4. Most Powerful
Selling Platform
For business
sellers: the
potential to drive
profitable sales
and build a brand
For consumer
sellers: an easy
way to declutter,
sell and make
money
A partnership not a
competition
Best Choice
Providing the
greatest selection
of inventory for
our buyers
From new,
everyday items to
rare and unique
goods
And incredible
deals only found
on eBay
Most Relevance
A shopping
experience that is
simple, data-driven
and personalized
Enabling buyers to
easily find, compare
and purchase items
they need and want
Highlighting the
unique value that
eBay brings
OUR
STRATEGY
5. EBAY INC AT A GLANCE
$2.1B
Revenue in Q1 2016
$20.5B
GMV in Q1 2016
162M
Global Active Buyers
57%
International
revenue
Q1 2016 data
$9B
Mobile Volume
314M
App downloads
6. EBAY MARKETPLACE AT A GLANCE
$19.6B
GMV in Q1 2016
9.5M
New listings added via
mobile per week
300M
Searches each day
63%
Transactions that
ship for free
(in US, UK, DE)
79%
Items sold as new
Q1 2016 data
~900M
Live listings
One of the world’s largest and most vibrant marketplaces
7. VELOCITY STATS
US
3 car parts or accessories are sold every
A smartphone is sold every
A dress is sold every
1 sec
4 sec
6 sec
UK
A necklace is sold every
A make-up product is sold every
A Lego product is sold every
10 sec
3 sec
19 sec
GERMANY
A truck or car is sold every
A pair of women’s jeans is sold every
A video game is sold every
5 min
4 sec
11 sec
AUSTRALIA
A pair of men’s sunglasses is sold every
A home décor item is sold every
A car or truck part is sold every
1 min
12 sec
4 sec
8. MOBILE VELOCITY STATS
US
A woman’s handbag is sold every
A car or truck is sold every
An action figure is sold every
10 sec
5 min
10 sec
UK
A tablet is sold every
A cookware item is sold every
A car is sold every
1 min
6 sec
2 min
GERMANY
A pair of women’s shoes is sold every
A watch is sold every
A tire or car part is sold every
20 sec
48 sec
35 sec
AUSTRALIA
A piece of jewelry is sold every
A baby clothing item is sold every
A motorcycle part is sold every
12 sec
46 sec
51 sec
9. THREE KEY TRENDS ARE
REDEFINING COMMERCE
Smart CommerceSeamless Commerce True Global Commerce
11. TRUE GLOBAL
COMMERCE of eBay’s business
is international57%
of commercial
sellers engage in
exporting*
95%
languages
8
*Sellers with $10,000 or
more/year in sales
18. BIG Data VVC
20
>50 TB/day new data
>100 PB/day
>100 Trillion pairs of information
Millionsof queries/day
>7500
business users & analysts
>50k chains of logic
24x7x365
99.98+%Availability
turning over a TB every second
Active/Active
Near-Real-time
>100kdata elements
Always online
Processed
>1.5 x 1012
new records/day
19. 21
TECHNOLOGY
EDW
Analytics
Application
Analysts and Data
Scientists
Management Integrators Business
Owners
Application
Servers
Data Processing
Clusters
Aggregation &
Summarization
Visualization &
Reporting
ClicktoInsights
Kylin
OVER Billings EVENTS FROM 162M EBAY BUYERS
CAPTURED, TRANSFORMED, SYNTHESIZED TO PROVIDE
ACTIONABLE INSIGHTS
20. 22
eBay has one of the largest most active
data platforms in the world.
eBay has one of the largest most active data
platforms in the world with a diverse set of
users.
24. Semi-Structured SQL++Structured SQL
Low End Enterprise-class System
Contextual-complex analytics, deep,
seasonal, consumable datasets
Production data warehousing,
large concurrent user base
Discover & Explore
Analyze & Report
Enterprise-class System
Unstructured JAVA / C
Structure the unstructured,
detect patterns
Commodity Hardware System
Singularity HadoopTeradata
Enterprise Data Warehouse
DISCOVER & EXPLOREANALYZE & REPORT
26
25. page
27
Biggest complexity drivers are
Maintaining separate databases
weekly/daily/hourly data transfers
Data inconsistencies
Data duplication
Increased complexity
Loss of centralized viz & control
DMs
A data mart cannot be cheap enough to justify its existence
26. PRESENTATION TITLE GOES HERE 28
...the wrong way
Data Marts in the Cloud
Customer
Customer
Customer
Customer
Customer
Product
Customer
Product
Customer
Product
Customer
Product
Trx
Customer
Product
Trx
Customer
Product
Trx
Customer
Product
Trx
27. PRESENTATION TITLE GOES HERE 29
Virtual Data Marts
Customer
Product Transactio
n
Behavior
Virtual
DataMart Virtual
DataMart Virtual
DataMart
Virtual
DataMart
Virtual
DataMart
Virtual
DataMartVirtual
DataMart
Virtual
DataMart
Virtual
DataMart
29. Semi-Structured SQL++Structured SQL
Low End Enterprise-class System
Contextual-complex analytics, deep,
seasonal, consumable datasets
Production data warehousing,
large concurrent user base
Discover & Explore
Analyze & Report
Enterprise-class System
Unstructured JAVA / C
Structure the unstructured,
detect patterns
Commodity Hardware System
Singularity HadoopTeradata
Deep-Data Platforms
DISCOVER & EXPLOREANALYZE & REPORT
31
Behavioral Data Centric
31. 33
Collaborative Analytics
Compose
Write and discover queries with ease; understand and reuse
code
easily; drives time and savings.
Catalog & Govern
Document and discover data and concepts; structured and crowd
sourced tagging of content in a stewarded environment.
Answers
Fast, trusted answers for everyone; search for analytic products
(metrics, reports, KPIs).
Forensics
Insightful IT and operational data to expose and
eliminateredundancies
Experts / Stewards
Govern
Simple Data
Management
Analyst
Compose
Better,Faster Queries
Business
Users
Answers
Google for your Data
IT
Forensics
Intelligence about your
data
32. Wiki + metadata repository
Alation SQL Assistant
Metadata repository
+ +
Storytelling
Mixing textual analysis with graphs
WHAT IT’S LIKECOLLABORATION TOOL
2013
2009
2014 AnswerHub Discussion forum moderated by support
DataHub + for data2010
COLLABORATION JOURNEY
2014
34. ENTERPRISE DATA PLATFORM
36
Data Warehouse Data Streams
Batch
Humans
Sets of data
Streams
Systems
Sets of data
Data Services
Services
Applications
Specific calls
Populated
Used by
How
Enterprise
Populated
Used by
How
35. DQRecon
Data Processing Ecosystem
37
Curated
Streams
Applications
Data Services
ApplicationAnalytics
Data
Scientists
Analysts
BU/PD
Leaders
Site DBs
Real-Time
Data
Sources
External
Data
Sources
ETL
Enterprise
Data
Warehouse
Deep Data
Analytics
Platform
Hadoop
Engineers
Stream
Processing
Caching
DOE
DQFirewall
Buyers/Sellers
38. Automated Signal Detection
40
Prediction – anomaly signal detection
Massively scalable and automated signal
detection and prediction
Phase 1: Signal detection
Phase 2: Root Cause analysis
39. 41
ANALYTICS IN EBAY
Measure Everything
Embedded in our daily life
Bottom-up & Top-down
Think and Live Analytics
Always
But know when to avoid Analysis Paralysis!
Analytics DNA
44. page
46
Diverse User Community
Data
Scientists
Financial
Planning &
Analytics
Site Analysts
Business
Analysts
Consumers
One-off
Analysis
Descriptive, Predictive &
Prescriptive Modeling
Experimentation &
Mining
Standard
Reports
Dashboards
Hadoop
R/SAS/SQL on
Teradata
Excel
Tableau
MicroStrategy
, Diverse Needs& Diverse Tools
45. 47
The Analytics Environment at eBay
Direct SQL access
User datasets
MicroStrategy
Tableau
Web based App
1000+ files
10,000+ tables
5000+ reports
10,000+
100+ named apps
Tough to find the right metrics and reports
Hard to build new metrics and reports
Impossible to know which metrics and
reports are correct vs old
50. Self-service Strategy changes
everything…
52
The data user experience is….
Incoherent
Isolated
Disjointed
Uncertain
Consolidates all knowledge about data for “Just-in-
Time” use
Unifies a consistent set of Data Products on the hub
Makes it easy to find and trace the path from Business
Insights and summaries to the underlying SQL, metrics
and metadata
Delivers transparency and build trust with Data
Governance
and Stewardship
51. Comprehensive & Documented -- Self-directed Experience
Insights Hub
ONE portal , ONE framework, ONE analytics app Store
Targeted & Simplified -- Self-service Experiences
SQL Writer Search Collaberation
Knowledge Management
Subject Matter Expert (SME)
Directory and Subject Domain
pages
Business Metrics Glossary
Certified data assets,
endorsements, descriptions.
MoreDetailedMoreSummary
TechnicalAnalysisBusinessInsight
Self Service Strategy, Governed Exploration for Analysis and
Business Insight
52. DATA GOVERNANCE
54
Business Glossary – Managed articles about logic and language.
Knowledge: What should it be?
Data Asset Certification
Trust: Is this the right view? Who says so? As of when?
Well Managed – Quality checks, release notes, load updates
Trust: Is it ok to use RIGHT NOW?
53. DATA GOVERNANCE
55
Business GlossaryData Asset Certification Well Managed
What we do: Data knowledge management and data
stewardship
Goals:
• Demystify our data warehouse of tens of thousands of
datasets
• Increase trust in data by increasing transparency
• Save analysts’ time and reduce their opportunities for error
58. 60
A COMPLETE VIEW OF OUR CUSTOMERS
Behavior Demographics & Interests
AttitudeValue to eBay
59. 61
DATA SCIENCE
Data Data
Science
Business
ImpactData Data Science Data Science Data Science
Business
Impact
Insights
Customer Insights used to make
decisions and set strategy
Predictive Models
Models that predict outcomes
to achieve optimal targeting
Segments
New ways to assess value and
attitudes of our customers
DNA
60. 62
CONVERSION MODEL
User Category Probability
111602**** 1564** 10.1%
111602**** 1562** 6.54%
111602**** 1569** 5.67%
111602**** 3564** 4.33%
111602**** 1397** 1.19%
111602**** 3877** 1.11%
111602**** 9282** 1.01%
111602**** 3607** 0.91%
111602**** 1040** 0.81%
111602**** 1564** 0.76%
111602**** 1040** 0.66%
111602**** 4250** 0.01%
111602**** 5235** 0.01%
• Cart data
• Watch data
• Mobile watch
• Search pages
• Browse data
• Purchase history
Models
eBay is the world’s most vibrant marketplace where the world goes to shop, sell, and give. Whether you are buying something new or used, luxurious or modest, rare or commonplace, trendy or one-of-a-kind – if it exists in the world, it’s probably for sale on eBay. Our mission is to be the world’s favorite destination for discovering great value and unique selection.
eBay connects millions of buyers and sellers around the globe, empowering people and creating opportunity. Our vision for commerce is one that is enabled by people, powered by technology, and open to everyone.
We give sellers the platform, solutions, and support they need to grow their businesses and thrive, but we never compete with them. We measure our success by our customers' success.
Our vision for commerce is one that is enabled by people, powered by technology, and open to everyone.
Our strategy is to drive the best choice, have the most relevance, and deliver the most powerful selling platform.
eBay Inc. is a global commerce leader including our Marketplace, StubHub and Classifieds platforms.
Collectively, we connect millions of buyers and sellers around the world.
The technologies and services that power our platforms are designed to enable sellers worldwide to organize and offer their inventory for sale and buyers to find and buy it virtually anytime and anywhere.
eBay Inc. employs approximately 11,600 people globally (as of Dec. 31, 2015)
Today’s eBay isn’t what it used to be - many people think of us only as an auction site, but that perception hasn’t kept up with reality.
The reality is that 79% of what is sold on eBay is new merchandise, available for purchase immediately.
We have more than 900 million items listed for sale and 162 million active buyers, effectively making us the world’s biggest shopping destination.
From our vantage point, we believe the impact of these three trends will transform the commerce landscape.
Seamless commerce is much more than a mobile experience. To engage with consumers in the “new retail”, brands must take a multi-screen approach.
We must stop thinking of experiences across individual devices - and start thinking of holistic shopping experiences, where consumers can seamlessly engage with your brand across multiple screens, literally from wherever they are.
Brands also recognize that online and offline are not mutually exclusive. Consumers want the best of both worlds, shopping online and across multiple devices, and offline in-store. The continued proliferation of mobile will deliver a richer consumer experience that help shoppers navigate seamlessly between the digital and physical worlds.
At eBay, we’re finding that the multi-screen consumer is more highly engaged. They visit sites more frequently, and they buy significantly more when online. Multiscreen is device agnostic, which means every screen is shoppable. Because we can’t predict what the next great device will be, we must focus on providing customers with the best possible experience - regardless of the device – so consumers can shop when they want, for what they want.
At eBay, we are innovating across devices, creating seamless buying and selling experiences for iOS, Android, desktop, and even wearables to make sure our customers can engage at every touch point. We are also allowing people to shop the way they want: online, offline and mobile are coming together in services like Click & Collect offered by eBay with Argos in the UK, which allows buyers to pick-up their purchase in-store if they choose.
Consumers are increasingly able to shop the world. Their market, or where they shop, is no longer defined by borders. They go online to explore the world – interests/likes come to life in different places.
Because the brands and products they love can be difficult to find in their markets, they’re willing to shop foreign websites. They tolerate friction in buying in order to access the selection that a global marketplace has to offer.
At eBay, 57% of our business is international and 95% of our commercial sellers engage in exporting. The eBay app is available in 190 countries, we host 25 localized websites across the globe and are available in 8 languages.
We are offering innovative approaches to eliminate friction points in global shopping, such as programs like the Global Shipping Program – which enables sellers to more easily ship to 64 countries around the world.
Consumers are overwhelmed by the number of choices they face day-to-day. Smart brands are using data to surface inventory to their consumers in ways that feel relevant, helpful and familiar.
At eBay, we are curating and simplifying content in ways that align to users’ stated (and sometimes unstated) preferences, serving up content in new, simplified interfaces that surprise and delight them. We are also experimenting with machine learning to help bridge the gap between intent and understanding.
大数据是一个数量级大于你习惯的数据, Grasshopper
This one take more time. Big Data – size, complexity, velocity. Intersection of #products with customers and activity cause huge volumes
All of this needs to be loaded and maintained – daily, hourly, 15-minutes, near-real-time
These users generate millions of requests per day, Add HA.
Make a big deal about 24x7, no place for batch or query windows. We are a global company with analysts and users all over the world. We load and process and query 24x7. If we take a backup, it has to happen with everything else.
100 PB/day, processed by our systems, going over data over and over against to find new patterns, etc. Vivaldi touches a TB/second itself. That’s 86 PB/day on one system.
Its easy to build a large PB store for 10s even 100s of PBs. But, accessing that data and use it in a meaningful way is the challenge. We design our systems for extremely high usage.
Our technology is proprietary – but leverages a lot of Open Source Stack
Most of the Data Processing heavy lifting happens on Hadoop Clusters. Majority of it – MR jobs.
We also leverage Scala/Scoobi and have a custom built framework (Cascading Based) through a host of libraries, all internally customized.
Our approach to reporting – is very ‘democratic”. Since a large part of the analytics are for internal consumption, we have to deal with a wide variety of data customers with different degrees of data knowledge and data handling maturity.
A strategy that has worked very well for us is to provide a top line Analytical Tool ( combination of reports and dashboards), depending on the use case and then, provide curated data sets – to allow for interactive querying and analysis.
What exactly do my teams do? Data engineering and technology development at scale
You can’t see/touch/feel most of what we do.
We build and manage platforms, used by over 10K distinct users in the last year. .
Fully integrated DP with history back to beginning. RJ saying it's our most powerful weapon
Emphasize engineering org and expertise. - scale and complexity,
Search science and best match
Include detail slides in deck, Advertising buildout example.- Ilari
And/or Buildout what is required to make trending campaign work in nous and customer Dna
Real time PLA
Data management slide
Similar to commerce OS for site Dev we do for data
Use product slide to answer the question -- but what exactly do you do -- emohaisze most resources are working on the platform -- some of what we finis very visible, but most is not as its a platform that enalrd others.
Thanks,
Darren
5 Stages to something they refer to as the SENTIENT ENTERPRISE
Framework for maximizing speed/value/agility of investments in Data
Data Management at eBay roughly follows a 5-stage model developed by
OLIVER RATZESBERGER | TeradataMOHAN SAWHNEY | Kellogg School of Management
I’ve discussed how agile businesses create a balance between imposing and loosening structure – centralizing the definition of data rules but decentralizing use cases to drive innovation.
Agile businesses are able to make more strategic decisions based on higher levels of both breadth and depth of data.
The Agile Data Warehouse moves traditional central DW structures to a balanced decentralized framework built for agility.
Centralized data – decentralized access. Data Labs that support experimentation and self service
Promotion process for VDM to Prod.
We avoid federation of DMs – no pooling, redundant data, inconsistencies, HC to manage, probably 10x more expensive than they appear
But DMs provide agility and speed. The way we do it is completely different. The right way to do cloud for analytics. When we provision a virtual DM, yes it has 100 GB of empty space, but it also has access to PB of reusable company data instantly. Also used for interative development and test.
Behavioral Data Platform
From Transactional to Behavioral Data. Value comes from behaviors rather than transactions
LinkedIn for Analytics
Harnessing the power of social & crowed sourcing to empower the enterprise to collaborate on analytics as scacle
Share, Follow, Like
Working with 3rd party vendor on building out our collaborative data hub
But these are just the initial steps towards an even more exciting future for the enterprise.
Our efforts today around creating agile and transparent data architectures and systems will enable us to create the sentient enterprise.
What do I mean by “sentient”? Do I want companies to have feelings?
It’s true that the word “sentient’ is derived from the Latin word “sentīre,” meaning “to feel,” and it refers to any entity that can feel or perceive things.
This is key to why we think it’s the perfect descriptor. At Teradata we work on helping companies on building a corporation that can sense when something’s wrong and report it to the humans in charge of fixing it.
In the sentient enterprise, an entire company operates like a single organism, where the left hand knows what the right hand is doing, and where human beings can get signals and suggestions that inform and guide their critical business decision
In the sentient enterprise, your data talks to you, like it has a brain of its own.
In the sentient enterprise, the CFO could have answers within hours instead of days.
In fact, imagine if long before she received her weekly revenue trends report, the CFO could get an alert from the enterprise pointing her to the root cause so she could do something about it.
Or, better yet, what if she didn’t get an alert at all – because the revenue dip was prevented in the first place?
ns.
Analytical Application Platform
Analytical Apps. From static applications and ETL to agile Self Service Apps.
From Extraction of Data to Enterprise Listening.
From centralized ETL heavy static code to agile frameworks (if you want your data integrated, conform to this service API)
From manual data extraction after the fact to real-time Data Listening – Streaming Transactions + Pulsar
Introduce EDP
We’ve been on the path of the agile data warehouse for a long time
We have the requisite user created data labs, and support experimentation and testing as well as highly integrated core (production) data
Our recent change has been the addition of enterprise data streams
Enterprise data streams are near real time streaming data designed to mirror the critical core data from the EDW but in real time – enhanced with history from the EDW so that actions and recommendations can be made based upon new (live) actions while taking into account rich context
Without this key enabler we could not progress into stage 5: autonomous decision making
Reusing what we learned from EDW in the other two areas
doe.corp.ebay.com
Enterprise data streams and real time services
Automated Decisioning Platform
Predictive Technologies and Algorithms. From 10% of time on decision making and 90% sifting through data to 90% on decision making with the help of automated algorithms.
Implementing Predictive Technologies and Algorithms at scale and operationalizing them throughout the enterprise
Let systems deal with the ever increasing combinations and intersections of data
Focus the human brain on making decisions
50K intersection points across our customer experiences modeled each day
Example: total GMV in Fashion, New listings in Electronics
5:00 - 2 minutes with next – 5:02
This joke reminded me of the situation at eBay in terms of how to use data. It’s very confusing, just like putting together an IKEA piece of furniture.
5:00 - 2 minutes with previous– 5:02
Same situation at eBay when it comes to finding/using data
Transition 1
Reality its much more confusing…also note no instructions
5:02 - 2 minutes with next – 5:04
Before I talk about how we are transforming analytics at eBay, let me tell you more about our analytic environment.
At eBay, a lot of what we do – the decisions we make – are driven by data. We have a large and diverse community of analysts, and we have an even larger and diverse community of data consumers – from Executives all the way through to Business users.
5:02 - 2 minutes with previous – 5:04
We have a diverse user community…
Transition 1
Diverse types of individuals that use data in their day-to-day job, from Data Scientists through traditional FP&A, from Site and Business Analysts to the Exec & Business consumer.
Transition 2
This diverse community has a diverse set of needs in using data – from one-off analysis to statistical modeling; from A B experimentation comparisons and deep data mining of unstructured data to standard reports and dashboards using transaction data.
Transition 3
And we have a number of tools to enable all of this – from Hadoop to R, SAS & SQL on the large Teradata stores that I have showed, reporting from Excel to visualization tools like Tableau and enterprise class tools like MicroStrategy. But such diversity of Users, needs and tools…
Transition 4
…does cause some chaos.
5:04 - 3 minutes – 5:07
Whaddya mean Chaos?
Users can have direct SQL access to the biggest data systems on the plant
They can create their own datasets
They have enterprise class tools like MicroStrategy
And slick visualization builders like Tableau and Excel
Transition 1
What’s the problem? ? Why is there a need to “transform analytics”?
Transition 2
Well, it is a classic problem that I am sure many of you share or recognize.
Transition 3
There are hundreds of files sent via email or squirreled away on SharePoint or shared network drives
There are thousands of tables built without consideration of reuse, retention or rationalization
There are over 5,000 reports in MicroStrategy with little capability to know if they are relevant or if the data/thinking is stale
And there are tens of thousands of workbooks in Tableau that share these same issues
Transition 4
So, like I said earlier, at eBay you are expected to “go use data”
but it is tough to find existing metrics and reports – there are so many
If you are adept at SQL and finding data, it is easy to build metrics – but you might be adding to the chaos
And in an unstructured environment, it is tough to know which metrics and reports are the right ones to use, or which ones are old or stale
So we need to think differently about solving these big data problems.