The Essential Soft Skills and Strategic Framework
for Leading a Goal-Centered Analytics Practice
ABOUT THIS COURSE
This unique course directly addresses the cultural, environmental, team and resource issues that impact the overall analytic function in medium and large organizations. It is designed and facilitated by active and highly seasoned strategic consultants who directly encounter the vast challenges and realities of modern organizational analytics.
“Advancing the Analytics-Driven Organization” presents a strategic framework required to arrive at 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. 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. Read the Series Overview to understand this event’s function within the full course series progression.
WHO SHOULD ATTEND
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.
- Data Scientists and 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.
KEY SKILLS YOU’LL TAKE AWAY IMMEDIATELY
- 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 and practice level
- Leave with the resources, contacts and actionable plans to substantially increase targeted analytic outcomes while minimizing dead ends
THE ORGANIZATIONAL CHALLENGE FOR ANALYTICS
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. The strategic actions of interpreting, adopting and acting upon this insight has proven to be a far greater challenge than the technical tasks of leveraging the technology, tools and algorithms themselves.
The vast majority of organizations approach analytics in a tactical and disjointed fashion. They start with data, software and an overpopulated team of data scientists tasked with finding patterns in data. This is akin to isolating a large team of mechanics to optimize an engine. They huddle under the hood without regard to the nature of the track, 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 actions taken 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 ongoing process monitoring. At the same time, analytic practitioners are often misguided by leaders who lack a basic analytic awareness 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 accuracy metrics instead of objective, goal-driven solutions.
Organizations that continue to operate without a strategic framework, a common analytic platform and purpose, as well as a fully-formed collaborative team for analytics will fail to capitalize on expensive upstream investments in data acquisition, storage, structure, quality and Big Data implementations. But even more impactful are the competitive gains suspended by most organizations that remain untrained, nonstrategic, and analytically impaired.
UPON COMPLETION, YOU WILL BE ABLE TO
- 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
- Understand a formal phased framework for establishing an internal modeling factory and analytic practice
- Make right-sized and properly-timed investments in analytic talent, software and supporting resources
- Clearly report the primary analytic contributors that impact performance metrics that are important to leadership
- Identify analytic opportunities; then validate, organize and prioritize projects for lowest risk and highest gain
- Establish a strategic foundation to support advanced analytics such as predictive modeling and data science
WHAT MAKES THIS COURSE UNIQUE
There is truly 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.
“Advancing the Analytics-Driven Organization” is the only known visionary course available that lays out critical strategic considerations to effectively qualify 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.
SESSION I – CORE CONCEPTS
- Orientation to data science and organizational analytics
- Trends within the analytically competitive organization
- The advent of data science
- The Arena: From business unit-based to IT department-based
- The Professionals: From analyst to data scientist
- The Analyses: From descriptive analyses / business intelligence
- What is predictive analytics’ role in Big Data?
- Big data needs advanced analytics …but does analytics need big data?
- You will never have a perfect model
- Market perceptions of big data
- ROI of big data and associated analytics
- Retail use case
- Guerrilla marketing use case
- Medical or government use case
- The future of big data and advanced analytics
to predictive analyses / data mining / machine learning
SESSION II – HOW TO THINK LIKE A DATA SCIENTIST
- Stats 101 in ten minutes
- A / B testing and experiments
- BI vs predictive analytics
- IT’s role in predictive analytics
- Statistics and machine learning: complementary or competitive?
- Primary project types
- Predicting a value given specific conditions
- Identifying a category given specific conditions
- Predicting the next step in a sequence
- Identifying groups
- Common analytic algorithms
- Decision Trees
- Neural Networks
- Genetic Algorithms
- Ensemble Modeling
- Popular tools to manage large-scale analytics complexity
- R and Python
- Hadoop, MapReduce and Spark
- Data Mining “workbenches”
- Performing a data reconnaissance
- Building the analytic sandbox
- Preparing train / test / validation data
- Defining data sufficiency and scope
SESSION III – THE CAO’s ROADMAP
- The Modeling Practice FrameworkTM
- The elements of an organizational analytics assessment
- Project Definition: the blueprint for prescriptive analytics
- The critical combination: predictive insights & strategy
- Establishing a supportive culture for goal-driven analytics
- Defining performance metrics to evaluate the decision process
- What is the behavior that impacts performance?
- Do resources support stated objectives?
- Leverage what you already have
- Developing and approving the Modeling Plan
- Selecting the most strategic option
- Planning for deployment
- What will the operational environment be?
- Who or what is the end consumer?
- How do results need to be purposed or presented?
- Measuring finalist models against established benchmarks
- Preparing a final Rollout Plan
- Monitoring model performance for residual benefit
SESSION IV – BUILDING THE ANALYTICS ENTERPRISE
- Attracting and hiring the right analytic talent
- The roles and functions of the fully-formed analytic project team
- Specialization in analytic project teams
- Analytic opportunity identification, qualification and prioritization
- Organizational resistance and developing a culture for change
- Project failure is not the worst outcome
- Staging the organizational mind shift to data-driven decisioning
- Motivating adoption by domain experts, end users and leadership
- Recording ongoing organizational changes
- Monitoring and advancing organizational analytic performance
- “Democratizing” analytics: Advantages and risks of “self-service”
- Watson Analytics
- Establishing performance dashboards
- Standing up an agile analytic modeling factory
- Knowledge retention and skill reinforcement
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.
Sign Up Early and Save
Predictive Analytics Webinar
Learn How to Get
Predictive Modeling Off
the Ground and Into Orbit
4pm EST / 1pm PST
Why Train With TMA?
“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”
ASUSTeK Computer inc.
“The instructor is also a consultant. He fully understands real world scenarios, and a project approach to effective execution.”
Application Development Manager
“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.”
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.”
Government of Alberta