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)

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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.

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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

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The data explosion meets the ever reduction
in per unit costs for computing capabilities

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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.

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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

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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

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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

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Driving towards a culture of data driven decisions
requires a foundation based on strong skills

12. Confidential and proprietary. Copyright © 2012 Teradata Corporation.12
AUDIO & VIDEO WITSML TEXT DTS/DAS LOGS GIS MRO ERP
TERADATA UNIFIED DATA ARCHITECTURE
Reservoir Engineers
Production Engineers
Asset Managers
OperatorsCustomers / PartnersGeologist
HSEMaintenance
CAPTURE | STORE | REFINE
Big Data
Management
LANGUAGES MATH & STATS DATA MINING BUSINESS INTELLIGENCE APPLICATIONS
DISCOVERY
PLATFORM
INTEGRATED
DATA WAREHOUSE
Big Data
Analytics

13. Confidential and proprietary. Copyright © 2012 Teradata Corporation.13
Business
Analyst
Discovery
Platform
Discovery
Big Data Discovery Platform Requirements
Data Sources
Structured
Data
Multi-
Structured
Data
Non
relational
Data
OLTP
DBMS’s
Users
Data
Scientist
SQL
MapReduce
Statistical
Functions
Discovery Tools
• Structured and multi-
structured data
• Doesn’t require
extensive data
modeling
• Doesn’t balance the
books
• Data completeness
can be good enough
• No stringent SLA’s
Possible Analytics
• Path to Machine Failure
• Historical Well Behavior
• Optimization Analytics
• Machine data/process
optimization

14. Confidential and proprietary. Copyright © 2012 Teradata Corporation.14
Discovery Analytics Visualization Options
• A visualization tool built upon Aster’s
SQL-MapReduce framework.
• Browser-based visualizations are
produced using result sets from popular
Aster operators such as nPath and cFilter.
• Visual SQL-MR functions are perfect for
visualizing path & pattern and graph for
iterative analysis.
• Connect to the Aster platform using
ODBC.
• Aster’s relational output is easily fed into
and visualized by these partner tools (and
other standard BI Tools).
• Great for visualizing query results and for
interactive reporting.
Visual SQL-MapReduce® Functions Traditional BI Tools

15. Confidential and proprietary. Copyright © 2012 Teradata Corporation.15
• Deliver valuable insight to
lines of business resulting
from deep analysis of all of
your data, all of the time
Uncover New Insights In Your Business
Fraudulent Paths
Golden Path to Application Submit
Paths To Attrition

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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)

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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…….

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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…….

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Opportunities in the mining world
Production, Processing, Logistics, Distribution, Core/Drilling,
Exploration, Social responsibility…….

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Opportunities in the mining world
Production, Processing, Logistics, Distribution, Core/Drilling,
Exploration, Social responsibility…….

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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.