Pre-Conference Workshop | 26 March 2018, Monday
Workshop A:Optimizing Machine Learning to Empower Business Units to Make Data-Driven Decisions
9:00 am - 12:00 pm Workshop A:Optimizing Machine Learning to Empower Business Units to Make Data-Driven DecisionsJohnson Poh - Head of Data Science/Practice Leader for Big Data Analytics DBS Bank
With an influx of data and the tools available, thinking along the business context is crucial to understand and drive business insights. However many companies struggle to grasp the application of data analytics in the business context. It is critical to implement a systematic approach to harness data analytics to deliver critical business insights.
This workshop will guide participants in building a comprehensive machine learning strategy to achieve business intelligence.
- Overview of machine learning application in businesses
- Building an IT infrastructure to collect, analyze and store data
- Collaborating with business functions to understand their needs and agree on right data sets to analyze
- Creating valuable predictive models with machine learning for your business
- Case studies and examples of machine learning application
Johnson PohHead of Data Science/Practice Leader for Big Data Analytics
12:00 pm - 1:00 pm Lunch
Workshop B: Putting Machine Learning into Practice: Top 5 Challenges & Ways to Overcome It
1:00 pm - 4:00 pm Workshop B: Putting Machine Learning into Practice: Top 5 Challenges & Ways to Overcome It
Many people understand the possibilities, potential, and benefits that machine learning can bring to an organization. However, operationalizing machine learning and being prepared for a difficult implementation are essential to maximise the benefits.
This workshop will open with an overview presentation and interactive group discussion on the “Top 5 Challenges” to operationalizing Machine Learning, including case studies on how an organization has overcome them.
The main group will then disperse into breakout sessions for a focused dialogue on:
- Defining key factors of success and overcoming common pitfalls
- Collaboration with IT on architecture,
- Communication planning, stakeholder management, and organizational redesign
- Governance, compliance, controls, audit and measuring success
- Data Science organization structure, roles, responsibilities, skillsets, service delivery methodology and interface to business/operations