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1 ©	Hortonworks	Inc.	2011	–2017.	All	Rights	Reserved
SPRINT’S	DATA	MODERNIZATION	JOURNEY
November	30,	2017
2 ©	Hortonworks	Inc.	2011	–2017.	All	Rights	Reserved2 ©	Hortonworks	Inc.	2011	–2017.	All	Rights	Reserved
3
©2016	 Sprint.	 This	information	 is	subject	 to	Sprint	 policies	 regarding	 use	 and	is	the	 property	 of	Sprint	 and/or	 its	 relevant	 affiliates	 and	may	contain	 restricted,	 confidential	 or	privileged	materials	
intended	 for	the	sole	use	of	the	intended	 recipient.	 Any	review,	use,	distribution	 or	disclosure	 is	prohibited	 without	 authorization.
10.1.2016
Version	 1.1	DSB
Sprint’s Data Modernization Journey
Putting a foundational framework for
empowering Analytics
November 30, 2017
4
©2016	 Sprint.	 This	information	 is	subject	 to	Sprint	 policies	 regarding	 use	 and	is	the	 property	 of	Sprint	 and/or	 its	 relevant	 affiliates	 and	may	contain	 restricted,	 confidential	 or	privileged	materials	
intended	 for	the	sole	use	of	the	intended	 recipient.	 Any	review,	use,	distribution	 or	disclosure	 is	prohibited	 without	 authorization.
10.1.2016
Version	 1.1	DSB
About Sprint
Sprint	is	one	of	the	largest	telecom	services providers	in	the	US	with	business	
operations	across	multiple	countries	offering	its	residential	and	commercial	
customers	a	variety	of	communication	and	media	services.
Multiple	Countries	-
US,	Mexico,	Caribbean
Wireless,	Internet,	
Prepaid,	Wired
Service	Offerings	-
B2B,	B2C
60M	Subscriber	
Base
5
©2016	 Sprint.	 This	information	 is	subject	 to	Sprint	 policies	 regarding	 use	 and	is	the	 property	 of	Sprint	 and/or	 its	 relevant	 affiliates	 and	may	contain	 restricted,	 confidential	 or	privileged	materials	
intended	 for	the	sole	use	of	the	intended	 recipient.	 Any	review,	use,	distribution	 or	disclosure	 is	prohibited	 without	 authorization.
10.1.2016
Version	 1.1	DSB
Our Modernization Demand Drivers
Paradigm	Shift	in	Customer	
Usage	– Data	now	outpaces	
Voice	usage	enormously
Data	Explosion	– More	data	
originating	from	all	
directions	and	new	types
Increased	Appetite	for	
Analytics	– Cost	Savings,	
Better	Customer	Experience
We	were	experiencing	a	paradigm	shift	in	how	the	data	landscape	was	
evolving	and	our	focus	was	on	how	we	can	harness	the	wealth	of	data to	
better	serve	our	customers	and	improve	our	business	operations.
6
©2016	 Sprint.	 This	information	 is	subject	 to	Sprint	 policies	 regarding	 use	 and	is	the	 property	 of	Sprint	 and/or	 its	 relevant	 affiliates	 and	may	contain	 restricted,	 confidential	 or	privileged	materials	
intended	 for	the	sole	use	of	the	intended	 recipient.	 Any	review,	use,	distribution	 or	disclosure	 is	prohibited	 without	 authorization.
10.1.2016
Version	 1.1	DSB
Our Modernization Goals
Our	data	modernization	goals	were	focused	on	business	imperatives in	order	
to	provide	a	richer,	personalized	customer	experience,	improve	business	
operations	and	gain	better	visibility	into	our	Customers.
Fraud	detection	– Expanded	
datasets	and	improve	data	
availability
Improve	visibility	to	gain	
better	insights	and	make	
timely	business	decisions
360	Degree	View	of	
Customer	– better	ways	to	
serve	and	reduce	churn
7
©2016	 Sprint.	 This	information	 is	subject	 to	Sprint	 policies	 regarding	 use	 and	is	the	 property	 of	Sprint	 and/or	 its	 relevant	 affiliates	 and	may	contain	 restricted,	 confidential	 or	privileged	materials	
intended	 for	the	sole	use	of	the	intended	 recipient.	 Any	review,	use,	distribution	 or	disclosure	 is	prohibited	 without	 authorization.
10.1.2016
Version	 1.1	DSB
Iterative Approach to Modernization
We	took	an	iterative	approach	towards	modernization	in	order	to	ensure	our	
journey	had	meaningful	and	achievable	targets.
Build	foundational	framework	
with	an	evolving	and	scalable	
architecture
Reduce	data	redundancy	by	
consolidating	data	points	
and	improve	data	availability		
Spend	less	time	on	lift	&	
transform	and	more	on	
Analytics
Core	Foundation Data	Consolidation Empower	Analytics
8
©2016	 Sprint.	 This	information	 is	subject	 to	Sprint	 policies	 regarding	 use	 and	is	the	 property	 of	Sprint	 and/or	 its	 relevant	 affiliates	 and	may	contain	 restricted,	 confidential	 or	privileged	materials	
intended	 for	the	sole	use	of	the	intended	 recipient.	 Any	review,	use,	distribution	 or	disclosure	 is	prohibited	 without	 authorization.
10.1.2016
Version	 1.1	DSB
Tips for Modernization
• Analysis and scoping
• Big data is not just volume but also more compute and analytics
• Scalable hardware which can be improved for more compute when required
• Network throughputs or improve bandwidth for large data volumes
• Prepare and plan
• Technology and Business Teams Adoption
• Plan real-time architecture ahead instead of rearchitecting later
• Communicate and manage expectations
Think	big,	start	small,	move	fast.
10©	2017	Diyotta	Inc.	All	Rights	Reserved.
Webinar:	Data	Modernization
Plan	Big,	Start	Small	and	Move	Fast
November	30,	2017
©	2017	Diyotta	Inc.	All	Rights	Reserved.
What’s	Driving	Modernization	For	You?
Expanded	Data	Sets	
Getting	access	to	the	
data	which	was	not	
accessible	before
Better	Computing
Transforming	data	in	
the	most	optimized	
compute	engine
Data	Availability
Getting	access	to	
actionable	data	at	the	
right	time	to	act
Advanced	Analytics
Gaining	insights	for	
better	business	
decisions	in	real-time
Cloud	Strategy
Cloud	based	sources,	
compute	in	cloud	or	
data	lake	in	cloud
Modern	Data	
Architectures
More	data	sources
More	processing	zones
More	downstream	or	consumer	applications
Emerging	technologies
Multiple	development	patterns
What	Matters	for	Data	Modernization
• Time	to	Value
• Data	Latency
• Data	Consumption	
• Cost
Technology
• Performance
• Scalability
• Maintainability
• Evolving	Technologies	Enablement
Business
Modern	Data	Strategy	Considerations
Invest	in	building	a	core	
Architectural	Framework	
with	modern	data	solutions
Leverage	existing	teams	towards	
building	in-house	expertise	for	
modern	technologies
Augment	teams	with	domain	
expertise	to	leverage	
industry	perspective
Diyotta	Enables	Modern	Data	Integration
Provision	data	for	
Advanced	
Analytics
Acquire	data	from	all	
data	points	in	batch	or	
real-time
Choose	any	compute	
zones	for	data	
processing
Server-less	ETL	
architecture	with	
no	bottlenecks
Resources
Diyotta	Solution	Page:	http://www.diyotta.com/solutions
Modern	Data	Integration	Whitepaper:	
https://www.diyotta.com/modern-data-integration-white-paper/
Request	Demo:	http://www.diyotta.com/demo
Diyotta	Test	Drive:	http://www.diyotta.com/try
Piet	Loubser
VP	Product	and	Solutions	Marketing
Hortonworks
18 ©	Hortonworks	Inc.	2011	–2017.	All	Rights	Reserved
The	New	Way	of	Business	Is	Fueled	By	Connected	Data
• Connected	Customers,	
Vehicles,	Devices
• Socially	crowd-
sourced	requirements
• Digital	design	and	
analysis
• Digital	prototypes	and	
tests	(simulations)
• Connected	Factories,	
Sensors,	Devices
• Human-robotic	
interaction
• 3D-printing	on	
demand
• Connected	Trucks,	
Inventory
• Location,	traffic,	
weather-aware	
distribution
• Real-time	inventory	
visibility
• Dynamic	rerouting
• Connected	
Customers,	Devices
• Omni- channel	
demand	sensing
• Real-Time	
Recommendations
• Connected	Assets
• Remote	service	
monitoring	&	
delivery
• Predictive	
maintenance
• OTA	Updates
Development Manufacturing Distribution Marketing/Sales Service
19 ©	Hortonworks	Inc.	2011	–2017.	All	Rights	Reserved
Modern	Data	Architecture
Telemetry	–
Connected	
Devices
Time–Series	
Historian
Machine
Learning
C L O U D
Sensors,	
SCADA,		
Control	Systems	
Edge	
Analytics
Exceptions	that	
Drive	Predictive	
Maintenance
360	View	of	
Operations,	
Equipment	
Failure	Analytics,	
etc.
Deep	Historical
Analysis
DATA 	C E N T E R
Stream	Analytics
Cyber	&	
Threat Data
20 ©	Hortonworks	Inc.	2011	–2016.	All	Rights	Reserved
Many	organizations	struggle	to	understand	the	difference	between	
Big	Data	and	Big	BI
Big	Business	Intelligence	vs.	Big	Data
Big	BI
DATA Big	Data
§ Same	analysis	as	before,	just	
more	data
§ Batch	or	warehouse-type	
processing
§ Informative,	but	not	really	
actionable
§ Joining	data	sets	never	before	
joined,	 asking	questions	never	
before	asked
§ Real-time	or	near	real-time,	
leading	to	
predictive/persuasive/preventive
§ Ability	to	extract	the	value	of	
data	and	quickly	translate	into	
action	– ”Data	Agility”
§ Action-oriented,	 driving	“Action	
at	the	Speed	of	Insight”Many	apply	traditional	reporting	methods	to	Big	Data	&	Advanced	
Analytics	resulting	in	missed	opportunities	and	limited	financial	return
21 ©	Hortonworks	Inc.	2011	– 2017.	All	Rights	Reserved
22 ©	Hortonworks	Inc.	2011	– 2017.	All	Rights	Reserved
EDW	Optimization patterns	:	3	Use	Cases
• Active	Archiving
• ETL	Offloading
• Data	Enrichment
ANALYTICS
Data	
Marts
Business	
Analytics
Visualization
&	Dashboards
DATA	SYSTEMS
Systems	of	Record
RDBMS
ERP
CRM
Other
23 ©	Hortonworks	Inc.	2011	– 2017.	All	Rights	Reserved
EDW	Plus	Hortonworks	Data	Platform	(powered	by	Apache	Hadoop)
Optimize	and	Reduce	Costs
Archive Cold Data away from EDW
• Move	cold	or	rarely	used	data	to	Hadoop	
as	active	archive	
• Store	more	of	your	data	longer,	cheaper
• Greater	historical	analysis
Offload costly ETL process
• Free	your	EDW	to	perform	high-value	functions	like	
analytics	&	reporting,	not	ETL
• Use	Hadoop	for	advanced	or	massive-scale	ETL/ELT
ANALYTICSDATA	SYSTEMS
Data	
Marts
Business	
Analytics
Visualization
&	Dashboards
Systems	of	Record
RDBMS
ERP
CRM
Other
ELT
°
°
°
°
°
°
°
°
°
°
°
°
°
°
°
°
°
°
°
N
Cold	Data,	
Deeper	Archive
&	New	Sources
Enterprise	Data	
Warehouse
Hot
Data
Science
OLAP	on	Hadoop
24 ©	Hortonworks	Inc.	2011	– 2017.	All	Rights	Reserved
EDW	Plus	Hortonworks	Data	Platform	and	Hortonworks	DataFlow
Land	and	Enrich	more	data	to	respond	faster	to	new	business	requests
Archive Cold Data away from EDW
• Move	cold	or	rarely	used	data	to	Hadoop	
as	active	archive	
• Store	more	of	your	data	longer,	cheaper
• Greater	historical	analysis
Offload costly ETL process
• Free	your	EDW	to	perform	high-value	functions	like	
analytics	&	reporting,	not	ETL
• Use	Hadoop	for	advanced	or	massive-scale	ETL/ELT
Land & Enrich more data to create
more value-add analytics
• Use	Hadoop	to	ingest	new	data	sources,	such	as	web	
and	machine	data	for	new	analytical	context	from	
unstructured	and	semi-structured	sources
• Create	an	analytical	sandbox for	advanced	data	
science
ANALYTICSDATA	SYSTEMS
Data	
Marts
Business	
Analytics
Visualization
&	Dashboards
Systems	of	Record
RDBMS
ERP
CRM
Other
ELT
°
°
°
°
°
°
°
°
°
°
°
°
°
°
°
°
°
°
°
N
Cold	Data,	
Deeper	Archive
&	New	Sources
Enterprise	Data	
Warehouse
Hot
Data
Science
OLAP	on	Hadoop
Clickstream Web	&	Social Geolocation Sensor	
& Machine
Server	
Logs
Unstructured
NEW	SOURCES
Ingest	 Stream	 Events
25 ©	Hortonworks	Inc.	2011	–2016.	All	Rights	Reserved
Advantages	of	Leveraging	the	Hortonworks	Solutions
Deeper	and	
Broader	Data	Sets	
Complete	Data	
Governance	&	Security
Rich	and	Open	
Analytic	Tools
Non-structured	and	
Structured	data
Lower	Total	Cost
of	Ownership
26 ©	Hortonworks	Inc.	2011	–2016.	All	Rights	Reserved
M A X I M U M C O M M U N I T Y I N N O V A T I O N
T H E
I N N O V A T I O N
A D V A N T A G E
P R O P R I E T A R Y
H A D O O P
T I M E INNOVATION
Innovation	happens	best	not	in	
isolation	but	in	collaboration
OPEN	
COMMUNITY
Our	Founding	Belief
27 ©	Hortonworks	Inc.	2011	–2016.	All	Rights	Reserved
M A X I M U M C O M M U N I T Y I N N O V A T I O N
T H E
I N N O V A T I O N
A D V A N T A G E
P R O P R I E T A R Y
H A D O O P
T I M E INNOVATION
Innovation	happens	best	not	in	
isolation	but	in	collaboration
OPEN	
COMMUNITY
Our	Founding	Belief
Unmatched	Economics
Enable	cost	effective	data-center	and	
cloud	architectures
Minimal	Vendor	and	Integration	Risks	
prevents	vendor	lock-in	and	speeds	
ecosystem	adoption	of	ODPi-compliant	
core
28 ©	Hortonworks	Inc.	2011	–2017.	All	Rights	Reserved
Thank	You

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