Program : September 29th 2020

As the financial services sector is undoubtedly the most data-intensive sector in the global economy, the influence of Big Data on the sector is tough to overestimate. Due to the increasing and changing customer expectations, business intelligence and data analytics having become a crucial element architectural optimization for banks. There’s a strong requirement for real-time models leveraging the data driven decisions making concept. In this talk, I’ll discuss the prudent approach for a multi-dimensional data exploitation and how Real-time and batch data can be analysed simultaneously at scale- allowing embedded analytic processing into the applications. This in turn enables programmatic decisions based on real-time analysis. We’ll discuss practical use cases for leveraging a real-time data integration pipeline to avoid all problems of classic Extract-Transform-Load tools, which are data processing latency, functional miscomprehension and metadata heterogeneity. The audience will infer the value of leveraging real time architectural approach towards enhancing the value-driven insights,reporting, quality and availability.

One of the first steps to successfully utilizing data is interpreting and understanding the data properly. Join our professional expert to learn what tools are best for enabling fast discovery and deeper insights.

Processing of complex geospatial big data has always been a challenge not only in the Information and Technology (IT) sectors but also in the Geo-Spatial domain. Efficiently handling geospatial big data is critical to extracting meaningful information. In many cases, we need to use a combination of different tools, technologies, and techniques to perform big data analysis. Open source python libraries and tools can be used to process large amount of geospatial data.

Explore the latest open-source API testing tools and how to establish which platform is right for you.

With the proliferation of data in the past years, most business critical decisions are heavily influenced by deep data analysis. As companies rely more on data for their functioning; storing, managing and accessing data intelligently and efficiently is more important than ever before.

Traditionally, any conversation around data would revolve around how one can effectively ingest data into a large data warehouse. As more business decisions are driven by data in real time, we require strong guarantees such as acceptable latencies, high data quality and system reliability. Moving from a full-reload to a delta model of ingesting quickly became the primary way to ingest large amounts of data at scale. A number of such ingest patterns showcased how a transaction support on such datasets could benefit use-cases immensely. 

A case study showcasing how organizations can implement and maintain proper strategies for managing data, through the various stages of its lifecycle, across all projects.

Blockchain and Data Analytics are changing the way how today’s utility distribution companies operate and are moving towards a DSO ready state. Today, Utilities can use emerging technology and governance to leverage this opportunity in proactively defining this change and help shape this evolving ecosystem.

With the first phase of a government review on open banking now released this session will take a look at the merits of open banking and affects on consumers. 

Natural Language Processing is the pivotal element of AI technologies, join us to understand the current NLP skills you need to know to stay ahead.

This session will host a case study analyzes of implemtning a chatbot, from the concept stage to an in-production product navigating through a matrized organization of IT, Digital & Business while doing Agile & Waterfall at the same time. There is a great deal of interest for large organizations in this sphere but the challenges of rocking the boat too much remains a concern (esp. Frontline/Customer Care org). 

Data synthesis methodologies are combating certain data challenges in AI and being used to overcome the limitations of available real-world data.

Panel experts discuss the methods of augmented data driving decision-making compared to relying on a combination of data and experience. 

Developments in quantum computing could mean breaking encryption faster and independently, this technology is predicted to change the digital landscape in a huge way. 

The ability to deliver applications to end-users over the network rather than  locally on their devices means better agility and lower costs - making this investments one of the best ones for your team. 

Learn everything your team needs to know in order to implement edge computing and find success from it. 

We are seeing tremendous growth for cloud services, the latest is the distributed cloud, this technology allows enterprises to go cloud native.

Experts tackle industry wide challenges we face while creating cloud migration strategies.

Learn the difference between two key software testing methods and which method is right for your team.

This session will present on how a DS team builds and ships things with the help of cloud platforms.

Data Science is at the core of building personalization and recommendation services in Loblaw Digital’s e-commerce website consumer journey and the omni-channel customer experience. In this talk, Xiaoming will present her work at Loblaw Digital to create engaging digital experiences for Canadian customers, and how data science teams could work together with the product team, Dev team and DevOps/SRE team to achieve a productive collaboration.
Xiaoming will also present a case study with how her team ships the ML models as managed  microservices, enabling the A/B testing and more sophisticated online multi-armed bandit testing for continuous improvement. She’ll discuss the ML engineering aspects of her team’s work to achieve faster and more reliable releases, compliance to standard policy enforcement and central observability.

There are more ways than one to migrate to the cloud, discover the difference between lift and shift migration methods to uncover which strategy best meets your needs.

Smartphones made a huge impact on consumers lives over the course of the former decade, making it easier to be connected at all times which also led to making it easier to gain insights on consumers. Going forward it's going to become important for consumers to demand better privacy, better security and better transparency for all that data they provide. 

5G is a next-generation mobile network technology that can enable an astonishing range of innovative new products and services and offers the promise of significant economic and social benefits as well. However, like many emerging technologies, it is equally likely to disrupt multiple industries.

5G and IoT devices also introduce critical concerns around data privacy, cybersecurity and risk management that IT, security and privacy professionals need to understand, mitigate and manage.

The arrival of 5G and the growth of IoT are guaranteed to affect the cybersecurity landscape, it is so imperative that companies proactively assess their security strategies.

This session will establish a clear understanding of the essentials around IoT from a privacy and security perspective. Why is it important? How will it impact businesses models and consumers?

The challenge to create strong and trusted identity management solutions is ongoing. Detection strategies and continuous risk assessments with richer data will be required to protect all parties involved in payments.

It is being more important then ever to be aware of the latest fakes in AI. 

As IOT technologies evolve issues around privacy have dominated the landscape for these connect devices. 

Session description coming soon.

Data vulnerability continues to arise even with numerous available security solutions, understand how your organization can work to find these vulnerabilities first and mitigate security risks. 

Deep learning has shown profound accuracy and success in a numerous amount of activities; and with bigger training sets the more data it has to learn from which means better results. The problem arises with cases in which the data is highly private and sensitive, Deep Privacy solutions are needed to protect the data and allow industries with ultra sensitive data to effectively use deep learning without major risks.