Newmont Mining Corporation is one of the world's largest gold producers, with operations across several countries. It is collecting increasing amounts of data from sources like sensors, mobile devices, and social media. This "big data" presents opportunities to improve areas like asset management, logistics, and understanding social impacts. However, big data projects require the right skills and must be driven by business needs rather than technology alone. Newmont will need data scientists, technical skills, and partnerships to successfully harness big data analytics.
Europe needs a clear strategy for leveraging Big Data
Economy in Europe. Our objectives are work at technical, business and policy levels, shaping the future through the positioning of Big Data in Horizon 2020. Bringing the necessary stakeholders into a sustainable industry-led initiative, which will greatly contribute to enhance the EU competitiveness taking full advantage of Big Data technologies.
Big Data Analytics: A New Business OpportunityEdward Curry
This talk introduces Big Data analytics and how they can be used to deliver value within organisations. The talk will cover the transformational potential of creating data value chains between different sectors. Developing a Big Data analytics capability will be discussed in addition to the challenges facing the emerging data economy.
Collaborative Data Management: How Crowdsourcing Can Help To Manage DataEdward Curry
Data management efforts such as MDM are a popular approach for high quality enterprise data. However, MDM can be heavily centralized and labour intensive, where the cost and effort can become prohibitively high. The concentration of data management and stewardship onto a few highly skilled individuals, like developers and data experts, can be a significant bottleneck. This talk explores how to effectively involving a wider community of users within collaborative data management activities. The bottom-up approach of involving crowds in the creation and management of data has been demonstrated by projects like Freebase, Wikipedia, and DBpedia. The talk is discusses how collaborative data management can be applied within an enterprise context using platforms such as Amazon Mechanical Turk, Mobile Works, and internal enterprise human computation platforms.
Topics covered include:
- Introduction to Crowdsourcing and Human Computation for Data Management
- Crowds vs. Communities, When to use them and why
- Push vs. Pull methods of crowdsourcing data management
- Setting up and running a collaborative data management process
- Modelling the expertise of communities
Europe needs a clear strategy for leveraging Big Data
Economy in Europe. Our objectives are work at technical, business and policy levels, shaping the future through the positioning of Big Data in Horizon 2020. Bringing the necessary stakeholders into a sustainable industry-led initiative, which will greatly contribute to enhance the EU competitiveness taking full advantage of Big Data technologies.
Big Data Analytics: A New Business OpportunityEdward Curry
This talk introduces Big Data analytics and how they can be used to deliver value within organisations. The talk will cover the transformational potential of creating data value chains between different sectors. Developing a Big Data analytics capability will be discussed in addition to the challenges facing the emerging data economy.
Collaborative Data Management: How Crowdsourcing Can Help To Manage DataEdward Curry
Data management efforts such as MDM are a popular approach for high quality enterprise data. However, MDM can be heavily centralized and labour intensive, where the cost and effort can become prohibitively high. The concentration of data management and stewardship onto a few highly skilled individuals, like developers and data experts, can be a significant bottleneck. This talk explores how to effectively involving a wider community of users within collaborative data management activities. The bottom-up approach of involving crowds in the creation and management of data has been demonstrated by projects like Freebase, Wikipedia, and DBpedia. The talk is discusses how collaborative data management can be applied within an enterprise context using platforms such as Amazon Mechanical Turk, Mobile Works, and internal enterprise human computation platforms.
Topics covered include:
- Introduction to Crowdsourcing and Human Computation for Data Management
- Crowds vs. Communities, When to use them and why
- Push vs. Pull methods of crowdsourcing data management
- Setting up and running a collaborative data management process
- Modelling the expertise of communities
Big Data and Hadoop Training batch in Pune is scheduled to commence on December 7th, 2013.This batch will be as per a new revamped four day schedule, contents and focus, based on feedback from participants of earlier courses. The training is conducted in a workshop like environment with an effective blend of hands-on practicals and assignments to augment the fundamental theory covered.
About the Faculty:
He is a Doctorate in Engineering and an industry veteran with more than twenty five years experience in launching new technologies, products and businesses. He has been involved in acquiring five patents for the company that he has worked for.
Big Data Analytics – Why?
Data is now generated by more sources and at ever increasing rates. Examples include Social Media sites, GPS based tracking systems, point of sale equipment, etc. The ability to process such data can provide that essential edge required for business success. Demand for Big Data professionals is rapidly increasing. Knowledge of Big Data can provide an advantage leading to faster professional advancement
About this course
This course on Big Data Analytics for Business is a combination of essential fundamentals, practical techniques, hands-on sessions on Hadoop, and case studies to cement all this together.
By completing this course you will be able to …
Understand fundamentals of analytics: Descriptive, Predictive and Prescriptive Analytics
Know what ‘Big Data’, Map Reduce and Hadoop are all about
Get a grip on the structure of Big Data applications
Effectively use Big Data techniques like Map Reduce and tools like Hadoop, Hive, Hbase, Pig
Choose the most appropriate tools to solve Big Data problems
Identify, propose and lead Big Data projects in your organizations
Course Content -
What is Big Data?
Overview of Big Data tools and techniques
In-depth coverage of Map-reduce techniques to manage Big Data
Hadoop - In Depth
HDFS – In Depth
Installing and managing Hadoop – Hands-on
Introduction to Hadoop Clusters
Hands-on session using native installation and Amazon EMR implementation of Hadoop
The Hadoop ecosystem: Pig, HIVE, HBase, Pig, SQOOP and Flume
Analytics: Descriptive, Predictive and Prescriptive
What is Big Data Analytics
Introducing Analytics in the enterprise: Case Studies
Trends in Big Data Analytics
The course takes a "hands-on" approach to ensure that the basics are understood very well and assimilated concepts are applied in practice.
Essential pre-requisite for practitioner course: Java programming language.
Note: Basic Java Module for participants those who are new to Java.
A top-down look at current industry and technology trends for Big Data, Data Analytics and Machine Learning (cognitive technologies, AI etc.). New slides added for Ark Group presentation on 1st December 2016.
Big data characteristics, value chain and challengesMusfiqur Rahman
Abstract—Recently the world is experiencing an deluge of
data from different domains such as telecom, healthcare
and supply chain systems. This growth of data has led to
an explosion, coining the term Big Data. In addition to the
growth in volume, Big Data also exhibits other unique
characteristics, such as velocity and variety. This large
volume, rapidly increasing and verities of data is becoming
the key basis of completion, underpinning new waves of
productivity growth, innovation and customer surplus. Big
Data is about to offer tremendous insight to the
organizations, but the traditional data analysis
architecture is not capable to handle Big Data. Therefore,
it calls for a sophisticated value chain and proper analytics
to unearth the opportunity it holds. This research
identifies the characteristics of Big Data and presents a
sophisticated Big Data value chain as finding of this
research. It also describes the typical challenges of Big
Data, which are required to be solved. As a part of this
research twenty experts from different industries and
academies of Finland were interviewed.
From Data Platforms to Dataspaces: Enabling Data Ecosystems for Intelligent S...Edward Curry
The Real-time Linked Dataspace (RLD) is an enabling platform for data management for intelligent systems within smart environments that combines the pay-as-you-go paradigm of dataspaces, linked data, and knowledge graphs with entity-centric real-time query capabilities.
The RLD contains all the relevant information within a data ecosystem including things, sensors, and data sources and has the responsibility for managing the relationships among these participants.
It manages sources without presuming a pre-existing semantic integration among them using specialised dataspace support services for loose administrative proximity and semantic integration for event and stream systems. Support services leverage approximate and best-effort techniques and operate under a 5 star model for “pay-as-you-go” incremental data management.
Big Data: Industry trends and key playersCM Research
Big data is data that cannot be analysed on a traditional database. Companies that develop the database platforms to analyse big data will make a fortune. This report looks at industry trends and the key players in this emerging industry.
Big Data and Hadoop Training batch in Pune is scheduled to commence on December 7th, 2013.This batch will be as per a new revamped four day schedule, contents and focus, based on feedback from participants of earlier courses. The training is conducted in a workshop like environment with an effective blend of hands-on practicals and assignments to augment the fundamental theory covered.
About the Faculty:
He is a Doctorate in Engineering and an industry veteran with more than twenty five years experience in launching new technologies, products and businesses. He has been involved in acquiring five patents for the company that he has worked for.
Big Data Analytics – Why?
Data is now generated by more sources and at ever increasing rates. Examples include Social Media sites, GPS based tracking systems, point of sale equipment, etc. The ability to process such data can provide that essential edge required for business success. Demand for Big Data professionals is rapidly increasing. Knowledge of Big Data can provide an advantage leading to faster professional advancement
About this course
This course on Big Data Analytics for Business is a combination of essential fundamentals, practical techniques, hands-on sessions on Hadoop, and case studies to cement all this together.
By completing this course you will be able to …
Understand fundamentals of analytics: Descriptive, Predictive and Prescriptive Analytics
Know what ‘Big Data’, Map Reduce and Hadoop are all about
Get a grip on the structure of Big Data applications
Effectively use Big Data techniques like Map Reduce and tools like Hadoop, Hive, Hbase, Pig
Choose the most appropriate tools to solve Big Data problems
Identify, propose and lead Big Data projects in your organizations
Course Content -
What is Big Data?
Overview of Big Data tools and techniques
In-depth coverage of Map-reduce techniques to manage Big Data
Hadoop - In Depth
HDFS – In Depth
Installing and managing Hadoop – Hands-on
Introduction to Hadoop Clusters
Hands-on session using native installation and Amazon EMR implementation of Hadoop
The Hadoop ecosystem: Pig, HIVE, HBase, Pig, SQOOP and Flume
Analytics: Descriptive, Predictive and Prescriptive
What is Big Data Analytics
Introducing Analytics in the enterprise: Case Studies
Trends in Big Data Analytics
The course takes a "hands-on" approach to ensure that the basics are understood very well and assimilated concepts are applied in practice.
Essential pre-requisite for practitioner course: Java programming language.
Note: Basic Java Module for participants those who are new to Java.
A top-down look at current industry and technology trends for Big Data, Data Analytics and Machine Learning (cognitive technologies, AI etc.). New slides added for Ark Group presentation on 1st December 2016.
Big data characteristics, value chain and challengesMusfiqur Rahman
Abstract—Recently the world is experiencing an deluge of
data from different domains such as telecom, healthcare
and supply chain systems. This growth of data has led to
an explosion, coining the term Big Data. In addition to the
growth in volume, Big Data also exhibits other unique
characteristics, such as velocity and variety. This large
volume, rapidly increasing and verities of data is becoming
the key basis of completion, underpinning new waves of
productivity growth, innovation and customer surplus. Big
Data is about to offer tremendous insight to the
organizations, but the traditional data analysis
architecture is not capable to handle Big Data. Therefore,
it calls for a sophisticated value chain and proper analytics
to unearth the opportunity it holds. This research
identifies the characteristics of Big Data and presents a
sophisticated Big Data value chain as finding of this
research. It also describes the typical challenges of Big
Data, which are required to be solved. As a part of this
research twenty experts from different industries and
academies of Finland were interviewed.
From Data Platforms to Dataspaces: Enabling Data Ecosystems for Intelligent S...Edward Curry
The Real-time Linked Dataspace (RLD) is an enabling platform for data management for intelligent systems within smart environments that combines the pay-as-you-go paradigm of dataspaces, linked data, and knowledge graphs with entity-centric real-time query capabilities.
The RLD contains all the relevant information within a data ecosystem including things, sensors, and data sources and has the responsibility for managing the relationships among these participants.
It manages sources without presuming a pre-existing semantic integration among them using specialised dataspace support services for loose administrative proximity and semantic integration for event and stream systems. Support services leverage approximate and best-effort techniques and operate under a 5 star model for “pay-as-you-go” incremental data management.
Big Data: Industry trends and key playersCM Research
Big data is data that cannot be analysed on a traditional database. Companies that develop the database platforms to analyse big data will make a fortune. This report looks at industry trends and the key players in this emerging industry.
Autoradio gps dvd kia forte 2009 avec ecranradiovoiture
Description rapide
Autoradio DVD GPS pour KIA FORTE 2009 compatible GPS/VCD/CD/MP3/MPEG4/DIVX/CD-R/USB/SD/WMA/JPEG/Ipod avec Ecran tactile et les fonctions TV et radio AM/FM intégrées.
JIMS IT Flash , a monthly newsletter-An Initiative by the students of IT Department, shares the knowledge to its readers about the latest IT Innovations, Technologies and News.Your suggestions, thoughts and comments about latest in IT are always welcome at itflash@jimsindia.org.
Visit Website : http://jimsindia.org/
IBM Watson Data Platform is an integrated platform of tools, services and data that helps companies accelerate their shift to become data-driven organizations. It is the IBM public cloud foundation designed to support the data and analytics vision of whole enterprises, delivering a fully integrated platform that sustains both analytical investigations and putting insights into active use in production at any scale. It delivers the user experiences that amplify the ability of every data professional to execute on that vision, allowing teams such as data scientists, developers and business analysts to work together across different languages and data models.
Watch full webinar here: https://bit.ly/2Y0vudM
What is Data Virtualization and why do I care? In this webinar we intend to help you understand not only what Data Virtualization is but why it's a critical component of any organization's data fabric and how it fits. How data virtualization liberates and empowers your business users via data discovery, data wrangling to generation of reusable reporting objects and data services. Digital transformation demands that we empower all consumers of data within the organization, it also demands agility too. Data Virtualization gives you meaningful access to information that can be shared by a myriad of consumers.
Register to attend this session to learn:
- What is Data Virtualization?
- Why do I need Data Virtualization in my organization?
- How do I implement Data Virtualization in my enterprise?
This Presentation is completely on Big Data Analytics and Explaining in detail with its 3 Key Characteristics including Why and Where this can be used and how it's evaluated and what kind of tools that we use to store data and how it's impacted on IT Industry with some Applications and Risk Factors
World’s 10 Best Data Integration Solution Providers 2022.pdfInsightsSuccess4
This edition features a handful of World’s 10 Best Data Integration sectors that are at the forefront of leading us into a digital future.
Read More: https://insightssuccess.com/worlds-10-best-data-integration-solution-providers-2022-june2022/
Data Pioneers - Roland Haeve (Atos Nederland) - Big data in organisatiesMultiscope
Roland Haeve is cross competence manager Big Data voor Atos Nederland. Roland heeft ruim 18 jaar ICT-ervaring in het aanbieden van complete oplossingen binnen onder andere Business Intelligence (BI) en Big Data (Analytics). Big Data is voor veel bedrijven nog pionieren en uitzoeken wat de mogelijkheden zijn. In zijn presentatie zal Roland ingaan op succesvolle Big Data cases. Hij zal hierbij niet enkel inzoomen op Nederland, maar ook bredere, Europese voorbeelden meenemen.
The Evolving Role of the Data Engineer - Whitepaper | QuboleVasu S
A whitepaper about how the evolving data engineering profession helps data-driven companies work smarter and lower cloud costs with Qubole.
https://www.qubole.com/resources/white-papers/the-evolving-role-of-the-data-engineer
Big Data Mining Keynote presentation Sept 2013 09012013
1. Harnessing Big Data and Analytics
Sept 24th, 2013
Julio Da Silva
Global IT Director of Enterprise Data Warehouse
2. Page 2
Overview of Newmont
Newmont Mining Corporation is primarily a gold producer, with significant assets
or operations in the United States, Australia, Peru, Indonesia, Ghana, New
Zealand and Mexico. Founded in 1921 and publicly traded since 1925, Newmont
is one of the world’s largest gold producers and is the only gold company included
in the S&P 500 Index and Fortune 500. Headquartered near Denver, Colorado, the
company has around 40,000 employees and contractors worldwide.
3. Page 3
It started with bits and bytes
Gigabyte (1 000 000 000 Bytes)
1 Gigabyte: A pickup truck filled with paper
Terabyte (1 000 000 000 000 Bytes)
10 Terabytes: The printed collection of the US Library of Congress
Petabyte (1 000 000 000 000 000 Bytes)
20 Petabytes: Production of hard-disk drives in 1995
Exabyte (1 000 000 000 000 000 000 Bytes)
5 Exabytes: All words ever spoken by human beings.
Zettabyte (1 000 000 000 000 000 000 000 Bytes)
4. Page 4
What is Big Data
Volume & Velocity
“From the dawn of civilization until 2003, humankind generated 5 Exabytes of data.
Now we produce 5 Exabytes every 2 days…. And the pace is accelerating.” Eric
Schmidt, executive chairman, Google.
5. Page 5
Where is this data coming from
- Variety
High speed networks (wireless and wired networks connecting):
Onboard computers on mobile equipment like trucks, shovels……
Sensors gathering data from: our high value production
machinery/equipment – trucks, shovels, conveyors, processing
RFID tags (people, shipments, inventory…..)
Mobile phones/tablets enabling greater collection of information (words,
photos, voice, video, gps….)
Social networks
Web 2.0 and collaborative solutions
Digitized Lab results
6. Page 6
The data explosion meets the ever reduction
in per unit costs for computing capabilities
7. Page 7
What does Nirvana in Big data look
like
You may have heard of IBM’s Watson…
Why Jeopardy?
The game of Jeopardy! makes great demands on its players – from the range of topical
knowledge covered to the nuances in language employed in the clues. The question IBM
had for itself was “is it possible to build a computer system that could process big data
and come up with sensible answers in seconds—so well that it could compete with
human opponents?”
A. What is the computer
system that played
against human
opponents on
“Jeopardy”…
and won.
8. Page 8
Getting started –
It starts with a vision & not technology
8
………..Be a data driven organization, making decisions that
drive industry leading performance……….
Guiding Principles:
1. Strategic and organization alignment (Top-Down)
2. Focus on Business Value
3. Trust the data quality
4. “Google” like speed to queries
5. Be easy to use
6. Be reliable
7. All at a low cost
9. Page 9
Driving towards a culture of data driven decisions
requires a foundation based on strong skills
Data Governance
Data Architecture
Data Integration/ETL
Reporting and Visualization
Business Analysts
10. Page 10
Driving towards a culture of data driven decisions
requires a foundation based on strong skills
Data Scientist
Data science seeks to use all available and relevant data to
effectively tell a story that can be easily understood by non-
practitioners
Incorporates varying elements and builds on techniques and
theories from many fields, including mathematics, statistics, data
engineering, pattern recognition and learning, advanced
computing, visualization, uncertainty modeling, data
warehousing, and high performance computing with the goal of
extracting meaning from data and creating data products
11. Page 11
Driving towards a culture of data driven decisions
requires a foundation based on strong skills
16. Page 16
Opportunities in the mining world
Production, Processing, Logistics, Distribution, Core/Drilling,
Exploration, Social responsibility…….
Asset Management Use case
Capture all sensor data & “black box” – structured and
unstructured
Capture all relevant ERP and transaction data
• ERP data (Work orders, Notifications, Preventative Maintenance schedules,
Equipment costs over life-time, future planned costs, Fleet/operations
performance data…...
Integrate production data plans
• Real-time reporting events
Condition Based monitoring systems and alerts leveraging a
Service Oriented Architecture
With integrated data one has the ability to navigate and analyze
“CONTEXT” in relation to the real-time event and make decisions.
“CONTEXT” = comparison data (view by equipment, site, region,
global); trends (History + planned), dependencies (production plans)
17. Page 17
Logistics Use case
Big data
• Capture all relevant ERP data (Inventory, purchasing, ..)
• Capture RFID data
• Capture Vendor data (Stock on hand, estimated duration to delivery…)
Real-time
• Stock-out can create alerts leveraging a Service Oriented Architecture
With integrated data one has the ability to navigate and analyze
“CONTEXT” in relation to the real-time event and make decisions.
“CONTEXT” = comparison data (view by site, region, global); trends
(history + planned), dependencies (maintenance orders, purchase
orders, shipments, reservations….)
Opportunities in the mining world
Production, Processing, Logistics, Distribution, Core/Drilling,
Exploration, Social responsibility…….
18. Page 18
Social responsibility Use case
Topsy, is a company based in San Francisco, that provides
analyses from Twitter postings (tweets).
There is also Social Relationship Management Software-as-a-
service (SaaS) technology that allows marketers to publish and
engage fans on social networks and customize a brand's look,
feel and message in an easy to use, scalable, and efficient
method.
The screen captures to follow show some examples of what is
possible in this space.
Opportunities in the mining world
Production, Processing, Logistics, Distribution, Core/Drilling,
Exploration, Social responsibility…….
19. Page 19
Opportunities in the mining world
Production, Processing, Logistics, Distribution, Core/Drilling,
Exploration, Social responsibility…….
20. Page 20
Opportunities in the mining world
Production, Processing, Logistics, Distribution, Core/Drilling,
Exploration, Social responsibility…….
21. Page 21
Summary
Big Data projects will fail if they are driven by technology. Companies that
have successful implementation of Big Data start with a strong Business
Case, and Big Data technology just happened to be used to solve the
business questions. This has to be a Business driven initiative.
Find your Data Scientists – Business resource who has ability to understand
data mining techniques and interpret predictive analyses
Enhance Technical Skills – IT resource who can develop with the Big Data
technologies; Hadoop, Aster, MapReduce………..
Search for SaaS solutions where possible. Extremely expensive to pioneer
new solutions and keep up with fast pace of change in this ever emerging
area.
Align with your local Universities to sponsor support Master/PHD programs
that will benefit both parties.