ABOUT THE FORUM
Today we have more data than ever. Customers are generating different kinds of data every second from various interactions they make. Having big data was a beneficial asset for the organisations for a while however knowledge hidden behind this information and using this data for the future decisions has become the major issues. If you have a crystal ball, great! But otherwise how can you identify issues and make wiser decisions for the future?
Research firms forecast the predictive analytics market to reach $5.2 to $6.5 billion by 2018/2019; they are calling it the game changer as it has helped reinvent 6 industries (Healthcare, Manufacturing, Marketing & Financial Services, Government, Credit Risk Insurance and Workforce Management). This is the why more and more organisations are turning to predictive analytics to increase their bottom line and competitive advantage.
Corporate Parity is pleased to present two days of learning through case study presentations & Interactive panel discussions, as well as multiple networking opportunities. Take the chance to hear from the pioneers sharing their ideas on how to shape the future of your organisation through effective data analysis.
- Role of predictive analytics in modern business strategies
- Data analytics techniques
- Applying predictive analytics to your business: where to look?
- Practical real-world use-cases from across the globe
- How should analytics be applied to infer the social context of customers?
- How does this work in practice for a CRM and targeted marketing programme?
- What are the practice and ethical issues that this raises?
- Lean Six-sigma framework
- Applying Lean Six Sigma in the IIoT age & challenges
- 3 steps approach : correlate data source, perform advanced analytics, derive and implement measures
- Benefits: What you can expect?
- Model capacity (aka expressiveness)
- Model interpretability (aka understandability)
- The enablers:
- Algorithmic improvements for learning
- Hardware speedups
- Data: the new currency
- Growth in semi-supervised and Bayesian techniques
- Reinforcement learning
- Differential Privacy