Getting to Caprica - Making VR Really Usable

In the quest to modernize the way they think about making decisions, organizations are turning to AI and automation wherever possible. AI models can help with creating predictions and labels, but to get deeper insights on the meaning behind the data, organizations are starting to embrace decision intelligence.

Discover how businesses can use decision intelligence to create strong decision models for their processes and how to map actions to outcomes.

Learn about the decision intelligence framework and hear from the experts on what DI can mean for your business. Viewers will hear more about:
- The impact of the pandemic on the quality of decisions made, especially in the context of business
- The emergence of decision intelligence
- How DI differs from AI 
- Eliminating errors and biases when making a decision
- Decision intelligence for critical business moments
- How data and analytics leaders can leverage DI to design, model, align, execute, monitor, and tune decision models and processes in the context of business outcomes and behavior

Moderator: Eric Topham - CEO & Co-Founder, The Data Analysis Bureau
Panelist: Peter Bailis - CEO at Sisu Data
Panelist: Bipin Chadha - VP Data Science at CSAA Insurance Group
Panelist: Pragyan Nayak - Chief Data Scientist at Hitachi Vantara Federal

This episode is part of The Business Intelligence Report original series with Eric Topham, Co-Founder & Data Science Director at The Data Analysis Bureau. We welcome viewer participation and questions during this interactive panel session.

Legacy financial services institutions consistently see big Tech as a competitive threat. The highly-scaled, AI-driven method of creating and nurturing client relationships seems to be the antithesis of what high-touch, relationship-driven investors pride themselves on.

However, the industry is already moving in this direction; with Charles Schwab’s purchase of TD Ameritrade and Morgan Stanley’s purchase of E*TRADE, we see action towards improving today’s digital experience. This is what consumers expect, and where the profits lie - research found by the World Economic Forum highlights that the industry is moving towards an era of self-driving finance, where returns are not owned by those who invest client money, but by those who own the customer’s experience.

So how can FS companies across the board view the digital client experience as a strategic advantage?

Join this discussion as we explore:
- What the digital customer experience encompasses
- How AI and automation can help you focus on deepening your relationships
- How AI can help streamline back-end operations

Speakers:
- Elena Kozhemyakina, Founder and CEO, Fintech4Funds
- Labhesh Patel, Chief Technology Officer and Chief Scientist, Jumio
- Chris Leong, Transformation & Change Director, Leong Solutions
- Amnah Ajmal, Group Executive - Merchants, Acceptance & Digital Partnerships, Mastercard

This episode is part of the "Driving Fintech Forward" series with Elena Kozhemyakina, Founder and CEO of Fintech4Funds. We welcome viewer questions and participation.

Explore how to leverage Tableau's strengths in unconventional ways to improve workflows in your organization. Amber will demonstrate how an enterprise risk management firm used Tableau to reduce its reporting workflow from 4 hours to only 2 minutes.

Geographics Information Systems (GIS) and Big Data have known an accelerated convergence of late, each technology multiplying the potential of the other.
This trend is enabling new Analytics and Big Data Management practices, whether it finds itself accelerated by novel approaches, services, hardware, platforms, or allows for brand new applications
In this context, we will first look at the new concepts underlying Big Geo-Data Analytics, whether it concerns the addition of Smart City infrastructure, the omnipresence of mobility and what it means for Track and Trace and Multi Sensing applications for the mass market and for industry and commercial applications, and the new requirements that applications must respect with respect to fraud, and cyberattacks, with new regulations applying to businesses in terms of protection of the personal information.
Then we will consider the technologies supporting these new advancements in Big Geo-Data Analytics, namely : Massive Data coming from new generations of IoT, Smart Cities, and the acceleration technologies enabling and supporting them such as GPU accelerated and / or in-memory databases, Fog & Edge Analytics, Artificial Intelligence including Fog & Edge AI, the different uses of Blockchain for geolocated information and in particular Proof of Location, and the particular place of Big Geo-Data Analytics for Machine Data Analytics combined with Geo IP.
Finally, we will look into industry use cases of these different new practices in Big Geo-Data Analytics, along with the architectures and infrastructures supporting them, in 3 real life use cases in the industry which are supported by the Mind in a Box integrated solution : Geotracking and proof of location for the transport of dangerous materials and the place of AI automation for it, real time track and trace for field operations, GeoIP and geo-fencing for cybersecurity and the place of Artificial Intelligence to extend SIEM capabilities.

AI generated Synthetic Data is one of the most exciting technologies currently entering the marketplace. It allows organizations to work with data in a privacy preserving way, to debias data and much more. In this talk Tobias Hann, CEO of MOSTLY AI will give an overview of what Synthetic Data is, its use cases, and how your organization can start benefiting from Synthetic Data today.

The Bank of International Settlement introduced the regulation proposal BCBS 239 for improved risk data aggregation capabilities for banks. It comprises 11 principles how risk data aggregation and reporting shall be organised and technically set up.

The financial crisis has shown that banks had huge difficulties to report their risk positions accurately and timely to the regulators. For a rather long period of time during the peak of the crisis it remained unclear how much the banking system suffered from specific systemic risks.

Since the publication 2013 banks are working to comply with BCBS 239. A progress report has shown that no significant bank in Europe was fully compliant by mid 2020 while most institutes had reported good progress on most of the goals apart from data architecture and IT infrastructure. However the new capabilites for the banks looked so tempting that the vast majority extended the scope of their BCBS 239 projects to benefit also in other fields of the 11 principles defined by BCBS 239.

So what is so tempting by BCBS 239 and what hampers banks from reaching compliance? In this talk I will give a brief overlook of how banks appoached the transformation. It will be shown how non-banks can profit from spirit of principles while avoiding the pitfalls. Further I will talk about architecture used for banks and which tooling concepts works best for non-banks.

Today, companies stay competitive and need to understand their customers. The global situation has changed the consumers’ behaviour and brands have to reshape their marketing and sales approach.
They can leverage data but they are uncertain on how to leverage and manage the information.
Artificial Intelligence is no more hype but a real opportunity to do that.
In the webinar we will go through:
● Data expectations
● Internal data
● External data
● Why leverage data form marketing and sales opportunities
● Why Artificial Intelligence is a tool that helps (with real case examples)

Why join us?
1. To understand your data and how to leverage them
2. To know more about your competitors and look at them as an opportunity
3. To say “WOW, I can do that!”

As more businesses are turning towards ML and as data is available in abundance, there is a high demand of adopting ML in the industry. This brings the focus to the right choice of ML tools for both development and operationalising ML in production. The ML tools ecosystem has responded well, with a wide plethora of tools to solve various problems associated with ML. This wide variety of choices for ML possess a new challenge to the ML users, on when to use which technology. The most significant challenge comes in productionising ML, as for real world use cases the challenges are manyfold like how to do ML over Big Data, do ML over high speed streaming data, light weight ML over edge devices, distributed ML over high speed clusters, fast ML supported with machine acceleration technologies, etc. We will try to address a high level overview of the landscape and walk through some of the popular industry cases where choosing the right tool in production becomes a challenge.

Does it seem like that the challenges facing enterprise analytics have a familiar ring to them? Do you also get the feeling that same challenges plague us year after year? Then, you are not alone. While words may have evolved, the challenges today are surprisingly similar to the ones faced decades ago. Systems thinkers will immediately recognize this as a situation where we are trying to solve for symptoms without addressing the root causes. Whenever this happens, the problems or the challenges have a tendency to keep coming back.
In this talk I will go over some interesting case studies from the past to highlight the challenges and what lessons we can learn from them.
Key Takeaways:
• Why some challenges persist year after year?
• What are the root causes?
• What can we learn from past mistakes and successes?

Wider usage of AI models brought the need for them being unbiased. While explainable AI makes AI models more understandable, most Explainable AI approaches make AI understandable only for data scientists. On the other hand, insurance underwriters and claims experts also need to understand how AI behaves so that they can trust it and include it in their daily business actions. In addition to being understandable by business, AI models need to be updateable by business and easy to use and continuous learning.
During this webinar, CEO & Co-Founder Prof. Zehra Cataltepe and Groupama CIO Serdar Birlikci go through how an easy, understandable, understanding and continuous learning AI could enable customer retention to be solved with an agile team of business experts in insurance.

Neural networks were originally conceived in 1943 but were largely put aside until the advent of sufficiently capable computing power to run these networks in the 1970's - we face the same challenge today but with data. Many organizations lack the capabilities to handle and combine the data they have in-house already to drive real business changing decisions, so how can they hope to be prepared for the future deluge of data that the growing connected world is bringing? Increasing automation of decision making (via event driven processing) and fierce competition within most industries is driving us all to make more decisions faster, if not instantaneously - a mirroring of the phenomenon that occurred within Wall Street with the advent of High Frequency Trading 25 years ago.

Today, that same millisecond half-life value of data is prevalent across industries and much disruption can be oversimplified to increasing timeliness of insights from data. While in the past on Wall Street, this could be solved by paying to have a server located physically closer to data feed, the future of many industries brings the promise of a new problem where the required data is distributed. In this brave new world the timeliness of your entire data pipeline will ultimately determine the level of intelligent decision making you can apply. Attend the talk to see why as with the past, present and future - "it's about time".

.