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Day 1

Chairperson Overview

Omar Moonis Former Citibanker IE Guest Speaker

Growing Customer Relationship Value Through Analytics

Kumar is an experienced digital transformation leader who has expertise in building Marketing Technology (MarTech) systems that enable the automation of customer lifecycle programs, marketing channels, advanced analytical data products across multiple business units within holding companies with positive business returns. He is also experienced in building, managing and leading cross-functional, cross-cultural teams spread in locations across the globe. As a highly skilled MarTech change agent, Kumar has devised a centralised MarTech capabilities roadmap for NTUC Enterprise’s subsidiaries and merchants. Using Agile best practices, he now leads and collaborates closely with teams across Sales, Marketing, Design, Research, Data, Tech and Engineering to strategise the product roadmap and develop a one-stop, self-serve, intelligent marketing engagement platform (Plus! Engage) that drives viability and profitability for NTUC Link. A recipient of a Masters in Analytics and honours in E-commerce, Kumar is also an invited speaker at many digital conferences and forums, most recently at Machine Learning Asia Summit 2018, where he spoke about the wonders of artificial intelligence. He was featured on national TV as an inspirational role model for youths and in the Straits Times on the magnetism of data driven customer experiences in Singapore.

Nanthekumar Tamilselvan Head of Marketing Tech & Automation NTUC Link

Managing the Enterprise Data Lake for Digital Products

Establishing the overall architecture of Data management and Analytics for Tetra Pak Group. Specialties: Business Intelligence, Advanced Analytics, Statistics, Master Data Management, Enterprise Data Warehouse Architecture, Big Data, Business Objects, Power BI, BI Strategy, Supply Chain Management.

Balaji Rajamani Business Intelligence Architect Tetra Pak

Icebreaking Session - Beyond Analytics Norm

This session is designed to help break the ice
and facilitate networking among delegates.

1. Please turn to your left or right and introduce yourself (name,
company, what do you do) 

2. Bring your "most
successful story" at work and letting people know your skills and challenges you are facing at work.

3.Please join the real-time voting system and discussing questions proposed on the screen.

Networking Coffee Break

How AI Adoption Could Bring Greater ROI, Efficiency and Technological Transformation

In his current role, Roy’s responsibilities include leading a team of 24 across Asia Pacific on the discipline of data science.

He is required to engage senior executive stakeholders to understand the alignment between business strategy and information requirements and to utilize models to gain insight into business performance, support fact-driven decisions, and communicate opportunities for sustained improvement.

Prior to joining MSD, Roy held positions of increasing responsibilities in the field of business analytics, market intelligence, data mining, market research and strategic planning.

Roy Goh Director, Service Delivery/ Mgt (Analytics) MSD

Algorithm vs. Data: What Every Executive Must Know Before Using AI

AI is fast becoming the cornerstone of digital transformation in business and industry. No longer just the enabler of hot consumer technologies, like facial recognition and self-driving cars, AI is a powerful tool in the business leader’s arsenal — providing causal understanding of essential commercial issues: “what makes a high-value customer”, “how can I predict who’s most likely to visit my store, and purchase a product” or “what distinguishes buyers on my app from customers who only purchase in-store?”. Virtually every technology vendor visiting the CXO suite, large and small, weaves in AI in their pitch.

One widespread misconception about AI, used to great advantage by vendors, is that its power derives from the algorithms, or techniques, used to implement learning and modeling. Terms like Deep Learning, or Quadratic Discriminant Analysis, are making their way into prime time TV slots at family meal times — through ads by some of the largest global software vendors. The subliminal message: sophisticated techniques lead to better outcomes. Nothing could be further from the truth. It turns out that the nature of the data used to train AI algorithms, specifically its quality, depth and breadth, impact outcomes far more than the technique itself. This session will explain this in detail, using common marketing and business analysis use-cases, and real world case-studies. The intent is to arm you with knowledge that can positively impact the AI and predictive modeling activities in your organizations, and make you a more informed assessor of technologies and products to employ.

Kajanan Sangaralingam Senior Data Scientist Mobilewalla

How Classical Management Practices Help Established Companies to Onboard Predictive Analytics

Companies created in the last 5-10 years are naturally data savvy and often build the whole business model around smart usage of new data technologies. The long-time established companies innovated business models and technologies before data became an asset. To change the proved business culture might be challenging, but classical management techniques and pragmatism can make adoption of predictive analytics more effective than a bumpy start-up mode.

The session will touch the following topics:

· How to add data analytics into existing processes and make it a tool for traditional business
· How to introduce new technologies without creating chaos and generating disappointment
· The role model to produce data-intensive solutions

Olga Sholomitskaya Manager, Data Analytics Solution DHL

Networking Lunch Buffet

Using Predictive AI to Provide an Optimal Ride Experience

Ride-hailing platforms have been increasingly popular in recent years in providing passengers with a convenient avenue to book a ride. For Grab, data plays a key role in improving ride allocation and providing an optimal user experience. In seeking to provide users with the perfect ride, data science and AI are embedded in every stage of a user’s trip. This session is a sharing on a few these use cases - user preference and mobility behaviour insights, and how they drive service efficiency.

Serene Ow Data Science Lead Grab

Beyond Games AI in the Gaming Industry

AI for games is not new and newer AI methodologies are finding increasing application in improving overall game experience with smarter and more realistic game play. However, AI plays an equally important role beyond games AI. In this talk, I will share Yoozoo’s experience in applying AI across the different business functions of customer service, marketing, and operations.

Chi Keong Goh Director, AI Technical Yoozoo Games

Networking Coffee Break

Fireside Chat - The Heart of Data Science

Johnson is currently Head Data Science / Practice Lead for Big Data Analytics at DBS Bank where he leads a team of data scientists and machine learning engineers in leveraging data driven tools and techniques. He holds an adjunct faculty appointment at SMU School of Information Systems and his focus areas include applied statistical computing, machine learning as well as big data. His professional experience also includes being Chief Data Scientist, ASEAN at Booz Allen Hamilton as well as Head Data Science for Ministry of Defence Singapore.

Johnson Poh Head of Data Science DBS Bank

Panel - Defining Business Goals for Predictive Analytics

What trends do you predict are most going to affect predictive analytics in 2019?

What technology has made the biggest change to your work in the last two years? How has it changed the way you work today?

How best have you utilized predictive analytics and AI tools to effectively manage huge amounts of data? Is too much data more of a hindrance that a help?

What examples can you give where you have used predictive analytics to spot trends that you have used in recent times to attract new customers?

Have you experienced push back in terms of budgetary or resource allocation when it comes to investing time and energy into predictive analytics and AI solutions; and what advice would you give to those in similar positions when they are pitching up the command chain for continued or increasing support in predictive analytics and AI?

What are some of the biggest dangers of using AI to make business decisions? What can be done to ensure that AI technology avoids human bias?

As AI continues to dominate the conversation, will predictive analytics become an arm of AI, if it hasn't already?

How big of an issue is talent retention within the predictive analytics field and how can we prepare for a potential talent gap?

Finally, what advice would you give to someone setting up a new analytics team at a startup?

Regina Ku Head of Business Intelligence Love Bonito
Lauren Clarke-Wiest Senior Director, Innovation & Analytics Aon
Omar Moonis Former Citibanker IE Guest Speaker
Jim Lim CTO Huawei Technologies
Geraldine Wong Head of Data Science Singtel

Networking Drinks

Day 2

Chairperson Overview

Joe Tusin CEO, Founder Chynge

Data to Decision with Predictive Analytics – A Not So Predictable Journey

Data analytics technology is maturing rapidly with newer algorithms demonstrating ever more fascinating capabilities for solving ever more challenging data problems. Nevertheless, successful ‘productisation’ of these solutions through effective integration into business processes that then start to actually change the way the organisation runs and make the predicted benefits a measurable fact, does not always follow a simple recipe. In this talk, we will highlight some of these challenges that prevent innovative & promising analytics technologies from ‘seeing the light of the day’ and share some thoughts around maximising the chances of a successful roll out.

Partha Dutta SVP, Data Science Sembcorp

Combating Financial Crimes with Laser Accuracy

Financial crimes comprising money laundering, terrorism financing and fraud are critical components in the overall risk profile of financial institutions The regulatory fines for systemic failures have amounted to over $350B since the financial crisis of 2008. Financial criminals have honed their skills and capabilities to exponentially increase the difficulties in trapping them. Financial institutions resort to cloud computing, big data, artificial intelligence, and deep learning to develop innovative weapons to combat financial crimes. Learn the practical challenges of regulatory compliance and data privacy that impede the effective behavioral modelling of financial criminals and how to overcome these with creativity and innovation.

Joe Tusin CEO, Founder Chynge

How a Data Driven Culture Enhances Predictive Analysis Adoption

Predictive Analysis is usually not given the due credit it deserves. Instead of covering technical data mining and machine learning methods, as a visual analytics expert, the speaker will showcase how to prep the dept/company culture to implement the various outputs of predictive analytics and prescriptive analytics in the scenarios covered to improve the data driven culture of an organization to make quick decisions and increase the analytics maturity of the company in general.

Sudhir Panda Ass. Director, Visual Data Analytics IE Guest Speaker

Networking Coffee Break

Predicting Turnover and Understanding the Effectiveness of HR Actions on Employee Retention.

Ridwan is a Data Scientist at Kraft Heinz. He currently is dabbling in analytics methodologies that can be applied to Talent Management and Employee Performance. Having worked in both analytics consultancies as well as in-house HR teams, he understands where analytics can provide the best value for businesses and more importantly, where it can’t.

Ridwan Ismeer Data Scientist Kraft Heinz

Achieving Operational Excellence with Transactions Forecasting

Achieving Operational Excellence in FMCG is often a super-human task falling on the shoulders of ever more burdened organization of Sales Representatives. With hundreds of SKUs, tens of customers and often conflicting priorities of ensuring base business health and growing new products, managing complexity of customer service, promotions and base sales becomes missions impossible. While Sales Force Automation systems do a good job in digitizing Sales Representatives daily routine, solutions available today have a long way to go in ensuring quality of embedded analytics. Most SFAs rely in their forecasting on averaging past values or simply provide Sales Representatives with fields to input numbers on how much they think they will sell. This approach might work if the Sales Representative have been working with an SKU for a long time. But in practice such situations are rare due to the rate of new product introduction and the workforce turnover. Modern forecasting methods, such as Bayesian Networks, Machine Learning Ensemble models and more familiar Time Series approaches can help produce high-quality forecast to feed SFA systems directly. However, we need to be aware that choosing the right forecasting method for predicting volume & frequency of transactions is only the start – a successful implementation leading to demonstrable business growth will be possible only when a series of seamless system-to-system interfaces are created and efficient Big Data Analytics system is implemented to present right numbers at the right time of the day to each Sales Representative. In my presentation I will share the experiences in implementation of transaction forecasting systems across domains of eCommerce, Modern Retail and Vending, sharing the best practices I have discovered so far.

Galina Voloshyna Director, Data & Analytics IT Coca-Cola

Employing the Power of Data Analytics in Central Banking

Data are becoming the new raw material for business” – Craig Mundie. Undoubtedly, organisations in the financial sector are increasingly leveraging on data analytics for problem solving and decision making. The availability of data has also enabled MAS to apply analytics in many of our central banking and corporate functions. In this presentation, I will be sharing on our data analytics journey, focusing on the use cases of data analytics and on how we progress together as an organisation towards a data-driven future.

Grace Thng Deputy Director, Data Analytics Monetary Authority of Singapore

Networking Lunch Buffet & Departure Coffee