Data Analytics Online Summit (Time Zone – SGT)

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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.

How we are leveraging user and customer first principles and building out self-serve data products and data foundations to deploy effective data analytics for business decisions.

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.

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In this session we will explore a list of charts categorised by their data visualization functions or by what you want a chart to communicate to an audience. While the allocation of each chart into specific functions isn't a perfect system, this session will focus on a useful guide for selecting chart based on your analysis or communication needs to make the best representation of data.

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.

In Artificial intelligence & What They Mean for BI. Next in the beginning, may be 5 to 10 years ago, data visualization ... "Big data: The next frontier for innovation, competition, and productivity". .... The next generation of NLG, pioneered, unifies two divergent aspects of Artificial Intelligence.

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Artificial intelligence and Roboticsare hardly new,but the
technologies have progressed substantially inrecent years. Advances
in machine learning techniques,improvements in sensors and
ever-greater computingpower have helped create a new generation of
hardwareand software robots with practical applications in
nearlyevery industry sector, especially in Banking and Financial
sector.At present, artificial Intelligence (AI) technologies are
increasingly being applied in the banking industry, mainly toward
knowledge management, identity authentication, market analysis,
customer relationship management, anti-money laundering, and risk
control.

But there are major challenges to decide/choose right architecture
strategy for different application This presentation intends to talk
about differnt architecture principles of AI&Robotics in Banking
sector .

Data & Analytics Leaders should consider how effective and useful data lakes will be in their overall data and analytics strategies. The key is to build data lakes for specific requirements of key user groups or analytics use cases. Failures can be avoided by reviewing the expectations of business units, availability of skills and infrastructure capabilities of the organisation.

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While the data analytics and insights have leapfrogged by many notches, are the businesses realizing the full potential of this revolution? The answer could be mixed. Business process and organization design plays a key role in enabling this which would be covered in this presentation. By making these changes, we have seen up to 10x improvements in process efficiency, cycle time and throughput using analytics.

User adoption or product adoption is the number one goal for many product organizations. But there are many challenges that come in the way, for example, product awareness issue, complex onboarding experience, business value, and lack of continuous feedback loop from the customer. In this presentation, you will learn an easy to use framework that we applied across our customer, partner, the customer-facing team (like customer success) and product development and design teams to get the right data at the right time to make better decisions.