Big Data Trends - WorldFuture 2015 ConferenceDavid Feinleib
David Feinleib's Big Data Trends presentation from the World Future Society's Annual Conference, WorldFuture 2015, held at the Hilton Union Square, San Francisco, California July 25, 2015.
What is the impact of Big Data on Analytics from a Data Science perspective.
Presented at the Big Data and Analytics Summit 2014, Nasscom by Mamatha Upadhyaya.
Cutting Edge Predictive Analytics with Eric Siegel Databricks
Apache Spark empowers predictive analytics and machine learning by increasing the reach and potential. But, before jumping to new deployments, it’s critical we 1) get the analytics right and 2) not overlook less conspicuous business opportunities. In this keynote, Predictive Analytics World founder and “Predictive Analytics” author Eric Siegel ramps you up on a dangerous pitfall and a critical value proposition:
– PITFALL: Avoiding BS predictive insights, i.e., “bad science,” spurious discoveries
– OPPORTUNITY: Optimizing marketing persuasion by predicting the *influence* of marketing treatments, i.e., uplift modeling
Trends in Big Data & Business Challenges Experian_US
Join our #DataTalk on Thursdays at 5 p.m. ET. This week, we tweeted with Sushil Pramanick – who is the founder and president of the The Big Data Institute (TBDI).
You can learn about upcoming chats and see the archive of past big data tweetchats here
http://www.experian.com/blogs/news/about/datadriven
Creating $100 million from Big Data Analytics in BankingGuy Pearce
A sanitized version of our presentation to the Teradata Marketing Summit in Los Angeles in March 2014, on how we created $94.95 million in incremental value for a bank by means of a customer-centricity strategy enabled by Big Data and Analytics
Big Data Trends - WorldFuture 2015 ConferenceDavid Feinleib
David Feinleib's Big Data Trends presentation from the World Future Society's Annual Conference, WorldFuture 2015, held at the Hilton Union Square, San Francisco, California July 25, 2015.
What is the impact of Big Data on Analytics from a Data Science perspective.
Presented at the Big Data and Analytics Summit 2014, Nasscom by Mamatha Upadhyaya.
Cutting Edge Predictive Analytics with Eric Siegel Databricks
Apache Spark empowers predictive analytics and machine learning by increasing the reach and potential. But, before jumping to new deployments, it’s critical we 1) get the analytics right and 2) not overlook less conspicuous business opportunities. In this keynote, Predictive Analytics World founder and “Predictive Analytics” author Eric Siegel ramps you up on a dangerous pitfall and a critical value proposition:
– PITFALL: Avoiding BS predictive insights, i.e., “bad science,” spurious discoveries
– OPPORTUNITY: Optimizing marketing persuasion by predicting the *influence* of marketing treatments, i.e., uplift modeling
Trends in Big Data & Business Challenges Experian_US
Join our #DataTalk on Thursdays at 5 p.m. ET. This week, we tweeted with Sushil Pramanick – who is the founder and president of the The Big Data Institute (TBDI).
You can learn about upcoming chats and see the archive of past big data tweetchats here
http://www.experian.com/blogs/news/about/datadriven
Creating $100 million from Big Data Analytics in BankingGuy Pearce
A sanitized version of our presentation to the Teradata Marketing Summit in Los Angeles in March 2014, on how we created $94.95 million in incremental value for a bank by means of a customer-centricity strategy enabled by Big Data and Analytics
Big Data and The Future of Insight - Future FoundationForesight Factory
As Big Data sweeps through consumer-facing businesses, we ask:
- If Big Data is truly a revolution, then what (and whom) will it eliminate or elevate?
- What value will still be derived from conventional market research and brand-building techniques?
- If every brand is backed by Big Data, can every brand prosper?
For more information please contact info@futurefoundation.net or visit www.futurefoundation.net
Big Data Trends and Challenges Report - WhitepaperVasu S
In this whitepaper read How companies address common big data trends & challenges to gain greater value from their data.
https://www.qubole.com/resources/report/big-data-trends-and-challenges-report
Activating Big Data: The Key To Success with Machine Learning Advanced Analyt...Vasu S
A whitepaper of Qubole that How to make all of your data available to users for a multitude of use cases, ranging from analytics to machine learning and artificial intelligence.
https://www.qubole.com/resources/white-papers/activating-big-data-the-key-to-success-with-machine-learning-advanced-analytics
Analytics: The Real-world Use of Big DataDavid Pittman
UPDATE: Register now to participate in the 2013 survey: http://ibm.com/2013bigdatasurvey IBM’s Institute for Business Value (IBV) and the University of Oxford released their information-rich and insightful report “Analytics: The real-world use of big data.” Based on a survey of over 1000 professionals from 100 countries across 25+ industries, the report provides insights into organizations’ top business objectives, where they are in their big data journey, and how they are advancing their big data efforts. It also provides a pragmatic set of recommendations to organizations as they proceed down the path of big data. For additional information, including links to a podcast with one of the lead researchers and a link to download the full report, visit http://ibm.co/RB14V0
Global Data Management: Governance, Security and Usefulness in a Hybrid WorldNeil Raden
With Global Data Management methodology and tools, all of your data can be accessed and used no matter where it is or where it is from: on-premises, private cloud, public cloud(s), hybrid cloud, open source, third-party data and any combination of the these, with security, privacy and governance applied as if they were a single entity. Ingenious software products and the economics of computing make it economical to do this. Not free, but feasible.
Valliappa Lakshmanan says: “Ask someone a question in Google and you are likely to receive a link to a BigQuery view or query rather than the actual answer”. That’s what we are doing at Travelstart! I’ll present our DataOps approach and a way to create a culture of DIY, avoiding the BI bottleneck.
Big Data Ppt PowerPoint Presentation Slides SlideTeam
Big data has brought about a revolution in the field of information technology. Our content-ready big data PPT PowerPoint presentation slides shed light on the importance and relevance of large volumes of data. The data management presentation covers myriad of topics such as big data sources, market forecast, 3 Vs, technologies, workflow, data analytics process, impact, benefit, future, opportunity and challenges, and many additional slides containing graphs and charts. The biggest benefit that this big data analytics presentation template offers is that it enables you to unearth the information that can be used to shape the future of your business. Moreover, these designs can also be utilized to craft your own presentation on predictive analytics, data processing application, database, cloud computing, business intelligence, and user behavior analytics. Download big data PPT visuals which will help you make accurate business decisions. Enlighten folks on fraud with our Big Data PPt PowerPoint Presentation Slides. Convince them to be highly alert.
Building a Data Platform Strata SF 2019mark 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.
[This is a new, changed version of the presentations of the same title from last year's Strata]
Big Data and The Future of Insight - Future FoundationForesight Factory
As Big Data sweeps through consumer-facing businesses, we ask:
- If Big Data is truly a revolution, then what (and whom) will it eliminate or elevate?
- What value will still be derived from conventional market research and brand-building techniques?
- If every brand is backed by Big Data, can every brand prosper?
For more information please contact info@futurefoundation.net or visit www.futurefoundation.net
Big Data Trends and Challenges Report - WhitepaperVasu S
In this whitepaper read How companies address common big data trends & challenges to gain greater value from their data.
https://www.qubole.com/resources/report/big-data-trends-and-challenges-report
Activating Big Data: The Key To Success with Machine Learning Advanced Analyt...Vasu S
A whitepaper of Qubole that How to make all of your data available to users for a multitude of use cases, ranging from analytics to machine learning and artificial intelligence.
https://www.qubole.com/resources/white-papers/activating-big-data-the-key-to-success-with-machine-learning-advanced-analytics
Analytics: The Real-world Use of Big DataDavid Pittman
UPDATE: Register now to participate in the 2013 survey: http://ibm.com/2013bigdatasurvey IBM’s Institute for Business Value (IBV) and the University of Oxford released their information-rich and insightful report “Analytics: The real-world use of big data.” Based on a survey of over 1000 professionals from 100 countries across 25+ industries, the report provides insights into organizations’ top business objectives, where they are in their big data journey, and how they are advancing their big data efforts. It also provides a pragmatic set of recommendations to organizations as they proceed down the path of big data. For additional information, including links to a podcast with one of the lead researchers and a link to download the full report, visit http://ibm.co/RB14V0
Global Data Management: Governance, Security and Usefulness in a Hybrid WorldNeil Raden
With Global Data Management methodology and tools, all of your data can be accessed and used no matter where it is or where it is from: on-premises, private cloud, public cloud(s), hybrid cloud, open source, third-party data and any combination of the these, with security, privacy and governance applied as if they were a single entity. Ingenious software products and the economics of computing make it economical to do this. Not free, but feasible.
Valliappa Lakshmanan says: “Ask someone a question in Google and you are likely to receive a link to a BigQuery view or query rather than the actual answer”. That’s what we are doing at Travelstart! I’ll present our DataOps approach and a way to create a culture of DIY, avoiding the BI bottleneck.
Big Data Ppt PowerPoint Presentation Slides SlideTeam
Big data has brought about a revolution in the field of information technology. Our content-ready big data PPT PowerPoint presentation slides shed light on the importance and relevance of large volumes of data. The data management presentation covers myriad of topics such as big data sources, market forecast, 3 Vs, technologies, workflow, data analytics process, impact, benefit, future, opportunity and challenges, and many additional slides containing graphs and charts. The biggest benefit that this big data analytics presentation template offers is that it enables you to unearth the information that can be used to shape the future of your business. Moreover, these designs can also be utilized to craft your own presentation on predictive analytics, data processing application, database, cloud computing, business intelligence, and user behavior analytics. Download big data PPT visuals which will help you make accurate business decisions. Enlighten folks on fraud with our Big Data PPt PowerPoint Presentation Slides. Convince them to be highly alert.
Building a Data Platform Strata SF 2019mark 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.
[This is a new, changed version of the presentations of the same title from last year's Strata]
Using Machine Learning to Understand and Predict Marketing ROIDATAVERSITY
Marketing is all about attracting, retaining and building profitable relationships with your customers, but how do you know which customers to target, which campaigns to run, and which marketing programs to invest in, to get most return for your dollar?
Join Alteryx and Keyrus as we demonstrate how to combine all relevant marketing, sales and customer data, and perform sophisticated analytics to deepen customer insight and calculate ROI of marketing programs.
You’ll walk away knowing how to:
Segment and profile your customers – take that raw data and translate it into real value
Build a marketing attribution model within Alteryx, creating a personal answer engine for your company.
Leverage R or Python code in an Alteryx workflow so data scientists can collaborate with non-coding stake holders in a code-friendly and code-free environment.
Join Alteryx and Keyrus and get the actionable insights you need to drive marketing ROI analytics, and answer million-dollar questions without spending millions of dollars on standardized solutions.
There are more and more organisations thinking about Open Data. We do think that Open Data needs tools. The Business Model Canvas could be one, but it's not totally appropriate. Here is the Open Data Canvas. Let's try it and improve it !
Product-thinking is making a big impact in the data world with the rise of Data Products, Data Product Managers, data mesh, and treating “Data as a Product.” But Honest, No-BS: What is a Data Product? And what key questions should we ask ourselves while developing them? Tim Gasper (VP of Product, data.world), will walk through the Data Product ABCs as a way to make treating data as a product way simpler: Accountability, Boundaries, Contracts and Expectations, Downstream Consumers, and Explicit Knowledge.
Business Intelligence & Data Analytics– An Architected ApproachDATAVERSITY
Business intelligence (BI) and data analytics are increasing in popularity as more organizations are looking to become more data-driven. Many tools have powerful visualization techniques that can create dynamic displays of critical information. To ensure that the data displayed on these visualizations is accurate and timely, a strong Data Architecture is needed. Join this webinar to understand how to create a robust Data Architecture for BI and data analytics that takes both business and technology needs into consideration.
Analytics thought-leader Thomas Davenport and leading industry experts discuss how—and why—organizations like yours use business analytics to empower more timely and precise decisions by bringing new insights into daily operations.
How to Successfully Apply Data & AI in the Marketing Value Chain. In this session Artefact will be starting with the role of data in the current world and what it has lead to currently: Where are we with Data & Artificial Intelligence? the future is definitely here. We will make it concrete and explore where to apply Data & AI in digital marketing? What can AI do and what can't it do (yet)?Possible areas are automation, optimization and more. Artefact will make it practical to conclude and explain what using Data & AI means practically for Digital Marketing? What are the actionable next steps in Planning, setup/workflow, getting control and creative.
Big Data Developer Career Path: Job & Interview PreparationIntellipaat
Youtube link : https://www.youtube.com/watch?v=iggl879a0s8
Intellipaat Big Data Hadoop Training: https://intellipaat.com/big-data-hadoop-training/
Read complete Big Data Hadoop tutorial here: https://intellipaat.com/blog/tutorial/hadoop-tutorial/
Building New Data Ecosystem for Customer Analytics, Strata + Hadoop World, 2016Caserta
Caserta Concepts Founder and President, Joe Caserta, gave this presentation at Strata + Hadoop World 2016 in New York, NY. His session covers path-to-purchase analytics using a data lake and spark.
For more information, visit http://casertaconcepts.com/
Building a Data Strategy – Practical Steps for Aligning with Business GoalsDATAVERSITY
Developing a Data Strategy for your organization can seem like a daunting task – but it’s worth the effort. Getting your Data Strategy right can provide significant value, as data drives many of the key initiatives in today’s marketplace – from digital transformation, to marketing, to customer centricity, to population health, and more. This webinar will help demystify Data Strategy and its relationship to Data Architecture and will provide concrete, practical ways to get started.
Emerging Trends in Data Architecture – What’s the Next Big Thing?DATAVERSITY
With technological innovation and change occurring at an ever-increasing rate, it’s hard to keep track of what’s hype and what can provide practical value for your organization. Join this webinar to see the results of a recent DATAVERSITY survey on emerging trends in Data Architecture, along with practical commentary and advice from industry expert Donna Burbank.
Building the Artificially Intelligent EnterpriseDatabricks
This session looks at where we are today with data and analytics and what is needed to transition to the Artificially Intelligent Enterprise.
How do you mobilise developers to exploit what data scientists and business analysts have built? How do you align it all with business strategy to maximise business outcomes? How do you combine BI, predictive and prescriptive analytics, automation and reinforcement learning to get maximum value across the enterprise? What is the blueprint for building the artificially intelligent enterprise?
•Data and analytics – Where are we?
•Why is the journey only half-way done?
•2021 and beyond – The new era of AI usage and not just build
•The requirement – event-driven, on-demand and automated analytics
•Operationalising what you build – DataOps, MLOps and RPA
•Mobilising the masses to integrate AI into processes – what needs to be done?
•Business strategy alignment – the guiding light to AI utilisation for high reward
•Agility step change – the shift to no-code integration of AI by citizen developers
•Recording decisions, and analysing business impact
•Reinforcement-learning – transitioning to continuous reward
Why Everything You Know About bigdata Is A LieSunil Ranka
As a big data technologist, you can bet that you have heard it all: every crazy claim, myth, and outright lie about what big data is and what it isn't that you can imagine, and probably a few that you can't.If your company has a big data initiative or is considering one, you should be aware of these false statements and the reasons why they are wrong.
Data Intelligence: How the Amalgamation of Data, Science, and Technology is C...Caserta
Joe Caserta explores the world of analytics, tech, and AI to paint a picture of where business is headed. This presentation is from the CDAO Exchange in Miami 2018.
Arthur C. Nielsen, the founder of ACNielsen said, “The price of light is less than the cost of darkness.” This is becoming even more important in the day and age of IoT devices and ubiquitous internet connectivity. The amount of data that is at the fingertips of our companies’ decision makers is colossal. Yet very few business leaders and their direct teams can analyze their data by themselves to uncover insights that will improve our products and services to delight their customers and grow their business.
With the rise of low-code/no-code tools, cloud infrastructure, and the convergence of AI and BI, the democratization of analytics can accelerate the time to answer a question while improving its relevancy.
In this presentation, we will cover the 12 critical capabilities to succeed in enabling self-service analytics and augmenting data literacy across the enterprise.
Reporting at Motorola - Predictive analytics & business insights 2014Patrick Deglon
In this presentation, Patrick Deglon will share his learnings and provide best practices when using open Google tools & API. He will present his daily email report that hundreds of key Motorola stakeholders are receiving to drive the business, as well as a mobile solution based on the latest web technologies, including Google Visualization, Bootstrap CSS and many of the Google APIs (Gmail, BigQuery, Analytics, Drive, App Engine, Users authentication, etc.).
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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.
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
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.
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
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).
2. 2
Patrick Deglon Bio
After a PhD in Particle Physics and ten years at the University of
Geneva studying the creation of the Universe, Patrick spent the next
decades driving business insights at eBay, Motorola Mobility, and Teradata.
At eBay, he led significant improvements in marketing effectiveness by
developing methods to measure incremental sales, and by running large scale
experiments on Internet marketing channels.
At Google’s Motorola Mobility, he raised the bar in Analytics and on-boarded
open Google tools and technologies.
In Dec 2016, he joined Teradata as the Vice President of Advanced Analytics
driving the strategy, direction, investment and realization of Teradata’s advanced
analytics portfolio, including the Teradata Database, Aster Analytics, and Open
Source Software.
He is married with two kids and moved to San Diego, California in Dec 2016.
3. The Role of Advanced Analytics
in the Modern Enterprise
21. 21
60%
Number of failed
Big Data projects
90%
Number of useless
data lakes by 2018
6 months
Static data projects
value duration
50%
of data in any project
is an exact repeat of
>5 other projects
80%
Of all projects is spent
preparing data rather
than creating value
5 months
Average to develop, test,
validate, deploy and scale
new analytical models
“Lakes are overwhelmed
with information assets
captured for uncertain
use cases”
“Not establishing
data governance and
management is
underlining value”
“We have institutionalized repetition
and redundancy
because of the way we
manage data”
“We lack discipline in
data management to generate
long term
value”
“Acting like a Fintech
is a lot easier said
than done”
“We keep buying promises,
and are not cynical enough about
the time it take to
realize them”
Endemic Challenges That Must Be Solved
29. 29
1st Example: How much is worth a human life?
1982: New chemical labeling in workplace (cost of labeling vs cost of life)
Occupational Safety and
Health Administration
Yes
Office of Management
and Budget
No
George H.W. Bush
Vice-President
?
30. 30
1st Example: How much is worth a human life?
https://www.nprillinois.org/post/how-value-life-statistically-speaking
Kip Viscusi
1982: New chemical labeling in workplace (cost of labeling vs cost of life)
Occupational Safety and
Health Administration
Yes
Office of Management
and Budget
No
George H.W. Bush
Vice-President
?
• US Worker risk of death: 1 in 25,000
• Dangerous jobs (arctic fishermen, oil rig workers, loggers) have higher risk
• By normalizing 200,000+ job profiles for education and skills, we can estimate that for $1,000 per
year more, worker are willing to take a extra 1 in 10,000 chance of dying on the job
• 10,000 workers = 1 estimated death
• so 10,000 * $1,000 = $10 millions (value of statistical life)
• Yet each life is priceless, especially for the love ones
31. 31
2nd Example: How much should you pay for the
keyword “red dress” on Google?
Google Shopping
(SKU-based, pay per impression, per click,
per sale, or for Return on Ad Spending)
Google Ads
(Keyword-based, pay per click)
Google Search
(Content-based, free)
32. 32
Experimental Design
Test Group
• Switch off Google AdWords
• 30% of USA
Control Group
• Keep Google AdWords
• 30% of USA
• Similar buying pattern/seasonality
than Test Group
US DMA – Designated Market Area
Google AdWords Locations Targetting
eBay Marketing
Experiment
36. 36 Don’t Do Marketing Do Marketing
No Purchase
Purchase
37. 37 Don’t Do Marketing Do Marketing
No Purchase
Purchase
L L
38. 38 Don’t Do Marketing Do Marketing
No Purchase
Purchase
L L
D D
39. 39 Don’t Do Marketing Do Marketing
No Purchase
Purchase
L L
D D
C
C
40. 40 Don’t Do Marketing Do Marketing
No Purchase
Purchase
L L
D D
C
C
?
?
41. 41 Don’t Do Marketing Do Marketing
No Purchase
Purchase
L L
D D
C
C
?
?
Cost
Direct Return
Incr Return
42. 42 Don’t Do Marketing Do Marketing
No Purchase
Purchase
L L
D D
C
C
?
?
Cost
Direct Return
Incr Return
Rule #1: Never, ever, spend money
unless you really-really have to
49. 49
Operational Simplicity
• Only SQL used
• One command to train the model
• One command to score
Verizon Results
GOAL RESULT
Avoid Data Movement / Duplication Met
Initial Accuracy of 64% or better Goal Exceeded: 69.8%
Model Training with >1M records to be <20 min Goal Exceeded: <13min
Model Scoring >200M records to be <30 min
(scoring the entire US customer base)
Goal Exceeded: 22.5min
“I’ve done this for a
long time. I really
haven’t seen this
result ever.”
- Ksenija Draskovic
Operational Results
In less than 40 minutes, they can refresh their
model and score their entire customer base,
with results live in their Teradata system
56. 56
Discover the Possibilities with the Teradata Vantage
Prediction
• How much revenues will we
have next month?
Segmentation
• Which prospects are the more
likely to purchase our product?
Understanding Causality
• Which customer events are
the most important to drive a
sale?
$
Text Mining
• Which offers include non-
compliant terms?
Networking Hypothesis testing
• Which customers are likely to
be fraudsters?
• Does our new website
generate significantly more
leads?
?
Re: Investment question
I can guarantee you a return on investment
of 10%, if you open a new saving account
with ACME Bank Inc. before the end of the
month.