Learn how organisations are able to drive better business outcomes from big data products and in turn, deliver personalised, real-time experiences to customers. We will walk through a common customer journey and demonstrate how to quickly test ideas and gather insights from data lakes, data pipelines, and sandboxes. See how to serve the needs of your business users, business analysts, and data scientists using AWS services for analytics, Big Data, and Machine Learning. Speaker: Vicky Falconer, Business Development Manager - Big Data, AWS

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Vicky Falconer
Business Development Manager, Big Data - Amazon Web Services
Better Business From Exploring
Ideas
Modern Data Architectures On AWS

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Let’s go on a Common
Customer Journey

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Meet AudibleEars, as they move from B2B to B2C
* This case is representative of a common customer journey, but AudibleEars isn’t an actual business
AudibleEars manufacturers hearing aids.
They ran a traditional business since
2005, selling through distribution
channels.
2005
In 2016, they launched their first “AudibleEars
App”. The hearing aid comes with the
AudibleEars mobile app which enables
customized control of the hearing device, voice
to text translation, music streaming. The device
syncs with the users mobile phone via
Bluetooth. The mobile app has optional GPS
tracking.
2016

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AudibleEars Needs To Answer New Questions
And Move Faster
Raymond, Head of ProductLim, Head of Finance
Are customers using the new features?
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?

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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/
visualise
Store Process/
analyze
1 4
0 9
5

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Outcome 1:
Modernise And Consolidate

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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)

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Ingest ServingData
sources
Modern Data Architecture
Insights to enhance business applications, new digital services
Transactions
ERP
Data analysts
DATA PIPELINES
Ingest/
Collect
Consume /
Visualise
Store Process /
Analyze
1 4
0 9
5

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Start Small and Iterate

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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

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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 optimised 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

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Characteristics Of A Data Lake
Future
Proof
Flexible
Access
Dive in
Anywhere
Collect
Anything

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Start With A Set Of Specific Questions To
Answer, Then Work Backwards To The Data
Required
Raymond, Head of Product
Are customers using the new features?
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

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Experiment, Validate, then Scale

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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

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Common Data Pipeline Configuration
Raw Data
Amazon S3
Highly decoupled configurations scale better, are more fault tolerant, and cost optimised
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

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Data security
and management
Encryption
Access Controls
Monitoring and Metrics
Audit Trails
Automation
Serverless Computing
Data Discovery and
Protection
Data Visualisation
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

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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

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Outcome 2:
Innovate For New Insight

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AudibleEars Has Its First Direct Relationship
With Consumers
Krzysztof, Data ScientistBala, Head of Research & Development
What are our customer segments, based on usage?
Can we predict changes to hearing loss from usage
of the app?
Can we give a better customer experience, for
example optimised settings, based on signals?
How are consumers using the hearing aids?
When do they turn them off? How are they
altering the settings throughout the day?
What kinds of people are in/decreasing usage?

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Start With A Set Of Specific Questions To
Answer, Then Work Backwards To The Data
Required
Media consumption
(Partner API)
Registration, usage
[time/place]
(Mobile app)
Bala, Head of Research & Development
How are consumers using the hearing aids?
When do they turn them off? How are they
altering the settings throughout the day?
What kinds of people are in/decreasing usage?

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Start With A Set Of Specific Questions To
Answer, Then Work Backwards To The Data
Required
Krzysztof, Data Scientist
Voice to text, GPS
DATA LAKE, OR NOT?
Registration, usage
[time/place]
(Mobile app)
LOAD TO DATA LAKE
What are our customer segments, based on usage?
Can we predict changes to hearing loss from usage
of the app?
Can we give a better customer experience, for
example optimised settings, based on signals?

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Sandboxes - Fast, Cheap, Low Risk

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

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Ingest ServingData
sources
Modern Data Architecture
Innovate for new revenues - personalisation 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

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Outcome 3:
Real-Time Engagement

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AudibleEars Offers A Personalised Life
Soundtrack
Personalised, based on
past preferences,
people with similar behaviors,
and environments detected

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Use AudibleEars Voice Enablement To Recommend
Settings
What are the
best settings for
watching a play?
Amazon Transcribe
/ Comprehend
Action: <RECOMMEND>
Category: THEATRE
Request settings
HISTORY
RECOMMENDATION!
PEOPLE LIKE YOU
Amazon Kinesis
Streams
Connected device data
Location: <FIND GPS>
Ambient Noise: <FIND Noise>

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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

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

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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

32. Thank You