AI, Data & Digital Strategies

Chairpersons welcome and opening remarks. 

To follow soon …

Align data strategy with business strategy by transforming a siloed data infrastructure into a modern programmable enterprise.

A/B testing or Experimentation plays a key role in decision making. Data driven approaches with science underpinnings provide the needed rigor to test hypotheses and make decisions. There are however a class of decisions where variable assignments based on Reinforcement Learning techniques make some tests feasible and add velocity (shorter experimentation duration) to others. These strategies don’t conform to the standard hypothesis testing template but serve the same end goal of data driven decision making. We will go through an overview of multi-arm bandits and bayesian parameter tuning as RL strategies applied to experimentation systems.

• Identifying and breaking down silos across departments and business data stacks.  
• How to maximise data sets and data pipelines.  
• Aligning data with specific business goals – making use of insights for targeted business e.g. seasonal buying cycles and customer demand.  

Governance can be intimidating for many large enterprises. Users have access to multiple tools and content, and establishing a uniform layer of governance on top of all your BI, Big Data, and Analytics tools is a daunting task. However, governance is critical, and the lack of proper governance leads to poor user engagement and low ROI from BI, big data, and AI initiatives.

Join this session to learn how you can…..
1. Establish a unified portal to provide a single source of truth for data insights
2. A Governance layer that provides analysts a way of documenting, categorizing, and certifying key BI reports and publishing them
3. Track usage and feedback of your analytics

AI in manufacturing is a hot topic around the industry, with multiple potential use cases, vendors, and methodologies.

The challenge that manufacturers face is that the path from concept to deployment to ROI is very resource and time-consuming, requiring months of work and multiple expertise. And in many cases, the ROI is never actually achieved.

In this session, we will explore the regular path from concept to ROI in manufacturing AI projects and how new technologies can shorten it to days instead of months.

We will showcase a real-life case study involving early fault detection model deployment in one week that resulted in substantial savings and efficiencies.

• Understanding the challenges businesses are facing and looking at how data can be the answer – not a one size fits all response.  
• Utilising the enterprises strategic advantage – how to unlock data’s value.  
• Studying current processes for capturing data and looking at next steps.

The what: While AI continues to evolve at a rapid pace – adoption rates growing day over day – projects still fail at a disappointingly high rate. The why: In the past, capturing data at scale and building models was the challenge, but today we’re confronted with the issue of making AI more robust while avoiding the risk of unintended consequences.  While the tools are new, many challenges remain the same.  This session will cover:

• Building blocks to develop requirements for AI-driven projects 
• Examples of identifying and mitigating project risks and unintended consequences 
• How to build the business case for an AI project (and get buy in) 

• Data Eco-System – From Tabular & Object Data to Graph Data
• Graph Data in Industrial Applications
• Fujitsu and Larus Partnership – From Graph Database to Graph AI
• Explainable Graph AI (GXAI) with EnsemBiz – Deep Tensor
• Deep Dive Example into Discovery GXAI Pipeline

• Instruments to manage risks between AI vendors and buyers. 
• Case studies of product differentiation through guarantees by AI vendors. 
• Outlook on technology risk transfer enabling the AI transformation

To follow soon …

• Why data teams require an observability platform designed specifically for data applications. 
• What multidimensional DataOps observability helps manage (performance, cost, data quality). 
• How DataOps observability isn’t just for engineers or operations but empowers different data team members across the DataOps lifecycle. 

Headspace is a values-first company: through 10 years of innovating the use of technology in meditation and mindfulness, the focus has never shifted from making sure that our approach to technological implementation is as mindful as our content.

Over the last year, much of that innovation has centered around ML-powered personalization: from in-app content recommendation to push notifications to AI-driven dynamic welcome flows. But implementing ML/AI solutions brings unique challenges: personalization is inherently intimate and demands great sensitivity in the ways we use and communicate member data.

In this talk, we’ll explore how Headspace balances the intimate and data-centric process of building ML solutions while maintaining our brand identity as a mindful actor.

Pre-recorded session 

View Day 2 content here:

Applied Data & Analytics

Data Optimisation | Big Data | Intelligent Decision Making | Data Storage | Industry Use-cases 

Transformational AI

Personalisation | Edge AI | Digital Transformation | Future Progressions | Industry Use-case