In this Advancing the Analytics-Driven Organization course, students will learn the he cultural, environmental, team and resource issues that impact the overall analytic function in mid-sized and large organizations. The course is designed to present practical applications of analytics and teach companies how to integrate analytics into their strategies. Upon successful completion of this course, students will earn 18 INFORMS Professional Development Units.
The ability to make effective and timely decisions driven by rapidly changing is critical to the success of modern businesses. The proliferation of big data, IOT platforms and reporting and analytic software have created an environment where managers across the organization must rely heavily upon their analysts and IT staff for critical insight. The 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 Advancing the Analytics-Driven Organization course will teach leaders how to synchronize all essential roles of the analytic team and bridge the gap between them that causes most projects to fall short.
Who Should Take this Advancing the Analytics-Driven Organization Course?
- Organizational Leadership
- Project Managers
- Data Scientists
- IT Specialists
- Big Data Team Members
This course has no prerequisites- sign up today and enhance your business skill set!
What Makes This Course Unique
There is truly no other training in the marketplace that presents a structured framework to specifically teach how to solve the complex analytic resource, environmental and cultural issues that exist in larger organizations. Learn how to synchronize all essential roles of the analytic team and bridge the critical translation gap between them that causes most projects to fall short of their potential.
Advancing the Analytics-Driven Organization is the only known visionary course 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 to predictive analyses / data mining / machine learning
- 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
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 Framework™
- 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 GOAL-CENTERED ANALYTICS OPERATION
- 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
|Certificates||Same day as course completion|
|Food||For Live, In-Person Events: Continental breakfast and snacks/beverages throughout the day, but not lunch.|
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