EarEcstasy is a company that manufactures headsets. They traditionally operated in a B2B model but have recently launched a direct-to-consumer "Smart Buds" product. To support their new business needs, EarEcstasy is exploring moving to a modern data architecture on AWS to gain insights from customer data and power new experiences. The document outlines three potential outcomes: 1) Modernizing and consolidating their data infrastructure, 2) Innovating for new revenue streams through personalization, and 3) Enabling real-time customer engagement through AI and analytics.

1. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Craig Stires
Head of AI, Analytics, Big Data - Asia Pacific, AWS
Better Business from Exploring Ideas
Modern Data Architectures on AWS

2. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Let’s go on a common customer journey

3. Meet EarEcstasy, as they move from B2B to B2C
* This case is representative of a common customer journey, but EarEcstasy isn’t an actual business
EarEcstasy manufacturers headsets. They ran
a traditional B2B business since 2005, selling
through distribution and retail channels.
2005
In 2018, they launched their first “Smart
Buds”. These wireless headsets have voice
enablement, GPS tracking, and heartrate
monitors built in, and the device syncs with
the users mobile phone via Bluetooth. The
mobile app also supports scene detection.
2018

4. EarEcstasy needs to answer new questions and move faster
Raymond, Head of ProductLim, Head of Finance
Which regions are the new earbuds selling well?
What is the demand forecast by product category?
What is the social sentiment about our products?
How do quality issues impact cost of production?
Can I look at supplier performance over time?
How can we reduce our inventory holding costs?

5. To answer new questions quickly, we look to a
modern data architecture design
Massive upfront costs
Overprovisioned capacity
Long implementation times
Pay as you go, for what you use
Decoupled pipelines and engines
Experimentation platform
Ingest/
Collect
Consume/
visualize
Store Process/
analyze
1 4
0 9
5

6. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Outcome 1: Modernize and consolidate

7. Start with a set of specific questions to answer, then work
backwards to the data required
Lim, Head of Finance
How do quality issues impact cost of production?
Can I look at supplier performance over time?
How can we reduce our inventory holding costs?
Order History /
Returns (CRM)
Inventory /
Production (ERP)

8. Ingest ServingData
sources
Modern data architecture
Insights to enhance business applications, new digital services
Transactions
ERP
Data analysts
DATA PIPELINES
Ingest/
Collect
Consume /
visualize
Store Process /
analyze
1 4
0 9
5

9. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Start small and iterate

10. Ingest ServingData
sources
Modern data architecture
Insights to enhance business applications, new digital services
Transactions
ERP
DATA PIPELINES
Data
Lake
expdp
Data Data analysts
Data Warehouse
Amazon Redshift
Direct Query
Amazon Athena
She asks for the SMALLEST amount of data to answer her questions.
If it isn’t good enough, she asks for another small slice to be loaded to the DATA LAKE

11. Amazon Redshift – Modern Data Warehousing
Fast, scalable, fully managed data warehouse at 1/10th the cost
Massively parallel, scales from gigabytes to exabytes
Queries data across your Redshift data warehouse and Amazon S3 data lake
Fast at scale
Columnar storage
technology to improve I/O
efficiency and scale query
performance
Open file formats
Analyze optimized data
formats on direct-attached
disks, and all open file
formats in S3
Cost-effective
Start at $0.25 per hour;
as low as $250-$333 per
uncompressed terabyte
per year
$
Secure
Audit everything; encrypt
data end-to-end; extensive
certification and compliance

12. Characteristics of a Data Lake
Future
Proof
Flexible
Access
Dive in
Anywhere
Collect
Anything

13. Start with a set of specific questions to answer, then work
backwards to the data required
Raymond, Head of Product
Which regions are the new earbuds selling well?
What is the demand forecast by product category?
What is the social sentiment about our products?
Trending /
Mentions (Social)
Order History /
Returns (CRM)
NOW IN THE DATA LAKE

14. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Experiment, validate, then scale

15. Ingest ServingData
sources
Modern data architecture
Insights to enhance business applications, new digital services
DATA PIPELINES
Data
Lake
He first looks to the DATA LAKE, and imports only the category data he needs
He imports JUST ENOUGH data to see if the market is responding to products.
Business users
Transactions
ERP
Social media
Data
Stream
Capture
Amazon
Kinesis
Events
Amazon
QuickSight
Data Warehouse
Amazon Redshift
Stream Data
Amazon
ElasticSearch

16. Common data pipeline configuration
Raw Data
Amazon S3
Highly decoupled configurations scale better, are more fault tolerant, and cost optimized
ETL (Hadoop)
Amazon EMR
Triggered Code
Amazon Lambda
Staged Data
(Data Lake)
Amazon S3
ETL & Catalog Management
AWS Glue
Data Warehouse
Amazon Redshift
Triggered Code
Amazon Lambda

17. Data security
and management
Encryption
Access Controls
Monitoring and Metrics
Audit Trails
Automation
Serverless Computing
Data Discovery and
Protection
Data Visualization
Data movement
Physical Appliances
Hybrid Storage
Private Networks
File Data
WAN Acceleration
Third-party Applications
Streaming Data
Complete set of building blocks
FileBlock
Object Archival
Storage types

18. Ingest ServingData
sources
Modern data architecture
Insights to enhance business applications, new digital services
Transactions
ERP
Data analysts
Business users
DATA PIPELINES
EVENT PIPELINES
Data
Event
Insights
Data
Lake
Social media

19. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Outcome 2: Innovate for new revenues

20. EarEcstasy has its first direct relationship with consumers
Krzysztof, Data ScientistBala, Head of Marketing
What are our customer segments, based on usage?
Can predict music preference from location and HR?
Are there additional signals in the voice commands?
Can we infer user activity, from scenes in pictures?
How are people using the Smart Buds?
How to understand what they listen to and when?
What kinds of people are in/decreasing usage?

21. Start with a set of specific questions to answer, then work
backwards to the data required
Bala, Head of Marketing
How are people using the Smart Buds?
How to understand what they listen to and when?
What kinds of people are in/decreasing usage?
Media
consumption
(Partner API)
Registration,
usage [time/place]
(Mobile app)

22. Start with a set of specific questions to answer, then work
backwards to the data required
Krzysztof, Data Scientist
What are our customer segments, based on usage?
Can predict music preference from location and HR?
Are there additional signals in the voice commands?
Can we infer user activity, from scenes in pictures?
HR, Voice, GPS,
Images (Device
data)
DATA LAKE, OR NOT?
Registration,
usage [time/place]
(Mobile app)
LOAD TO DATA LAKE

23. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Sandboxes - fast, cheap, low risk

24. Ingest ServingData
sources
Modern data architecture
Insights to enhance business applications, new digital services
Transactions
Data scientists
Business users
Connected
devices
DATA PIPELINES
EVENT PIPELINES
Data
Event
Insights
Data
Lake
Sandbox
ML / Analytics / DLWeb logs /
clickstream

25. Ingest ServingData
sources
Modern data architecture
Innovate for new revenues - personalization and forecasting
Transactions
ERP
Data analysts
Data scientists
Business users
Connected
devices
DATA PIPELINES
EVENT PIPELINES
Data
Event
Insights
Data
Lake
ML / Analytics
Social media
Web logs /
clickstream

26. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Outcome 3: Real-time engagement

27. EarEcstasy offers a personalized life soundtrack
Personalized, based on
past preferences,
people with similar behaviors,
and environments detected

28. Use EarEcstasy voice enablement to play music
I’m tired, play me
some music!
Amazon Transcribe
/ Comprehend
Action: PLAY
Category: MUSIC
Genre: <RECOMMEND>
Request content
HISTORY
Twenty One Pilots!
PEOPLE LIKE YOU
Amazon Kinesis
Streams
Connected device data
Location: <FIND GPS>
Mood: <FIND HR>

29. Use the mobile app to take a picture to identify
activity
A QUIET OFFICE
Amazon SageMaker
Image Classification
Amazon Rekognition
Image
CHAIR
LAPTOP
LAMP
DESK
97%
95%
88%
82%
Object Identification
WORKING!
<HISTORY>

30. Ingest ServingData
sources
Modern data architecture
Real-time engagement and interactive customer experiences
Transactions
ERP
Data analysts
Data scientists
Business users
Engagement platformsConnected
devices
Automation / events
DATA PIPELINES
EVENT PIPELINES
Data
Event Action
Insights
Data
Lake
ML / Analytics
Predict /
Recommend
AI Services
Social media
Web logs /
clickstream

31. Business Outcomes on a Modern Data Architecture
Outcome 1 : Modernize and consolidate
• Insights to enhance business applications and create new digital
services
Outcome 2 : Innovate for new revenues
• Personalization, demand forecasting, risk analysis
Outcome 3 : Real-time engagement
• Interactive customer experience, event-driven automation, fraud
detection

32. Ready to build better business from your ideas?
Short list projects that
directly impact
customer engagement
and adoption
Build simple data
pipelines that allow you
to test new ideas, and
fill your data lake
Ask our solution architects
and professional services
teams to help you build

33. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Summit sessions to dive deeper:
Wednesday (04-April-18)
14:00 Architecting a Serverless Data Lake on AWS
15:10 Machine Learning in Practice
16:20 Artificial Intelligence to Delight Your Customers

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