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Guidance and results for the data-rich, yet information-poor.



The Essential Soft Skills and Strategic Framework

for Leading a Goal-Centered Analytics Practice

Classroom: Two Days
Online: Four Half-Day Live Sessions
$1,795 USD
INFORMS Professional Development Units: 18


This unique course directly addresses the cultural, environmental, team and resource issues involved with the overall analytic function in medium and large organizations. Unlike any other analytic training available, this event applies a structured framework for establishing a sustainable modeling practice with goal-driven purpose. It is designed and facilitated by active and highly seasoned analytic consultants who directly encounter the vast challenges and realities of modern organizational analytics.

Organizations that populate a deep bench of data scientists tasked with discovering patterns in data to generate ‘interesting insights’ will not be competitive in the modern analytic landscape. “Advancing the Analytics-Driven Organization” outlines the strategic foundation for generating analytic results that are purposeful, measurable, understandable, actionable, residual and adopted by stakeholders. Those who attend this course will acquire analytic leadership traits that are in high demand in large organizations. Participants will develop rare soft skills to significantly advance their professional profile and stand out in the competitive analytics marketplace.


While the density of material establishes an aggressive pace in the presentation of content, no preparation is required for this course. Content is presented in nontechnical terms. Exercises are conveyed through demonstrations and guided discussion only.  If you wish to drill into more detailed content for comprehensive assessment and project design, consider the Project Planning course. And if you wish to directly experience modeling methods, algorithms and the natural messiness of data and modeling through hands-on exercises, consider the Model Development course. Read the Series Overview to understand this event’s function within the full course series progression.


This course 1) builds critical soft skills required to establish a sustainable internal analytic practice and collaborate effectively with various organizational team members – from leadership to IT, subject matter experts, analytic practitioners, external enrichment vendors, and the deployment team; 2) establishes a strategic mindset to organize and oversee an ongoing goal-driven analytic function; and 3) serves as a structured platform for those wishing to proceed into more comprehensive analytics projects with greater confidence.

“Advancing the Analytics-Driven Organization” is intended for the following roles:

    • Organizational Leadership – who desire a greater understanding of analytics’ true capabilities, limitations, risks, rewards and high-level function from an unbiased, vendor-neutral perspective to confidently set expectations, define goals, establish mindset and sell the vision.
    • Functional Managers – who are seeking clarity on how to assemble a fully-formed and right-sized analytic team and oversee the implementation of a low-risk / high-impact analytic function that provides measurable data-driven decisioning.
    • Analytic Practitioners – who are motivated to enhance their tactical quantitative experience with a highly pragmatic strategic layer in order to tightly align with organizational project goals while broadening their capability and value on the analytic team.
    • Experienced Statisticians – who may be tactical experts and statistical wizards, but realize they will be more valuable to the organization upon adapting their classical training to a more goal-driven mindset for prescriptive analytics.
    • IT Specialists – who wish to gain a better appreciation of the overall analytic process to more effectively prepare resources for data analysis and integrate resulting decision models within today’s sophisticated data and deployment environments.
    • Big Data and BI Team Members – who seek a strategic orientation to organizational analytics before drilling down into advanced analytics methods such as data mining, predictive modeling, machine learning, knowledge discovery and unstructured text analysis.


    • Focus on key broader analytic issues, strategies and mindset required for organizational data-driven decision making
    • Unify project teams – from practitioners to leadership – in order to establish a common strategy, implementation framework and monitoring process for greater coordination, efficiency, clarity and impact:
        • For leadership to better understand and trust what the analyst delivers
        • For analysts to obtain clear direction on analytic goals and delivery requirements
        • For integrators to qualify and prepare for integration in advance of modeling
        • For subject matter experts to be leveraged for application context and results translation without adding bias to otherwise objective models
    • Leave ad hoc and esoteric statistical exercises behind in lieu of targeted, insightful and understandable analytic outcomes that drive organizational decisioning with residual benefit
    • View content through a progressive series of demonstrations, brief exercises and guided discussion in order to closely experience realistic implementation issues at the strategic project level
    • Leave with the resources, contacts and actionable plans to substantially increase targeted analytic outcomes while minimizing dead ends


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 proliferation of big data platforms as well as the advancement of reporting and analytic software suites have created a complex environment where managers across the organization must rely heavily upon their analysts, subject matter experts and IT staff for critical insight. Interpreting, adopting and acting upon this insight has proven to be a far larger obstacle in organizational analytics than leveraging the algorithms and technology used to derive the insights.

The vast majority of organizations approach analytics in a linear, tactical and disjointed fashion. They start with data, software and a team of data scientists tasked with finding patterns in data. This is akin to isolating a team of mechanics to optimize an engine. They huddle under the hood without regard to the rules of the race and what it takes to win. 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.

It is typically not the responsibility of data scientists and analytic practitioners to focus strategically. Yet, analytics will fall short of its potential without adequate context, clear problem definition, effective results translation, targeted reporting for leadership, actionable deployment, and process monitoring. At the same time, analytic practitioners are often misguided by leaders 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 convey artificial metrics instead of objective, goal-driven solutions.

Organizations that continue to operate without a strategic framework, common analytic platform and purpose, as well as a fully-formed collaborative team for analytics will fail to capitalize on very expensive upstream investments in data acquisition, storage, structure, quality and Big Data implementations. But even more impactful are the dramatic gains and competitive acceleration overlooked by most organizations that remain untrained, nonstrategic, and analytically impaired.


    • Evaluate and address cultural, environmental and talent issues that frequently derail analytic deployments
    • Integrate and function more effectively with coordinated and qualified roles across a fully-formed analytic team
    • Follow a seven phase Modeling Practice Framework™ for establishing an internal modeling factory and analytic practice
    • Classify the four major types of analysis projects and match the best suited core analytic methods
    • Discover, monitor and clearly report the primary analytic contributors that impact performance metrics that are important to leadership
    • Populate analytic opportunities; then validate, organize and prioritize projects for lowest risk and highest gain
    • Establish a solid strategic foundation for more advanced analytic practices such as predictive modeling and data mining


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.


There is no other event in the marketplace that presents a structured framework to specifically address and organize the complex analytic resource, environmental and cultural issues that exist in larger organizations. The intent of this course is to synchronize all essential roles of the analytic team and bridge the critical translation gap between them that cause most projects to fall well short of their potential.

Concurrently, a framework for establishing a sustainable internal analytic practice is presented. “Advancing the Analytics-Driven Organization” is the only known course of its kind with a strategic emphasis that builds rare soft skills required to effectively assess and design analytic projects, and lead implementation teams. These are truly the key issues that prevent most organizations from being effective and competitive in today’s analytic landscape. Those who are truly intentional about leveraging analytics for measured gain and residual impact are perfect candidates for this course.


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
    • Model Development in Three Steps
        • Training
        • Testing
        • Validation
    • A Basic Guide to the Modeling Practice Framework™
        • Assess
            • Who is on the project team?
            • What is the attitude of leadership and the team toward analytics?
            • What is the current practice for analytics?
            • What are all candidate projects?
            • What are objectives, requirements and qualifyers for each candidate project?
            • What is the current baseline level of performance?
            • What are cost-benefit thresholds?
            • What are existing and desired performance benchmarks?
            • Where are all the data sources?
        • Plan
            • What are the roles and experience required for each team member?
            • What’s involved in a data reconnaisance?
            • What is the quality of the data sources?
            • Does the environment, resources and situation support each modeling objective?
            • Define data sufficiency and scope
            • How should a data sandbox be developed?
            • What will the operational environment be?
            • What are options and specifications for model deployment?
            • Which projects are truly viable and how should they be prioritized?
        • Prepare
            • What criteria should drive the analytic culture and mindset shift?
            • How should team responsibilities be refined?
            • 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?
            • What modeling methods should be considered?
            • How do I build my Training/Test/Validation data sets?
            • What is in a Modeling Plan and who should review and approve it?
        • Model
            • 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
            • How ready are we for deployment?
        • Validate
            • Are we selecting the most strategic option?
            • Have we maintained an focus on ‘organizational performance’?
            • Is validation expressed in context with the original defined goal?
            • How do finalist models perform against established  benchmarks?
            • Have we formed a consistent implementation strategy?
            • What is the outcome of a dress rehearsal and trial deployment?
            • How to pick a ‘Challenger’
            • Confirming we picked a good Challenger
            • Preparing a final rollout plan
        • Deploy
            • Reviewing all project functions
            • Evaluating the expected performance of our Challenger
            • Adoption by domain experts
            • Adoption by end users
            • Preparing leadership for a new decision process
            • Project failure is not our worst outcome
        • Monitor
            • Adapting to a changing environment
            • Creating a maintenance schedule
            • Determining model life expectancy
            • Establishing maintnenance triggers
            • Assigning monitoring responsibilities
            • Buiding the performance dashboard
            • Identifying new data sources
            • Recording ongoing organizational changes
            • Preparing for the next Framework cycle

      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

The Modeling Agency, LLC, is registered with INFORMS (the INstitute For Operations Research and the Management Sciences) as a Recognized Analytics Continuing Educational Provider for the CAP® (Certified Analytics Professional) program. The CAP® credential provides analytics professionals with a means to distinguish themselves and demonstrate to employers, colleagues, and the public that they are knowledgeable analytics professionals. Courses provided by The Modeling Agency, LLC, are automatically accepted by INFORMS when claimed by credential holders as evidence of continuing education. For more information about the CAP® program, including requirements, eligibility, benefits, preparation, and exam dates, please visit the INFORMS website at www.informs.org/certification.

Upcoming Sessions

October 20 & 21, 2016
November 7 – 10, 2016
12:00p – 5p US EST | 9:00a – 2p US PST
17:00 – 22:00 UTC / GMT
January 23 – 26, 2017
12:00p – 5p US EST | 9:00a – 2p US PST
17:00 – 22:00 UTC / GMT

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Events Limited to 20 Seats

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Attendee Comments

“Data Analytics is not merely about the statistical techniques, but also the ability to understand the context of the problems, to ask the right questions, and to establish the performance metrics that are relevant and crucial to answer these questions. It requires a service design mentality, and that’s what this class delivers”

Ting-Shuo Yo
Data Scientist
ASUSTeK Computer inc.

“The instructor is also a consultant. He fully understands real world scenarios, and a project approach to effective execution.”

Jean M. Cotis
Application Development Manager
UPS Freight

“I look forward to applying what we learned during this course to current and new projects. We will be more organized, efficient and effective by applying the concepts and techniques delivered in this class.”

Sean Schmitt
US Customs and Border Patrol

“No fluff at all. The instructor consistently provided specific, practical and understandable advice about complex ideas and processes. He explains things very clearly, backs up his answers with real-world exampls and is patient with questions.”

Partner Conference Production

“As someone without a formal degree, analytics can be a daunting word. This course is approached in a very consumable way and without the blunt theory of academia. It is practical and applicable, which is exactly what analytics needs in order to support organizational objectives.”

Carmen Schwesinger
Government of Alberta