Name: NoSQL, Hadoop and MapReduce: Building a Modern Data Infrastructure that Works
Start: 2013-08-21T18:00:00Z
End: 2013-08-21T18:59:55.000Z
Location: BrightTALK
Rating: 4.38

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.

A monolithic approach to building systems rarely succeeds.  And yet there is clear value in having knowledge workers access data across an enterprise.  The Data Mesh concept put forth by Dehghani proposes a divide and conquer approach to tackling monolithic complexity by aligning implementation to the idea of domains.  In this workshop we will discuss an engineering approach for Data Mesh deployment that provides a practical architecture and governance framework to deliver connectivity across domains while encouraging a decentralized approach to stewardship and implementation. 

Learn how to accelerate delivery of data products using a domain-driven design approach.
Learn about best practices in data engineering aligned to a domain-driven approach to delivery of data products.
Learn how to deliver using autonomous, decentralized teams while avoiding anarchy in architecture and deployment.

An Engineering Approach to Implementation of the Data Mesh Concept

According to a recent Gartner study, it is estimated that 85% of big data projects fail. Main reason for this failure being that datasets significantly grow over time n size and get more complex; thus, the traditional BI solutions can’t keep pace to manage them. Augmented Analytics has proven to be the best solution for the dilemma. By utilizing AI and Machine Learning capabilities to BI solutions you can gather precise appropriate data, achieve comprehensible information patterns, perfect the data analysis models and anticipate better BI insights.

In this session we will learn how AI is helping the BI industry to transform into a better, more insightful data representations with more accurate predictions and recommendations. Using multiple AI elements, Augmented Analytics are revolutionizing the BI industry through the power of AI and Machine Learning. It is what every industry in any sector needs today to get the best out of Big Data and BI.

Takeaways:

What are Augmented Analytics and who are they for?
The benefits of adopting Augmented Analytics for BI
The AI elements for Augmented Analytics
Augmented Analytics challenges
Best practices and examples of utilization

Augmented Analytics: Discover How AI is Revolutionizing the BI Industry

Orchestrating data science and machine learning in an industrial setting is difficult. The data landscape is challenging, littered with complex problems and without the necessary freedom to design experiments or create observational studies on real-world processes. On top of that, you must manage stakeholders, operate cloud technologies, write software or wrap your solution into a product required to run predictions 24/7/365 – all while supporting business operations! With all this on your plate, you will need strategies to successfully incorporate MLOps in your organisation. In this talk, we will explore:


How to “bootstrap” ML Engineering (MLEng) and MLOps practices in your organisation.
Different ways to organize your MLOps teams to create optimum value.
Where to start on your MLOps journey.
How to avoid some common “gotchas” of starting out in MLOps.

Just Build It! How to Make MLOps a Reality In Your Organisation

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

Key Takeaways: 
• Understand the new production model and the role of big data analytics within. 
• Present the global scientific discussion about Big Data Analytics as well as the technologies and  techniques of Big Data Analytics. 
• Describe and explain the Strategic Alignment Framework. Understand the different elements of  SAF: Strategy, Structure, Environment and how Advanced Data Analytics interact with each element. 
• How SAF will guide the appropriate queries  
• How to attain alignment and fit in SAF and Advanced Data Analytics 
• Case studies will be given for further study on the subject

Strategic Alignment Framework for Big Data Analytics: Mastering Quality

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

In a whirlwind of big data tools like MapReduce, NoSQL, Hadoop, and their cousins and brothers, it’s difficult to understand the stack you need to make your data as useful as possible. How do you decide which tools to use, and once you do decide, how do you make the jump? 

Join this roundtable led by big data infrastructure experts to: 
*Understand the ingredients of a modern data infrastructure 
*Learn how to assess your needs 
*Make a blueprint for building a modern data architecture that works for you

NoSQL, Hadoop and MapReduce: Building a Modern Data Infrastructure that Works

hadoop

mapreduce

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NoSQL, Hadoop and MapReduce: Building a Modern Data Infrastructure that Works

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