the modern data analytics academy

Get the best Data Management, Business Intelligence, and Analytics training available in Canada. We cover end-to-end education experiences on the core pillars of analytics and data management. We designed them to bring you from basic concepts and best practices to practical skills and tactics that you can use as soon as you are back at the office.
Learn More

courses

our
"data management and analytics" 
series

These courses are timeless and provide skills that will remain with the student no matter what solution you have in place. it will give you the concepts to improve your current state and the frameworks to plan the future state.

courses

our
"MANAGING DATA FOR MODERN ANALYTICS"
series

These courses are aimed at those wishing to put modern data analytics into action with practical concepts and advice on how to go about it.

A SIMPLE APPROACH TO TRAIN YOUR TEAM

in-person or live on-line

where (option 1)

on your site, privately

We come to you. You get the team and the room ready, and we deliver the class.

where (option 2)

on-line, privately

Alternatively, we can use a conferencing service and teach any of our courses online. Your team can be anywhere.

format

half or full day

All our courses come in half-day and full-day formats. The half-day formats don't have labs or practices, for example. It depends on your budget, the time you have and how in-depth you need to go.

how much

daily fixed rate

We charge a daily rate. Whether you have a full-day course for 10 people, or two half-day classes for 20 people, it's one, fixed price.

booking

how to book

Contact us to discuss the options and what you need. The best way is to have a conversation.

Contact us for more details by scheduling a call

courses

detailed syllabus


Introduction to Prescriptive Analytics Using Simulation Models


Prescriptive analytics enables managers to explore different scenarios and evaluate new business opportunities by playing the “what-if” game. It enables the evaluation and comparison of different options as part of the decision making process. This leads to a deeper understanding about how to define and achieve business and operating goals.

Implementing prescriptive analytics using simulation methods within a Business Intelligence (BI) program provides additional capabilities to existing BI programs. Answers to advanced business questions starting with "why" and "what if" can now be answered. Maintaining the models in a calibrated and reliable manner over time requires rigorous data management practices based on principles of integration and quality.

Prescriptive analytics using simulation extends and enhances the capabilities of BI programs and BI programs enable the utility and maintainability of the necessary data and models.

This course provides an introduction to prescriptive analytics using simulation models applied to areas that are relevant to business analysts, operations planners, decision makers, functional managers and BI team members. The basic concepts are introduced and a framework is provided that positions simulation analytics within a broader BI Program. Categories of models are described that provides an overview of the breadth of potential opportunities for prescriptive analytics within diverse organizations. 

o Basic capabilities of simulation
o Categories of models and modeling techniques
o Domains of applicability
o How to build and implement simulation models
o Data management requirements for simulation
o How business problems can be defined and solved
o The role of experimental design
o How insights can be generated
o How to explore and discover possible routes to successful outcomes
o How business intelligence, analytics, and simulation are related disciplines 

o BI program leaders
o BI architects and project managers
o Business analytics team members
o Business managers and decision makers
o Functional analysts
o Operations managers
o Process improvement specialists


Modern Business Intelligence – A Systems Thinking Approach


The term Business Intelligence is not well understood in the industry and is used inconsistently by many IT and business professionals alike. Although the term was defined in the mid 1990’s, the meaning of Business Intelligence continues to evolve as practitioners learn more about its capabilities and challenges.

This online training course introduces a “holistic” view of Business Intelligence and presents it as a complex system composed of many sub-systems that must be aligned and work together to produce the desired business results. The real success of BI within an organization can only be achieved if a holistic understanding is developed that shapes how the various components are designed and implemented. In addition to the extensive overview, the course makes Business Intelligence real and tangible by illustrating the concepts, principles, and practices using a detailed case study. 

o Business Intelligence concepts and terminology
o The purpose and capabilities of successful Business Intelligence and how value is actually generated within organizations
o How people, information, technology and business objectives are all critical components of BI success
o The common challenges and risks encountered in BI implementations
o How to utilize Systems Thinking concepts to describe Business Intelligence holistically and how it depends on the integration of many different types of components that must work together 

o Business Managers and Executives
o Technology Managers and Executives
o Business Analysts
o Business Measurement and Performance Analysts
o IT Analysts and Developers
o Data Management Analysts
o Technology and Business Architects
o BI Program Managers and Team Members 


Introduction to Diagnostic Analytics Using Control Charts


The field of Diagnostic Analytics includes the capabilities to detect abnormal conditions and to estimate root causes to those conditions. This course is focused on the “detection” aspect of diagnostic analytics and introduces Statistical Process Control (SPC) as a suitable approach for defect detection. Root cause analysis of the identified defects is beyond the scope of this course.

SPC includes a set of analytical techniques that measure and detect abnormal changes in the behavior of a managed process. SPC helps managers respond to unexpected changes in critical variables and take corrective action as necessary to maintain the desired levels of product quality and process performance over time.

SPC has been successfully applied to a wide range of business, technology and production processes that all have measurable outputs. It is based on the application of statistical techniques implemented in the form of control charts used to monitor the variation of important process variables or attributes.

The reduction in variation of process behavior is critical for improving both process and product quality. Successful implementation of SPC requires management commitment to continuous process improvement over time. SPC tools provide measurement and analytical inputs to an overall Quality Management framework.

This online training course provides an introduction to the concepts, techniques and applications of SPC within the context of information management practices and processes. The theory of SPC is introduced and the design of control charts is discussed as a basis for describing how a diverse range of data and process quality management challenges can be addressed. 

o Identify methods for detecting defects and abnormal conditions
o Define and describe some common process building blocks
o Describe the concepts and theory behind “statistical control”
o Describe how statistical methods can be used to measure and estimate process variation
o Identify and categorize major causes of process variation
o Describe how process variation is directly related to product quality
o Discuss the principles of control charts used to detect and generate process alarms
o Present the basic concepts of quality management initiatives and practices and how it relates to the scope of Statistical Process Control
o Describe how to apply solutions to address process, data and related quality management challenges
o Provide the context necessary to implement effective solutions 

o Big Data Analytics Professionals
o Data Quality Analysts
o Data Governance Leaders
o Process Improvement Analysts
o Business Analysts
o Information Technology Professionals
o Data Warehousing Team Members
o Data Warehousing and Big Data Professionals
o Program Managers and Project Managers leading various types of Business Improvement Programs
o Functional Business Managers 


Introduction to Data Warehousing in the Age of Big Data


For over twenty years, data warehouses have served organizations in the areas of data integration, provisioning, management, and information delivery. Use cases ranging from basic reporting to advanced analytics have been successfully implemented by companies of many different sizes.

Due to rapid growth of non-traditional data sources, availability of new technologies and growing expectations of managers to compete on analytics, the traditional data warehouse is re-defined and presented within a broader modern context. A corporate data ecosystem is evolving and presents new opportunities for creating business capabilities that were not previously possible. Amidst these changes, the data warehouse continues to play foundational and integral roles within the expanding data landscape.

This course re-defines the scope of the “modern” data warehouse. The need for planning and the role of architecture are described and clarified, followed by a discussion about the challenges related to gathering useful information requirements. This is followed by a discussion of design approaches, development, testing and quality management techniques.

The true value delivered to the organization by a data warehouse depends on operational and service activities that leverage the data components previously implemented. It is through this combination of data and technology assets with managed operations and services that desired analytical and business capabilities are created. The design and implementation approaches of operations and services are provided to highlight this key requirement. The course material presents a full life cycle of the data warehouse including business context, scope, requirements, design, implementation and operations. 

o The components that define a data warehouse platform
o What trends impact the modern data warehouse
o To position the data warehouse platform in the big data era
o Architectural options and considerations
o Development options and approaches
o The requirements gathering process
o Necessary design activities
o How operations and service processes enable business capabilities 

o Data warehousing program and project managers
o Data warehouse architects
o Data scientists and analytics professionals
o Big Data practitioners
o Data warehouse designers and developers
o Data warehouse maintenance and support specialists
o Data integration and management practitioners
o Anyone who is new to data warehousing


Planning Your Data-Driven Success Using a Capability Lens


Companies continue to increase their dependencies on data and analytics for safely managing their infrastructure, providing customer service, pursuing new growth opportunities, generating shareholder value, and maintaining regulatory compliance. New advances in data storage platforms, novel data sources, analytic algorithms, visualisation techniques and open source software are causing challenges for companies to understand how everything fits together to create meaningful business results.

A capability based reference model can help organizations understand how to harness new and evolving analytic concepts in a manner that helps them enhance their business performance and achieve desired results. This workshop introduces a capability based framework that describes the building blocks that are needed to successfully execute a data-driven strategy to align new analytic opportunities with key areas of business impact. Attendees will .combine content from the lecture with practical group activities to understand how to apply a Capability Framework within their own company. 

o How business capabilities can be transformed using advances in analytics
o How analytics capabilities can be defined, assessed, and classified
o How data capabilities drive business impact
o Why governance capabilities are fundamental for sustainable success
o How to identify new opportunities for additional analytics solutions. 

o Leaders of Analytics Programs and Initiatives
o Business and Technology Managers Leading Data-Driven Business Improvements
o Business Intelligence, Data Warehousing and Big Data Architects
o Performance Improvement Specialists
o Governance Professionals
o Business Analysts and Decision Makers Seeking Data-Driven Impact
o Data Management Professionals at all Levels
o Data Science and Analytics Practitioners and Sponsors
o Anyone Planning to Help their Organizations become more Data-Driven


Managing Data for Modern Analytics: Rethinking Data Architecture


The technology revolution of recent years has dramatically changed the way organizations design, deploy, and manage data-delivery and analytics systems. Hadoop, NoSQL, and the cloud have ushered in a new era of scale-out, elastic, and real-time computing while new data preparation, catalog, and analysis tools aided by advanced machine learning and search technologies have radically changed the information supply chain.

The traditional world of data warehousing and business intelligence has been flipped upside down. Instead of serial data pipelines of relational data managed, modeled, and batch loaded exclusively by IT, the modern analytics ecosystem supports multiple, continuous real-time data flows designed by data engineers and data analysts close to the business. Abundant data sources and multiple use cases result in many data pipelines—maybe as many as one for each use case. The ability to find the right data, manage data flow and workflow, and deliver the right data in the right forms and at the right speed is essential to success with modern analytics. 

o How modern analytics reshapes the consumerism, business, and data management
o The shortcomings of legacy architectures in the world of modern analytics
o Stages and processes of the modern information supply chain
o How the “data quake” has shaken the foundations of data management
o How data lakes and data warehouses work well together
o A framework for modern data management topology
o Architectural principles for data pipelines and data services
o How to adapt from traditional BI architecture to a modern analytics ecosystem 

o Business and IT leaders struggling with the paradoxes of modern data management
o Analytics and BI designers and developers who are dependent on fresh and relevant data for every analytics use case
o Data management professionals at all levels from architects to engineers
o Data architects, analytics architects, and BI architects, and anyone in an architecture role that intersects with data


Managing Data for Modern Analytics: Curating and Cataloging Data


As the world of data management grows and changes, the roles and participants in data ecosystems must adapt. With the convergence of several influences – big data, self-service analytics, self-service data preparation tools, data science practices, etc. – we’re moving rapidly into an age of data curators and data shoppers. Data shopper describes anyone who is seeking data to meet analytics needs.

Data curator is a role that is responsible to oversee a collection of data assets and make it available to and findable by data shoppers. Cataloging is an essential curation activity to create and maintain a vital, valuable, and valued data marketplace. Curating and cataloging work together to meet the data needs of business and data analysts, to provide self-service data to complement self-service analytics, and to realize the promise of democratizing data analytics. 

o The concepts, responsibilities, and skills of data curation
o The role of the data curator in data governance and the differences between a data curator and a data steward
o The needs and wants of data consumers and the characteristics of vital and valuable data services
o The purpose, content, and uses of a data catalog
o The state of data cataloging tools and technology
o Guidelines for getting started with data curating and cataloging 

o Business and IT leaders struggling with the paradoxes of modern data management
o Analytics and BI designers and developers who are dependent on fresh and relevant data for every analytics use case
o Business analysts, data analysts, and data scientists who routinely need to find, evaluate, and access data
o Data management professionals at all levels from architects to engineers
o Data governance professionals – especially data stewards who need to adapt to the changing world of modern data management 


Managing Data for Modern Analytics: Rethinking Data Governance


Conventional data governance practices come from a simpler time when data management was free from many of today’s challenges, such as self-service reporting and analytics. Traditional data governance focuses on enforcement of controls with “locked gates” as the primary enforcement mechanism. Gates and enforcement continue to be necessary, but these methods must be complemented with support for the autonomy and agility of the self-service world. Modern data governance practices use a combination of guides, guardrails, and gates to support a continuum that begins with prevention, uses intervention when prevention doesn’t work, and relies on enforcement as last resort data protection.

Much of modern data governance is a cultural shift. The need to exercise controls is minimized when curating, coaching, crowdsourcing, and collaboration are integral parts of governance processes and practices. In a self-service world, every data stakeholder plays a part in data governance. 

o Where governance fits within modern data ecosystems, from point of ingestion to reporting and analysis
o How various technologies support governance through the ecosystem
o Process challenges for governing self-service; supplementing controls with collaboration and crowd sourcing
o Engagement models for governing self-service
o Organizational challenges for governing self-service; moving from data stewards to stewardship, curation, and coaching
o Operational challenges for governing self-service; implementing a combination of gates, guardrails, and guides 

o Business and IT leaders struggling with the paradoxes of modern data management
o Chief Data Officers
o Data governance professionals at all levels of the organization
o Data curators and data catalog administrators 


Best Practices in Modern Data Management


The world of data management has changed radically in recent years. Long-standing practices that worked well in the simple world of on-premises RDBMS no longer meet today’s data management needs. Big data, cloud, NoSQL, self-service analytics, data lake architecture, and more have shaken the foundations of data management—think of it as the “data quake.” In the post-quake era we must rethink data management and remake data management practices with attention to agility, adaptability, collaboration, and resilience. 

o Data architecture and topology in the age of data lakes
o Data warehousing in the age of data lakes
o Data quality management in the world of big data
o Data modeling in the age of NoSQL
o Data governance in a self-service world
o Data pipelines and data services
o Data curation and cataloging
o The continuing evolution of data management including data fabric and data hubs 

o Business and IT leaders struggling with the paradoxes of modern data management
o Chief Data Officers and Chief Analytics Officers
o Data management professionals at all levels from architects to engineers
o Data architects, analytics architects, technology architects, and BI architects 

one-hour webinar

any subject

Instead of hunting for the best Youtube video you can find that "sort-of" makes the point you are trying to put across, let us talk to your team. We are experts and can cover any Data Management and Analytics subject there is and give you just what you need to know about it. Online. One hour. Call us to find out more.

Contact us for more details by scheduling a call

team

meet the instructors

This subset of our senior team will not only get through the content but will be able to answer yoyr questions about how it can apply to your reality.

Mark Peco

senior instructor and consultant

Mark has been teaching for over 20 years wityh TDWI. He is CBIP certified and many of the courses here were created by him.

Marc-Eric LaRocque

senior instructor and consultant

Marc-Eric is also CBIP certified and has also thought Data Management and Analytics over the past 20 years.