Panel: AI - The Backbone of the Modern Startup

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".

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For many businesses, marketing teams are autonomous in driving leads and proving that their marketing activities are bringing in ROI. They work on their own tech stacks, working on analytics, and delivering their own reports. But now, it’s not so simple. There is a clear shift in customer expectations and how they are interacting with brands. Furthermore, there are more potential customer touchpoints than ever before.

How are businesses keeping up with these changes? Well, they are adopting new tools and technologies to gain deeper insights and improve processes. However, this switch has created new challenges, and a new level of coordination and accountability is required, particularly between marketers and IT. If not addressed, businesses will fall behind and data silos will only continue to surge.

In this webinar, we will address how we are using an all-inclusive martech-specific reference model to help our clients to centralize and share information throughout the entire business, aligning the needs of all stakeholders to the same goals.

Discover how this is our go-to model to:
• Apply technology and data to unify, measure, and analyze end-to-end business success.
• Provide smooth collaboration and better decision-making with aligned goals, people, processes, and data.
• Drive results by saving time and improving visibility.
• Develop a new level of confidence, through a culture of transparency, accountability, and predictability.

We believe it’s this strategic shift in aligning teams towards a single goal that encompasses the full customer life cycle to become truly customer-centric.

The case for a Financial Technology (Fintech) data lake.
Fintech is impacting business models and its concepts require testing.
Experimentation in a controlled way using a regulatory sandbox.
Fintech concepts to be checked for reliability and robustness
A data lake for back-testing, out of sample experiments and forward performance checks.

It is hard to become a data-driven company. Despite spending millions in building data and AI capabilities only 29.2% of companies achieving transformational business outcomes, and just 30% have developed a well-articulated data strategy according to the recent survey of Fortune 1000 companies. In this webinar, I’ll share battle-tested methodologies to gain the maximum of your company’s data potential. We will cover the following areas:
Defining measurable, achievable, and focused outcomes for data-driven transformation
Aligning operational models to fit the company’s data strategy
Architecting an efficient fit-for-purpose data platform that creates value

Too many science-based companies lose their investment in knowledge soon after their staff generates it. Once the problem is solved, the improvement is realized or the R&D project is complete, the knowledge work including the data, analysis and resulting predictive models are lost, or at least inaccessible to others.

Regenerating analytical knowledge carries higher risks, incurs unnecessary costs, and delays achieving business goals. The future success of technical problem solving and process innovation requires a modern knowledge management strategy. Companies that adopt a modern knowledge management strategy will dramatically reduce the amount of time and effort in the daily work of engineers and scientists, thereby not only preventing the needless duplication of experiments, but also extending the use of past experiments and improvement initiatives.

Wayne will present a use case to demonstrate the power of managing analytical knowledge through CoBase, an enterprise-level knowledge management system that enables engineers and scientists share access to and collaborate mutually on past analyses and relevant supporting data.

Join this panel of industry experts and discover how enterprises are transforming their big data management strategies to drive significant improvements to data wrangling and improve data accuracy and efficiencies.
You'll learn about:
- The latest tools organizations are utilizing to govern, prepare and analyze their data
- Opportunities and challenges to be aware of when building an enterprise data strategy
- Best practices for managing and analyzing big data for enterprise insights
- And more!

Data itself has become a commodity, and the mere possession of abundant amounts of data is not enough. But the ability to monetize data effectively (and not merely hoard it) can undoubtedly be a source of competitive advantage in the digital economy. However, the world is changing as fast as the data is growing. No longer do we have the luxury of adding data to a data lake or warehouse , and then perform analytics. We now must enable analytics and response to what the data tells us in real time. You must interact with your clients at the speed of a tweet. Now we need to change our thinking into a microsecond mindset. How do we enable the thinking let alone the capability to initiate in real time?

Since data analysis and information does not naturally present one obvious conclusion, how can directors, senior leadership and management teams better frame complex decisions and judgment?

Take Away:
- If you cannot see the paradox, you are blind to consequences!
- A model to explain quantum risk and why it is creating an issue for boards and senior management teams
- A new framework and tool to help create simple linear narratives from complex data problems