Name: Pre-Processing Big Data: Techniques to improve quality of big data analysis
Start: 2016-10-12T12:00:00Z
End: 2016-10-12T12:47:41.000Z
Location: BrightTALK

Make smarter moves with your big data management

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

Traditionally data had been produced and stored at multiple sources and given the high cost of
the traditional Data warehousing system, only pristine data could be used for analytics and reporting.
When a data analyst needed to derive business value from this data, he/she required communication across teams, integrations between products, and a high level of coordination to get the information they needed.

There was a clear need for a central platform, being the single source of truth for all the data and
providing quick access to the data in a self-service manner. With the advent of Big Data and Cloud
systems, the storage and compute got cheaper which means that even the raw and intermediate data can also be made available for analytics by bringing them to the central location. This led to the
evolution of Enterprise data lake within organizations. The concept of data lake has evolved from a
Hadoop-based on-premise data lake to an object-storage cloud-based data lake with compute being
provisioned only when the need arises.


Big Data and Cloud Engineering team at Abzooba has been playing a key role in helping organizations achieve their goal of digital transformation. We have been a trusted partner for our clients in their Data Modernization, Cloud Migration and Advanced Analytics journey and this has also helped us to learn some of the best practices while designing cloud-native big data solutions.


During this Webinar, following topics would be discussed

1. Best Practices of designing a data lake solution
2. Introduction to Olive and Pine, building blocks for Data Lakes
3. Demo on Olive (Data Ingestion Framework)

About Bijoy Bora: Bijoy is the SVP & Global Head, BD&C Engineering practice, Abzooba Inc. He is also a Professor of Practice, in their MS Data Science program, School of Engineering & Computer Science, University of the Pacific, San Francisco. CA. Bijoy was also the Co-founder of Zaloni Inc.

Best practices for designing a cloud-native data lake solution

Join this expert panel, moderated by Cory Minton (Founder of the Big Data Beard podcast), and discover the latest tools, techniques and strategies industry experts are using to effectively manage and leverage their data lakes for business intelligence and analytics use cases.

Speakers:
Moderator: Cory Minton, CEO and Founder of Big Data Beard

Panelists: 
Ramesh Menon - VP of Products for Infoworks - 
Ozzy Johnson - Deputy CTO for Domino Data Lab 
Jim Youldassis, North America Cognos Analytics Program Manager, IBM

Refine Your Data Lake Strategy for Analytics

Failing to protect sensitive information can put a lot of people at risk of being exploited by cybercriminals, and can make a company face enormous legal penalties.

The way information is shared and stored can put the information at risk.

It is risky to store personal information on portable devices, which are easily lost or stolen.

In addition, the consequences of a data breach can be devastating. Identity theft could lead to financial losses, and a company could face lawsuits and legal penalties.

This presentation will cover what kinds of personal information must be protected & guidelines for keeping this info safe.

After viewing this session, attendees should:
1.       Understand how Privacy is defined
2.       Highlight some of the Privacy regulations from around the globe
3.       Understand what information to safeguard
4.       Understand how these privacy regulations affect organizations that handle personal information

Data Privacy & Why it Matters

The data revolution has resulted in a seemingly endless variety of tools and techniques designed to extract insights from big data and gain a decisive competitive edge in the marketplace. Many successful organizations across major industries like Financial Services, Healthcare and Retail, now analyze the data generated by everyday business activities and use the resulting insights to inform critical business decisions.

Tune in to learn more about how industry leaders are becoming Modern BI powerhouses and get expert panelist advice on how to accelerate your own business intelligence and analytics strategy in 2019 and beyond.

Join BrightTALK's LinkedIn Group for BI & Analytics!
- http://bit.ly/2PJlPHm

Moderator: 
Cory Minton, Founder & CEO of Big Data Beard

Panelists:
Pratik Verma, CEO of BlueTalon
William McKnight, President of McKnight Consulting Group
Rob Antczak, CTO of PowerAdvocate

Tools and Techniques to Accelerate Your Modern BI Strategy

Join this expert panel, moderated by Cory Minton (Founder of the Big Data Beard podcast), and discover the latest tools, techniques and strategies industry experts are using to effectively manage and leverage their data lakes for business intelligence and analytics use cases.

Join BrightTALK's LinkedIn Group for BI & Analytics!
- http://bit.ly/2PJlPHm

Speakers:
Moderator: Cory Minton, CEO and Founder of Big Data Beard

Panelists: 
Ramesh Menon - VP of Products for Infoworks - 
Ozzy Johnson - Deputy CTO for Domino Data Lab 
Jim Youldassis, North America Cognos Analytics Program Manager, IBM

The data revolution has resulted in a seemingly endless variety of tools and techniques designed to extract insights from big data and gain a decisive competitive edge in the marketplace. Many successful organizations across major industries like Financial Services, Healthcare and Retail, now analyze the data generated by everyday business activities and use the resulting insights to inform critical business decisions.

Tune in to learn more about how industry leaders are becoming Modern BI powerhouses and get expert panelist advice on how to accelerate your own business intelligence and analytics strategy in 2019 and beyond.

Moderator: 
Cory Minton, Founder & CEO of Big Data Beard

Panelists:
Pratik Verma, CEO of BlueTalon
William McKnight, President of McKnight Consulting Group
Rob Antczak, CTO of PowerAdvocate

Join BrightTALK's BI & Analytics group on LinkedIn: http://bit.ly/2PJlPHm

This webinar is part of BrightTALK's What's Big in BI series.

The data revolution has resulted in a seemingly endless variety of tools and techniques designed to extract insights from big data and gain a decisive competitive edge in the marketplace. Many successful organizations across major industries like Financial Services, Healthcare and Retail, now analyze the data generated by everyday business activities and use the resulting insights to inform critical business decisions.

Tune in to learn more about how industry leaders are becoming Modern BI powerhouses and get expert panelist advice on how to accelerate your own business intelligence and analytics strategy in 2019 and beyond.

Moderator: 
Cory Minton, Founder & CEO of Big Data Beard

Panelists:
Pratik Verma, CEO of BlueTalon
William McKnight, President of McKnight Consulting Group
Rob Antczak, CTO of PowerAdvocate

[Ep.2] What's Big in BI: How to Accelerate Your BI & Analytics Strategy

The data revolution has resulted in a seemingly endless variety of tools and techniques designed to extract insights from big data and gain a decisive competitive edge in the marketplace. Many successful organizations across major industries like Financial Services, Healthcare and Retail, now analyze the data generated by everyday business activities and use the resulting insights to inform critical business decisions.

Tune in to learn more about how industry leaders are becoming Modern BI powerhouses and get expert panelist advice on how to accelerate your own business intelligence and analytics strategy in 2019 and beyond.

Moderator: 
Cory Minton, Founder & CEO of Big Data Beard

Panelists:
Pratik Verma, CEO of BlueTalon
William McKnight, President of McKnight Consulting Group
Rob Antczak, CTO of PowerAdvocate

With just a few weeks to the UK's largest data & analytics event, we've gathered some of the elite speakers who will be taking the stage to debate the latest trends, hottest solutions and the biggest opportunities (and challenges) for businesses in a data-driven world. 

* Fast Data & DataOps
* Self-Service Analytics
* Artificial Intelligence
* Customer Experience
* Data Governance

What will they be talking about at The Olympia, London, on the 13-14 November 2018, what do they want to hear about, what are they looking forward to?

Join this panel discussion and arm yourself for excellence in this brave new data-driven world.

Richard Corderoy, Chief Data Officer, Oakland Data and Analytics
Andy Mott, Senior Consultant, Arcadia Data

Embracing the Data Revolution - What to Expect at Big Data LDN 2018

The recent data explosion is a huge challenge for storage and IT system designers. How do you crunch all that data at a reasonable cost?  Fortunately, your familiar SAS comes to the rescue with its new 24G speed. Its flexible connection scheme already allows designers to scale huge external storage systems with low latency.  Now the new high operating speed offers the throughput you need to bring big data to its knobby knees!  Our panel of storage experts will present practical solutions to today’s petabyte problems and beyond.

Flash Storage with 24G SAS Leads the Way in Crunching Big Data

For years, banks have been sitting on a goldmine of customer data. Only recently have they started exploiting that, although not surprisingly for their own benefit.  
Personal data can give great insights to drive bank outcomes by decreasing credit losses and reducing fraud losses. In this webinar Paul Clark, CTO, will look at how we can use customer data to;
* Drive customer’s own advantage
* Avoid slip ups
* Dodge nasty charges
* Optimise the customer’s finances end to end.

How to Leverage Big Data for Customers: Lessons from a Purpose-Driven Bank

Implementing AI applications based on machine learning is a significant topic for organizations embracing digital transformation. By 2020, 30% of CIOs will include AI in their top five investment priorities according to Gartner’s Top 10 Strategic Technology Trends for 2018: Intelligent Apps and Analytics. But to deliver on the AI promise, organizations need to generate good quality data to train the algorithms. Failure to do so will result in the following scenario: "When you automate a mess, you get an automated mess."

This webinar covers: 

- An introduction to machine learning use cases and challenges provided by Kirk Borne, Principal Data Scientist at Booz Allen Hamilton and top data science and big data influencer.
- How to achieve good data quality based on harmonized semantic metadata presented by Andreas Blumauer, CEO and co-founder of Semantic Web Company and a pioneer in the application of semantic web standards for enterprise data integration.
- How to apply a combined approach when semantic knowledge models and machine learning build the basis of your cognitive computing. (See Attachment: The Knowledge Graph as the Default Data Model for Machine Learning)
- Why a combination of machine and human computation approaches is required, not only from an ethical but also from a technical perspective.

Semantic AI: Bringing Machine Learning and Knowledge Graphs Together

How are financial service firms around the world using machine learning systems today to identify and address risk in transactional datasets? 

This webinar will look at a new approach to transaction analysis and illustrate how the combination of traditional rules-based approaches can be augmented with next-generation machine learning systems to uncover more in the data, faster and more efficiently. 

We will span the various applications in banking, payments, trading, and compliance; looking at a variety of use cases from bank branch transaction analysis to trading data validation. 

Anyone interested in financial technology, next-generation machine learning systems and the future of the financial services industry will find this webinar of specific interest.

About the speaker: 
Erik McBain, CFA is a Strategic Account Manager for MindBridge Ai, where he specializes in the deployment of emerging technologies such as artificial intelligence and machine learning systems in global financial institutions and corporations. Over his 10-year career in banking and financial services(Deutsche Bank, CIBCWM, Central Banking), Erik has been immersed in the trading, analysis, and sale of financial instruments and the deployment of new payment, banking and intelligent technologies. Erik's focus is identifying the various opportunities created through technological disruption, creating partnerships, and applying a client-centered innovation process to create transformative experiences, products, and services for his clients.

Audit Ex Machina: Digital Learning Systems and Transactional Data

Data in the real-world is almost always dirty, incomplete, scattered or inconsistent. For data scientists, 'janitor work' is a key hurdle to data insights. 

Whether you use big data for analytics or data science, with increasing variety and velocity of big data, the data pre-processing step can be the most time-consuming step in your data pipeline. 

Featuring engineering concepts and practical examples in Python and R, this webinar will focus on technical considerations and data engineering techniques to optimise data preparation to get the most value from your big data pipeline.

Pre-Processing Big Data: Techniques to improve quality of big data analysis

python

data

data science

data preparation

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Pre-Processing Big Data: Techniques to improve quality of big data analysis

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