NoSQL, Hadoop and MapReduce: Building a Modern Data Infrastructure that Works

Commercial application of the subdomain of AI known as machine learning is in a state of solid growth, where interest in applying the technology within new domains is at an all-time high. Though demand is great, studies demonstrate that over 85% of AI projects do not make it to production and lack management buy-in. One main reason is that our abilities to understand the technology remain at an early stage of development and this inhibits the broader application of “mysterious black-box algorithms” in society. Here we describe our tactics to overcome this challenge by providing machine learning practitioners and data scientists with software tools that provide greater transparency and more intuitive visuals of machine learning models. In addition to providing means to streamline the production of AI products, detailed visuals with rich analytics provided by our next-generation of software tools open new strategies to understand, explain, test and audit machine learning products. Tailored to a general audience of technology professionals and enterprise business leaders, the chief executive officer of Zetane will demonstrate these visuals of AI and our software tools during the discussion. Additional topics covered will include how Zetane leverages open-source standards to promote innovation in machine learning and the company’s recent patent of an innovation that helps power unique features of software and hardware used in the field. Audience members will have the opportunity to download the free software demoed in this presentation so they too can view the internal workings of popular machine learning models used in research and enterprise.

Enterprise IT Operations teams are finding themselves increasingly in the hot seat while having to guarantee impeccable experience for the growing base of users of the applications they support, while juggling digital transformation and a multi-cloud landscape. The “break and fix” models widely embraced for decades are finding little endorsement in today’s environment. Prediction has been a non-trivial problem, thus far, especially when precision and recall levels are concerned. Thanks to recent advances in Machine Learning models coupled with deep experience managing large enterprise application and infrastructure environments, we can now make preventive healing a reality. In this webinar, we will describe the key elements of AIOps and machine learning techniques that make this new paradigm possible.

Bringing a Data Science program into large, entrenched companies poses significant challenges on two fronts: First, Enterprises' internal processes, organizational structure and culture pose a barrier to building a successful Data Science program. Second, data science and machine learning come with their own intrinsic challenges. Some problems take longer to solve than others. Furthermore, not all problems are solvable by data science. The speakers share their experience and views on both the Internal and Intrinsic challenges of establishing Data Science as a Core function within Enterprises.

Matt Shearer, Chief Product Officer at specialist data engineering and innovation company Data Language, outlines a winning formula for transforming your data strategy, as well as some “Dos” and “Don’ts” based on the company’s wide experience in this area with global brands on their digital transformation journeys.

Modern day medicine becomes increasingly ruled by data driven decision making. In its core clinician & patient driven. Fueled by technology and empowered by medical AI, Data & IT . Thus, the opportunity for non-linear change is imminent.

According to Harvard & MIT published reviews -two different ideas come at play. The pivot from pipeline to medica AI early precision diagnostics; open api white-label platform / eco-system. Another is the business model innovation, leveraging Value Based care to balance it out.

Which in its turn creates the opportunity to move from “pay per sick” to “fee for service”, -in essence the monetized value of a disease that will never happen. (QALY- reversed)

That leaves us with strategic choices. A “box 2”: manage the present and selectively forget the past. -like changing gears on a bike. Choose to replace & accelerate.

What you will learn:

1. Leverage Analytics & patient data, paradigm shift the moment of intervention. Creating ripple effects through the vertical and horizontal value chain. Both for patient, caretaker & business.

2. The travel time on patient care path will be significantly altered. By such technologies as AI ECG. Creating ripple effects that impact care delivery and also P&L & R&D tracks for corporate.

3. Poor countries, like Africa & India, the opportunity to create a “first time right system’ materializes. Democratizing healthcare in similar trend as Fintech. Due to lack of infrastructure.

4. Rich countries - can learn and take advantage. If, with a willingness to learn, we can move past “sunk cost” of existing infrastructure.,

5. Harness AI & ML patient & business analytics to get a forward moving innovative business model & adjacent eco system.

Modern data architecture discipline can really produce a value as long as business understands, supports, and partakes in this initiative. The goal of this program is to define a roadmap for data product, self-service decisions tools, and reusable data service, to socialize data anomalies. The accountability is on enterprise architecture department to bring the inner climate under one common roof to serve better frameworks and solutions.

2020 changed the business landscape beyond recognition and as a result companies are increasingly looking at AI technology to transform their data management strategies, automate data governance, and accelerate business insights. In fact, 30% of organizations are planning to increase their AI investments, according to a Gartner report from September 2020.

However, staying abreast of solutions and strategies on the market isn’t easy, and it can be hard to keep up with the latest innovations and advancements in AI that are advancing enterprise insights.

In this panel, AI and business insight experts are sharing the trends and predictions you need to know that will help you create a strategy that’ll uncover the value in your data and give you a competitive edge.

Join us to learn:
- Predictions for where AI and analytics will be in 2022
- Current AI and BI trends that future-oriented businesses are making the most of
- How AI can help businesses make the most of the data economy in 2021
- How AI-based analytics solutions have evolved in the past year and what they now offer
- And more

Panelists:
Ramesh Dontha, Managing Partner at Digital Transformation Pro
Mrunalini Jagtap, Cofounder & Director at AshArth Asia Pte Ltd (Singapore)
Myron Mallia-Dare, Corporate & Technology Lawyer at Miller Thomson LLP

Analytics trends that defined 2020 will continue to make their mark in 2021. By the end of 2024, 75% of enterprises will shift from piloting to operationalizing AI according to Gartner. As data science becomes more readily adopted as part of an enterprise insight strategy, join this panel of industry experts to learn more about where the big data & analytics industry is heading.

This month's episode of The Business Intelligence Report with Eric Topham will look at the defining trends in data and analytics and the long-term impact of digital transformation.
- The analytics trends that defined 2020 and will continue to make their mark in 2021
- Where is the big data & analytics industry headed in terms of the "next big thing?"
- Will we see more commercialized AI?
- Will data science become more readily adopted as a part of enterprise insight strategy?
- Will predictive analytics become more sophisticated?
- Will the cloud remain the go-to space for conducting more cost-effective analytics initiatives?
- Expert recommendations, best practices, and predictions for the BI&A industry space.

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.

Speakers:
-Eric Topham, Co-Founder & Data Science Director at The Data Analysis Bureau
-David Yakobovitch, Sr Manager Education Delivery at Single Store
-Jérémie Farret, VP Actionable Intelligence at Inmind Technologies
-Berit Hoffman, VP of Product at Sisu Data

Innovations in Artificial Intelligence (AI) have been powering financial services for years, by helping analyze vast amounts of data, reducing costs, and driving efficiencies at every level.

Discover how innovations in AI are powering the new trends in fintech and payments, especially B2B payments. Learn how AI can be used to digitize direct payments and fee revenue, as well as to enable risk mitigation, and a variety of other use cases.

This month's episode of "Driving Fintech Forward" will explore the latest AI trends in fintech with a special focus on payments. Viewers will hear more about:
- The shift to digital and real-time payments and electronic invoices
- The push for more automation due to the pandemic 
- Why fintech's future lies in AI
- AI and smarter payments
- Digitizing direct payments (ACH) and fee revenue
- The next step beyond mobile wallets
- How AI helps mitigate risk and fight fraud


Panellists:
- Dan Dolev, Managing Director, Senior Analyst, FinTech Equity Research, Mizuho
- Nick Dryden, CEO, Stathler
- Oded Zehavi, CEO & Co-founder, MESH


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

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.