Name: Getting to Caprica - Making VR Really Usable
Start: 2016-11-29T17:00:00Z
End: 2016-11-29T17:43:08.000Z
Location: BrightTALK

Data is the foundation of any organization and therefore, it is paramount that it is managed and maintained as a valuable resource. 

Subscribe to this channel to learn best practices and emerging trends in a variety of topics including data governance, analysis, quality management, warehousing, business intelligence, ERP, CRM, big data and more.

Data and analytics have always been critical to organizations, but in today's competitive landscape they are essential to business success. Companies must be able to connect data, analytics and governance, and tie these to business processes, decisions and outcomes.

This panel of experts explores the secrets behind developing a data analytics strategy and selecting the platforms and tools that will bring your business to the next level.

Join us to learn how to:
--Streamline data prep and processing
--Ensure BI and analytics tool outputs match business information needs
--Define the metrics that matter
--Develop tailored reporting and visualization methods
--Take advantage of augmented and predictive analytics
--Consistently deliver insights that benefit decision-making

Achieving Business Value with Analytics

At the core, Machine Learning Operations (MLOps) takes an experimental machine learning model into a production system. MLOps is an emerging practice distinct from traditional DevOps. ML lifecycle involves using patterns from training data, making MLOps workflow sensitive to data changes, volumes, and quality. Additionally, matured MLOps should support monitoring both ML lifecycle activities and production model monitoring.

Key Takeaways:
• Build cloud-native serverless analytics pipeline. 
• Learning different data automation pattern including data lake, lake house and data mesh architecture.
• Automate end-to-end data pipeline with MLOps.

Decoding Data Automation With Cloud-Native Analytics Pipeline and MLOps

Promises, promises… everybody’s swearing that your unstructured data is a goldmine. All you have to do is buy their software, engage their services, hire fancy tech pros and magic will happen. Buy, buy, buy, right? But you’re not so sure.

The real secret to lucrative results is no secret at all. You’ve got to align data and analytics with your everyday business goals and practices. It’s not rocket science, yet you must understand and manage the process.

In this session, you will learn:
• How to spot the easiest opportunities to profit through data analytics. 
• Why technical staff can’t produce the results you want without active management involvement.
• When to integrate unstructured data with predictive analytics to support decision making.

Make Your Data Pay -- What Non-technical Executives Need to Know

The extraction of actionable information from data is a key enabler for optimizing workflows, automating processes, and providing meaningful insights to decision makers. But we live in an unstructured world, and unstructured sources of data can be particularly difficult to parse. In this session, we will share the latest developments in the field, including the modern tools and machine learning techniques employed by insight engines to intelligently extract information from unstructured data. We’ll also address the following questions:

  *   What are the challenges we face in processing unstructured data sources?
  *   Which tools are available to extract insights from this data?
  *   How can we use extracted insights to automate processes and workflows?

We will close the session by providing a real-world example of process automation employed to assist with bid management.

Business Transformation Through Insights-Driven Automation

Organizations looking to maximize business intelligence (BI) use may consider constructing a business intelligence team consisting of four key roles -- the expert, designer, analyst and steward. In this session, we'll detail these four important roles -- how to hire them, how to inspire them and how to manage their work.

Attendees will learn:

• How to integrate existing informal skills and practices into a new practice.
• The steps to build BI team from scratch.
• Inspiring and retaining key roles
• Pitfalls -- personal and technical -- to watch for .
• Expanding new capabilities such as data science and AI

Keys to building a successful business intelligence team

Agility has become a buzzword over the past two years — and even more so when it comes to business intelligence. According to research, 93% of the organizations that had fully implemented an agile BI strategy before the pandemic outperformed those that hadn’t. As the market adjusts to a new normal of constant change, how can adopting agile BI practices help your organization ride the waves of dynamic demand?
Tune into May’s episode of the Business Intelligence Report to learn from leaders in the field when it comes to implementing an agile BI strategy. Viewers will walk away with understandings of how agile analytics foster quicker ROI and increase capacity to adapt to changing business needs. 

Topics for discussion include the following:
• What defines agile BI today
•The most common hurdles to agile BI adoption
• The advantages of agile BI, from decreasing time to market and increasing ROI to increasing user adoption
•How your business can get started
•And more!

Speakers:
Gowri Selka, Data Analytics Strategist & Advisor at Volantsys Analytics
Stephanie Sibert, Director, Business Intelligence & Analytics at Ironclad
Kate Schiffelbein, Director of Business Intelligence at Kentik
Abeer Shrivastava, Director - Business Intelligence & Advanced Analytics at SubroSmart

What Agile BI Can Do for Your Business

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.

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

At the core, Machine Learning Operations (MLOps) turns an experimental machine learning model into a production system. MLOps is an emerging practice distinct from traditional DevOps. The ML lifecycle aggregates training data, making MLOps workflow sensitive to data changes, volumes, and quality. Additionally, matured MLOps should support monitoring both ML lifecycle activities and production model monitoring.

Key Takeaways:
• Build cloud-native serverless analytics pipeline. 
• Learning different data automation pattern including data lake, lake house and data mesh architecture.
• Automate end-to-end data pipeline with MLOps.

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.

Setting the Budget for the ML Stack for Analytics

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

Advanced BI Features Help Democratize Data

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.

Bad Analytics: What They Are and How To Avoid Them

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.

Ensure customer trust with data automation and security best practices

The importance of data visualization continues to rank highly among IT and business leaders across categories. As the Data Era evolves rapidly in a time of unprecedented digital transformation, what are the cutting-edge data visualization techniques and trends to be aware of as 2022 unfolds?
 
Join our April episode of the Business Intelligence Report to hear from experts in the data discovery and analysis field to understand best practices for getting the most out of your business’s data. Topics for discussion include the following:
•The increasingly critical role of AI in data discovery & visualization
•The advantages of real-time data visualization
•The implementation of video visualization
•What augmented and virtual reality can bring to the picture
•And more!

Speakers:
- Gowri Selka, Data Analytics Strategist & Advisor at Volantsys Analytics
- Miguel Encarnação, Head of Data Visualization at Regions Bank
- Kyle Dupont, CEO at Ohalo
- David Ciommo, Data Visualization Principal at Humana

Top Practices for Data Discovery & Visualization

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.

The Essentials to Data Science Success within Enterprises

We are witnessing ever increasing innate human desire to gain insight in every industry and every walk of life. Our professional and personal lives are reliant on data and frictionless data technology to make sense of the insight to achieve significant outcomes. This insatiable human demand can only be fulfilled provided data and frictionless technologies are enabled with free and open approach.

Join me and industry leaders to explore industry trends to democratise data for humans to thrive in a digital data economy, we will discuss:

• What are significant challenges corporate humans (I.e., employees) face?
• Why should they care about this?
• What impact it has on their customers and their company’s success?
• Can AI/ML models ever make these humans effective in a closed data ecosystem?

Debarshi Bandyopadhyay, Principal Consultant & Delivery Partner, Infosys UK Limited
Hari Yedoti, Group Head of Data, Liquid Telecom

The Human Data Democracy

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.  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 2022 and learn:  

-What trends to look out for in 2022 
-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

Panelists:
Pragyansmita Nayak, Ph.D., Chief Data Scientist at Hitachi Vantara Federal
Sandra Carrico, VP, Machine Learning at Sorcero
Sean Hearne, Director, Program Management Office at Fiserv

Trends in Big Data and Analytics in 2022

Today, big data has the potential to transform functions, industries, organizations, and governments with an unprecedented impact. Complacency is not an option. You may be asking, “Where are the opportunities?”, “How can we drive business value using data visualization with bottom-line impact?” Join me as we explore these questions and more.

Leveraging Big Data Analytics & Visualization for transformational growth

With data quickly evolving into an organizations most vital asset, it has become increasingly important to invest in visualization tools that help generate insights. Companies such as Tableau, Power BI, and Looker have made it easy to create visuals and share information across an organization, but just creating a visual isn’t always enough. There must be a compelling narrative, anchored by facts, to facilitate the communication that ultimately drives decision making. In this session, you will learn the science behind the message your visuals give off and how you can create more engaging visuals for your audience.

The Art of Storytelling through Engaging Visuals

This webinar will look at the challenges currently facing VR across a variety of "serious business" use cases from education and training to data visualisation and what the technology needs to do in order to get beyond the "wow" and move into being a productive, useful and truly in-demand technology.

Getting to Caprica - Making VR Really Usable

Virtual Reality

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Getting to Caprica - Making VR Really Usable

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