases
  • Dashboard Requirements
    • Business Scope and Stakeholders
      • Operational Dashboards
      • Tactical Dashboards
      • Strategic Dashboards
    • Features and Functions
    • Performance Indicators
    • Cascading and Dependency
  • Dashboard Design
    • Design Tips
    • Item Placement
    • Element Layout
    • Filters
    • Drill-Down
    • Help
  • Design Tips and Techniques
    • Avoid Decoration
    • Simplify
    • Adapt Charts to Fit Viewers
    • Choosing Charts
    • Formatting Tables
    • Pre-attentive Processing
    • Choosing Fonts
    • Color Blindness
  • Dashboard Design Examples
    • Balancing Sparsity and Density
    • What’s Wrong with This Picture?
    • What’s Right with This Picture?

Performance Scorecards

  • Implementing Scorecards
    • From Planning to Production
    • The Design Phases
  • Scorecard Requirements
    • Business Stakeholders
    • Ownership and Accountability
    • Features and Functions
    • Performance Indicators
    • Cascading and Dependency
    • Drill to Analysis
  • Scorecard Design
    • Scorecards vs. Dashboards
    • Tabular Views
    • Expand and Collapse
    • Embedded Scorecards
    • Linking Metrics to Graphs
  • Performance Indicators
    • Metrics vs. Performance Indicators
    • Elements of Performance Indicators
    • Performance Targets
    • Displays and Encodings
    • Interpreting KPIs

Summary and Conclusion

  • Integrated Performance Management
  • Summary of Key Points


BI program and project managers; BI and performance management architects, designers, and developers; business executives and managers seeking performance improvements; dashboard and scorecard designers and developers; anyone with a role in defining, creating, or applying business metrics

Business Analytics: Exploration, Experimentation, and Discovery
Chris Adamson


Analytics is at the forefront of business intelligence. The promise of BI is found in data analysis that provides insight and drives innovation. Data-driven investigation, exploration, and experimentation lead to the kinds of discoveries that uncover opportunities and help answer future-looking questions. Analytics is a hot topic in business management, and quantitative analysis has rapidly become the in-demand skill for data management. What was once a specialty field exclusive to statisticians and mathematicians has become mainstream. Today’s business analysts combine understanding of business, data, statistics, math, visualization, and problem solving to meet business-critical needs for information, understanding, and insight.

Learning Objectives

  • How models are used to define and frame analytic needs
  • Model development techniques, including influence diagramming, spreadsheet engineering, and parameterization
  • Model refinement techniques, including sensitivity analysis, strategy analysis, and iteration
  • Discovery-oriented techniques, including heuristic analysis, subjective probability, hypothesis formation, and experimentation
  • Statistical foundations of data analysis, including histograms, standard deviation, and regression
  • The data side of analytics: data preparation, data cleansing, data visualization
  • The human side of analytics: communication, conversation, collaboration
  • A bit about analytics tools from free and open source to advanced analytics technology

Seminar Outline

Introduction to Business Analytics

  • Defining Business Analytics
    • What is Analytics?
    • Why Analytics?
    • Business Analytics vs. Performance Management
  • · Analytic Processes
    • Decision Analysis: Define, Disassemble, Evaluate, Decide
    • Problem Analysis: Frame the Problem, Model the Problem, Model the Solution, Evaluate & Learn
    • Data Analysis: Data Profiling, Problem Statement, Modeling, Analysis, Interpretation
  • The Analytics Environment
    • Analytics and BI
    • Enterprise Data, Data Warehousing, User Data, and Local Data
    • People, Tools, Organizations, and Analytic Culture
    • Collaborative Analytics and Agile Analytics

Analytic Modelling

  • Modelling Concepts
    • Modelling Purpose
    • Modelling Skills
    • Problem Modelling vs. Solution Modelling
  • Problem Modelling
    • Framing the Problem
    • Influence Diagramming
    • Goal-Question-Metric-Measure (GQMM)
  • Solution Modelling
    • Spreadsheet Engineering
    • Parameterization
    • Beyond Spreadsheets
  • Model Refinement
    • Sensitivity Analysis
    • Strategy Analysis
    • Testing and Iteration
    • Error and Calibration

Discovery Analytics

  • Heuristic Analysis
    • Human Thought Processes
    • Uncertainty and Incompleteness
    • Analyzing Intangibles
    • Rules of Thumb
  • Subjective Probability
    • Estimation - Best Guesses of the Experts
    • Quantifying Estimates
    • Variation and Disagreement
    • Confidence Intervals and Confidence Levels
  • Hypothesis and Experimentation
    • Framing the Problem – Finding Interesting Hypotheses
    • Designing Experiments
    • Testing Hypotheses
    • Keeping the Data in Sight
    • Feedback and Learning

Statistics and Data

  • From Data to Statistics
    • The definitional section from current analytics course
  • Statistics and Analytics
    • Histograms
    • Distribution and Deviation
    • Randomness
    • Correlation
    • Regression
  • Data and Analytics
    • Finding Data
    • Observations, Populations, and Samples
    • Raw Data vs. Summary Data
    • Data Preparation and Cleansing
    • Data Visualization

People and Technology

  • The Human Side of Analytics
    • Communication
    • Conversation
    • Collaboration
  • The Technology Landscape
    • Statistical Analysis Tools
    • Data Visualization Tools
    • Open Source Analytics
    • Data Analysis in Excel
    • Advanced Analytics

Summary and Conclusion

  • Summary of Key Points
  • References & Resources


Practicing business analysts and those who aspire to become business analysts; business functional managers responsible for analysing performance and risk; BI program managers, architects, and project managers; BI and IT professionals seeking to know more about business analytics.

Predictive Analytics Fundamentals
Chris Adamson


Predictive analytics is a set of techniques used to gain new knowledge from large amounts of raw data by combining data mining, statistics, and modelling. Predictive analytics goes beyond insight (knowing why things happen) to foresight (knowing what is likely to happen in the future). Predictive models use patterns in historical data to identify and quantify probabilities of future opportunities and risks. Virtually every industry—insurance, telecommunications, financial services, retail, healthcare, pharmaceuticals, and many more—uses predictive analytics for applications such as marketing, customer relationship management, fraud detection, collections, cross-sell and up-sell, and risk management.

This course introduces predictive analytics skills, which encompass a variety of statistical modeling techniques, including linear and logistic regression, time-series analysis, classification and decision trees, and machine-learning techniques. Beyond statistics skills, predictive analytics requires knowledge of problem framing, data profiling, data preparation, and model evaluation.

Learning Objectives

  • Definitions, concepts, and terminology of predictive analytics
  • Common applications of predictive analytics
  • How and where predictive analytics fits into a BI program and the relationships with business metrics, performance management, and data mining
  • To distinguish among various predictive model types and understand the purpose and statistical foundations of each
  • Organizational considerations for predictive analytics, including roles, responsibilities, and the need for business, technical, and management skills

Seminar Outline

Predictive Analytics Concepts

What and Why of Predictive Analytics

  • Predictive Analytics Defined
  • Business Value of Predictive Analytics

The Foundation for Predictive Analytics

  • Statistical Foundation
  • Data Mining Foundation

Predictive Analytics in BI Programs

  • Predictive Analytics in the BI Stack
  • Predictive Analytics in the BI Roadmap
  • Business, Technical & Data Dependencies

Common Applications for Predictive Analytics

  • What Business Needs to Predict

Data Mining Fundamentals

Statistics and Data Mining

  • Data Description Basics
  • The Shape of the Data
  • Variables
  • Relationships and Dependencies
  • Probability

Data Mining Processes

  • Data Mining Projects
  • CRISP-DM & SEMMA Compared

Data Mining People

  • A Team Effort
  • Roles and Responsibilities

Data Mining Models

  • What Are Models?
  • Kinds of Models
  • Some Examples
  • How Are They Built?

Data Mining Techniques

  • Classification
  • Segmentation
  • Association
  • Sequencing
  • Forecasting

Data Mining Technology

  • Features and Functions Overview
  • The Tools Landscape

Data Mining Algorithms

  • What and Why
  • Some Examples

Predictive Mining and Modelling

Introductory Concepts

  • Distribution View
  • Model Types View
  • Process View
  • Process Overview

Business Understanding

  • Activities and Deliverables
  • Pragmatics

Data Understanding

  • Activities and Deliverables
  • Pragmatics

Data Preparation

  • Activities and Deliverables
  • Pragmatics


  • Activities and Deliverables
  • Pragmatics


  • Activities and Deliverables
  • Pragmatics


  • Activities and Deliverables
  • Pragmatics

Human Factors in Predictive Analytics

Analytic Culture

  • Executive Buy-In
  • Strategic Positioning
  • Enterprise Range and Reach
  • Decision Processes

People and Predictive Analytics

  • The Range of People
  • The Range of Knowledge
  • Readiness
  • Trust and Motivation
  • Expectations and Intent
  • Getting from Analytics to Impact

Ethics and Predictive Analytics

  • Why Ethics Matters
  • Data and Ethics
  • BI, Analytics, and Ethics
  • Managing Ethics in Business

Getting Started with Predictive Analytics

Predictive Analytics Readiness

  • Readiness Checklist
  • Executive Commitment
  • Organizational Buy-In
  • Data Assets
  • Human Assets
  • Technology Assets

Predictive Analytics Roadmap

  • A Plan to Evolve
  • An Evolving Plan

Predictive Analytics Success Factors

  • Pragmatism and Realism
  • Best Practices
  • Mistakes to Avoid

Building Skills and Competencies


BI program managers, architects, and project managers; business analysts who want to extend from gaining insight to providing foresight; business managers who need new tools to help them shape the future of the business; anyone interested in the basics of predictive analytics

Tools and Techniques for Analysing Big Data
Mike Ferguson


This session looks at tools and techniques available to data scientists, business analysts and traditional DW/BI professionals to analyse multi-structured data. It looks how different types of developers and users can exploit Big Data platforms such as Hadoop and NoSQL databases using programming techniques, self-service BI tools as well as how vendors are making it easier to gain access both the NoSQL/Hadoop world and the Analytical RDBMS world by using data virtualisation.

Learning Objectives

  • To understand how data and analytical characteristics of big data dictate the platform used for analysis
  • To understand the options for analysing data on Hadoop
  • To understand the tools and techniques for analysing multi-structured data in Hadoop and the pros and cons of each
  • To understand graph analytics and when to use it
  • To understand real-time streaming analytics for analysing data in motion
  • To understand how to analyse data in multiple big data platforms with traditional data warehouse data

Seminar & Workshop Outline

  • Data and analytical characteristics of Big Data
  • Beyond the data warehouse –  new big data analytical workloads
  • Analysing multi-structured data in Hadoop
    • Creating Sandboxes for Data Science projects
    • Stages of the analytical process
    • Preparing and cleaning data in Hadoop
    • Text and social media analytics
    • Clickstream analytics
    • Options for analysing data on Hadoop
    • Developing analytics using MapReduce
    • NoSQL BI Tools and applications for Hadoop e.g. Datameer, Karmasphere, Platfora, IBM Customer Insight
    • Creating search indexes on multi-structured big data in Hadoop
    • Using search to analyse multi-structured big data
    • Building dashboards and reports on top of search engine indexed content
    • SQL connectivity initiatives to Big Data – e.g.Impala, Hive, Stinger, Pivotal HawQ, IBM BigSQL
    • Analysing big data from an RDBMS using external table functions and SQL MapReduce
    • Analysing Big Data using Self-Service BI Tools e.g. Tableau, QlikView, Spotfire, SAS Visual Analytics, MircoStrategy, SAP Lumira,
    • Big data analytics – query performance enablers
  • NoSQL analytics – a look at graph databases and graph analytics
  • Tools and techniques for real-time streaming analytics
  • Multi-platform analytics – joining Hadoop and DW data for analysis
  • Using Data virtualisation to simplify access Big Data and traditional DW/BI systems
  • Data visualisation and in-memory data in a big data environment


  • Business Analysts
  • Data Scientists
  • Enterprise Architects
  • BI developers
  • Database administrators
  • BI Project Managers
  • CIOs

Presenter's Biographies

Chris Adamson

Chris Adamson, CBIP, is an independent consultant, educator, and author. He works with customers in all industries to develop data warehouse strategies, define and prioritize projects, and design solutions. He teaches dimensional design at TDWI conferences and for TDWI Onsite Education. Chris's latest book is Star Schema: The Complete Reference. His other work includes Data Warehouse Design Solutions and Mastering Data Warehouse Aggregates. Chris blogs about data warehousing at Star Schema Central.

Mike Ferguson Mike Ferguson is Managing Director of Intelligent Business Strategies Limited. As an analyst and consultant he specialises in business intelligence, data management and enterprise business integration. With over 31 years of IT experience, Mike has consulted for dozens of companies, spoken at events all over the world and written numerous articles. He is an expert on the B-EYE-Network. Formerly he was a principal and co-founder of Codd and Date Europe Limited – the inventors of the Relational Model, a Chief Architect at Teradata on the Teradata DBMS and European Managing Director of DataBase Associates.

In-House Training
If you require a quote for running this course in-house, please contact us with the following details:

  • Subject matter and/or speaker required
  • Estimated number of delegates
  • Location (town, country)
  • Number of days required (if different from the public course)
  • Preferred date

Please contact:
Jeanette Hall
E-mail: jeanette.hall@irmuk.co.uk
Telephone: +44 (0)20 8866 8366
Fax: +44 (0) 2036 277202