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1 ©	Hortonworks	Inc.	2011	– 2016.	All	Rights	Reserved
Understanding	Big	Data	Maturity-
Retail	and	Consumer	Goods	
April	2017
2 ©	Hortonworks	Inc.	2011	– 2016.	All	Rights	Reserved
Contents
à Big	Data	in	Retail
– Digital	Revolution
– Explosion	of	Data
à Big	Data	Maturity	Analysis
– Hortonworks	Big	Data	Maturity	Scorecard	
– Retail	and	CPG	Maturity	Analysis
à Big	Data	Use	Cases
– Retail	Use	Case	Maturity	Map
– Single	View	of	Customer
à Big	Data	in	Action
– Retail	Case	Study
– Call	to	Action
45%	of	F	200	firms	want	to	
become	and	Integrated	
Digital	eco-system	provider
47%	of	firms		will	introduce	a	new	digital	product	portfolio	in	18	months
69%	say	improving	their	
data	strategy will	be	key	
to	their	relationship	with	
the	customer	
48%	believe	sustainability	as	a	
key	reason	to	change	their	
digital	business	model	by	2019
78%	of	F	500	organizations	
have	a	medium	to	poor									
Big	Data	and	Analytics	
capabilities
63%	of	$10bn+	firms	are	
witnessing	their core	
business	model	disrupted
Only	36%	CEOs	have a shared	a	
digital	transformation	vision		
although	93%	of	the	employees	
believe	it	is	the	right	thing	to	do
Digital	and	the	Fourth	Industrial	Revolution	through	the	numbers	lens	…
4 ©	Hortonworks	Inc.	2011	– 2017		All	Rights	Reserved Hortonworks	 Confidential.	 For	Internal	Use	Only.
Significance	of	Big	Data	in	Retail
Source:	McKinsey,	Press	Search
Improvement	
potential	in	retail	
operating	margins	
through	 Big	Data	
Improvement	in	
Marketing	potential	
through	 Big	Data
Retail	companies	to	
use	Beacons	in	the	
next	5	years	
Estimated	annual	
economic	impact	of	
IOT	in	Retail	by	
2025
Consumers	now	
use	a	device	or	in-
store	technology	 	
during	 shopping
Estimated	cross-
channel	retail	
sales	in	the	US	by	
2017
60% 70%80% $1.8T>$500B15-20%
5 ©	Hortonworks	Inc.	2011	– 2016.	All	Rights	Reserved
Contents
à Big	Data	in	Retail
– Digital	Revolution
– Explosion	of	Data
à Big	Data	Maturity	Analysis
– Hortonworks	Big	Data	Maturity	Scorecard	
– Retail	and	CPG	Maturity	Analysis
à Big	Data	Use	Cases
– Retail	Use	Case	Maturity	Map
– Single	View	of	Customer
à Big	Data	in	Action
– Retail	Case	Study
– Call	to	Action
6 ©	Hortonworks	Inc.	2011	– 2017		All	Rights	Reserved Hortonworks	 Confidential.	 For	Internal	Use	Only.
There	are	several	challenges	that	are	inhibiting	companies	adopt	Big	Data	
– According	to	Gartner	Value	is	#1	
Source:	Gartner
Determining	how	
to	get	value	from	
big	data
Obtaining	
skills	and	
capablities	
needed
Risk	and	
governance	
issues
Funding	for	big	
data-related	
initiatives
Defining	our	
strategy
Integrated	
multiple	data	
sources
Integrating	big	
data	technology	
with	existing	
infrastructure
Infrastructure	
and/or	
architecture
Leadership	or	
organizational	
issues
Understanding	
what	is	"Big	
Data"
Greater	than	50%
30%	to	49%
20%	to	29%
Less	 than	19%
Only	15%	firms	
are	able	to	
calculate	ROI	for	
any	Digital	
Initiative	
-Mckinsey	Digital
7 ©	Hortonworks	Inc.	2011	– 2016.	All	Rights	Reserved
Tailor	a	Value-based	Path	to	the	Summit	of	Data-driven	Excellence	
Data	Driven	Futuristic	Organization
• Stage	1:	AWARE	
Big	data	is	discussed	but	not	reflected	in	business	strategies	or	
processes	beyond	historical	analysis
•
• Stage	2:	EXPLORE	
Emerging	consensus	on	the	potential	of	big	data	
and	localized	experiments	 and	results	
• Stage	3:	OPTIMIZING	
Operational	performance	is	optimized	in	up	to	
three	dimensions:	customer	lifecycle,	product	
lifecycle,	an	facility	lifecycle	
• Stage	4:	TRANSFORMING																																			
Data	embraced	as	currency,	as	business	value	
streams	are	created	through	predictive	analytics
Hortonworks	Big	Data	Maturity	Scorecard	helps	you	start	that	journey
Stages	of	Hortonworks	
Big	Data	Maturity	Scorecard
8 ©	Hortonworks	Inc.	2011	– 2016.	All	Rights	Reserved
Companies	need	to	understand	where	they	stand	in	terms	of	big	data	maturity	so	that	they	can	progress	and	identify	the	required	
initiatives.	
Our	Big	Data	Scorecard	helps	in	assessing	your	company’s	 current	state	along	
five	key	capability	domains:
1) Sponsorship;	
2) Data	and	Analytics;	
3) Technology	and	Infrastructure;	
4) Organization	and	Skills;	 and
5) Process	Management.	
Within	each	of	these	capability	domains,	 we	identify	four	key	focus	areas	that	
indicate	maturity,	and	then	assess	each	area	according	to	their	specific	 maturity	
level	
Five	capability	domains	of	Hortonworks	Maturity	Model
Although	the	purpose	of	our	framework	is	to	evaluate	your	company’s	 maturity	level	in	these	areas,	we	believe	it’s	far	more	important	to	
understand	how	to	capitalize	on	your	existing	capabilities,	 and	to	invest	in	those	focus	areas	where	we	can	best	maximize	progress	toward	defined	
business	 objectives.
9 ©	Hortonworks	Inc.	2011	– 2016.	All	Rights	Reserved
Sponsorship
Data	&	Analytics	
Practices
Technology	and	
Infrastructure
Organization	
and	Skills
Process	
Management
Overall	Findings	for	Retail	and	Consumer	Goods	Sector
1.5
1.2
2.0
1.71.61.71.7
1.51.5
2.0
1.71.71.61.5
1.9
1.81.92.1
1.6
1.9
2.9
2.6
3.1
2.92.92.82.92.82.8
3.1
2.82.82.9
2.6
3.03.03.03.13.02.9
Cross	Functional	Practices
In	house	or	Outsourced
Operations	Security	Gov
Planning	and	Budgeting
Functionality
Investment	Focus
2.9
Data	Processing
Data	Storage
Data	Collection
Hosting	Strategy
Business	Case
Advocacy
Funding
Data	Analysis
Leadership	Model
Vision	Strategy
Program	Measurement
Analytic	Dev	Skills
Integration
Analytic	Tools
1.7
• Overall,	firms	are	still	in	the	
Exploration	stage	–
– Firms	lack	enterprise	vision	 around	Big	
Data	with	little	executive	sponsorship	
– Firms	are	primarily	using	structured	data	
and	are	outsourcing	Big	Data	projects
– They	are	starting	to	adopt	analytical	
tools	for	project	specific	objectives
• In	the	next	2-3	years,	firms	are	
expected	to	be	in	Optimizing phase
– With	enterprise-wide	vision	and	
alignment	with	sponsorship	 and	funding
– Firms	will	have	data	lake	with	
unstructured	data	and	integrated,	
analytical	tools	on	top	of	it
– Will	leverage	mix	of	in-house	 and	
outsourced	resources
Current	 In	2-3	YearsBig	Data	Maturity	Scores	(Average)
Key	Takeaways
In	the	next	few	slides,	we	analyze	the	Big	Data	scorecard	results	along	the	5	capability	domains
10 ©	Hortonworks	Inc.	2011	– 2016.	All	Rights	Reserved
Capability	Domain:	Sponsorship
29%
6%
65%
35%35%
24%
6%
4321
6%
12%
53%
29%
41%
24%24%
12%
4321
6%
24%
41%
29%
41%
29%
24%
6%
4321
12%
47%
35%
6%
35%35%
24%
6%
321 4
• Vision	and	Strategy: Currently,	most	
of	the	firms	are	in	early	stages	of	
establishing	enterprise-wide	vision	on	
Big	Data	and	in	2-3	years	most	of	
them	will	have	one	
• Funding: Big	data	projects	are	
primarily	driven	by	IT	projects	and	
budget.	But	Big	Data	programs	will	be	
part	of	cyclical	budgeting	process	in	
the	near	future
• Advocacy: There	is	some	level	of	
executive	sponsorship	which	will	only	
increase	with	better	alignment	
among	the	leadership
• Business	Case: Most	of	the	firms	
although	don’t	have	business	case	
right	now,	they	plan	to	have	one	in	
the	next	2-3	years
Vision	and	Strategy Funding
Business	CaseAdvocacy
Current In	2-3	Years
%	of	firms	at	a	Maturity	Level
Key	Takeaways
11 ©	Hortonworks	Inc.	2011	– 2016.	All	Rights	Reserved
Capability	Domain:	Data	and	Analytics	Practices
29%
35%35%
41%
24%
29%
6%
4321
6%
12%
35%
47%
35%
29%
35%
0%
4%3%2%1%
6%6%
18%
71%
18%
35%35%
12%
1 432
6%
12%
18%
65%
29%
35%29%
6%
4321
Data	Collection Data	Storage
Data	AnalysisData	Processing
Current In	2-3	Years
%	of	firms	at	a	Maturity	Level
• Data	Collection: Although	most	of	
the	firms	still	use	structured	data,	
they	expect	to	make	big	strides	and	
will	deploy	automated	mechanisms	
to	collect	both	structured	and	
unstructured	data	in	2-3	years	
• Data	Storage: Most	still	discard	
majority	of	the	data	but	are	planning	
to	have	“data	lake”	to	keep	their	data
• Data	Processing: Currently,	
processing	is	manual	but	firms	expect	
to	have	enterprise-wide	metadata	
standards	in	the	near	future
• Data	Analysis: Most	of	the	firms	
focus	mainly	on	business	metrics	
reporting	that	is	going	change	to	
more	advanced	and	predictive	
analytics	in	the	next	2-3	years
Key	Takeaways
12 ©	Hortonworks	Inc.	2011	– 2016.	All	Rights	Reserved
Capability	Domain:	Technology	and	Infrastructure
6%
24%
59%
12%
29%29%29%
12%
4321
0%
24%24%
53%
24%
41%
29%
6%
4%3%2%1%
6%
24%
35%35% 35%35%
29%
0%
1 432
0%
6%
41%
53%
18%
53%
18%
12%
4321
Hosting	Strategy Functionality
IntegrationTools
Current In	2-3	Years
%	of	firms	at	a	Maturity	Level
• Hosting	Strategy: Most	of	the	firms	
currently	store	data	on-premise	but	
expect	to	deploy	hybrid	hosting	
infrastructure	going	forward
• Functionality: Majority	of	the	firms	
currently	deploy	EDW	data	
warehouses	and	are	in	the	process	of	
complementing	it	with	Hadoop-based	
clusters
• Tools: Firms	are	starting	to	adopt	
analytical	tools	for	project	specific	
objectives	and	will	increasingly	have	
central	administration	of	these	tools
• Integration: Currently,	there	is	little	
integration	between	the	tools	but	
with	the	Hadoop	deployment	there	
will	be	better	integration	and	cross-
functional	analysis
Key	Takeaways
13 ©	Hortonworks	Inc.	2011	– 2016.	All	Rights	Reserved
Capability	Domain:	Organization	and	Skills
6%
12%
29%
53%
41%
29%
12%
18%
4321
0%
12%
29%
59%
18%
47%
35%
0%
4%3%2%1%
6%6%
41%
47%
24%
41%
29%
6%
1 432
6%6%
35%
53%
29%
41%
18%
12%
4321
Analytical	and	Development	Skills In-house	or	Outsourced
Cross-functional	PracticesLeadership	Model
Current In	2-3	Years
%	of	firms	at	a	Maturity	Level
• Analytical	and	Development	Skills:
Currently,	the	Big	Data	skills	are	
mostly	located	within	the	IT	
organization	but	firms	are	investing	a	
lot	to	gain	advanced	analytical	skills	
across	the	organization
• In-house	or	Outsourced: For	
majority	of	the	firm,	significant	work	
is	being	outsourced	but	firms	will	
deploy	mix	of	in-house	and	
outsourced	skill-set	in	the	near	future
• Leadership: Majority	firms	are	also	
expected	to	have	a	centralized	
analytics	group	to	help	drive	the	Big	
Data	programs
• Cross-functional	Practices: With	
centralized	group,	firms	will	have	
increasing	capability	for	cross-
functional	collaboration	and	analysis
Key	Takeaways
14 ©	Hortonworks	Inc.	2011	– 2016.	All	Rights	Reserved
Capability	Domain:	Process	Management
0%
35%
29%
35%
41%
35%
18%
6%
4321
6%
12%
29%
53%
35%
24%
41%
0%
4%3%2%1%
35%
0%0%
24%
76%
18%
35%
12%
4321
0%
18%18%
65%
29%
35%29%
6%
1 432
Planning	and	Budgeting
Operations,	Security	and	
Governance
Investment	FocusProgram	Measurement
Current In	2-3	Years
%	of	firms	at	a	Maturity	Level
• Planning	and	Budgeting: Although	
there	is	little	formal	planning	and	
budgeting	for	Big	Data	programs	
currently,	the	firms	expect	to	have	
one	in	2-3	years
• Operations,	Security	and	
Governance: Firms	vary	in	this	
dimension	– majority	will	have	
enterprise-wide	policy	and	protocol	
in	the	near	future
• Program	Measurement: Majority	of	
the	firms	expect	to	monitor	the	
outcomes	from	Big	Data	programs	
along	with	the	formal	planning
• Investment	Focus: Although	
investment	is	currently	made	on	ad	
hoc	basis,	it	is	expected	to	change	to	
find	new	sources	of	revenue	and	
business	models	moving	forward
Key	Takeaways
15 ©	Hortonworks	Inc.	2011	– 2016.	All	Rights	Reserved
Sponsorship
Data	&	Analytics	
Practices
Technology	and	
Infrastructure
Organization	
and	Skills
Process	
Management
Case	Study:	A	leading	European	Retailer
1.01.0
3.03.0
2.02.0
3.03.03.03.03.0
2.0
3.0
1.0
3.0
2.0
3.03.03.03.0 3.03.0
4.04.04.04.04.04.04.04.04.04.04.0
3.0
4.04.04.04.04.04.0
2.5
3.9
Investment	Focus
Program	Measurement
Operations	Security	Gov
Planning	and	Budgeting
Cross	Functional	Practices
Business	Case
Advocacy
Funding
Vision	Strategy
Leadership	Model
In	house	or	Outsourced
Analytic	Dev	Skills
Integration
Analytic	Tools
Functionality
Hosting	Strategy
Data	Analysis
Data	Processing
Data	Storage
Data	Collection
• Retailer	is	already	in	Optimizing	stage
and	will	attain	the	highest	maturity,	
Transforming,	in	Big	Data	in	2-3	years	
– Has	an	enterprise-wide	vision	and	
strategy	– on	which	rest	of	the	key	
elements	of	Big	Data	depend
– Has	started	to	ingest	unstructured	data	
and	rarely	discard	data
– Has	adopted	Hadoop	to	accomplish	
different	workloads	with	integration	of	
analytical	tools	across	the	organization	
– Is	investing	in	Big	Data	skills	for	
advanced	analytics	and	leverages	both	
inhouse	and	outsourced	resources	
– Has	already	included	Big	Data	programs	
in	its	budgeting	and	planning	cycle
Current In	2-3	Years
Big	Data	Maturity	Scores
Key	Takeaways
16 ©	Hortonworks	Inc.	2011	– 2016.	All	Rights	Reserved
Contents
à Big	Data	in	Retail
– Digital	Revolution
– Explosion	of	Data
à Big	Data	Maturity	Analysis
– Hortonworks	Big	Data	Maturity	Scorecard	
– Retail	and	CPG	Maturity	Analysis
à Big	Data	Use	Cases
– Retail	Use	Case	Maturity	Map
– Single	View	of	Customer
à Big	Data	in	Action
– Retail	Case	Study
– Call	to	Action
17 ©	Hortonworks	Inc.	2011	– 2016.	All	Rights	Reserved
Transformation
--- Maturity Stages à
OptimizationExplorationAwareness
---MaturityStagesà
Marketing
Merchandising
IT Ops
Digital
Store Operations
Purchasing &
Logistics
2
6
7
4
10
11
13
1a
1b
12
8
3
95
15
14
16
17
Peer	Competitive Scale
Standard	among	peer	group
Common	among	peer	group
Strategic	among	peer	group
New	Innovations
Retail	Industry	– Use	Case	Maturity	Roadmap
No Use	Case	Name
1a Single	View	of	Customer
1b Single	View	of	Customer
2 Basket Analysis
3 Social	Listening	
4 Enriched	Basket	Analysis
5 Clickstream	Analysis
6 Recommendation	 Engine
7 Price Optimization	
8
Beacon/Sensor	Monitoring	
and	Ingest
9 Store	Communications
10 Email	Management
11 EDW	Enhancement
12 Inventory	Optimization	
13 Path	to	Purchase
14 Supply	Chain	Telemetry
15 Customer	Service	Analysis
16 Preventative	Maintenance
17 Machine	Learning	/	AI
Discussed	in	
subsequent	slides
18
Use	Case:	Single	View	of	Customer
• Ability	to	identify	#	unique	customers	which	directly	impacts	both	the	top-line	ROI	measurement	and	bottom–line	optimization
• Increased	customer	loyalty,	LFL	sales,	average	basket	size,	redemption	propensity	on	promotional	activity,	listing	fees	
• Dynamic	real	time	targeted	pricing	which	results	in	better	margins	from	your	most	loyal	customers	
Business	
Value
• Better	customer	experience	leading	to	increased	loyalty	and	customer	advocacy	
• Increased	Marketing	Effectiveness	leading	to	higher	ROI	on	every	£	spent	
• Cross	selling	and	predictive	promotional	propensity	means	greater	number	of	manufacturer	partnerships	
Why	
Do	It?
• Currently,	Retailers,	CPG	firms	and	other	manufacturers	create	shopper	profiles	based	on	historical	data,	SKU	level	data	and	
Basket	data	– They	however	struggle	to	marry	that	data	with	the	behavioral	data	from	multiple	other	channels	(mobile,	Social	
Media,	etc.)	to	map	out	the	DNA	of	the	customer	and	fail	to	predict	futuristic	buying	patterns	of	customers	across	categories
and	products
• Single	View	of	the	customer	not	only	allows	organizations	the	ability	to	create	targeted	campaigns	based	on	shopping	patterns
but	also	opens	up	new	avenues	of	revenue	streams	through	advanced	marketing	efforts	such	as	cross	device	marketing,	
beacon	sensing,	proximity	mktg.	etc.	
Idea	
Summary
The	Single	View	of	Customer	combines	historical	sales	data	from	structured	systems	with	new,	unstructured	and	semi-structured	
data	from	social	media,	sentiment	analysis,	web	activity,	and	blog	posts.	Single	View	of	the	customer	helps	create	the	DNA	of the	
consumer	that	can	be	used	to	target,	re-target,	personalize	messaging	to	help	address	issues	around	loyalty,	churn,	cross-selling,	
increasing	the	top	line	etc.	
Innovate	–Grow	&	Enable
19
Contents
à Big	Data	in	Retail
– Digital	Revolution
– Explosion	of	Data
à Big	Data	Maturity	Analysis
– Hortonworks	Big	Data	Maturity	Scorecard	
– Retail	and	CPG	Maturity	Analysis
à Big	Data	Use	Cases
– Retail	Use	Case	Maturity	Map
– Single	View	of	Customer
à Big	Data	in	Action
– Retail	Case	Study
– Call	to	Action
Ø Quick	Facts
Quick	Facts
• For	direct	marketing,	the	lack	of	visibility	into	a	customer’s	credit	and	financial	situation	
restricted	retailer's	ability	to	pre-screen	“right”	customers	to	send	the	mailers
• Mismatch	between	Inventory	Merchandising	Ad	Planner	and	Warehouse	Inventory	led	
to	incomplete	sales
• Generation	of	various	business	reports	took	days	to	complete	and	even	after	that,	not	all	
the	information	was	available	to	the	Business	stakeholders
Situation	Analysis
Innovation	Strategy
• Retailer	built	an	Enterprise	Analytics	platform	based	on	Hortonworks	Data	Platform,	breaking-
down	silos	and	increasing	historical	depth	of	data	available	for	analysis
• Drove	targeted	marketing	strategy	with	insight	driven	customer	segmentation	analysis,	
leveraging	new	data	sources,	including	the	available	history
• Implemented	near-real	time	simulation	of	new	Credit	Strategy	with	respect	to	approval	or	
decline	of	application	process	by	collecting	exhaustive	set	of	variables	needed	for	credit	policy	
coding	for	all	customers
Business	Impact
• Reduced	Spend	on	Direct	Mailers	by	optimizing	mailing	by	Customer	Segment:	$3M	in	first	10	
months	of	2016	($4.5	to	$5.0M	expected	run	rate	savings)
• Reduced	ads	effectiveness	analysis	in	Product	Performance	report:	300x	improvement	in	
turnaround
• Reduced	associate	time	in	coding	for	red-flags	and	lookups	for	decline	rules:		500x	time	
reduction in	implementing	credit	policy
$3M
Marketing	dollars	saved	to-
date	from	trimming	the	direct	
mailers
Up	to	500x
Time	improvement	in	
implementing	credit	policy
Up	to	45x
Time	improvement	in	
generating	Inventory	
Merchandising	Ad	Planner
21 ©	Hortonworks	Inc.	2011	– 2016.	All	Rights	Reserved
Call	to	Action
Take	Big	Data	Scorecard	Survey	on	
Hortonworks	website
Collaborate	with	Hortonworks	to	
create	value-based	Big	Data	roadmap

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Big Data Maturity as a Business: A Retail Case Study

Editor's Notes

  1. Use the tombstone
  2. Digital revolution is transforming the industries. Data strategy is key part of this strategy and 2/3rd are working towards improving it.
  3. In retail, there is big potential of Big Data .. 60% improvement potential in operating margins, 15-20% improvement in marketing potential. This is driven by big transformational changes in retail – mobile devices, IOT sensors and that measure customer movements in store. Cross-channel is becoming important where customers shop across the stores and online
  4. Use the tombstone
  5. Retail firms are in the exploration stage where firms don’t currently have full-fledged Big Data vision and strategy or primarily using structured data. But they are actively working on it – across the board average maturity scores will improve and most of them will be in Optimizing phase. This will be driven by having enterprise-wide vision and strategy, usage of unstructured data and leveraging inhouse and outsourced skill set. Most of the firms are currently in the Exploration stage Firms lack enterprise vision around Big Data and funding is unbudgeted Although most of the firms still use structure data, some have started to collect unstructured data as well Firms have also started to adopt analytical tools for project specific objectives Big Data skills are mainly located among technologists and most of the work is outsourced There is lack formal process for planning Big Data programs In 2-3 years, firms are planning to attain Optimizing stage Firms plans to attain enterprise-wide vision and alignment with sponsorship and funding Firms expect to make big strides in storing their data through Data Lake Firms will use tools that fit the purpose with centralized administration of tools and integration among the tools Organizations are investing to gain advanced analytical skills and will leverage mix of in-house and outsourced skill set Planning and budgeting for Big Data will be part of cyclical budgeting process
  6. In vision and strategy, majority of the firms currently lack enterprise-wide vision. Funding is unbudgeted and seems to come from IT projects, there is little executive sponsorship and there is very little business case development around Big Data. In 2-3 years, most of the firms will have enterprise-wide vision and strategy for Big Data. Funding will be part of cyclical budgeting process. There will be increased alignment among executive sponsors to support Big Data with Big Data business cases being developed
  7. In Data and Analytics domain, most of the firms are still using structured data while discarding most of the data they collect. The firms are also focused mainly on measuring key business metrics for their business rather than doing advanced analytics. Over the next 2-3 years, firms plan to leverage unstructured data, store it in the data lakes while keeping the data even if it isn’t being in use at that time. Firms are planning to perform advanced and predictive analytics on top of this data lake.
  8. Currently firms store their data on-premise in the traditional EDWs. They are staring to adopt analytical tools for specific objectives but aren’t able to conduct cross-functional analysis as there is little integration of tools across the organization. W.r.t. technology, the firms are mainly moving toward hybrid hosting strategy of on-prem and cloud based storage and analysis. As discussed, firms are planning to have data lake, which will be based on multiple Hadoop clusters with tools on top of it that will be integrated and with ability to provide cross-functional insights.
  9. In terms of organization and skills, whatever Big Data skills firms have, are located in the IT organization currently. Firms outsourced quite a bit of work for Big Data projects. Firms also don’t yet have CoE to enable best practices in the whole organization and to achieve cross-group collaboration. But firms expect things to be different in 2-3 years. Most of the firms are investing in gaining advanced analytical skills and expect mix of in-house and outsources Big data skills set within the organization. Majority of the firms are also planning to have centralized COE group for cross-functional collaboration and institutionalizing best practices.
  10. Within process management, firms currently lack planning around Big Data programs as projects seem to be driven within IT, by IT budgets. Given this, there is hardly any evaluation of results from Big Data projects. In future, majority of the firms are planning to have budgeting at either business-unit level or at the enterprise-wide. This will result in businesses looking for Big Data programs that drive new value streams and business models. With this, will come more effective measurement around Big Data projects and their outcomes. Firms do have wide spectrum of capabilities when it comes to data security. Some have basic security and governance process while others do have enterprise-wide standards in these areas. These capabilities will only improve in 2-3 years.
  11. We analyzed the maturity scorecard data for one of the top European retailer. The retailers already has an enterprise-wide vision and strategy, which drives rest of the organization. The form ingests and analyzes unstructured data in Hadoop to perform advanced analytical and predictive skills. It is investing in Big data skills with has already included Big Data programs in its budgeting and planning cycle. The firm is already in Optimizing stage and will be in Transforming stage in 2-3 years
  12. Use the tombstone
  13. Use the tombstone