Every Executive that has a Big Data Hadoop Cluster and their Staff, this is a must see! Getting your big data house in order.
The misalignment and clutter issues waste much of the precious time for critical decisions.
Closing the data source discovery gap and accelerating data discovery comprises three steps: profile, identify, and unify. This white paper discusses how the Attivio
platform executes those steps, the pain points each one addresses, and the value Attivio provides to advanced analytics and business intelligence (BI) initiatives.
Joe Caserta, President at Caserta Concepts, presented "Setting Up the Data Lake" at a DAMA Philadelphia Chapter Meeting.
For more information on the services offered by Caserta Concepts, visit our website at http://casertaconcepts.com/.
Introduction to Data Science (Data Summit, 2017)Caserta
At DBTA's 2017 Data Summit in New York, NY, Caserta Founder & President, Joe Caserta, and Senior Architect, Bill Walrond, gave a pre-conference workshop presenting the ins and outs of data science. Data scientist has been dubbed the "sexiest" job of the 21st century, but it requires an understanding of many different elements of data analysis. This presentation dives into the fundamentals of data exploration, mining, and preparation, applying the principles of statistical modeling and data visualization in real-world applications.
These slides use concepts from my (Jeff Funk) course entitled analyzing hi-tech opportunities to analyze how Big Data is becoming economically feasible for health care. These slides describe how the cost of sensors, data processing, data storage and data analyzing are falling, how new and better forms of storage and algorithms are being implemented, and what this means for sustainable health care. These changes are enabling a move towards personalized health care.
Are you tired of saying “no” when it comes to data? IDC and Talend share insights into how you can deliver data governance with a “yes”.
The reliability of data, and your company’s reputation for protecting it, have become essential to doing business in the data age. Modern data governance works at the speed of business, the scale of data, and still has a human touch so you can say “yes” and deliver trusted data.
Closing the data source discovery gap and accelerating data discovery comprises three steps: profile, identify, and unify. This white paper discusses how the Attivio
platform executes those steps, the pain points each one addresses, and the value Attivio provides to advanced analytics and business intelligence (BI) initiatives.
Joe Caserta, President at Caserta Concepts, presented "Setting Up the Data Lake" at a DAMA Philadelphia Chapter Meeting.
For more information on the services offered by Caserta Concepts, visit our website at http://casertaconcepts.com/.
Introduction to Data Science (Data Summit, 2017)Caserta
At DBTA's 2017 Data Summit in New York, NY, Caserta Founder & President, Joe Caserta, and Senior Architect, Bill Walrond, gave a pre-conference workshop presenting the ins and outs of data science. Data scientist has been dubbed the "sexiest" job of the 21st century, but it requires an understanding of many different elements of data analysis. This presentation dives into the fundamentals of data exploration, mining, and preparation, applying the principles of statistical modeling and data visualization in real-world applications.
These slides use concepts from my (Jeff Funk) course entitled analyzing hi-tech opportunities to analyze how Big Data is becoming economically feasible for health care. These slides describe how the cost of sensors, data processing, data storage and data analyzing are falling, how new and better forms of storage and algorithms are being implemented, and what this means for sustainable health care. These changes are enabling a move towards personalized health care.
Are you tired of saying “no” when it comes to data? IDC and Talend share insights into how you can deliver data governance with a “yes”.
The reliability of data, and your company’s reputation for protecting it, have become essential to doing business in the data age. Modern data governance works at the speed of business, the scale of data, and still has a human touch so you can say “yes” and deliver trusted data.
WHAT IS A DATA LAKE? Know DATA LAKES & SALES ECOSYSTEMRajaraj64
As the name suggests, data lake is a large reservoir of data – structured or unstructured, fed through disparate channels. The data is fed through channels in anad-hoc manner into these data lakes, however, owing to the predefined set of rules orschema, correlation between the database is established automatically to help with the extraction of meaningful information.
For more information visit:- https://bit.ly/3lMLD1h
How to understand trends in the data & software marketmark madsen
The big challenge most analytics and IT professionals face today is dealing with complexity. Trends are still not clear. It helps to look at the past and current state to understand what’s really happening in the data technology market – a whole lot of reinvention and some innovation, but not where you expect it.
We have the (well-understood) problems that we have, with their (well-understood) limitations and intractabilities.
We deal with them in the world in which they were first codified and framed. Paradigms (world views) change as a function of political, economic, technological, cultural, use and growth, however, and when the world changes we’ll have a criteria for framing not just the problems/shortcomings/intractabilities of the prior paradigm, but that paradigm itself.
At that point, however, it will have ceased to matter because we’ll be dealing with fundamentally new problems/shortcomings/intractabilities.
Discovering Big Data in the Fog: Why Catalogs MatterEric Kavanagh
The Briefing Room with Dr. Robin Bloor and Waterline Data
Good enterprise data can drive positive business outcomes. But if that data isn’t organized and accessible, information workers are left with an incomplete picture. Knowing the location, lineage and permissions of data across the enterprise can lead to more accurate and insightful searches, and ultimately, knowledge discovery.
Register for this episode of The Briefing Room to learn from veteran Analyst Dr. Robin Bloor as he discusses how the success of big data projects relies on understanding your data. He’ll be briefed by Todd Goldman and Mohan Sadashiva of Waterline Data, who will explain how their solution can facilitate discovery via automation and crowd sourcing. They’ll demonstrate how combining the value of tribal knowledge with rationalized data can enable self-service analytics, improve data governance, and reduce data redundancy.
Big Data Expo 2015 - Trillium software Big Data and the Data QualityBigDataExpo
Successful Big Data initiatives rely on accurate, complete data, but the information they draw on is often not validated when it enters an organization. In this session we will look at the challenges big data brings to an organization, and how data quality principles are adapting to ensure business goals and return on investments in big data are realised. We will cover:
- Challenges of big data
- Turning data lakes into reservoirs
- How data quality tools are adapting
- Why data governance disciplines remain crucial
Dark Data: A Data Scientists Exploration of the Unknown by Rob Witoff PyData ...PyData
Modern Data Science is enabling NASA's engineers uncover actionable information from our "dark" data coffers. From starting small to operating at scale, Rob will discuss applications in telemetry, workforce analytics and liberating data from the Mars Rovers. Tools include iPython, Pandas, Boto and more.
Assumptions about Data and Analysis: Briefing room webcast slidesmark madsen
In many ways, moving data is like moving furniture: it's an unpleasant process dubbed an occasional necessary evil. But as the data pipelines of old decay, a new reality is taking shape: the data-native architecture. Unlike traditional data processing for BI and Analytics, this approach works on data right where it lives, thus eliminating the pain of forklifting, narrowing the margin of error, and expediting the time to business benefit. The new architecture embodies new assumptions, some of which we will talk about here.
Register for this episode of The Briefing Room to hear veteran Analyst Mark Madsen of Third Nature explain why this shift is truly tectonic. He'll be briefed by Steve Wooledge of Arcadia Data who will showcase his company's technology, which leverages a data-native architecture to fuel rapid-fire visualization and analysis of both big data and small.
Big Data LDN 2018: FROM PROLIFERATION TO PRODUCTIVITY: MACHINE LEARNING DATA ...Matt Stubbs
Date: 14th November 2018
Location: Self-Service Analytics Theatre
Time: 13:50 - 14:20
Speaker: Stephanie McReynolds
Organisation: Alation
About: Raw data is proliferating at an enormous rate. But so are our derived data assets - hundreds of dashboards, thousands of reports, millions of transformed data sets. Self-service analytics have ensured that this noise is making it increasingly hard to understand and trust data for decision-making. This trust gap is holding your organisation back from business outcomes.
European analytics leaders have found a way to close the gap between data and decision-making. From MunichRe to Pfizer and Daimler, analytics teams are adopting data catalogues for thousands of self-service analytics users.
Join us in this session to hear how data catalogues that activate data by incorporating machine learning can:
• Increase analyst productivity 20-40%
• Boost the understanding of the nuances of data and
• Establish trust in data-driven decisions with agile stewardship
Caserta Concepts, Datameer and Microsoft shared their combined knowledge and a use case on big data, the cloud and deep analytics. Attendes learned how a global leader in the test, measurement and control systems market reduced their big data implementations from 18 months to just a few.
Speakers shared how to provide a business user-friendly, self-service environment for data discovery and analytics, and focus on how to extend and optimize Hadoop based analytics, highlighting the advantages and practical applications of deploying on the cloud for enhanced performance, scalability and lower TCO.
Agenda included:
- Pizza and Networking
- Joe Caserta, President, Caserta Concepts - Why are we here?
- Nikhil Kumar, Sr. Solutions Engineer, Datameer - Solution use cases and technical demonstration
- Stefan Groschupf, CEO & Chairman, Datameer - The evolving Hadoop-based analytics trends and the role of cloud computing
- James Serra, Data Platform Solution Architect, Microsoft, Benefits of the Azure Cloud Service
- Q&A, Networking
For more information on Caserta Concepts, visit our website: http://casertaconcepts.com/
Augmented analytics will push the analytics adoptionPolestarsolutions
The world of data analytics is no longer restricted to data scientists, IT, and analysts. Augmented analytics combines the best aspects of ML and human curiosity to assist users get quicker insights, consider data from unique angles, increase productivity and assist users of all skill levels to make smarter decisions based on AI analytics.
Big data is used for structured, unstructured and semi-structured large volume of data which is difficult to
manage and costly to store. Using explanatory analysis techniques to understand such raw data, carefully
balance the benefits in terms of storage and retrieval techniques is an essential part of the Big Data. The
research discusses the Map Reduce issues, framework for Map Reduce programming model and
implementation. The paper includes the analysis of Big Data using Map Reduce techniques and identifying
a required document from a stream of documents. Identifying a required document is part of the security in
a stream of documents in the cyber world. The document may be significant in business, medical, social, or
terrorism.
When you list your company, you are allowing the shares of your company to be publicly traded. This means that these shares can either be bought or sold by any investors including existing shareholders. Just like in any marketplace, each of these shares will have a value or a reference price attached to it.. Hence, by listing your company, you will be able to know the market value of your company. This creation of a public market for your shares gives you an opportunity to unlock the value of your company and realise your investments.
WHAT IS A DATA LAKE? Know DATA LAKES & SALES ECOSYSTEMRajaraj64
As the name suggests, data lake is a large reservoir of data – structured or unstructured, fed through disparate channels. The data is fed through channels in anad-hoc manner into these data lakes, however, owing to the predefined set of rules orschema, correlation between the database is established automatically to help with the extraction of meaningful information.
For more information visit:- https://bit.ly/3lMLD1h
How to understand trends in the data & software marketmark madsen
The big challenge most analytics and IT professionals face today is dealing with complexity. Trends are still not clear. It helps to look at the past and current state to understand what’s really happening in the data technology market – a whole lot of reinvention and some innovation, but not where you expect it.
We have the (well-understood) problems that we have, with their (well-understood) limitations and intractabilities.
We deal with them in the world in which they were first codified and framed. Paradigms (world views) change as a function of political, economic, technological, cultural, use and growth, however, and when the world changes we’ll have a criteria for framing not just the problems/shortcomings/intractabilities of the prior paradigm, but that paradigm itself.
At that point, however, it will have ceased to matter because we’ll be dealing with fundamentally new problems/shortcomings/intractabilities.
Discovering Big Data in the Fog: Why Catalogs MatterEric Kavanagh
The Briefing Room with Dr. Robin Bloor and Waterline Data
Good enterprise data can drive positive business outcomes. But if that data isn’t organized and accessible, information workers are left with an incomplete picture. Knowing the location, lineage and permissions of data across the enterprise can lead to more accurate and insightful searches, and ultimately, knowledge discovery.
Register for this episode of The Briefing Room to learn from veteran Analyst Dr. Robin Bloor as he discusses how the success of big data projects relies on understanding your data. He’ll be briefed by Todd Goldman and Mohan Sadashiva of Waterline Data, who will explain how their solution can facilitate discovery via automation and crowd sourcing. They’ll demonstrate how combining the value of tribal knowledge with rationalized data can enable self-service analytics, improve data governance, and reduce data redundancy.
Big Data Expo 2015 - Trillium software Big Data and the Data QualityBigDataExpo
Successful Big Data initiatives rely on accurate, complete data, but the information they draw on is often not validated when it enters an organization. In this session we will look at the challenges big data brings to an organization, and how data quality principles are adapting to ensure business goals and return on investments in big data are realised. We will cover:
- Challenges of big data
- Turning data lakes into reservoirs
- How data quality tools are adapting
- Why data governance disciplines remain crucial
Dark Data: A Data Scientists Exploration of the Unknown by Rob Witoff PyData ...PyData
Modern Data Science is enabling NASA's engineers uncover actionable information from our "dark" data coffers. From starting small to operating at scale, Rob will discuss applications in telemetry, workforce analytics and liberating data from the Mars Rovers. Tools include iPython, Pandas, Boto and more.
Assumptions about Data and Analysis: Briefing room webcast slidesmark madsen
In many ways, moving data is like moving furniture: it's an unpleasant process dubbed an occasional necessary evil. But as the data pipelines of old decay, a new reality is taking shape: the data-native architecture. Unlike traditional data processing for BI and Analytics, this approach works on data right where it lives, thus eliminating the pain of forklifting, narrowing the margin of error, and expediting the time to business benefit. The new architecture embodies new assumptions, some of which we will talk about here.
Register for this episode of The Briefing Room to hear veteran Analyst Mark Madsen of Third Nature explain why this shift is truly tectonic. He'll be briefed by Steve Wooledge of Arcadia Data who will showcase his company's technology, which leverages a data-native architecture to fuel rapid-fire visualization and analysis of both big data and small.
Big Data LDN 2018: FROM PROLIFERATION TO PRODUCTIVITY: MACHINE LEARNING DATA ...Matt Stubbs
Date: 14th November 2018
Location: Self-Service Analytics Theatre
Time: 13:50 - 14:20
Speaker: Stephanie McReynolds
Organisation: Alation
About: Raw data is proliferating at an enormous rate. But so are our derived data assets - hundreds of dashboards, thousands of reports, millions of transformed data sets. Self-service analytics have ensured that this noise is making it increasingly hard to understand and trust data for decision-making. This trust gap is holding your organisation back from business outcomes.
European analytics leaders have found a way to close the gap between data and decision-making. From MunichRe to Pfizer and Daimler, analytics teams are adopting data catalogues for thousands of self-service analytics users.
Join us in this session to hear how data catalogues that activate data by incorporating machine learning can:
• Increase analyst productivity 20-40%
• Boost the understanding of the nuances of data and
• Establish trust in data-driven decisions with agile stewardship
Caserta Concepts, Datameer and Microsoft shared their combined knowledge and a use case on big data, the cloud and deep analytics. Attendes learned how a global leader in the test, measurement and control systems market reduced their big data implementations from 18 months to just a few.
Speakers shared how to provide a business user-friendly, self-service environment for data discovery and analytics, and focus on how to extend and optimize Hadoop based analytics, highlighting the advantages and practical applications of deploying on the cloud for enhanced performance, scalability and lower TCO.
Agenda included:
- Pizza and Networking
- Joe Caserta, President, Caserta Concepts - Why are we here?
- Nikhil Kumar, Sr. Solutions Engineer, Datameer - Solution use cases and technical demonstration
- Stefan Groschupf, CEO & Chairman, Datameer - The evolving Hadoop-based analytics trends and the role of cloud computing
- James Serra, Data Platform Solution Architect, Microsoft, Benefits of the Azure Cloud Service
- Q&A, Networking
For more information on Caserta Concepts, visit our website: http://casertaconcepts.com/
Augmented analytics will push the analytics adoptionPolestarsolutions
The world of data analytics is no longer restricted to data scientists, IT, and analysts. Augmented analytics combines the best aspects of ML and human curiosity to assist users get quicker insights, consider data from unique angles, increase productivity and assist users of all skill levels to make smarter decisions based on AI analytics.
Big data is used for structured, unstructured and semi-structured large volume of data which is difficult to
manage and costly to store. Using explanatory analysis techniques to understand such raw data, carefully
balance the benefits in terms of storage and retrieval techniques is an essential part of the Big Data. The
research discusses the Map Reduce issues, framework for Map Reduce programming model and
implementation. The paper includes the analysis of Big Data using Map Reduce techniques and identifying
a required document from a stream of documents. Identifying a required document is part of the security in
a stream of documents in the cyber world. The document may be significant in business, medical, social, or
terrorism.
When you list your company, you are allowing the shares of your company to be publicly traded. This means that these shares can either be bought or sold by any investors including existing shareholders. Just like in any marketplace, each of these shares will have a value or a reference price attached to it.. Hence, by listing your company, you will be able to know the market value of your company. This creation of a public market for your shares gives you an opportunity to unlock the value of your company and realise your investments.
A detailed understanding about the technology and its implementations in various sports along with its limitations in Cricket. Comment your queries and mail me if you want to discuss upon this with me at abhinaybandaru@hotmail.com
Les chatbots, ces robots de conversation gèrent les messageries de Booking ou Expedia. Messenger de Facebook mute en solution business et nombre d’agences web renforcent la relation client avec une fonction chats en ligne pilotée par le prestataire. Du live avec une personnalisation du contact !
Humain ou chatbots, quels avantages, inconvénients et précautions ?
Comment développer sa relation client via les chats ?
Pourquoi le chat est une brique supplémentaire pour sa distribution ?
Quels messages diffuser sur les chats ?
Messenger est-il le Graal promis ?
THEME : Relation client – IA – Chat – Innovation Salle : Salle Estérel
ANIMATEUR : Thomas Yung (Artiref) CIBLE : Entreprise
INTERVENANTS :
► Guillaume POULAIN – (TOM TRAVEL ON MOVE)
► Diane BONHOMME – Area Manager(Expedia Inc)
► Martin SOLERS
We’re in the difficult middle years of the information age, where a nexus of factors like cheap storage, rich HD media, ubiquitous connectivity and more sophisticated SaaS products are generating more data than we can affordably store or meaningfully process.
Data observability is a collection of technologies and activities that allows data science teams to prevent problems from becoming severe business issues.
Architecting a Data Platform For Enterprise Use (Strata NY 2018)mark madsen
Building a data lake involves more than installing Hadoop or putting data into AWS. The goal in most organizations is to build multi-use data infrastructure that is not subject to past constraints. This tutorial covers design assumptions, design principles, and how to approach the architecture and planning for multi-use data infrastructure in IT.
Long:
The goal in most organizations is to build multi-use data infrastructure that is not subject to past constraints. This session will discuss hidden design assumptions, review design principles to apply when building multi-use data infrastructure, and provide a reference architecture to use as you work to unify your analytics infrastructure.
The focus in our market has been on acquiring technology, and that ignores the more important part: the larger IT landscape within which this technology lives and the data architecture that lies at its core. If one expects longevity from a platform then it should be a designed rather than accidental architecture.
Architecture is more than just software. It starts from use and includes the data, technology, methods of building and maintaining, and organization of people. What are the design principles that lead to good design and a functional data architecture? What are the assumptions that limit older approaches? How can one integrate with, migrate from or modernize an existing data environment? How will this affect an organization's data management practices? This tutorial will help you answer these questions.
Topics covered:
* A brief history of data infrastructure and past design assumptions
* Categories of data and data use in organizations
* Data architecture
* Functional architecture
* Technology planning assumptions and guidance
This talk is an introduction to Data Science. It explains Data Science from two perspectives - as a profession and as a descipline. While covering the benefits of Data Science for business, It explaints how to get started for embracing data science in business.
Big Data is a concept that has become popular since 2012 to
express the exponential growth of the data to be processed.
These big data go beyond intuition and human analytical abilities. They require new tools to store, query, process and view information.
INTRODUCTION TO BIG DATA AND HADOOP
9
Introduction to Big Data, Types of Digital Data, Challenges of conventional systems - Web data, Evolution of analytic processes and tools, Analysis Vs reporting - Big Data Analytics, Introduction to Hadoop - Distributed Computing
Challenges - History of Hadoop, Hadoop Eco System - Use case of Hadoop – Hadoop Distributors – HDFS – Processing Data with Hadoop – Map Reduce.
8 Guiding Principles to Kickstart Your Healthcare Big Data ProjectCitiusTech
This white paper illustrates our experiences and learnings across multiple Big Data implementation projects. It contains a broad set of guidelines and best practices around Big Data management.
Big Data and BI Tools - BI Reporting for Bay Area Startups User GroupScott Mitchell
This presentation was presented at the July 8th 2014 user group meeting for BI Reporting for Bay Area Start Ups
Content - Creation Infocepts/DWApplications
Presented by: Scott Mitchell - DWApplications
Using Data Lakes to Sail Through Your Sales GoalsIrshadKhan682442
Using Data Lakes to Sail Through Your Sales Goals Most Popular Busting 5 Common CRM Myths Fail-Proof Ways to Hire A-Lister in Sales Our Recommendations Retail Redefined - Where does the innovation takes us?
To know more visit here: https://www.denave.com/resources/ebooks/using-data-lakes-to-sail-through-your-sales-goals/
The volume, variety, velocity and veracity of big data are getting increasingly complex
each passing day. The way the data is stored, processed, managed and shared with
decision-makers is getting impacted by this complexity and to tackle the same, a
revolutionary approach to data management has come into picture. A data lake.
Busting 5 Common CRM Myths Most Read Fail-Proof Ways to Hire A-Listers in Sales Fail-Proof Ways to Use Data Lakes to Achieve Your Sales Goals Recommendations from Us Where does innovation lead us with respect to retail redefined?
Paradigm4 Research Report: Leaving Data on the tableParadigm4
While Big Data enjoys widespread media coverage, not enough attention has been paid to what practitioners think — data scientists who manage and analyze massive volumes of data. We wanted to know, so Paradigm4 teamed up with Innovation Enterprise to ask over 100 data scientists for their help separating Big Data hype from reality. What we learned is that data scientists face multiple challenges achieving their company’s analytical aspirations. The upshot is that businesses are leaving data — and money — on the table.
Gdpr CCPA Why Benchmarks of Billions of rows are as meaningful as compliance ...Steven Meister
GDPR/CCPA …, Fortune C Levels, What has been communicated to you is NO LONGER accurate. Data Compliance with your volumes is now viable! BigDataRevealed’s Architecture and Methodologies combined with the latest Spark & Apache, have broken the Compliance/Scalability Code. Billions of rows can now be processed for Compliance in minutes to hours. Video Benchmarks Spreadsheet & Demo = https://youtu.be/VTZ16LcgLmU
Gdpr ccpa steps to near as close to compliancy as possible with low risk of f...Steven Meister
How to become GDPR & CCPA Compliant. See the complete 5 page GDPR, CCPA Compliancy Plan
Here is the CCPA / GDPR 3 Day Training PowerPoint - https://www.slideshare.net/StevenMeister/ccpa-and-gdpr-three-day-training-with-actual-deliverables-and-the-whys-and-hows-to-do-so
847-440-4439 https://www.youtube.com/channel/UC3F-qrvOIOwDj4ZKBMmoTWA?view_as=subscriber
GDPR 16 page PPT Plan - https://www.slideshare.net/StevenMeister/gdpr-ccpa-automated-compliance-spark-java-application-features-and-functions-of-big-datarevealed-april-version-35
https://youtu.be/JGoQwoicUxw
Comprehensive Metadata Catalog Video for GDPR / CCPA - https://youtu.be/xryESgfzRcc
Gdpr ccpa automated compliance - spark java application features and functi...Steven Meister
GDPR – CCPA Automated Technology, 16 Page PowerPoint with Features, Functions, Architecture and our reasons for choosing them. Be on your way to compliance with Technology created with compliance as its goal. Expect to add years of development without technology built specifically for compliances, such as GDPR, CCPA, HIPAA and others.
After scrolling through this PowerPoint you will realize just what is required and be able to better estimate the efforts it will take for your company to meet these regulatory requirements with technology and then without technology.
Spend just 5-10 minutes that might save your company, and your Customers, all the negative ramifications of the inevitable 2 breaches a year a company can expect to suffer.
This PowerPoint covers the critical aspects and needs that are present in any project designed to meet regulatory requirements for GDPR, CCPA and many others.
Complete Channel of Videos on BigDataRevealed
https://www.youtube.com/watch?v=3rLcQF5Wsgc&list=UU3F-qrvOIOwDj4ZKBMmoTWA
847-440-4439
#CCPA #GDPR #Big Data #Data Compliance #PII #Facebook #Hadoop #AWS #Spark #IoT #California
GDPR, CCPA, Analytics & Big Data applications. Beta this Comprehensive Regulatory Compliance & Analytics Accelerator engine delivering results on laptops, servers & AWS / Clouds. Analytics and extensive Metadata Catalogs, assist companies in developing marketing strategies, increase profits, and understand their customers and Data Protection Regulations.
Steven Meister GDPR and Regulatory Compliance and Big Data Excelerator Profes...Steven Meister
Steven Meister Cover Letter and CV
My Expertise is in Data Regulatory Compliance like (EU GDPR), California Cyber Security and most every countries Data Privacy and Security Regulations and accelerating the building of Big Data Frameworks and platforms in Hadoop and AWS S3.
Recent Accomplishments: https://youtu.be/roPC1NSgRGg
https://youtu.be/nwwqZTY_6Gc https://youtu.be/ZcNGXR2eLT0
Privacy Assurance Initiative
Description:
Much has been written about the importance of adopting a consumer data privacy program that can withstand the scrutiny of regulators mindful of enforcing the General Data Protection Regulations as adopted in the European Community in 2018. Many have developed solutions that go to great lengths to protect consumer data that has been identified as falling within the guidance of GDPR. But few have devised the means of identifying the data housed within your four walls, within the cloud solutions you employ and within the platforms you employ to perform some functions of your commercial ventures that involve the use of consumer data.
GDPR BigDataRevealed Readiness Requirements and EvaluationSteven Meister
This GDPR methodology can evaluate your GDPR readiness. For those feeling GDPR ready, you may uncover complex issues often neglected. For those that have waited, you can gain knowledge providing for a more successful GDPR outcome.
https://youtu.be/uE4Q7u0LatU https://youtu.be/R37S9mIiVAk https://youtu.be/AQf3if7DnuM
Are you prepared for eu gdpr indirect identifiers? what are indirect identifi...Steven Meister
What is your solution for GDPR’s Indirect Identifiers? Many aren’t sure what they are and will probably be unsuccessful when attempting to become GDPR compliant. Allow me to explain.
As a software development manager, I must confess that the Discovery & Remediation of Indirect Identifiers was the most complex project I have managed in my 33 years in the industry.
First, let me explain what an Indirect Identifier is. According to the “Privacy Technical Assistance Center of the U.S. Department of Education, it means “Indirect identifiers include information that can be combined with other information to identify specific individuals, including, for example, a combination of gender, birth date, geographic indicator and other descriptors.”
I have listed 3 informative youtube videos on the eu gdprSteven Meister
I have listed 3, of what I consider very informative yet very different viewpoints on the EU GDPR and most definitely expressed differently by each set of presenters
Eu gdpr technical workflow and productionalization neccessary w privacy ass...Steven Meister
GDPR = General Data Protection Regulations or GDPR = Get Demand Payment Ready when your hacked or audited.
A Realistic project plan for GDPR Compliance. Another reality is the 95% not ready and even the 5% that say they are, will not like what they see in this plan in the hopes of becoming GDPR compliant.
There is just not enough time or people to get it done in the next 8 months and even if you had
2 years. This is a harsh reality and without the use of software technology and strict yet flexible, repeatable methodologies, it just won’t happen. Look at this Project plan of what needs to be done, do the math, see the complexity of data movement and code and programs needed then give us a call.
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Show drafts
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
1. The Intelligent Catalog for Your Data Lake
Congratulations, you have a data lake, and
you even have some successes using it
But your have heard of failures and you
are beginning to understand the
frustrations of others and your own
Business moves at a fast pace, but big data
is shackled by IT backlogs responsible to
create access views
This blocks using big data for the truly
important things, the surprises that shape
markets
2. Without BigDataRevealed With BigDataRevealed
Once data arrives in the big data
environment, it is stripped of the identity
information from other environments.
Worse, it is optimized for storage, meaning
that if you don’t carefully manage it, you
may never find the data again in your data
lake.
BigDataRevealed can accept, store and
manage metadata created in other
environments giving your Data Scientist an
enormous head start. Non-technicians can
add metadata to the environment.
For data without existing metadata,
BigDataRevealed provides fully automated
processes designed to identify critical data
and assist in cataloging all other data.
Your Data Lake
Your have heard the horror stories of data scientists spending their time just searching for the information
needed in their analysis. Let data scientists use their time productively so that managers can be armed with
relevant, accurate information. We believe we have a better way, the BigDataRevealed intelligent catalog.
3. Numbers Social
Those who need
to eradicate PII
A Data Scientist’s
Best Friend
A Compliance
Officer’s dream
A Product
Manager’s enabler
Legacy systems have stored
information with a fair amount of
creativity. Fields designed to hold
comments may have been used
for Credit Card or Social Security
numbers, maybe medical
diagnosis data or other personal
identity information.
Once you replicate this data into
Hadoop these fields become
exposed / lost and potentially
create huge liabilities for you and
your company.
BigDataRevealed's extensive
pattern matching capabilities scan
all fields in all rows to identify
these risks and provide a
mechanism to locate the
troublesome data..
Data Scientists require
unambiguous clean data in their
data lake if they are to create
meaningful and reliable analytics.
BigDataRevealed processes every
field in every row to insure data
scientist know exactly what is
contained in a file.
There is no possibility of
confusion or of overlooking
troublesome data that would
result if only every 10th row were
processed. Data Scientist’s are
freed to create their ‘magic’ by
knowing exactly what is in their
data lake.
Compliance is all about the
identification of problematic
patterns and repeat offenses of
the same patterns. Quickly and
completely findings these patterns
is critical.
BigDataRevealed allows you to
pre-schedule pattern detection
processing so that no file will
circumvent the process. The
scheduled pattern matching
capabilities of BigDataRevealed
give the compliance officer the
ability to track patterns and their
results, Now and over time,
delivering the Compliance Officer
with a roadmap and complete
view of the Compliance Officers
potential nightmares.
Product Managers are always looking for
an edge in the market, whether it comes
from social media, trade organizations or
other sources as well of course from
legacy data.
The ability to analyze patterns from
incoming streams gives product
managers the ability to make sense from
the chaos that exists in the marketplace,
and perhaps act on a pattern that much
faster than their competitor thus
decreasing the risk, harm and marketing
nightmares of an unexpected, untimely
hack.
How your business benefits from the intelligent catalog
4. The Data Swamp
The Managed Swamp Market Sourced Data, usage organized
The opportunistic Swamp
Information, where ever it is sourced, is
published to the big data environment for
analysis. Thousands of tables make the data to
big, each with a level of programming to make it
appear as it were a traditional table with context
provided by non-tabular information
Usage is sparse because no one can find
anything in the data swamp due to poor folder
naming standards and lack of File / Columnar
Naming
Using sentiment and proximity analysis of social
media, blogs and other sources to determine
whether the branding messages and
promotional data is resonating in the
marketplace.
High profile initiatives are destined to the big
data environment, where IT gets involved to
create views for long term opportunities.
The ability to use the big data environment for
time sensitive uses is limited by the backlog and
throughput of the team who creates views of the
big data environment for consumption by non-
technicians.
For patterns that are known or relationships
that are assured, the managed swamp can
provide answers or identify cues that market
sentiment is not supporting business as usual
assumptions.
For PII and fraud analyses, known patterns can
be researched to identify and remediate
anomalies.
Typical Drivers
Data Scientist
driven integration
Data Lineage
Information Usage
Resistance
Typical Drivers
Data Clutter
Cost
Complexity
IT
Typical Drivers
Pattern based
management
Centralization of
metadata aligned
to the data lake
Collaboration and
Open Metadata
Repository
Typical Drivers
Internal/External
Audits
Public Filings
Marketing
Campaigns
Competitive
Analysis
Which most typically represents your data lake?
Typically Data Swamps
Typical Intelligent Catalog Environments
Typical Big Data scenarios
TargetQuadrant
TypicalMatureEnvironmentTypicalCAODrivenQuadrant
Typically Late to Disruptions
Thrives in disruption, but not stable markets
OpportunisticQuadrant
5. Information Inventory
A dynamic information inventory, which constantly is in a state of flux
to meet the current competitive landscape of the business community
is required. BigDataRevealed, which provides an as exists catalog,
with all the metadata and rules that apply to the information at hand.
This prevents your dynamic data lake from becoming a swamp.
Workflow
There will be items that require research. BigDataRevealed
contains the workflow of items researched and an ability to
build cases of repeat offenses so that questionable items are
identified, eradicated, and do not become repeat offenses.
Streaming
You will have needs where the information that will make a
difference is not housed in your environment and changes at a
moments’ notice. Inclusion of streams of data is mandatory,
and the fact that BigDataRevealed is installed in the Hadoop
environment means it will be able to keep up with the streams.
Exactly when does BigDataRevealed help?
Your business climate changes regularly, so should your data lake
Information clutter without context is the biggest reason for the creation of data swamps
INFORMATION
INVENTORY
Workflow
6. CHANGE
VISION
CLUTTER
MAPPING
Because business process changes
rapidly, the mapping of information
will similarly change. This will
require information models to be
highly fluid & Metadata Driven.
Algorithms serve as early warnings
when processing information from
outside the organization. They serve
as triggers for action and patterns
such as PII regulatory violations.
Information no longer mapped is
clutter and will be either archived or
de-emphasized or removed from
the Data Lake.
The intelligent data catalog should
be the foundation for your data lake
as it ensures that your team can find
data in a highly fluid data lake.
THE NEW
ORDER
Getting your big data
house in order
In order to participate in
the digital age, the data
lake must stay in lock step
with business intents to
be relevant and valuable.
Your data lake should be dynamic
to support the current population
of issues being tackled.
BigDataRevealed’s Intelligent
catalog is the perfect solution for
managing the dynamic properties
of the data lake.
7. Assign a Data Analyst, Data Scientist along with a Privacy,
Compliance and Risk Officer or Consultant and a Data Steward
How to get started
Information only has value if it is utilized to
achieve a value proposition and valued end
solution. This could be monetary, market
share, loyalty, branding, auditable, predictable
and easily accessible and understood.
8. What is the Intelligent Data Catalog?
BigDataRevealed Hadoop Ecosystem Stored
Catalog / Metadata
Pattern
Folder / File
Column
Locations
Pattern(s)
with % found
Columnar
Metadata
naming with
User Metadata
Data Discovery
of Data
Completes and
integrity
Assist in
originating
Source Loading
of Data
Metadata Catalog
Store
Legacy IOT
Third
Party
Data
BI,
Predictive
Analytics, AI
Assist in
determining
masking and
zone
encryption
Lineage, Job
Status by
Users
Cataloguing and Metadata with History
9. Investigate and Discover Data
Discover patterns in the
data lake that impact
every facet of your
business operations
Data preparation accounts for about
80% of the work of data scientists
There will be a shortage of talent necessary for
organizations to take advantage of big data. By 2018, the
United States alone could face a shortage of 140,000 to
190,000 people with deep analytical skills as well as 1.5
million managers and analysts with the know-how to use
the analysis of big data to make effective
decisions. McKinsey & Company
10. Data preparation accounts for about
80% of the work of data scientists
Data scientists spend 60% of
their time on cleaning and
organizing data. Collecting
data sets comes second at
19% of their time, meaning
data scientists spend around
80% of their time on
preparing and managing
data for analysis. Forbes
Article
The misalignment and clutter issues waste much
of the precious time for critical decisions
Need proof of the misalignment?
11. Native
Hadoop
File
System
HBASE
Hive or
Impala
MAPREDUCE
Hadoop
Components
Hive
Spark
Pig
Drill
Analytic Libraries
Impala
TIKA
Hbase
Spark Stream
D3.JS Interactive GUI
BigDataRevealed
Callable Java Modules
Map/Reduce, Spark,
NLP, Deep Learning
Externalized Callable
Modules
Departmentalized
BDR-Apache-VMWare
CLOUD
MySQL
Databases
(Oracle, DB2, SQL)
Teradata
BDR Architecture - Powered with Apache™ Hadoop®
BDR Lineage
( For the technicians, how we reside in the data lake )
KERBEROSSECURITYFRAMEWORK
APIFRAMEWORK
BDR
EXECUTABLES
BDR Workflow
BDR Metadata
BDR Alert
IntelligentCatalog
BDR Learning
KERBEROSSECURITYFRAMEWORK
APIFRAMEWORK
Optional VMWare
Portable
Environment
WIZARD
WIZARD
Rules
Engine &
Scheduler
Wizards
12. BigDataRevealed Discovers and Isolates Personally Identifiable & Potentially Risky(Outlier/Anomaly) information/data in your Hadoop ecosystem!
Q & A
BigDataRevealed Data Discovery for Big Data Hadoop
Steven Meister
CTO & Founder, BigDataRevealed
steven.meister@bigdatarevealed.com
847-791-7838
www.bigdatarevealed.com
Content contributions by
by Mark Albala, President, InfoSight Partners
mark@infoSightPartners.com
(201) 895-1666
Editor's Notes
BigDataRevealed Technical Overview. Shows BDR Architecture resides in the Hadoop Eco-System and Framework.