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Upcoming Webinar Series 2022

By |Last Updated: May 15th, 2024|

WEBINAR SERIES 2022

Free Upcoming Webinars: The Big Data Framework explained

The Enterprise Big Data Webinar Series will cover the six core capabilities that enterprises need to consider to obtain long-lasting value from Big Data.
The topics range from the technical components of setting up a Big Data architecture, the the establishment of data processes, to the soft skills required to set up a Big Data center of excellence. The webinar will run every six weeks starting from June-November 2022

Throughout the different webinars, we will cover some of the most critical components of the Enterprise Big Data Framework. Don’t miss this chance to learn more about Big Data, and join the webinars free of charge.

Read more about each upcoming webinar:

June 06, 2022 – Formulating a Big Data Strategy

A comprehensive enterprise-wide Big Data strategy can provide enterprises with a significant competitive advantage in the marketplace.

An effective Big Data strategy means a business strategy that includes Big Data. A well-defined and comprehensive Big Data strategy makes the benefits or Big Data actionable for the organization. It sets out the steps that an organization should execute to become a “Data Driven Enterprise”.

The session will discuss the 5-step approach for organizations can follow to formulate their Big Data strategy.

Everyone presently studying the domain of Big Data should have a basic understanding of how Big Data environments are designed and operated in enterprise environments, and how data flows through different layers of an organization.

Understanding the fundamentals of Big Data architecture will help system engineers, data scientists, software developers, data architects, and senior decision makers to understand how Big Data components fit together, and to develop or source Big Data solutions.

The session will provide an overview of the NIST Big Data Reference Architecture (NBDRA), the basics of distributed storage/processing as well as an overview of the Hadoop open source software framework.

Algorithms can perform calculation, data processing and automated reasoning tasks. By applying algorithms to large volumes of data, valuable knowledge and insights can be obtained.

The application of algorithms, and its subsequent use for Big Data, is grounded in the scientific domain of statistics.

The session will discuss essential statistical operations and provide common algorithms that are used in Big Data analysis and analytics solutions.

Embedding Big Data in Enterprises is as much about change management as it is about Big Data.

The design of a Big Data organization (digital transformation) therefore needs to be user led and have participation from all levels in the enterprise from the start. Organizational culture, organizational structures, and job roles have a large impact on the success of Big Data initiatives.

The session will review some ‘best practices’ on how to establish a data-driven organization.

To avoid the potential pitfalls that Big Data brings, processes can help enterprises to focus their direction. Processes bring structure, measurable steps and can be effectively managed on a day-to-day basis.

Setting up Big Data processes in the enterprise might be a time-consuming task at first but provides the benefits in the long run.

The session will discuss how Big Data processes can provide structure in the analysis of data as well as the big data three main sub-processes.

Artificial Intelligence can be considered a complete domain of science by itself, it is strongly interwoven with Big Data because the volume and variety of data sources are often massive (in terms of volume) and diverse (in terms of sensors). Additionally, many of the statistical and machine learning algorithms that are used to analyze Big Data sets, are similar to the ones used in Artificial Intelligence.

The session will provide an overview of the operational definition of AI in an enterprise context and will focus on cognitive analytics as an extension of the Big Data analytics techniques.

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