Webinars

Webinars

  • Do More With Data We Have: Engineering Strategies To Match Good Data to Products

    With a gigantic increase in data capturing capacity, corporations, government agencies, and research organizations face complexity in determining the best variables for their data models. Feature engineering techniques like exploratory data analysis are valuable for establishing the right variables for different statistical and machine learning models. In this session, attendees will learn the basic steps in R programming (and comparable techniques for Python) with use cases to apply these techniques in a sample training data model. Topics will be based on the following: R programming, R Studio, Python, Machine Learning, Data Science , APIs, Feature Selection/Feature Engineering, Exploratory Data Analysis, and Dimensionality Reduction. This session is meant for developers and managers using R or Python with an interest in statistical models or machine learning. The session will cover a few basic data type concepts. Prior experience with R or Python is not necessary. Read More

  • Setting the Budget for the ML Stack for Analytics

    Organizations today need a broad set of enterprise data cloud services with key data functionality to modernize applications and utilize machine learning. They also need a platform designed to address multi-faceted needs by offering multi-function data management and analytics to solve the enterprise’s most pressing data and analytic challenges in a streamlined fashion. Above all, they need a worry-less experience with the architecture and its components. A complete machine learning infrastructure cost for the first modern use case at a midsize to large enterprise will be anywhere from $2M to $14M. Get this data point as you take the next steps on your journey. Read More

  • DataOps: Strategy is the key to success

    Hear from Jameika Hawkins, CEO of 2 The ResQ, a full-service IT consulting firm as she explores the following pressing questions: 1. What exactly is DataOps? 2. Why choose DataOps? 3. How to get the best of your DataOps framework? Read More

  • Advanced BI Features Help Democratize Data

    The amount of global data creation and replication will grow at a compound annual rate of 23% for the next three years, according to research firm IDC. That makes managing enterprise data more critical than ever, and business intelligence is playing an important role. As adoption of BI increases across enterprises, businesses are looking to advanced capabilities to address challenges with sophisticated use cases and allow all levels of stakeholders to perform tasks that previously required the skills of a data scientist. In this panel discussion, experts in the field will explore the features that will help organizations move toward a data-driven culture. These include augmented analytics, embedded analytics, self-service and interactive enablement, mobile application delivery, and natural language querying. Join us to find out: --Which BI features can best support your business model and goals --Augmented analytics capabilities that give business users power to build their own queries --Guided analysis that steers users through the appropriate workflow --How self-service and interactive tools can improve productivity and expand data comprehension and usage Moderated by: Craig Stedman, Industry Editor, TechTarget Panelists: Ryan Ries, Practice Lead, Mission Donald Farmer, Principal, TreeHive Strategy Pierre DeBois, Founder and CEO, Zimana Read More

  • Reimagining and optimizing DataOps to drive evergreen business value

    The growing value of data has been understood for some time, yet many organizations still struggle to build, run and evolve their data capabilities and services for full utilization. These challenges are compounded by mounting business needs and expectations, and tighter competition within industry sectors. Data operations (DataOps) is therefore becoming much more than just "lights on", it is foundational for driving evergreen business value. Reimagining your DataOps strategy is a critical component to define and deliver your organization's Enterprise Data Strategy. While companies have customized their individual operations approach, certain factors remain consistent across the most successful insight-driven organizations. Their approach to DataOps is driven by a clear and actionable mission, value-oriented governance framework, and a comprehensive suite of enabling capabilities that drive high quality and high value execution. Join Deloitte to hear how reimagined DataOps is a critical success factor to unlocking the full value of your data-driven business strategies, and how new technologies and shifting operating models are transforming the delivery of DataOps services. Read More

  • Bad Analytics: What They Are and How To Avoid Them

    While there are a lot of discussions on what defines good analytics and reliable insights, how do we recognize bad analytics? Bad analytics involves more than lacking good insights. Unless companies can actively identify bad analytics, they cannot prevent them. In this presentation, Dr. Prashanth Southekal, Data Analytics Consultant, Author and Professor, and inventor f the DEAR model for data-driven decision-making, discusses the 10 key characteristics of bad analytics. Here are the key takeaways for the audience upon attending my presentation. 1. Understand good and bad analytics. 2. Spot for patterns for bad analytics based on 10 key characteristics. 3. Improve the adoption of data and analytics solutions in the organization. Read More

  • Ensure customer trust with data automation and security best practices

    As data is increasingly critical in order to draw insights and expand business potential, companies must be able to process this data faster, more accurately and in time to monetize on critical business advantages. That's when automation of data processing techniques become essential. Data security is another critical aspect to consider as companies increasingly handle more customer data. In this chat, we will look at certain data automation and security principles that an organization should follow in order to best ensure the trust of their customers. Read More

  • Accelerate IT Operations and App Development Across Distributed Infrastructures

    Data and digital operations fuel business success. But as infrastructure environments scale and become more distributed, complexity mounts and often cripples operational efficiency. In this keynote presentation, Scott Sinclair, Practice Director for Cloud, Infrastructure and DevOps at ESG, will explore why IT leaders need to modernize their approach to IT infrastructure by leveraging advantages in integrated intelligence and automation. With a wealth of innovation in this space, learn what IT and application development leaders are doing to minimize scaling burden on the pace of their operations to ultimately deliver success in digital business. Read More

  • The Future of Regulatory Compliance in Defi Space

    Coinfirm AML Oracle was the first smart contract-based solution for AML compliance of the burgeoning decentralized finance (DeFi) scene. The Oracle unlocks a key barrier that DeFi presented to previously developed blockchain compliance technology on the market. Because DeFi protocols run on-chain smart contracts – meaning that any compliance layer needs the same format of technology required to interact with a DeFi provider such as a decentralised exchange (DEX) through client smart contracts – a specifically tailored solution is necessary. Currently operated with Ethereum and RSK protocol, the Coinfirm AML Oracle is that solution – enabling decentralized entities to continue their lending, staking and general DeFi activities without fear of falling afoul of nefarious actors in their system – with an added emphasis on security and cost-effective gas usage. The Financial Action Task Force's updated draft guidance from 09.03.2021 massively expands the types of entities falling under the FATF’s purview. The updated guidance requests member states to ensure DeFi platforms are covered by AML laws, even if there is no single party responsible for live networks. The Coinfirm AML Oracle offers both a partial and fully decentralized solution to proactively manage compliance risks and comply with regulations. When users interact with a decentralized finance provider, their wallet address is queried for risk through the Coinfirm AML Oracle, which in turn passes data from Coinfirm’s API to generate a report ID C-Score based on 330+ risk evaluation scenarios that relays the information to the DEX. Read More

  • Blockchain Adoption, Management, and Application

    In today’s complex environment organizations are embarking on Digital Transformation journey. Compared to existing systems, blockchains are faster, cheaper, more reliable, and much more secure. For example, in healthcare, patients often have medical history scattered among various medical records maintained in several health-care establishments. The data is available only in the scope of those specific systems. Having a Blockchain Medical Profile to store an individual’s medical profile that is accessible from anywhere at the time of need. This omnipresence of the medical data will be a boon for the field of medical informatics. Similarly, in financial institutions, transactions that happens within and among financial institutions can be delivered, provided immediately, shared, and completely transparent stored on an immutable ledger via Blockchain technology. The faster the information is received and the more accurate it is, the better. Blockchain is ideal for delivering that information because it provides immediate, shared and completely transparent information stored on an immutable ledger that can be accessed only by permissioned network members. Blockchain technology and its infrastructure (hardware, software, and facilities, connectivity, mobility, performance, and security and such can be easily deployed and managed using ITIL best practices. Speakers: Waseem Ahmed, VP IT Production Operations, Citibank NA Atif Farid Mohammad PhD, Adjunct Professor, Artificial Intelligence, Machine Learning, Blockchain, UNC Charlotte Read More