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Acknowledgements	
This	paper	was	developed	by	the	Emerging	Cloud	Services	Working	Group,	comprised	of	members	from	the	
following	companies/organisations:	
1. Mr	Arun	Sundar,	Chairman	of	the	Emerging	Cloud	Services	Working	Group,	and	Chief	Strategy	Officer,	
TrustSphere	
2. Magnus	Young,	Director	of	Programmes,	Asia	Cloud	Computing	Association	
3. Bernie	Trudel,	Chairman,	ACCA,	and	Head	of	Data	Centre	Technologies,	Cisco	
4. Eric	Hui,	Treasurer,	ACCA,	and	Director	of	Cloud	and	Service	Provider	Markets,	Equinix	
5. Oyvind	Roti,	Head	of	Solutions	Architecture	–	International,	Google	Cloud	Platform	
6. Sudith	Devanarayana,	VP	–	Emerging	Business,	Telstra		
7. Madhwesh	Kulkarni,	Director	–	Digital	Applications	Global	Delivery	Network,	CSC	
8. Manish	Pratap,	General	Manager	–	Cloud	Business	Unit,	Dimension	Data		
With	assistance	from	the	ACCA	Secretariat	team:	
9. Lim	May-Ann,	Executive	Director	
10. Evelyn	Widjaja,	Director	of	Programmes	
11. Magnus	Young,	Director	of	Programmes	
	
Published	Oct	2016	by	the	Asia	Cloud	Computing	Association.	All	rights	reserved.	
Cover	design	by	booCreatives	Pte	Ltd.
Asia	Cloud	Computing	Association	|	Data	Analytics	to	Bridge	Knowledge	Gaps	|	October	2016	|	Page	3	of	16	
	
	
	
Data	Analytics	to	Bridge	Knowledge	Gaps	
An	ACCA	White	Paper	on	Supply	and	Demand	for	Big	Data	
Analytics	in	Asia	Pacific	
Oct	2016
Asia	Cloud	Computing	Association	|	Data	Analytics	to	Bridge	Knowledge	Gaps	|	October	2016	|	Page	4	of	16	
	
	
Table	of	Contents	
Executive	Summary	......................................................................................................................................................	5	
A.	Introduction	..............................................................................................................................................................	7	
B.	Supply	and	demand	of	data	analytics	......................................................................................................................	8	
C.	Data	analytics	awareness	.........................................................................................................................................	9	
D.	Barriers	to	increased	use	of	analytics	......................................................................................................................	9	
E.	Comparison	across	industry	verticals	....................................................................................................................	10	
F.	Conclusion:	Future	Demand	for	Analytics	.............................................................................................................	11	
Appendix	A:	Methodology	.........................................................................................................................................	12	
Appendix	B:	Data	Analytics	Questionnaire	...............................................................................................................	13
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Executive	Summary	
Data	 analytics	 is	 a	 fast-growing	 practice	 area	 and	 demand	 for	 data	 scientists	 and	 analytics	 professionals	 is	
soaring.	This	is	clear	from	the	noise	level	around	big	data	and	analytics,	that	this	is	the	new,	cloud-enabled	
frontier	for	companies	capitalise	on,	to	speed	up	business	growth	and	develop	and	maintain	their	competitive	
advantages.		
Through	dialogues	with	industry	and	users,	members	of	the	Asia	Cloud	Computing	Association	(ACCA)	noted	a	
trend	where	the	demand	for	data	analytics	seemed	greater	than	its	supply.	Sentiments	shared	with	us	through	
dialogues	suggested	gaps	in	the	market,	where	the	demand	for	analytics	was	not	met	by	the	current	analytics	
services	or	skillsets	available.		
As	a	result,	the	ACCA	developed	this	white	paper	to	examine	this	trend.	We	make	five	observations	from	our	
findings,	and	offer	four	suggestions	to	plug	the	gaps.		
Finding	1:	Users	need	analytics	services	to	overcome	in-house	data	gaps	
Prospective	analytics	users	overwhelmingly	indicate	that	they	rely	on	insights	based	on	data	that	they	do	not	
possess	in-house.	They	understand	that	analytics	services	can	help	them	perform	better	by	providing	insights	
based	on	analysis	of	the	inputs,	processes	and	output	of	the	company.	And	the	majority	of	analytics	services	rely	
on	data	inputs	from	the	users	–	in	particular	related	to	cost	drivers.	
Plugging	the	Gap	#1:	Analytics	providers	that	can	play	the	dual	role	of	assisting	customers	in	identifying	and	
procuring	the	right	data,	along	with	providing	the	analytics	services	–	will	be	best	placed	to	meet	demand	for	
analytics	services.		
Finding	2:	Analytics	providers	must	simplify	the	process	of	implementing	analytics	
Analytics	users	do	not	perceive	their	own	awareness	and	skills	as	major	barriers	to	the	use	of	analytics.	While	
analytics	users	understand	the	purpose	and	use	cases	of	analytics,	they	do	not	understand	common	tools	and	
technologies	 related	 to	 analytics.	 Our	 survey	 found	 that	 analytics	 users	 are	 not	 familiar	 with	 analytics	
technologies,	 common	 database	 management	 solutions,	 and	 processing	 tools	 for	 big	 data,	 indicating	 that	
analytics	customers	may	lack	the	technical	skills	to	implement	complex	analytics	models	for	big	data.		
Plugging	the	Gap	#2:	Analytics	providers	that	offer	simple	solutions	customised	to	user	needs	will	accelerate	
adoption	of	analytics	among	new	users.		
Finding	3:	Analytics	providers	must	demonstrate	the	value	of	their	services	
The	greatest	barrier	to	adoption	as	perceived	by	procuring	analytics	services	is	cost.	Justifying	and	calculating	
the	 return	 on	 investment	 (ROI)	 in	 analytics	 is	 a	 somewhat	 subjective	 exercise.	 It	 is	 often	 couched	 in	 cost-
avoidance	terms	i.e.	“investing	in	analytics	could	prevent	excess	spending	in	another	area”,	making	it	difficult	for	
prospective	users	like	CFOs	to	justify	the	expense.	A	related	finding	is	that	analytics	providers	may	overestimate	
the	extent	to	which	users	prioritise	analytics	as	critical	to	the	future	growth	of	their	companies.		
Plugging	the	Gap	#3:	Providers	that	effectively	communicate	the	value	of	their	analytics	services	through	use	
cases	will	spur	investments	into	analytics	services	and	are	better	placed	to	attract	new	analytics	customers.
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Finding	4:	Effective	analytics	is	powered	by	cloud	computing	
Companies	in	some	industries	perceive	industry	regulation	as	a	barrier	to	adopting	cloud,	which	is	an	obvious	
technological	enabler	of	analytics.	As	a	result,	these	companies	are	unlikely	to	adopt	effective	data	analytics	
solutions.		
Plugging	the	Gap	#4:	Facilitating	the	use	of	cloud	services	is	an	important	step	to	accelerate	adoption	and	
innovation	in	analytics.	
Finding	5:	Analytics	insight	is	key	to	staying	competitive	
The	majority	of	companies	surveyed	use	data	analytics.	However,	less	than	half	of	the	companies	use	analytics	
for	finance	related	decisions	–	and	only	one	in	20	companies	use	analytics	for	human	resource	decisions.	This	
indicates	that,	while	analytics	is	mainstreaming,	there	is	great	potential	for	analytics	to	improve	decision-making	
in	 many	 areas.	 Companies	 that	 change	 their	 thinking,	 act	 on	 analytical	 insights	 and	 become	 data-driven	
businesses	are	the	winners	of	tomorrow.	
Plugging	the	Gap	#5:	Access	to	data	is	not	enough;	senior	management	must	use	analytics	to	drive	their	
decision-making	and	become	part	of	the	story	of	their	company.	
What’s	next?	Recommendations	for	unlocking	data	analytics	on	cloud	
Based	on	our	findings,	we	offer	some	recommendations	on	how	to	unlock	the	potential	of	data	analytics	on	cloud	
to	four	stakeholders:	analytics	providers,	regulators	and	public	sector	organisations,	innovators,	and	ecosystem	
players.	
Analytics	providers	
Recommendation	#1	–	Build	and	train	capacity.	To	generate	demand	for	their	own	services,	providers	should	
develop	more	capacity	building	services,	including	training	and	consulting	for	their	clients.	This	is	perhaps	the	
area	where	providers	to	date	are	the	most	successful.	
Recommendation	#2	–	Simplify	data	analytics	tools.	To	meet	future	demand,	providers	should	develop	simpler	
data	analytics	platforms	and	real-time	analytics	software	that	can	be	customised	to	the	end	user,	especially	in	
sectors	that	have	yet	to	adopt	analytics	on	a	meaningful	scale.	This	could	further	transition	to	providing	end-to-
end	services	to	help	customers	transform	their	business	into	a	data-driven	business	through	analytics.			
Regulators	and	the	public	sector	
Recommendation	#3	–	Transparent	public	education	on	use	of	data	insights.	Regulators	should	promote	privacy	
regulations	that	reflect	the	reality	of	big	data	and	analytics.	Government	organisations	should	facilitate	informed	
use	of	open	data	for	analytics	that	can	improve	citizens’	lives	and	provide	insights	that	help	businesses	plan	for	
the	future.	The	government	should	also	educate	the	public	so	that	citizens	can	make	informed	decisions	on	how	
data	collected	from	their	daily	lives	are	used	for	analytics	by	third	parties.	
The	innovator	
Recommendation	#4	–	Keep	innovating	–	and	show	the	way	for	others!	Start-ups	and	innovators	fall	into	two	
categories	 of	 innovations:	 those	 that	 disrupt	 entire	 industries	 through	 innovative	 application	 of	 new	
technologies,	 and	 those	 that	 bring	 incremental	 change.	 The	 former	 leverage	 analytics	 to	 defeat	 traditional	
business	models	in	different	industry	verticals.	The	latter	can	increase	the	adoption	rate	of	analytics	by	bringing	
analytics	services	that	meet	the	demand	of	prospective	users.
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While	there	is	a	mature	market	for	customer	and	cost	driver	analytics,	there	is	strong	demand	for	analytics	
services	that	enable	improved	business	decisions	and	increase	the	analytics	users’	revenue	and	profit	margins.	
Innovation	in	these	areas	will	enable	faster	adoption	of	analytics	if	they	are	matched	with	evidence	and	case	
studies	from	the	market	that	articulate	the	value	proposition	and	approach	of	the	analytics	services.	
Ecosystem	players	
Recommendation	#5	–	Cross-pollinate	sectors	with	analytics.	Ecosystem	players,	including	investors,	accelerators,	
incubators	 and	 third-party	 consultants,	 can	 accelerate	 the	 development	 as	 well	 as	 adoption	 of	 analytics	 in	
business.	Market	disruptions	typically	take	place	when	analytics	and	data	services	are	allowed	to	cross-pollinate	
with	existing	products	and	services	to	bring	a	better	and	more	tailored	experience	to	the	users	of	these	products	
and	services.	This	includes	disruptions	in	retail	(e-commerce	companies),	media	(music	and	video	streaming	
services)	and	transport	(taxi-	and	other	ride-sharing	apps).	Similar	disruptions	will	take	place	in	other	sectors	–	
the	question	is	not	whether	it	will	happen,	but	when.	Analytics,	enabled	by	cloud	computing,	will	drive	this	
innovation.			
	
A. Introduction	
Data	analytics	is	a	fast-growing	discipline	and	demand	for	data	scientists	and	analytics	professionals	is	soaring.	
This	is	clear	from	the	wide	usage	of	data	analytics	as	a	tool	across	verticals	to	speed	up	business	growth	and	
develop	and	maintain	competitive	advantages	in	companies.		
This	trend	implies	that	demand	for	data	analytics	is	greater	than	its	current	supply	of	analytics.	In	other	words,	
the	demand	cannot	be	met	by	the	services	and	skills	currently	offered	by	the	market.		
This	white	paper	examines	this	trend.	It	compares	the	supply	and	demand	of	data	analytics	in	Asia	Pacific	to	
identify	current	demand	for	and	supply	of	data	analytics.	The	paper	focuses	on	analytics	use	in	Asia	Pacific,	
including	barriers	to	increased	data	analytics	use,	and	points	towards	where	demand	for	the	service	will	be	
tomorrow.		
Global	trends,	Asian	insights	
The	report	draws	on	insights	from	analytics	providers	and	users	in	Asia	Pacific.	Data	collected	using	surveys	and	
interviews	provide	insight	into	the	supply	and	demand	of	analytics	in	markets	that	are	underrepresented	in	
comparable	research.	Industry	verticals	included	are	advisory,	consulting,	e-commerce,	education,	retail	and	ICT.	
The	report	also	benefits	from	insights	from	both	large	traditional	analytics	providers	as	well	as	smaller	ones	that	
understand	the	needs	of	niche	users.	
The	25	companies	surveyed	represents	a	diverse	set	of	analytics	providers	and	users.	Interviews	and	focus	group	
discussions	complemented	the	survey	findings.		
The	majority	(67%)	of	the	analytics	providers	in	the	study	have	a	global	remit	and	operate	across	the	globe.	The	
analytics	users	surveyed	are	companies	from	Asia	Pacific	that	operate	in	one	(46%)	or	more	(54%)	markets	in	
Asia	Pacific.
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Figure	1	–	Survey	Response:	Breakdown	of	Analytics	Providers	and	Users	
B. Supply	and	demand	of	data	analytics	
We	present	some	key	insights	in	this	section.	It	is	important	for	both	analytics	providers	and	users	to	develop	a	
deeper	understanding	of	each	other.	The	former	needs	to	understand	the	limitations	in	analytics	skillsets	among	
users	and	provide	simpler	and	more	customised	services,	while	the	former	needs	to	communicate	their	needs	
and	demands	in	a	coherent	manner	to	analytics	providers.	
Data-driven	decision-making	has	become	mainstreamed	
Data	 analytics	 is	 becoming	 a	 part	 of	 decision-making	 processes,	 and	 crucial	 to	 companies	 developing	 and	
maintaining	competitive	advantages.	Analytics	providers	help	users	better	understand	their	customers,	enable	
customer	segmentation	and	targeting,	improve	process	efficiencies	and	the	quality	of	services,	and	inform	the	
development	of	new	products	and	services.	62%	of	the	companies	surveyed	currently	use	data	analytics	in	their	
daily	operations.	This	is	indicative	of	the	mainstreaming	of	data	analytics,	and	presents	a	strong	potential	for	
growth	in	the	field	through	targeting	new	customers.	
Strong	demand	for	customisation	
Current	and	prospective	users	of	analytics	demand	greater	customisation	of	analytics	services	and	interfaces	for	
their	needs.	With	the	exception	of	e-commerce,	companies	in	most	industries	find	it	difficult	to	leverage	analytics	
for	effective	customer	targeting	and	predictive	analyses	that	feed	into	marketing	and	the	development	of	new	
products	and	services.	Therefore,	industries	outside	of	e-commerce	demand	greater	customisation	of	analytics	
services	to	meet	their	specific	needs.	
Analytics	providers	must	help	users	overcome	data	gaps	
All	analytics	users	in	the	survey	indicated	that	their	analytics	services	draw	on	data	that	they	do	not	currently	
possess.	13%	of	the	companies	surveyed	rely	on	analytics	services	that	exclusively	analyse	third-party	data,	
whereas	75%	rely	on	analytics	that	utilises	both	in-house	data	and	third-party	data.	
This	is	indicative	of	the	two	main	challenges	for	users	before	taking	the	plunge	into	analytics:	data	management	
and	the	know-how	to	implement	data	analytics.	Analytics	providers	must	also	help	customers	with	their	data	
needs	–	users	know	the	purpose	they	want	analytics	to	serve,	but	they	either	do	not	possess	the	data,	or	they	
do	not	know	what	data	is	needed	to	execute	the	data	analysis.		
46%
54%
Analytics	Users
APAC	Single	Market APAC	Regional
33%
67%
Analytics	Providers
APAC	Single	Market Global
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Many	analytics	solutions	for	customer	segmentation	and	cost	drivers	
There	 is	 a	 large	 market	 for	 analytics	 services	 that	 help	 companies	 understand,	 segment	 and	 target	 their	
customers,	and	also	ample	supply	of	analytics	to	help	companies	understand	their	cost	drivers.	However,	less	
than	a	third	of	prospective	analytics	users	indicate	that	they	are	looking	for	customer	analytics	services,	and	
only	 14%	 demand	 analytics	 to	 understand	 their	 cost	 drivers.	 This	 could	 indicate	 that	 users	 are	 either	
experienced	 enough	 in	 managing	 and	 understanding	 their	 customer	 analytics	 services	 and	 cost-driver	
analytics,	or	–	more	likely	–	are	hesitant	or	ignorant	of	these	solutions.		
Strong	demand	for	analytics	that	drive	strategic	decisions	
Analytics	users	indicate	strong	demand	for	analytics	that	can	help	inform	strategic	business	decision.	Companies	
in	Asia	Pacific	want	to	use	analytics	to	improve	the	quality	of	their	existing	services,	as	well	as	to	drive	investment	
decisions	but	have	not	used	analytics	for	those	purposes.	There	is	a	gap	in	the	market	for	strategy-focused	
analytics,	which	creates	a	business	opportunity	for	innovators	and	analytics	providers	targeting	analytics	for	
strategic	decision-making.	
	
C. Data	analytics	awareness	
Analytics	users	have	high	awareness	of	the	potential	use	of	data	analytics,	but	do	not	always	know	how	to	
implement	effective	analytics	for	their	data.	Data	analytics	is	topical,	and	all	companies	know	they	have	to	do	
“something”,	but	struggle	with	execution.		
Knowledge	gaps	on	analytics	tools		
Current	 and	 prospective	 analytics	 users	 have	 an	 equally	 good	 grasp	 of	 concepts	 such	 as	 data	 mining	 and	
predictive	analysis	as	analytics	providers	do.	However,	there	is	a	significant	gap	between	analytics	providers’	and	
users’	knowledge	of	tools	and	technologies	related	to	big	data	and	data	analytics.		
Analytics	users	have	a	limited	grasp	of	the	technicalities	and	implementation	models	for	analytics	on	big	data.	
Users	 are	 less	 familiar	 with	 technologies	 and	 mechanisms	 such	 as	 SQL	 and	 NoSQL.	 Cassandra,	 a	 database	
management	solution	for	big	data	across	distributed	servers,	and	MapReduce,	a	model	for	implementing	and	
processing	big	data	on	a	cluster,	are	understood	by	0%	and	8%	of	users,	respectively.	
To	 overcome	 the	 knowledge	 gaps,	 analytics	 providers	 should	 focus	 on	 customisation	 and	 ease	 of	 use	 of	
analytics	services	as	well	as	effective	articulation	of	how	analytics	can	create	value	for	the	user	and	how	to	get	
started.	
	
D. Barriers	to	increased	use	of	analytics	
If	there	is	unmet	demand	for	analytics	services,	there	must	be	barriers	that	are	stopping	users	from	employing	
more	analytics	services,	as	well	as	barriers	for	providers	to	deliver	the	services	that	are	demanded.	These	barriers	
must	be	overcome	in	order	to	close	the	gap	between	supply	and	demand	for	analytics	services.		
Barriers	to	adopting	analytics	could	include	security	concerns	related	to	moving	data,	such	as	customer	data,	to	
and	from	the	cloud;	reputational	concerns	related	to	harvesting	data	from	customers	to	derive	commercial	
benefits;	and	privacy	issues	when	using	data	about	particular	individuals.
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Prospective	analytics	users	concerned	about	costs		
On	the	demand	side,	analytics	users	see	the	cost	of	procuring	analytics	services	as	the	greatest	barrier,	followed	
by	 concerns	 about	 data	 permissions	 and	 centralised	 data	 capture.	 The	 cost	 of	 data	 collection,	 analytics	
implementation,	and	procurement	of	analytics	services	are	the	key	concerns	for	prospective	analytics	users.	
Overcoming	these	concerns	by	providing	evidence	from	use	cases	to	show	the	return	on	investments	in	analytics	
are	key	to	accelerating	adoption	and	analytics	use.		
Skills	gap	on	data	management	and	security		
Users	may	not	know	how	to	execute	on	their	plans	and	ambitions,	and	the	setup	of	the	required	infrastructure	
and	 software	 is	 seen	 as	 cumbersome.	 Prospective	 customers	 also	 worry	 that	 data	 management	 and	 data	
permissions	may	hamper	their	ability	to	implement	analytics	services.	When	customers	move	to	an	aggregated	
“data	lake”,	the	data	permissions	within	an	organisation	may	present	a	barrier.		
Companies	that	have	successfully	implemented	analytics	have	often	needed	to	employ	dedicated	data	scientists	
that	can	incorporate	analytics	in	business	and	strategic	decisions,	which	may	not	be	an	option	for	SMEs.	To	
accelerate	adoption,	analytics	providers	must	help	their	customers	overcome	the	internal	barriers	to	data	
management.	
Analytics	providers	worry	about	awareness	and	skills		
Analytics	 providers	 believe	 that	 the	 greatest	 barriers	 to	 adopting	 analytics	 are	 prospective	 users’	 lack	 of	
awareness	on	how	to	execute	analytics	services	and	the	lack	of	skilled	employees	among	potential	analytics	
users.	Analytics	providers	understand	that	the	cost	of	procuring	analytics	services,	the	cost	of	improving	data	
capture,	and	the	lack	of	customisation	of	analytics	services	are	seen	as	barriers	by	users,	but	are	more	convinced	
that	the	benefits	outweigh	the	costs	than	users	are.	
Providers	disagree	on	the	gravity	of	some	potential	barriers.	Analytics	providers	that	are	also	cloud	service	
providers	are	more	concerned	about	data	permissions	and	the	challenges	related	to	establishing	a	centralised	
data	capture	within	an	organisation	than	non-CSP	analytics	providers.		
Providers	and	users	disagree	on	barriers	
Analytics	users	are	more	concerned	about	the	lack	of	industry	standards	and	data	classification	systems	than	
providers.	The	fact	that	analytics	is	not	a	strategic	priority	for	end-users	is	also	seen	as	a	greater	barrier	by	the	
users	than	the	providers	–	indicating	that	analytics	providers	may	overestimate	the	extent	to	which	potential	
analytics	users	are	actively	looking	to	increase	the	use	of	analytics	services.		
The	biggest	gap	is	seen	in	concerns	about	awareness	of	how	to	implement	analytics	services,	and	the	availability	
of	employees	with	the	right	skills	and	qualifications.	Providers	see	these	as	primary	concerns,	while	analytics	
users	clearly	see	these	concerns	as	secondary	to	their	concerns	about	data	capture,	permissions,	and	the	costs	
associated	with	procuring	analytics	services.	
	
E. Comparison	across	industry	verticals	
There	may	not	be	an	industry	skew	in	terms	of	potential	extraction	of	value	from	data	analytics,	but	there	is	an	
industry	skew	in	terms	of	adoption.		
Banks	and	financial	institutions	are	very	strong	on	“traditional”	data	analytics	–	especially	of	structured	data	
–	with	very	sophisticated	models	for	areas	such	as	fraud	detection,	projections	and	simulations.	However,	they
Asia	Cloud	Computing	Association	|	Data	Analytics	to	Bridge	Knowledge	Gaps	|	October	2016	|	Page	11	of	16	
have	 been	 lagging	 behind	 on	 adopting	 analytics	 for	 unstructured	 data	 and	 on	 customer	 segmentation	 and	
targeting.		
The	retail	sector,	and	in	particular	e-commerce,	is	leading	the	way	on	analytics	for	inventory	management	and	
customer	segmentation.	E-commerce	companies	have	advanced	data	analytics	for	customer	segmentation	and	
targeting.	The	speed	at	which	targeted	ads	appear	online	is	indicative	of	their	sophistication.	Companies	in	this	
bracket	–	including	other	digital	and	“born	in	the	cloud”	companies	such	as	mobile	gaming	and	streaming	services	
–	use	data	analytics	as	a	means	to	stay	competitive.	
Lagging	industries	are	hampered	by	privacy	and	data	protection	concerns	–	both	real	and	perceived	–	that	stop	
them	from	using	customer	data.	In	some	cases,	regulations	may	inhibit	cloud	use	and	data	aggregation,	while	
in	other	cases	companies	may	believe	that	industry	regulations	are	a	barrier	to	data	analytics	–	even	if	that	is	
not	the	case.	This	is	indicative	of	the	need	for	regulatory	clarity	as	well	as	policies	and	regulations	that	enable	
cloud	use	and	data	mobility.	
F. Conclusion:	Future	Demand	for	Analytics	
Unstructured	data	
Analytics	providers	largely	cater	to	analysis	of	structured	data.	With	an	estimated	80%	of	business	information	
consisting	of	unstructured	data,	and	the	volume	of	unstructured	data	growing	by	62%	per	year,1
	unstructured	
data	will	be	a	key	drive	of	analytics	demand	in	the	future.	
Alternate	commercial	models	and	outcome-based	pricing	
Mainstreaming	of	analytics	will	likely	lead	to	new	service	delivery	and	pricing	models,	including	analytics-as-a-
service	and	outcome-based	pricing.	Given	the	challenges	in	illustrating	the	return	on	investments	into	analytics,	
analytics	providers	that	are	able	to	sell	the	results	of	their	analytics	services	rather	than	selling	data	and	
software	services	will	thrive.	
Public	sector	and	smart	societies	
Governments	are	increasingly	working	to	maximise	the	utility	of	their	data	and	leverage	data	analytics	in	order	
to	create	value	for	their	citizens.	Cities	and	national	governments	are	increasingly	moving	away	from	treating	
data	primarily	as	a	risk	towards	championing	the	use	of	data	as	a	driver	of	more	effective	and	innovative	public	
service	delivery	models.	Smart	city	and	smart	nation	initiatives	will	continue	to	drive	demand	for	new	analytics	
applications.	
Analysis	of	third-party,	social	and	environmental	data	
Few	analytics	providers	currently	focus	on	analytics	of	external	social	and	environmental	data.	A	staggering	75%	
of	analytics	users	–	and	80%	of	potential	users	–	indicate	a	desire	to	adopt	analytics	services	that	analyse	
external	social	and	environmental	data.	
There	is	a	potentially	huge	demand	for	analytics	services	that	look	into	the	social	and	environmental	performance	
of	 organisations.	 This	 includes	 analytics	 that	 cover	 aspects	 such	 as	 human	 capital	 development	 within	 an	
organisation	and	the	external	environmental	impact	of	a	company’s	products	and	services.	
																																																													
1
	Deloitte,	2014,	Technology	in	the	mid-market	http://www2.deloitte.com/content/dam/Deloitte/us/Documents/Deloitte%20Growth%20Enterprises/us-
dges-deloitte-techsurvey2014-081114.pdf
Asia	Cloud	Computing	Association	|	Data	Analytics	to	Bridge	Knowledge	Gaps	|	October	2016	|	Page	12	of	16	
Appendix	A:	Methodology	
This	report	is	based	on	two	phases	of	research:		
Phase	1	
The	first	phase	was	based	on	interviews	with	a	taskforce	consisting	of	data	analytics	providers	and	cloud	industry	
experts	in	Australia,	Hong	Kong	and	Singapore.	Interviews	were	conducted	in	two	steps:		
1. Taskforce	participants	were	sent	a	questionnaire	via	email	for	them	to	provide	input	on:		
a. Customer	awareness	of	analytics;	
b. Different	uses	of	analytics	by	industry	verticals;	
c. Initiatives	and	technologies	that	enable	greater	use	of	analytics;	and		
d. Key	analytics	champions	in	different	industries.	
2. The	taskforce	was	then	interviewed	either	in	person	or	over	the	phone	to	provide	context	and	more	
detail	 on	 their	 responses	 to	 the	 questionnaire,	 as	 well	 as	 to	 solicit	 input	from	the	taskforce	 on	the	
questions	for	the	survey	in	the	second	phase	of	the	research.		
Phase	2	
The	second	phase	mapped	the	data	analytics	landscape	across	14	countries	in	Asia	Pacific,	and	pointed	to	where	
demand	for	data	analytics	will	be	tomorrow.	Using	an	online	survey,	data	was	collected	from	a	broad	set	of	
providers	and	users	of	data	analytics,	with	a	view	to	identify:	
1. Gaps	between	demand	and	supply	of	analytics	services;	
2. Gaps	between	providers’	and	users’	understanding	of	analytics	capabilities;	and		
3. Gaps	in	providers’	and	users’	perception	of	barriers	to	greater	use	of	data	analytics.	
The	survey	was	disseminated	in	a	strategic	manner	to	achieve	three	objectives:	
1. Direct	engagement	of	analytics	providers	to	ensure	high	response	rate	of	senior	decision-makers	/	CXOs	
for	the	supply-side	analysis;	
2. Direct	engagement	of	analytics	users	in	healthcare	and	finance	sectors	to	ensure	high	response	rate	of	
senior	decision-makers	/	CXOs	for	demand-side	analysis	in	two	priority	industries;	and	
3. Wider,	general	outreach	to	potential	analytics	users	across	a	broad	range	of	industries	to	compare	the	
responses	of	potential	analytics	users	with	those	of	senior	decision-makers	in	the	two	priority	industries.	
Analysis	and	validation	
See	Appendix	B	for	the	survey	questionnaire.	Responses	were	analysed	to	evaluate	differences	in	the	demand	
for	and	understanding	of	analytics	in	the	different	markets	and	industries.	
	
Findings	from	the	survey	were	validated	with	the	taskforce	participants	from	Phase	1	and	socialised	with	the	
members	of	the	ACCA.	Their	input	formed	part	of	the	analysis	in	the	final	report.
Asia	Cloud	Computing	Association	|	Data	Analytics	to	Bridge	Knowledge	Gaps	|	October	2016	|	Page	13	of	16	
Appendix	B:	Data	Analytics	Questionnaire		
The	 survey	 respondent	 sees	 different	 questions	 depending	 on	 whether	 he	 is	 an	 analytics	 provider,	
analytics	user,	or	a	prospective	analytics	user	that	currently	has	no	significant	analytics	activities.	
Questionnaire:	Analytics	PROVIDER		
Q1.	What	do	your	data	analytics	services	aim	to	
offer	your	clients?	Please	select	all	that	apply.	
Better	understanding	of	our	clients'	customers;	
Better	segmentation	/	targeting	of	our	clients'	customers;	
Better	understanding	of	our	clients'	cost	drivers;	
Improve	quality	of	our	clients'	existing	services;	
Analytics	/	research	for	our	clients	to	develop	new	services;	
Other	(Please	specify)	______________.	
Q2.	What	sources	of	data	do	you	capture	and	
analyse	for	your	customers?	Please	select	all	
that	apply	
Supplier	data;	
Operations	data;	
Customer	profiles;	
External	social	and	environmental	data.	
Q3.	What	constructs	of	data	do	you	capture	and	
analyse	for	your	customers?	
Structured	data;	
Unstructured	data.	
Q4.	What	frequency	of	analysis	do	you	deliver?	
Please	select	all	that	apply.	
Data	for	ad-hoc	deep	analysis;	
Data	for	automated	and/or	real-time	analysis.	
Q5.	How	familiar	are	you	with	the	following	
tools	and	technologies	related	to	big	data	and	
analytics?	
Cassandra;	
Data	mining;	
Hadoop;	
Machine	learning;	
MapReduce;	
Predictive	analysis;	
SQL	/	NoSQL;	
Tableau.	
Q6.	What	do	current	and	potential	data	
analytics	users	see	as	the	biggest	barriers	to	
increased	adoption	of	data	analytics?	Please	
rank	all	that	apply.	
Computing	power	/	speed	of	analysis;	
Concerns	about	vendor	lock-in;	
Concerns	about	security	and/or	data	privacy;	
Data	analytics	is	not	a	strategic	priority;	
Data	analytics	services	are	not	customised	to	our	needs;	
Data	permissions	/	no	centralised	capture;	
High	cost	of	improving	data	capture	and	data	management;	
High	cost	of	procuring	data	analytics	services;	
Lack	of	awareness	of	how	to	implement	data	analytics;	
Lack	of	industry	standards,	data	classification	systems,	or	
standardisation.	
Q7.	Which	countries	or	regions	do	you	currently	
serve	with	your	analytics	services?	
[FREE	TEXT]	
Q8.	What	is	your	role	as	an	analytics	provider	in	
helping	customers	adopt	data	analytics?	How	
successful	do	you	think	you	are	at	doing	this?	
[FREE	TEXT]	
	
Questionnaire:	Analytics	USER		
Q1.	Do	you	see	the	use	of	data	analytics	as	
critical	to	the	future	growth	and	
competitiveness	of	your	organisation?	
YES/NO	
Q2.	Who	is	responsible	for	data	analytics	at	your	
organisation?	
Separate	dedicated	Data	Analytics	department	/	team;	
Data	Analytics	is	a	function	of	an	existing	department	(i.e.	not	a	separate	
department).
Asia	Cloud	Computing	Association	|	Data	Analytics	to	Bridge	Knowledge	Gaps	|	October	2016	|	Page	14	of	16	
Q3.	Do	you	use	open-source	services	such	as	
Hadoop	or	OpenStack	for	data	analytics?	
YES/NO	
Q4.	Who	do	you	believe	is	best	placed	to	be	
your	data	analytics	provider?	Please	rank	the	
below	options.	
Our	telecommunications	provider;	
One	of	our	software	vendors;	
A	cloud	service	provider;	
A	solution	integrator.	
Q5.	What	is	your	motivation	for	using	data	
analytics?	
Better	understanding	of	our	customers;	
Better	segmentation	/	targeting	of	our	customers;	
Better	understanding	of	our	cost	drivers;		
Improve	quality	of	our	existing	services;	
Analytics	/	research	for	our	clients	to	develop	new	services;	
Other	(Please	specify):	
Q6.	What	sources	of	data	do	you	want	to	
capture	and	analyse?	Please	select	all	that	
apply.	
Supplier	data;	
Operations	data;	
Customer	profiles;	
External	social	and	environmental	data.	
Q7.	Ownership	and	control	of	data:	Please	
select	which	types	of	data	you	rely	on	in	your	
data	analytics?	
Only	in-house	data?	(i.e.	data	that	you	collect	and	own	inside	your	
organisation);	
Only	third-party	data?	(i.e.	data	that	you	procure	from	third-parties	/	do	
not	own);	
Both	in-house	and	third-party	data?	
Q8.	How	familiar	are	you	with	the	following	
tools	and	technologies	related	to	big	data	and	
analytics?	
Cassandra;	
Data	mining;	
Hadoop;	
Machine	learning;	
MapReduce;	
Predictive	analysis;	
SQL	/	NoSQL;	
Tableau.	
Q9.	What	do	you	see	as	the	biggest	barriers	to	
increased	adoption	of	data	analytics?	Please	
rank	all	that	apply.	
Computing	power	/	speed	of	analysis;	
Concerns	about	vendor	lock-in;	
Concerns	about	security	and/or	data	privacy;	
Data	analytics	is	not	a	strategic	priority;	
Data	analytics	services	are	not	customised	to	our	needs;	
Data	permissions	/	no	centralised	capture;	
High	cost	of	improving	data	capture	and	data	management;	
High	cost	of	procuring	data	analytics	services;	
Lack	of	awareness	of	how	to	implement	data	analytics;	
Lack	of	industry	standards,	data	classification	systems,	or	
standardisation.	
Q10.	Which	countries	or	regions	do	you	
currently	operate	in?	
[FREE	TEXT]	
	
Questionnaire:	Prospective	analytics	users	that	currently	have	no	significant	analytics	activities	
Q1.	Do	you	see	the	use	of	data	analytics	as	
critical	to	the	future	growth	and	
competitiveness	of	your	organisation?	
YES/NO	
Q2.	Who	do	you	believe	is	best	placed	to	be	
your	data	analytics	provider?	Please	rank	the	
below	options.	
Our	telecommunications	provider;	
One	of	our	software	vendors;	
A	cloud	service	provider;	
A	solution	integrator.	
Q3.	What	is	your	motivation	for	using	data	
analytics?	
Better	understanding	of	our	customers;	
Better	segmentation	/	targeting	of	our	customers;	
Better	understanding	of	our	cost	drivers;		
Improve	quality	of	our	existing	services;	
Analytics	/	research	for	our	clients	to	develop	new	services;	
Other	(Please	specify)		______________.
Asia	Cloud	Computing	Association	|	Data	Analytics	to	Bridge	Knowledge	Gaps	|	October	2016	|	Page	15	of	16	
Q4.	What	sources	of	data	do	you	want	to	
capture	and	analyse?	Please	select	all	that	
apply.	
Supplier	data;	
Operations	data;	
Customer	profiles;	
External	social	and	environmental	data.	
Q5.	How	familiar	are	you	with	the	following	
tools	and	technologies	related	to	big	data	and	
analytics?	
Cassandra;	
Data	mining;	
Hadoop;	
Machine	learning;	
MapReduce;	
Predictive	analysis;	
SQL	/	NoSQL;	
Tableau.	
Q6.	What	do	you	see	as	the	biggest	barriers	to	
increased	adoption	of	data	analytics?	Please	
rank	all	that	apply.	
Computing	power	/	speed	of	analysis;	
Concerns	about	vendor	lock-in;	
Concerns	about	security	and/or	data	privacy;	
Data	analytics	is	not	a	strategic	priority;	
Data	analytics	services	are	not	customised	to	our	needs;	
Data	permissions	/	no	centralised	capture;	
High	cost	of	improving	data	capture	and	data	management;	
High	cost	of	procuring	data	analytics	services;	
Lack	of	awareness	of	how	to	implement	data	analytics;	
Lack	of	industry	standards,	data	classification	systems,	or	
standardisation.	
Q7.	Which	countries	or	regions	do	you	currently	
operate	in?	
[FREE	TEXT]
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asiacloudcomputing.org
is.gd/accacloud
@accacloud
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