TRANSFORMING
DATA TO DECISIONS

A Roadmap to Revenue for Data Analytics

ABOUT THIS COURSE

This two-day course will prepare functional managers and business practitioners to finally make sense of data analytics and take control of the analytic process. “Transforming Data to Decisions” develops core skills for business analytics and lays the foundation for data-intensive analytic projects that deliver insight, clarity, confidence and actionable decision support.

Attendees will learn the four major types of problems encountered in commercial and public sector applications, and walk through a structured development process to grasp key issues they will face for each problem type. Unlike any other analytic or statistical training, this course maintains a decision strategy focus supported by work-along exercises on multiple business data sets. The exercises allow attendees to experience and organize the natural messiness of data analytics while reinforcing the skills to effectively drive their own projects. Participants return with a rich set of course notes, analytic process guides, exercise session files and follow-on resources.

PREREQUISITES

The density of material establishes an aggressive pace in the presentation of content. Statistical background is helpful, but not required.

WHO SHOULD ATTEND

This course 1) builds core capabilities for those who are new to data analytics; 2) provides a more functional, streamlined and strategic mindset for those with classical statistical training; and 3) serves as a structured platform for those wishing to proceed into more advanced analytics projects with greater confidence. “Transforming Data to Decisions” is suited for the following roles:

• Functional Managers – who desire a practical appreciation of core analytic methods in a fast-paced, impact-focused course in order to interact more effectively with analysts and IT staff on departmental data analysis projects, regardless of their prior statistical background.
• Business Analysts – who need to transform their quantitative capabilities into a purposeful approach to impactful decision analytics by letting go of ad hoc statistical techniques and focusing specifically on a framework of core analytic strategies most appropriate for tackling common business problems.
• Experienced Statisticians – who may be statistical wizards at the quantitative level, but realize they will be far more valuable to the organization upon learning how to adapt their classical training into more of a business mindset for streamlined, agile and targeted data analytics that translate into measurable impact for leadership.
• IT Specialists – who wish to gain a better appreciation of the overall analytic process in order to more effectively prepare resources for data analysis and integrate resulting decision models within the infrastructure of today’s sophisticated data storage and access environments.
• Business Intelligence Team Members – who realize the need to take a step back and reinforce their essential core analytic capabilities in order to gain solid traction when proceeding into more advanced analytics fields such as data mining, predictive modeling, machine learning, knowledge discovery, unstructured text analysis, and data-driven decision support.

BENEFITS OF ATTENDING

• Focus on core analytic issues, strategies, methods and techniques most appropriate for business decision making
• Understand in plain English what the algorithms do, and how true descriptive analytics relate to decision support
• Unify project teams – from statisticians to leadership – in order to establish a common strategy, framework and process for greater coordination, efficiency, clarity and impact
  • For analysts to obtain clear direction on analytic goals and delivery requirements
  • For leadership to better understand and trust what the analyst delivers
• Leave ad hoc, esoteric and academic statistical exercises behind in lieu of targeted, insightful and understandable analytic outcomes that drive decision models with residual benefit
• View content through an organized series of live work-along exercises in order to directly experience strategic issues as well as how to tactically implement at the project-level
• Leave with the resources, contacts and actionable plans to substantially increase analytic capabilities while minimizing dead ends

THE BUSINESS CHALLENGE

The ability to make effective and timely decisions driven by valuable information hidden within a rapidly increasing mass of data is critical to the success of modern organizations and managers. The advancement of analytic and reporting options, along with the proliferation of big data delivery platforms and analytic software suites create an environment where functional managers must rely heavily upon their analysts and IT staff for critical insight.

It is typically not the responsibility of analysts and IT specialists to focus on strategic-level decision processes. Yet, analytics will fall short of its potential without adequate context, sound problem definition and results translation. At the same time, statisticians and IT professionals are often misguided by managers who lack core analytic skills to effectively communicate their needs, or fully understand the results.

The gap between these roles leaves the manager to subjectively interpret results from analytical models that emphasize quantitative sophistication and artificial metrics instead of objective, data-driven solutions. And in business, the product of data analysis is not the analysis. The real products of the analysis are the insights gained and the impact measured.

The intent of this course is to bridge the critical translation gap between team members that cause the vast majority of analytic projects to fall short of their potential. Managers who attend this class will establish a stronger appreciation of core analytic methods and process to better interpret, evaluate and trust what the analyst delivers. Statisticians, IT professionals and analysts will learn how to approach data analysis in a structured and purposeful way that translates well for leadership, and directly impacts business performance.

WHAT YOU WILL LEARN

• Discover, monitor and clearly report the primary analytic contributors that affect strategic decision processes and their impact on performance metrics that are important to leadership
• Classify the four major types of analysis projects and match the best suited core analytic methods
• Receive a six-step methodology for evaluating and validating business insights
• Structure and organize a wide range of data analysis topics from a project-oriented, decision-process perspective
• Identify critical characteristics of available data fields and leverage treatments to significantly enhance their contribution to the decision making process
• Observe a structured process to evaluate the effectiveness of individual attributes and their value to enhancing a predefined objective
• Establish a solid foundation for more advanced analytic practices such as predictive modeling and data mining

WHAT YOU SHOULD BRING

Participants are welcome but not required to bring laptop computers. Live application Illustrations will be conveyed in the form of work-along sessions. Attendees may either watch the instructor work through the exercises, or take their own approach in parallel. Consider whether you learn better by watching or doing. Completed worksheets will be given to all attendees at the end of the class. All other course materials will be available at the training site.

WHAT MAKES THIS COURSE UNIQUE

This course directly addresses the critical yet common lack of effective coordination and hand-off between analysts, IT professionals and management. While each may be fully effective in their own domain, there is a significant loss of translation between roles in which critical context is lost. “Transforming Data to Decisions” targets this costly gap and enables all players to execute effectively across roles.

Unlike training that drills into various analytic techniques and leaves it to the attendee to figure out how to use them effectively, this intensive two-day course conveys analytics-in-action through work-along sessions. The sessions convey the four types of problems that dominate commercial and public sector implementations:

• Value Estimation
  • Estimating a value given specific conditions
  • Projecting the next step in a sequence
• Classification
  • Identifying a category given specific conditions
  • Identifying groups in large, complex environments

Attendees learn how to apply core analytic methods to defined problem types in order to reveal insights critical to organizational success. Attendees are then given a six-step development methodology to act as an analytic framework from which team members may collectively take control of a fully productive data analysis practice.

Analytic Model Development Process

To best convey and reinforce the analytic model development process, each topic is labeled in the course material as an “Issue, Insight or Illustration”:

• Issue: Brief lecture segments that present analytic methods, techniques and strategies
• Insight: Application tips, tricks and case examples that translate Issues into actionable context leveraged by the instructor’s seasoned experience in real-world implementations
• Illustration: Live demonstrations with optional work-along exercises that allow participants to directly experience and implement Issues and Insights to apply back at the office

TOPIC COVERAGE

This course follows a formal analytic model development process through four basic project types with reinforcement cycles. As such, the following outline is a topic list only; not an ordered schedule.

• Introduction
  • Core concepts
  • Using technology effectively
  • Big Data versus fat data for analytics
• The Four Basic Project Types
  • Predicting a value given specific conditions
  • Identifying a category given specific conditions
  • Predicting the next step in a sequence
  • Identifying groups
• The Motivation for Analytic Modeling
  • Enhanced performance: An incremental strategy
  • You will never have a perfect model
  • Think of business as a game
  • Performance metrics: Your compass to progress
  • A ‘Rear View Window’ perspective makes it hard to drive forward
  • Conditional decision-making given expected circumstances
  • The critical combination: Information & Strategy
• Mathematical Modeling
  • Formulas and their parts
  • Anticipating outcomes from environmental conditions
  • Projecting profit
  • How you think about an outcome is essential
  • People are inconsistent, unreliable and messy
  • There is never enough data
  • Samples and populations
• Model Development in Three Steps
  • Training
  • Testing
  • Validation
• A Basic Guide to the Process
  • Plan
    • What is the business goal of the analysis project?
    • What are the performance metrics for evaluating success of the decision process?
    • What is the scarce resource subject to allocation?
    • What is the behavior that impacts performance?
    • Is there sufficient data for the target behavior to develop an adequate model?
    • What is the current baseline level of performance?
    • Who is on the project team?
  • Prepare
    • What data should I include in my data sandbox?
    • What does a record look like?
    • What does the outcome or target variable look like?
    • What data representations should I use?
    • What data transformations should I use?
    • How do I select variables for my model?
    • How do I build my Training/Test/Validation data sets?
  • Build
    • Algorithms give us formulas, not answers
    • Formulas create a composite perspective
    • There is no such thing as a ‘good’ algorithm
    • Selecting the right tools for the job
    • The environment is also not consistent
    • Does the story make sense?
    • Not all data is created equal
    • Some data is not ‘math compatible’
    • Data Attributes
      • Qualitative
      • Nominal
      • Ordinal
      • Interval
    • Multiple models are usually needed
    • Adoption by domain experts, end users and leadership
    • Project failure is not the worst outcome
    • Some ‘cool’ features are just ‘nice junk’
    • Variability: Sometimes we want it, sometimes we don’t
    • Perfect correlation is not a good thing
    • No correlation is a waste of time
  • Confirm
    • Does our math make business sense?
    • Evaluation only makes sense ‘in context’
    • ‘Business Performance’ is the only priority
    • Consistent implementation strategy is critical
    • Our models are in competition with each other
    • How to pick a ‘Challenger’
    • Confirming we picked a good Challenger
  • Adopt
    • Evaluating the expected performance of our Challenger
    • Adoption by domain experts
    • Adoption by end users
    • Adoption by leadership
    • Project failure is not our worst outcome
  • Replace
    • Adapting to a changing environment
    • The environment always changes
    • Our business goals also change
    • Sometimes, we just want a better answer
• Development Process by Project Type
  • Predicting a Value Given Specific Conditions
    • Relationships in Data
    • Estimating the future value of an outcome based on known current conditions
    • Additional precision is more difficult to obtain and may put your project at risk
    • Build (observe or work-along)
  • Identifying a Category Given Specific Conditions
    • Shifting our thought process when the target outcome can take on a limited set of values
    • The business world is not normally distributed
    • The unknown attribute we are trying to predict is critical
    • The misuse of regression is dangerous to your financial health – and to your business
    • Playing the odds
    • The world is round and other non-linear realities
    • We need a different kind of formula… sort of
    • Classification is concerned with proportions, not precision
    • Build (observe or work-along)
  • Predicting the Next Step in a Sequence
    • Time series problems
    • Estimating the future value of an outcome by considering the direction and distance of change relative to our known position
    • Signal versus Noise
    • Plan
    • Build (observe or work-along)
  • Identifying Groups
    • Big Data versus Big Data Analytics – The business issues
    • Putting Fat Data on a Diet
    • Plan
    • Build (observe or work-along)
  • Wrap-up
    • The complexity of large-scale analytics
    • Specialization in project teams
    • The power of adapting core analysis skills
    • Where to go from here
      • Predictive analytics, business intelligence and other advanced technologies
      • Resources to get you on your way

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