Internet of Things (IoT) envisions that everything in the physical world is connected seamlessly and is securely integrated through Internet. New products are innovated under the umbrella of IOT and opening up different opportunities. This webinar will discuss the future potential of IOT and the trend in which it is moving in adoption and standardisation.
RecordedNov 10 201746 mins
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Pragyansmita Nayak, Ph.D., Chief Data Scientist @ Hitachi Vantara Federal
International Accounting Standards Board (IASB) defines asset is a resource controlled by the entity as a result of past events and from which future economic benefits are expected to flow to the entity. In this context, "Data is an asset" is an understatement. This cliche note is as widely cited as the reference to analytics as the "new oil", "new electricity"; essentially to depict data as a basic utility without which one cannot sustain a good quality of life. Before the patterns and trends were proficiently leveraged for a myriad variety of challenging tasks from disparate domains, it was often treated as a residual of a software process and a liability to maintain for archival reasons ranging from regulatory compliance to operational transparency, and so on other trivial purposes. Ease of access to data, computing resources and advanced algorithms contributed in varying degrees to this drastic change of perperception from liability to asset. Data governance principles with effective DataOps for the data lifecycle and data curation and tagging for a shared language across the echelons of a data-driven initiative is the necessary backbone. This will aggressively move the data ownership from being a Fixed asset to a Current asset.
Elena Kozhemyakina, Fintech4Funds | Lisa Kimball, Finicity | Panos Skliamis, SPIN Analytics
Managing risk is a necessity for lenders, insurers, and the customers they serve. When a lender wants to understand if someone is qualified for a loan, they need to understand debt & credit history, employment history, and past histories of default. Insurers have equally cumbersome criteria for gauging risk. Regulatory compliance adds in another layer of complexity. This process is long and inefficient, and in a modern marketplace, being quick and agile keeps you competitive. AI can help streamline the process, and knowing how to employ AI can be the difference between success and failure.
Join this webinar to hear about:
- The benefits of using AI in risk management
- How regulatory compliance can improve with AI
- Case studies of how AI is driving value for insurance companies and lenders
- Lisa Kimball, SVP, Product & Strategic Programs, Finicity
- Panos Skliamis, CEO & Founder, SPIN Analytics
This episode is part of the "Driving Fintech Forward" series with Elena Kozhemyakina, Founder and CEO of Fintech4Funds. We welcome viewer questions and participation.
Eric Topham | Mark Balkenende | Dedy Kredo | William McKnight
Gartner predicts that “by 2023, more than 33% of large organizations will have analysts practicing decision intelligence, including decision modeling”. In 2020, data analytics has evolved together with AI to produce more accurate predictions and educated suggestions. Join Eric Topham and a panel of industry experts and thought leaders to find out how data and analytics will transform human-machine relationships.
This month's episode of The Business Intelligence Report will look at the defining data and analytics trends to watch in 2021. Some of the topics to be covered during this session will include:
- The growing importance of external data
- Trends of how organizations are finding and using external data
- How data visualization enriches analytics and helps reveal correlations
- The evolution of dashboards. Where are we now and where are we headed?
- Data visualization in real-time for timely, actionable insights
- Visual analytics use cases. Building a data narrative to achieve business results.
- Mark Balkenende, Senior Director, Product Marketing at Matillion
- Dedy Kredo, VP of Customer-Facing Data Science at Explorium
- William McKnight, President of McKnight Consulting Group
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.
Elena Kozhemyakina, Fintech4Funds | Alix Melchy, Jumio | Katie King, AI in Business | Yali Harari, Innovesta
While AI promises to streamline business processes and increase ROI in financial services, it also has significant implementation challenges. Many financial services organizations have tried to implement AI into their tech stack only to fail. To succeed, leadership needs to embrace AI usage and find the right talent and data. But where should financial services institutions get started to stay competitive?
Join this webinar to learn:
- The initial criteria to consider when looking at different AI tools
- Why data quality and talent are needed to implement
- Challenges and pitfalls to avoid a successful implementation
- Alix Melchy, VP, AI at Jumio
- Katie King, CEO, AI in Business
- Yali Harari, CEO and co-Founder of Innovesta
This episode is part of the "Driving Fintech Forward" series with Elena Kozhemyakina, Founder and CEO of Fintech4Funds. We welcome viewer questions and participation.
Melanie Turek, VP & Fellow, Connected Work at Frost & Sullivan
Data and analytics are changing the way companies do business—in everything from the customer experience to human resources and recruiting, from finance and accounting to production and R&D. This session will get you ready for the new year by highlighting key trends to watch in 2021, calling out the technology and business transformations that you must adopt to stay competitive in a rapidly changing world. We’ll also discuss security, compliance and privacy concerns; address how Millennials and Gen Z workers are impacting the data-driven workplace; and suggest best practices for getting from zero to 60 in the coming months.
Vinay Rao, Co-founder & CEO and Santi Adavani, Co-founder of RocketML
Current deep learning approaches require large amounts of labeled data. The creation of labeled data is expensive, error-prone, and time-consuming. Despite these challenges, in the last decades, tremendous successes in machine learning have been achieved in the area of supervised learning that requires the compilation of large datasets with labels (for example, grouping pictures based on the person in the image). In contrast, unsupervised learning algorithms do not require labels and require minimal human participation. However, due to significant technical difficulties, they haven’t been as successful as supervised learning algorithms.
Self-supervision overcomes these technical difficulties to extract value from very large unlabeled datasets using machine learning with minimal human intervention in cybersecurity, precision medicine, and predictive maintenance applications. The new method circumvents these difficulties and clears the way to scaling unsupervised learning algorithms to large and complex datasets. We will show applications of these methods in cybersecurity and drug discovery problems.
Moderator: Premsai Sainathan at Roambee. Panelists from UN/CEFACT, World Economic Forum & Research Institutes of Sweden
Access to on-demand, real time and end-to-end data is key to unlocking the full potential of logistics and supply chains. From Manufacturers, Logistic Service Providers (LSP)/Third Party Logistics providers (3PLs), port authorities and terminal operators through to regulatory bodies, Beneficial Cargo Owners (BCO) financiers and the end consumer; all stand to benefit from information sharing.
In this session our panel of industry experts discuss
-The supply chain data and information landscape
-The top supply chain opportunities with information sharing
-Information sharing bottlenecks and challenges
-What can logistics and supply chain professionals do about it?
Join this session to learn how business intelligence and information leaders will maximise the RoI of, and unlock new business models through, information sharing for data driven logistics and supply chains.
Erin Waldron, Owner & Data Visualization Specialist at Data Dozen
In this 60-minute live demo, Tableau expert Erin Waldron will walk through the step-by-step building process behind a dashboard series analyzing COVID-19 data from the United States. As she creates each visualization in real time, she will introduce Tableau’s signature features accompanied by the internal dialogue that underpins each analytic decision. This parallel discussion of technical possibilities and ethical considerations, Erin will guide the audience through how to recognize and document the analytic decisions we make while building and their consequences for our audience. By the end of this talk, folks will walk away with a new perspective on:
- Data visualization best practices
- Picking the right chart type for the right data
- Tableau’s drag-and-drop user interface
- Dashboard design for public audiences
- Ethical problem solving and communication
Bipin Chadha, PhD., Chief Data Scientist at Ascential
While there are many predictions about how the future of analytics will unfold, we don’t have to always wait for them to come true for analytics to be a value producing component of the business. It is desirable to proactively shape the future of analytics in a way that produces value while mitigating the undesirable effects such as bias, lack of privacy, etc. A holistic approach is required for the benefits to materialize, just focusing on acquiring data or AI algorithms is not enough. In this webinar I will share key challenges to achieving these goals and what actions businesses can take to shape a future that is more likely to produce the outcomes we want.
Our actions today have a large impact on the future of analytics.
What are the critical issues we should focus on?
What steps we can take to ensure that analytics evolves in a form that is desirable?
What are the potential pitfalls to avoid?
Marius Moscovici, CEO of Metric Insights | Mike Smitheman, VP of Sales and Analytics of Metric Insights
The ability to drive consistent use and widespread adoption of Business Intelligence is an ongoing challenge for many companies, and the inability to achieve this consistency and uniform adoption can significantly impede their progress in becoming information and data-driven organizations.
Departmental siloes, tool proliferation, end-user data literacy, and other challenges too often produce an environment in which a shared, common understanding of the organization’s key performance indicators fail to materialize. In addition, metrics and measurements – the much-discussed “single-source-of-truth” – often fail to take shape, which in turn leads to competing versions of the truth; a lack of trust in available decision-making data; and, degradation in decision-making speed and effectiveness.
In this webinar, we will:
• Explore the underlying conditions that lead to the challenges of driving consistent and company-wide adoption of Business Intelligence
• Examine case studies of companies that have successfully solved these challenges
• Suggest solutions to the issues preventing organizations from building the necessary but elusive "Single Source of Truth"
Peter Kacandes, Sr. Technical Product Marketing Lead, Imply.io
Enterprise Analytics are currently often the purview of a narrow group of data scientists and trained analysts, with business users bottlenecked by those experts, or left to rely on static BI dashboards that are too slow for data exploration, but the economy increasingly demands that front-line business decision makers need real-time self-service analytics to make decisions faster and improve the customer experience.
In this talk, Peter Kacandes, a Sr. Technical Product Marketing Lead at Imply.io, will discuss:
The origins of this problem in the architecture of the current analytics tech stack, and the emerging hot analytics technology stack, which opens up self-service analytics on real-time big data to hundreds or thousands of business people at a company.
How a real-time data store that is 10-100X faster than data warehouses or data lake query engines, combined with an intuitive and high-speed UI, can empower untrained “citizen analysts” such as marketing, product, and operations managers, to make myriad daily decisions faster and data-driven.
The learnings of well-known companies who have implemented hot analytics for use cases such as user behavior, network, application and service performance monitoring, and real-time fraud detection.
Moderator: Ivan Roche at Disguise, Panelists: Richard Corderoy & Abel Aboh
The global big data and business analytics market was valued at 168.8 billion U.S. dollars in 2018 and is forecast to grow to 274.3 billion U.S. dollars by 2022, according to Statista. So what’ll happen in 2021?
As there's more data being made available on nearly anything in the world, so is the number of ways businesses can leverage that data. Big data management solutions and technologies are evolving to give companies the best data-driven analytics and predictive insights with unparalleled accuracy.
While there are endless possibilities in what can be done with big data, companies need to know not only how to organize their data but leverage it to get the most out of it.
Join this panel to stay up to date with what you need to know to plan for 2021 and learn:
-What trends to look out for in 2021
-How to sort through and leverage big data to get quality data-driven insights
-How to use common Big Data technologies
-How to keep your company up and track real-time data
Dr. Umesh Rao. Hodeghatta, Chief Data Scientist, N-U Sigma U2 Analytics Lab
AI systems and algorithms are trained and modeled and validated by humans on the data generated by humans or sensors or other automated systems. This poses challenge and risk of introducing human bias into the models, though many of us assume the AI systems are unbiased. As we are marching towards 2021 and making significant progress towards more sophisticated algorithms and models, we should also consider reducing bias in AI. Without managing bias, any AI initiative may fall apart due to the lack of trust and confidence by CEOs and leadership. In this session, we will be discussing some of the ethical and trust issues of AI systems due to AI Bias, and how organizations can model and validate AI systems and algorithms to reduce AI Bias.
Far too many companies struggle with data-related issues such as integrating online and offline data, a lack of collaboration between teams, low data accuracy, and poor data accessibility. You already know that creating a single source of truth for your customer data is the first step to good customer data management. Having your data all in one place provides a single data set that everyone in the company can use. This is the key to making your tools and teams work better together. A Customer Data Platform (CDP) can do all this and more. CDPs are designed to normalize large amounts of data across a wide variety of systems gathering data on the same individual in very different ways.
Have you heard of a CDP before and the value it brings?
In this webinar, you’ll learn:
-What is a CDP?
-What are the advantages of a CDP over a data lake?
-How can a CDP enable better business intelligence reporting?
-How CDPs can be leveraged with use cases designed for marketing, product and operations teams?
-And so much more!
About the presenter:
Aimee applies her vast analytics, project management, and leadership experience to help our client teams deliver the best analytics consulting solutions possible. Specifically, she works closely with clients to develop reports and analyze data, identify trends and relationships between different types of data, draw appropriate conclusions, and translate findings into marketing and sales strategies that drive revenue.
Aimee has more than 10 years of experience with web analytics solutions, including Adobe Analytics and Google Analytics, applying data-driven methodologies to business, product, sales, and growth initiatives. In addition to her strong analytics consulting expertise, she is a strategic thinker with the ability to influence decision making and a strong leader who ensures on-time, on-budget project completion.
Hema Murthy, Vice President, Customer Engagement at EnglishHelper Education Technologies
The EdTech Market is growing rapidly at a 16.3% CAGR, according to estimates from HolonIQ. Spurred by higher adoption rates due to COVID-19 and the availability of reliable low-cost solutions, the market is expected to touch $404B by 2025. EnglishHelper is an EdTech company providing low cost solutions to the under-served low income market in India and globally.
From a pilot in 100 schools across India, EnglishHelper has grown in massive scale to reach millions of students, teachers and schools in the K-12 space in just a matter of years. Along the way, it has conducted more than 100,000 technologically enabled assessments creating massive social impact for the under-served lowest income group. If you are serving a low-income market you are obligated to keep costs down while punching in more ROI into your systems and processes. In this session, we will share with you how we have deployed technology and tools at EnglishHelper to stay innovative and customer-oriented.
Giovanni Lorenzoni, CEO and Co-founder, BitBang, Data Management and Data Insights Consulting service
Companies are developing at speed and adopting innovative solutions for competitive differentiation and sustainable growth.
Efficient data management and analytics solutions are at essence for leaders to adopt a data-driven culture. Insights must be reliable to make more accurate decisions, optimize go-to-marketing strategies and achieve operational excellence. However, disparate systems and data silos hinder an organization’s ability to do this, instead leading to a growing amount of inaccurate data and overhead expenses.
It is a Mosaic approach of platforms and capabilities that will provide a truly valuable analysis where tools and analytics services are vital ‘participants’ in a data ecosystem. Integrating tools and combining data into a data environment so that it can be seamlessly accessed by data management services and used by end-users is key to monetize data and automate data-driven processes. Companies will need a continuous procedure for identifying, engaging with, and evaluating new data platforms and partners and, when appropriate, integrating these elements into analytics processes.
Rather than simply investing in data warehousing and advanced analytics tools, it is important that organizations take a step back and look at the systems of insights to build every project up from a mosaic of platforms and services to build an interconnected set of elements and organize them in a way that achieves data-driven insights. Understanding the interconnection between the systems of insights, people, processes, and microbiome of the entire organization will take you to 2021 and beyond.
Eric Topham, Co-Founder & Data Science Director at The Data Analysis Bureau (T-DAB)
Machine Learning and AI are beginning to show value across multiple industries for those organisations actively deploying them at scale. However, many are often trapped carrying out Proof of Concept projects, experimenting and developing models with teams struggling to implement solutions and reach production.
During this talk from The Data Analysis Bureau, we’ll explore the value of breaking the PoC cycle and how to retain the services in demand and deploy them through a development pipeline to reduce the costs of innovation. We’ll address how you move between R&D and application, get out of the PoC loop, the criteria, tools and timescales should you apply, and how you assess the value to achieve rapid deployment. We’ll share our lessons and a case study from working with academia and industry to move machine learning and deep learning models from R&D into production.
-Deliver value quickly and affordably – even if it isn't with the most complex algorithm
-Understand the difference between application and research, and when to transition between the with a proof of concept
-Put the horse before the cart when moving from POC to production
-Start delivering with the data infrastructure you already have
-Engage your human workforce
Genady Chybranov, CTO Financial Services Industry at Hitachi Vantara
Financial Services is one of the most data rich industries with analysable data volume growing exponentially. However, statistics show that businesses use only 0.5% of available data. As existing data analytics architectures in financial services reaching its limit, new approach is needed to leap forward with advanced ML and real-time analytics. In this webinar, you will learn:
-How to generate new business models with accelerated analytics in Financial Services
-How to accelerate existing data engineering and ML workflow by 100 times
-Best practices in architecting lightning fast and cost-efficient data engine
Simon Beaumont, Global BI Centre of Excellence Director and Fiona Gordon, Global BI Strategy Director at JLL
Analytics and insights have never been more important. As we embark on the fourth industrial revolution, leveraging technology to automate processes and empower people, organisations will be increasingly reliant on data to demonstrate return on investment and the outcomes achieved. With a new way of working comes with it an increasing need to empower people through an evolving set of analytical skills and experiences, ensuring teams embrace a data culture that is seen as complimentary to their personal growth and that of the wider organisation.
In this talk, learn how Simon Beaumont and Fi Gordon have led a data revolution within JLL:
Recognise how, across a large and complex global division, the promotion of a shared vision and common values have maximised engagement and minimised fear of the unknown.
A drive for innovation and continuous learning has energised their analytical workforce and supported the identification of future talent, both internal and external to the organisation.
Through JLL’s commitment to people they have inspired a vibrant internal community of analysts:
-Gamified learning has been leveraged to create a sense of shared achievement with personal growth being actively celebrated amongst their peer group
-Democratising achievement and recognition through virtual innovation such as e-badges to reward achievement and celebrate success
-The development of internal standards have led to consistency in design, minimising the time from concept to delivery for analysts and maximising insights for stakeholders
Recognising that democratisation of data extends beyond analysts, with stakeholders being supported through a commitment to collaboration and shared learning. Ensuring data is a skill for consumers as data as well as those developing it.
People are an organisations biggest asset and the exciting future of big data and analytics should be embraced as an opportunity to empower our people.
Managing and analyzing data to inform business decisions
Data is the foundation of any organization and therefore, it is paramount that it is managed and maintained as a valuable resource.
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