Training Session

Part 1: Understand the imperative of Digital Transformation.
Limiting the field of data application from the start will hinder creativity and vision of participants as they engage in projects. By understanding the spectrum of possibilities, participants will grow bolder in the analytics they undertake.
Part 2: Architect systems for agility and scalability in a true partnership between IT and business.
By avoiding initial critical errors, participants can design ultra-lean and efficient frameworks that they will not only tailor to their needs but also control. Understand what should drive the implementation of systems to best position them in your organization:

  • Learn the core system components of the analytics stack to best leverage and connect them
  • Become comfortable with technology to engage with confidence and impact
Part 3: Harness data with ease and transparency
Data is a raw material, and pre-analytic processing is almost as important as the final analysis. Understanding and recognizing data types enables the design of elegant and efficient models and preparation processes that will lead to faster insight with minimal error risk.

  • Seize the breadth and depth of the data universe
  • Understand databases and how different technologies apply to different business needs
  • Learn the data families (Facts, Master Data, Meta-Data, Calculated Data) to optimize their leverage for fast and flawless analytics
  • Understand what are the paradigm changes with Big Data
Part 4: Lead Data Quality and Master Data Management for best analytics results
A large and often hidden part of data management relies on quality control and alignment work. Mastering the base of these techniques is key to make progress without being hindered by bad data.
Part 5: Becoming an advocate of a data culture and a change leader
The most common weakness in data projects is the human factor. Identifying the internal and external dynamics of projects and knowing their potential impacts will help leaders to manage transition and foster engagement pro-actively.

  • Understand the required managerial changes that must happen with data projects
  • Identify and mitigate resistance to change
  • Thoroughly communicate upward and downward during the project
  • Leading with curiosity and design thinking mindset
Part 6: Design processes for performance and sustainability
System, data, and people must be organized around processes that achieve the subtle balance between what technology can deliver, the state of the data sources (quality, completeness), and the readiness of the team. If best practices are always a good base from which to start, a focus on speed and repeatability will free up the time to create additional value.

  • What makes processes sustainable?
  • Identify the key components of any data process
  • Learn how to position systems and people optimally
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