APAC Data Analytics Day

Registration/Conference Overview

Artificial Intelligence (AI) systems have achieved major milestones in the past few years. Some industries have successfully leveraged AI and machine learning, proving its potential to have a tremendous impact on various aspects of humanity life, such as the detection of skin and breast cancer more reliably than trained clinicians. There is no doubt that this progress has been made possible with advances in machine learning, the amount of data available and the computational power. The successful path to explore and leverage these opportunities however, can benefit from a thoughtful design framework, guiding us through the process. We will explore the design thinking framework for a successful AI journey, from problem identification to implementation, that enables us to identify risks early on and arrive at a meaningful solution free of bias.

Machine learning models are being increasingly used to make decisions that affect people’s lives. To paraphrase Uncle Ben from the Spiderman comics (even though I am a DC fan) “with great power comes great responsibility”, this is true as the models that we build can adversely or favourly affect demography’s of a population. So, to ensure that the model predictions are fair and not discriminating we need to ensure our models do not contain an inherent bias by those who design them.
In my presentation I look to explore the idea of inherent model bias and potential strategems to tackle this issue.

With a mission to become truly data driven, companies make significant investment in establishing data analytics platforms but many of them fail to realise the full potential due to poorly executed strategy underpinned by ineffective organisation design. In order to embed data analytics culture at the heart of the organisation, the importance of fit-for-purpose organisational model cannot be overlooked. How do you source, retain and deploy talent? Should you go with centralised, decentralised or hybrid model? How do you build a COE to that can maximise knowledge sharing, yet operate effectively to deliver value?
This talk helps you explore what type of model would be most effective for your organisation and how best to implement them.

Lunch Break

Existing information systems overlook the Master Data Management which supports data quality dimensions such as data believability and ease of understanding. Using a design science research paradigm, this session introduces and demonstrates an integrated framework that facilitates the traceability and accountability of information products.

Unlike typical software, Machine Learning applications do not have straightforward rules and usually act as a black box. Lack of explainability and interpretability reduces the trust of the product owners, legal counsels, and non-technical end-users. This talk helps you to understand the importance of interpretability and the trade-offs between interpretability and model accuracy among various machine learning models. It gives you some ideas to make machine learning more transparent and less of a black box.

The Data Fabric’s automated data engineering provides the business with the mechanisms to manage sparse data sources so it can create an integrated information and analytics Virtual Data Platform, and agile business self-service while guaranteeing 100% data quality.
For all intent and purpose, the ‘Virtual Data’ Platform (Data Fabric) refers to the set of data objects, including business entity types and business rules, that represent a Conceptual ‘Target’ Business Data Model (schema), which exist in memory within the Data Fabric Platform, and may or may not be persisted in a physical repository or a Virtual Data Warehouse (VDW). Once the Single View process applies all data integrity and data quality rules, the data objects are available to be accessed by the end-users and consumer applications directly from the Data Fabric Platform. This presentation explains a process that allows for agile business self-service to exploit business intelligence tools such as Qlik Sense, PowerBI, Tableau, MicroStrategy, etc., while providing for data provenance and data lineage as it relates to self-service.

Drinks & Networking session