ZDNet Big Data Priorities 2013 PDF

8/22/2019 ZDNet Big Data Priorities 2013 PDF

1/35Copyright 2013 CBS Interactive Inc. All rights reserved

Analytics/Big Data strategies, challenges and

implementation priorities

Big Data Priorities 2013

Sponsored by:


2/35Copyright 2013 CBS Interactive Inc. All rights reserved.

2BIG DATA PRIORITIES 2013

Con

tents

4Executive Summary

12 About This Report12 About Big Data Priorities

12 Report and Webcast Release Schedule

12 About ZDNet and CBS Interactive

13Strategic Perspectiveson Analytics/Big Data

13 The Growing Potential o Analytics/Big Data

14 Exploitation o Analytics/Big Data Lags Potential

15 Leadership Team Members Share Responsibility or

Driving Analytics/Big Data

16 Current Use of Analytics/Big Data16 Analytics in Day-to-Day Business

17 The Business Functions Where Analytics/Big Data is

Being Applied

18 Outcomes Targeted with Analytics/Big Data

19 The Reasons Why Businesses Arent Using

Analytics/Big Data

20 The Obstacles That Stop Businesses Getting Maximum

Benefts rom Analytics

21Which Data is Key for

Analytics/Big Data?21 Types and Sources o Data Used or Analytics

23 Businesses are Tackling a Range o Data Issues

24 Data-as-a-Service Providers (DaaS)




Con

tents

25Big Data Project Owners

and Budget Holders

25 The CIO and CEO are the Key Players

26 The Financials of Analytics/Big Data26 Analytics/Big Data Produces Measurable

Financial Returns

27 Organizations Seek Aggressive Payback on

Analytics/Big Data Investments

28 Approach to Analytics/Big Data Infrastructure28 Types o Technologies Used in 2012

29 Implementation o Analytics/Big Data Technologies

Will Grow Sharply in 2013

30 Perspectives on Big Data Is It Just Hype?

31Technology Directions for

Analytics/Big Data

31 Traditional Tools Rule in 2012

32 OLAP is the Leading Technology or Analysis

33 Survey Methodology33 Approach and Sample

33 Scope and Terminology

34 Timeline

34 Respondents

34 Respondent Demographics




Data has become a critical commodity in the

21st century economy. Recent new technologies

have accelerated the growth in sheer volume

o data collected, and devices such as sensors,

smart phones and tablets are ueling the data

explosion, leading to a doubling o the worlds

digital data in just the past two years. At

the same time, the latest data warehouses,

distributed fle systems, analytical tools and

aordable cloud-sourced computing power

provide ways to fnd meaning and value in the

mountains o data.

The combination o massive increases in data and better tools and

methods or processing it creates competitive opportunities and

challenges or almost all organizations. Those that ace the challenges

and orge ahead can prosper, while those that dont will be let behind.

In this research, we examine how organizations are responding to the

digital data explosion. We look at strategic perspectives on analytics

and big data, at how organizations use analytics in day-to-day decision-

making and to what purpose, and we identiy the executives driving the

application o analytics.

Execu

tiveS

ummary




Many businesses can see the potential o analytics/big data, and see that potential

increasing in uture years. At the same time, they are not exploiting analytics/big data to

anywhere near ull potential.

Currently, just under one-quarter o organizations (23.8%) say analytics/big data

has high potential to have a major inuence on their business perormance. When

respondents look a year ahead (2013), that grows to 37.1%, and it doubles to 50.3%

when they look ahead to 2014.

Almost 90% o organizations concede they are not currently exploiting the business

potential o analytics/big data. This doesnt mean they dont care about the topic, but

it does signiy they have other investment priorities, or an inability to exploit analytics/

big data, or both.

At a strategic level, senior executives are sharing responsibility or driving the exploitationo analytics/big data in their organizations. However, at a deployment level, the CIO and

CEO are clearly most involved as managers o analytics/big data budgets and plans.

Just under one-hal o businesses (47.1%) in North America use analytics in everyday

decision-making and business processes today, while 52.9% do not. The breadth o

analytics usage varies.

5.2% use analytics across the organization, and regard it as a core competency, and

5% use it in most departments.

14.4% use analytics in a limited number o departments, and 22.5% have just started

using analytics.

18.1% will deploy analytics capabilities or the frst time in 2013, raising membership o

the Analytics club to 65.3% o organizations.

Organizations with 100+ sta are much more advanced on the analytics deployment

path.

Almost one hal o organizations (44.1%) use analytics in marketing (17.4% to a great

extent, and 26.7% to a major extent). While analytics eorts are most ocused on

marketing, the utilization o analytics is quite evenly shared across dierent business

unctions.

Strategy is the second most common application o analytics/big data (43.1 %),

ollowed by production and/or operations (39.5%), and then sales (38.7%).

31% o businesses use analytics in general management to a great or major extent.

That this is their lowest ocus or analytics demonstrates the even spread o analytics

eorts across business unctions.

Execu

tiveS

ummary




For most businesses, analytics/big data is all about producing

outcomes in revenue, customers, productivity and markets.

Almost two-thirds (62.6%) o organizations are prioritizing

revenue generation outcomes either as a top priority (24.8%)

or major priority (37.8%).

Creating a deeper understanding o clients is also high

up the priorities list, with 57.4% saying its a top or major

priority. Similar proportions cite productivity gains as a

priority outcome.

Businesses are also using analytics to create a deeper

understanding o their markets (54.1% state this is a major

or top priority).

While analytics-using organizations are almost twice as likely to

use internally-sourced data as they are to use data rom external

sources, adoption o the latter has grown rapidly.

More than three-quarters o analytics-using businesses

(77.4% ) source data rom systems like Finance, Enterprise

Resource Planning (ERP), Customer Relationship

Management (CRM) or use in day-to-day decision-making.

By contrast around one-third (34.4%) are using data

sourced rom social networking and media. Given the

relatively recent development o social media, this is arelatively high level o adoption, and i it continues it will

quickly become a widespread practice.

Trac and activity on an organizations own website is also an

important data source on which analytics is perormed. The

emergence o sensors as a major data source attests to the

growth o the machine-to-machine (M2M) ecosystem.

Businesses also source and buy data rom commercial third-

party data suppliers, a practice that is becoming known as Data-

as-a-Service (DaaS), and its a practice that will increase.

More than one-quarter (26.7%) use data supplied bycommercial third-party DaaS suppliers such as Experian,

StrikeIron, Dun & Bradstreet.

Mobile devices are another important data source almost one-

third o businesses (23.7%) use data rom mobile devices.

Execu

tiveS

ummary

The majorityo businesses

report

measurable

fnancial benefts

rom analytics/

big data

initiatives.




Execu

tiveS

ummary All but one-tenth (9.9%) o businesses report measurable nancial benets rom analytics/big data initiatives, but the extent varies.

Roughly one-fth (19.3%) have achieved major fnancial benefts, and 6.4% report

achieving fnancial benefts to a great extent.

One-third report theyve achieved fnancial benefts to a medium extent, and a urther

one-third to a minor extent.

A majority o businesses expect to see payback on analytics/big data investments sooner

rather than later.

Organizations have aggressive expectations or payback on analytics/big data. Almost

two-thirds (61.4%) expect payback within two years or ewer. Roughly one-quarter

(22.7%) are looking or payback within one year.

A urther one-quarter (25.8%) expect a return on their investment within two to three

years, while just 12.9% expect the payback period to exceed three years.

The 2012 analytics/big data technology landscape is in its relative inancy.

Almost one-hal o organizations (48.3%) have neither an analytics nor a big data

inrastructure.

Almost one-quarter (23%) perorm analytics using data directly rom operational

systems.

Big Data and data warehouse inrastructure is not yet widely employed

18.8% perorm analytics on data sourced rom a data warehouse.

Just 5.4% use a big data inrastructure based on Hadoop, Map Reduce or similar

toolsets.

4.7% perorm analytics with data sourced both rom data warehouse and big data

environments.

2013 is the year the majority o businesses will utilize analytics and/or big data

capabilities.

The proportion o businesses using analytics will grow rom 51.7% in 2012 to 72.3% in

2013.

By the end o 2013, 21.3% o respondents will be using a combination o data

warehouse and big data platorms. This amounts to our-old growth in one year.




Organizations are evenly split about the importance and urgency

o big data.

Almost one-hal say its key (comprising the 39.1% who say

its a core competency, and the 10% who say its the most

important capability or their business).

The remainder see big data as having possibilities but not as a

priority (18.9%), or important but not transormational (27.8%).

4.3% o respondents think big data is just marketing hype.

Traditional analytics tools are three times more commonly used

today than big data tools

In October 2012, when this survey was felded, more thanone-hal o businesses (58.4%) used traditional analytics

technologies such as OLAP tools, relational database

management systems (RDBMS) and data warehouses to

support and achieve their analytics objectives.

A urther 14.2% plan to implement traditional technologies

by the end o 2012, and another 16.7% will deploy by

the end o 2013 by which time a shade under 90% o

analytics-using business will have deployed traditional

OLAP/data warehouse technologies.

Big data technologies will grow aster, but rom a much

smaller base

Packaged big data appliances i.e. pre-confgured

combinations o hardware, data warehouse tools, integration

tools and versions o the Hadoop distributed fle system

(DFS) - are currently the most widely deployed big data

toolset. 16.3% o respondents have already deployed these

appliances, and predicted deployment will grow to 47.2% o

businesses by the end o 2013, a growth actor o 2.9X.

The astest growth in deployment will be o open source

distributed tools such as Hadoop, which respondentsindicate will grow by a actor o six by the end o 2013

by which time 50.2% o organizations will be utilizing that

approach.

Next generation data warehouse oerings are already used

by 13.3% o respondents, and that will grow by a actor o

4.7 to 63% by the end o 2013.

Execu

tiveS

ummary

The use o data

visualization

and statical

analysis tools

will also become

widespread.




New analytical tools will increasingly take root in businesses by the end o 2014

Traditional OLAP tools such as Cognos and Sybase have been widely deployed in

analytics-using businesses, and that will continue.

Predictive analytics tools are quickly becoming common, and by 2014 these will be the

most widely deployed analysis tools.

The use o data visualization and statistical analysis tools will also become widespread.

The times they are a-changin

The big picture ormed by the many small data points in this survey is one o change, a sense that

a tipping-point is upon us. The analytics/big data initiatives trail-blazed by internet businesses over

the past 10 years are now seeping into the culture and business operations o more traditional

organizations. The seeping will become more o a ood, as businesses look to leverage both thenew technologies pioneered by early adopters, and the accessibility o cost-eective computing

resources rom cloud service providers. Businesses that arent on board will have challenges.

Those moving to the new analytics-powered model have challenges o their own: in applying

Analytics to the appropriate business problems; in sourcing, managing and stewarding data; in

deploying unamiliar open-source toolsets; and not least in fnding and developing the appropriate

skillsets. Those that persist have an opportunity to outsmart their competitors in the market.

Execu

tiveS

ummary




How should businesses react? Recommendations:

1. Dont be let behind

Forty-seven percent o businesses are already using analytics in everyday decision-making and

processes, and another 18% will start in 2013. I your business is not doing so, youre probably at

a competitive disadvantage. Devise a clear plan to assess how analytics and big data can help your

business compete, and then begin to consider the ways to incorporate analytics into your business.

2. Ask yoursel: What do I wish I knew?

Organizations sometimes dont know where to start with analytics/big data. Begin by asking

yoursel what critical pieces o inormation youd love to know about your business. What would you

love to know about your customers, about the potential customers you dont have who currently buy

rom competitors, and about the markets in which you operate? The answers to these questions will

provide a priority list rom which you can begin your analytics/big data journey.

3. Think about where you can nd and manage the inormation or data you lack

You probably already have much o the data you need rom operational systems in your business,

and in the behavioral data you collect rom your website trafc and your social presence. But is it

properly organized, is it available to people who make business decisions, do you have appropriate

data governance with rules about data ownership, standardization and rules or sharing? Look

outside the organization or data that plugs the holes in the data you already have. Good sources

include public data supplied through open government initiatives, commercial data-as-a-service

providers, and even social media eeds. Consider data sharing arrangements with non-competing

organizations.

4. Develop, hire and retain employees with data skills

Data science is a specialized crat. Its a combination o statistics, mathematics and technology

know-how aligned with business domain knowledge and the ability to ask the right penetrating

questions about the data an organization holds. These people work with business leaders to unlock

the value o data in a business, and can answer the questions you wish you knew. You need to

fnd and retain the best people.

5. Gut eel? Get Real! Inculcate an analytics culture across your organization

An analytics mindset is not only or the data specialist. Businesses must oster the use o data

across the organization, and move away rom decision by intuition, or gut eel. This is a change

process like any other youve used to move your organization rom one cultural paradigm to another.

The senior leadership team must lead by example, and should employ the deep data skills o data

science specialists to coach line managers and their sta on how best to use data to inorm their

decision-making every day.

Execu

tiveS

ummary




6. Do it!

Select the most important I wish I knew topic and get started.

Make the business case or your priority data project, state the investment and targeted fnancial

return and challenge your business leaders and data specialists to deliver. Youll learn rom your

mistakes, fnd opportunities you werent aware o, and youll develop your institutional data

competencies.

Execu

tiveS

ummary




About Big Data Priorities

Part o ZDNets IT Priorities research series, this is the frst North

American edition o Big Data Priorities, a survey by CBS Interactive

about the analytics/big data strategies, challenges and implementation

priorities o IT leaders.

Please reer to the methodology chapter at the end o this report or

more inormation about the report scope, respondent base and topics

covered.

Report and Webcast Release Schedule

The release date or the Big Data Priorities 2013 report is

January 24, 2013.

The key fndings will be reported in a special ZDNet/TechRepublic

webcast on January 24 2013 and on demand aterwards. Report author

Angus Macaskill will outline key themes, and discuss the fndings with

analytics thought leaders David Boyle o EMI Music Group and Hilary

Mason o bitly, inc. and Ken Wincko o Dun & Bradstreet. The audience

will also submit questions or discussion and response by the panel.

About ZDNet and CBS Interactive

ZDNet (www.zdnet.com) is where technology means business. The site

attracts an enthusiastic and interactive audience o business technology

inuencers, many o whom visit or the latest coverage and analysis o

how technology impacts business.

Business leaders and decision makers including CEOs, CIOs and IT

proessionals at all levels value the site due to its extensive resources,

enabling them to make the most out o technology or their business

challenges.

A

bou

tthisReport




The growing potential o analytics/big data

QUESTION: What is the potential or data analytics/big data to have a major inuence on your

organizations perormance?

Organizations clearly see the growing

potential o analytics/big data. The

proportion that say analytics/big data

will have a major infuence on their

business doubles rom 2012 to 2014.

Currently, just less than one-

quarter o organizations (23.8%)

say analytics/big data has

high potential to have a major

inuence on their business

perormance (rating 8 or above

on a 10-point scale).

But that number grows to

37.1% when respondents

look a year ahead (2013), and

doubles to 50.3% when they

look ahead to 2014.

The chart shows the proportion of organizations rating analytics/big data potential at 8 or above on a

10 point scale (1=No potential, 10=Great Potential).

Larger organizations see greater potential in analytics/big data

23.8% o all respondents say analytics/big data has high potential to signifcantly

inuence business perormance.

30.6% o larger businesses (100+ sta) say analytics/big data has high potential to

inuence business perormance, compared to 20.4% o smaller organizations (




Exploitation o Analytics/Big Data lags potential

QUESTION: How would you rate your organizations current exploitation o data analytics/

big data compared to the potential exploitation?

Almost 90% o organizations concede they are not currently exploiting the business

potential o analytics/big data. This is not conclusive evidence they dont care about the

topic, but it does convey they have other investment priorities, or an inability to exploit

analytics/big data, or both.

Only 12.1% o respondents say their exploitation o analytics/big data is close to

reaching its ull potential.

Larger businesses (100+ sta) and smaller ones (




Leadership team members share responsibility or driving

analytics/big dataQUESTION: To what extent are the ollowing stakeholders driving your organization to exploit

data analytics/big data?

Senior executives are sharing responsibility or driving the exploitation o analytics/

big data in their organizations.

The CIO and CEO are most widely involved to a great extent (rated 10 on a 1 to 10

scale) in driving the use o analytics.

The involvement o other C-level executives (CFO, COO etc.) is clear when the measure

is 8 or higher on the 1 to 10 scale.

One-third o business also say their line o business heads, such as the sales director

or marketing director, are also pushing analytics initiatives.

Line of

Business Heads

Other Senior

Leadership

CEO

CIO/IT

leaders

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

7.2% 6.0% 5.2% 12.1% 9.6% 9.9% 11.1%11.2% 11.7% 15.9%

9.9% 8.2% 7.2% 11.6% 7.0% 9.7% 9.1%

4.2%

22.7% 10.4%

7.6% 9.1% 5.5% 13.3% 7.7% 10.6% 9.7% 6.9% 21.3% 8.4%

7.2% 6.2% 6.9% 11.2% 11.6% 11.7%10.9% 8.4% 6.4%19.6%

Percentage of Organizations, n=596

21. Not at all 10. To a great extent3 4 5 6 7 8 9

Stra

teg

icp

erspec

tiveson

Analyt

ics/

BigData




Analytics in day-to-day business

QUESTION: To what extent do you use data analytics in everyday decision-making andprocesses in your organization?

Just under one-hal o businesses (47.1%) in North America use analytics in everyday

decision making and business processes today. These organizations use analytics at

varying levels o intensity.

5.2% use analytics across the organization, and regard it as a core competency.

5% use analytics in most departments, 14.4% use analytics in a limited number o

departments, and 22.5% have just starting using analytics.

A urther 18.1% will deploy analytics capabilities in 2013, raising members o the

analytics club to 65.3% o organizations.

Organizations with 100+ sta are much more advanced on the analytics deployment path.

Around one-hal o North American businesses (52.9%) dont use analytics in everyday

decision-making and business processes today.

Note: This chart shows the use of analytics generally, irrespective of the method of producing the

analytics, or whether the underlying data sources are operational systems, data warehouses or

distributed data infrastructure such as Hadoop, Map Reduce or similar systems.

CurrentuseofA

na

lytics/BigData

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Percentage currently using/not using Analytics/Big Data, n=596

34.7% 18.1% 5.2%22.5% 14.4% 5.0%

25.4% 15.9% 25.4% 21.6% 7 .3%

4.3%

40.5% 18.6% 21.9% 8.6%

4.1%

6.3%

41.1% 22.1% 16.8%

2.1% 4.2%

13.7%Not Disclosed




The business unctions where analytics/big data

is being appliedQUESTION: Does your organization use data analytics in the ollowing?

Analytics is being applied in the marketing unction more than any other business area.

44.1% o organizations use analytics in marketing (17.4% to a great extent, and 26.7%

to a major extent).

Although there is a bigger emphasis on marketing, the deployment o analytics is not

ocused on any one business area.

Strategy is the second most common application o analytics/big data (43.1 %),

ollowed by production and/or operations (39.5%).

The least common applications or analytics/big data are risk management (31.7% use

analytics to a great or major extent), and general management (31%).

Analytics/big data is used at similar levels in areas like sales, fnancial management,

product research and customer experience.

Note: The sample for this slide and a number of subsequent slides drops to 281(or lower in some

cases) because respondents who indicated they dont use analytics in their day-to-day business

were not presented with this range of questions.

CurrentuseofA

na

lytics/BigData

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

To a great extentTo a major extentTo some extentTo a minor extentNot at all

General management

Risk management/mitigation

Customer experience

Product research and development

Customer service / customer care

Financial management

Sales

Production and/or Operations

Strategy

Marketing

Percentage Using Analytics/Big Data, n=281

12.1% 22.1% 34.9% 22.1% 8.9%

14.9% 23.1% 30.2% 18.9% 12.8%

11.4% 17.8% 35.2% 22.4% 13.2%

14.9% 19.6% 29.2% 22.1% 14.2%

12.1% 18.1% 32.4% 20.6% 16.7%

10.0% 19.6% 32.7% 22.1% 15.7%

14.9% 17.8% 28.5% 27.0% 11.7%

14.9% 17.8% 30.6% 23.8% 15.7%

6.4% 14.9% 35.6% 23.8% 19.2%

10.0% 17.1% 28.8% 26.7% 17.4%




Outcomes targeted with analytics/big data

QUESTION: Which business outcomes are you prioritizing with your data analytics/big data

initiatives?

For most businesses, analytics/big data is all about outcomes in revenue, customers,

productivity and markets

Almost two-thirds (62.6%) o organizations are prioritizing revenue generation

outcomes either as a top priority (24.8%) or major priority (37.8%).

Creating a deeper understanding o clients is also high up the priorities list, with 57.4%

saying its a top priority (29.6%) or major priority (27.8%). Similar proportions cite

productivity gains as a priority outcome

Businesses are also using analytics to create a deeper understanding o their markets

(54.1% state this is a major or top priority).

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Top PriorityMajor PriorityMedium PriorityLow PriorityNot a Priority

Social Listening / sentiment analysis:

e.g. track what social media updates

say about companies, brands, products

Build data products: create and sell

data that has value to other businesses

Logistics

Risk Assessment/modelling:

financial market modelling and simulations;

assess risks and exposure of financial

markets/assets; detect fraud patterns etc.

Product/Service: Create deeper understanding of

product or service, product or service development,

product or service lifecycle, product servicing

Financial management

Customer acquisition: Use enhanced understanding ofcustomers / prospects to acquire new business

Markets, marketing analysis: Create deeper under-

standing of markets, campaign effectiveness analysis etc.

Productivity gains, costs savings

Clients/Stakeholders: Create deeper understanding

of clients (or stakeholders if Government/

Not for profit organization) e.g. customer

analytics, customer churn analysis.

Revenue generation: e.g. recommendation

engine, offer triggers, growing

customer value, cross-selling etc.

16.3% 28.9% 28.5% 16.3% 10.0%

29.6% 18.9% 17.0% 21.9% 12.6%

15.9% 17.8% 31.1% 24.8% 10.4%

13.0% 15.6% 31.5% 24.1% 15.9%

9.3% 15.9% 29.6% 27.8% 17.4%

9.3% 14.4% 28.1% 26.7% 21.5%

7.0% 8.9% 21.5% 37.8% 24.8%

7.4% 8.5% 26.7%

6.7% 12.6% 24.8% 29.3% 26.7%

28.5% 25.6%13.3%7.8% 24.8%

27.8% 29.6%

28.9% 24.1%13.7%9.6% 23.7%

Percentage Using Analytics/Big Data, n=270

CurrentuseofA

na

lytics/BigData




The reasons why businesses arent using

analytics/big dataQUESTION: You mentioned you dont use data analytics/big data. What are the main reasons?

The primary reason given by repondents or not using analytics/big data is theyre not

in an industry sector that has a lot o data. The second most common reason is that big

data isnt currently a priority or some businesses even though they can see potential

returns.

More than one-third (34%) o businesses say their industry sector doesnt have a lot o

data.

29.8% say they see potential in big data but it isnt a priority right now. By contrast,

13.8% say theyve considered analytics/big data but cant see a suitable return.

Around one-fth report they dont have the in-house skills to make big data work.

0% 5% 10% 15% 20% 25% 30% 35%

We've looked at Analytics/big data

but don't see a suitable return

We can see a potential return from big databut we don't have the in-house skills to make it work

We can see a potential return from big

data but it's not a priority for us right now

We're not in an industry sector

that has a lot of data34%

29.8%

22.4%

13.8%

Percentage Of Organizations

Not Using Analytics/Big Data, n=362

CurrentuseofA

na

lytics/BigData




The obstacles that stop businesses getting maximum

benefts rom analyticsQUESTION: Which o the ollowing (i any) are the biggest obstacles to your organization deriving

maximum benefts rom analytics?

Some organizations say they just dont have analytics embedded in their DNA, or they

dont have sucient analytics skills nor data skills. The two obstalces are closely related

without analytics skills its hard to engender an analytics culture, and hard to infuence

and educate the senior leadership team about the potential o analytics.

Lack o an analytics culture (stated by 20% o organizations), and lack o analytics skills

(16.3%) are the two biggest obstacles to organizations deriving beneft rom analytics.

Priority unding or other initiatives (12.6%) and lack o senior executive support (11.5%)are also cited as key challenges or many organizations.

0% 5% 10% 15% 20%

None of the above

Inability to demonstrate

the return on investment

Inability to prioritise

funding for big data

Inability to agree ownership of dataacross the organization

Lack of senior executive

leadership and support

Other initiatives are

given funding priority

Lack of skills in the organization in the

areas of analytics / data / data science

Lack of an analytics culture

in the organization


Using Analytics/Big Data, n=270

20%

16.3%

12.6%

11.5%

8.1%

8.9%

8.9%

13.7%

Some organizations say they just dont have

analytics embedded in their DNA, or they dont

have sufcient analytics skills nor data skills.

CurrentuseofA

na

lytics/BigData




Types and Sources o data used or analytics

Question: Which types and sources o data does your organization use or decision-making on a

day-to-day basis?

While analytics-using organizations are almost twice as likely to use internally-sourced data

as they are to use data rom external sources, adoption o the latter has grown rapidly.

More than three-quarters o analytics-using businesses (77.4% ) source data rom systems

like Finance, Enterprise Resource Planning (ERP), Customer Relationship Management

(CRM) or use in day-to-day decision-making.

By contrast, roughly one-third (34.4%) are using data sourced rom social networking

and media. Given the relatively recent development o social media this is a relatively high

adoption, and i it continues it will quickly become a widespread practise.

Trac and activity on an organizations own website is also an important data source on

which analytics is perormed.

Almost one-hal (44.8%) use data generated rom internet inquiries, purchases, etc.,

and 27.4% use click-stream data showing where visitors spend time on their site.

Whichda

taiskey

for

Ana

lytic

s/BigData

?

Operational Data e.g. from Finance,

ERP, CRM and other internal applications

None of the above

Mobile Devices, location data e.g. smartphones, tablets

Internet transactions data e.g. from

purchases, inquiries, requests etc.

Social Networking and Media e.g. tracking and

analyzing social media updates, tweets, blog postsNetworked Devices and Sensors e.g. electronic devices

such as IT hardware, smart energy meters, temperature

sensors, chips in products etc.

Data as a Service (DaaS) i.e.the aggregation,integration, automation

and dissemination of 3rd party information from suppliers such as

StrikeIron, Experian,TheWebService, Dun & Bradsteeet, Data.com etc.)

0% 10% 20% 30% 40% 50% 60% 70% 80%

Percentage of organizations disclosing, n=209

Internet Clickstream data e.g. analyzing

where visitors go on your website

77.4%

44.8%

34.4%

28.9%

27.4%

26.7%

23.7%

6.3%

The emergence o sensors as a major data

source attests to the growth o the machine-

to-machine (M2M) ecosystem.




The emergence o sensors as a major data source attests to

the growth o the machine-to-machine (M2M) ecosystem.

28.9% o organizations that use analytics source and

analyze data rom sensors (e.g. electronics embedded

in products, smart energy meters, etc.).

A separate research study by ZDNet on M2M published

in Jan 2013 supports this fnding, showing 29% already

use M2M or are implementing it now, and a urther

14.7% will implement it in 2013.

Businesses also source and buy data rom commercial third-party data suppliers, a

practice that is becoming known as Data-as-a-Service (DaaS), and its a practice that will

increase.

More than one-quarter (26.7%) use data supplied by commercial third-party DaaS

suppliers such as Experian, StrikeIron, Dun & Bradstreet.

An accidental omission in this survey is a question on the sourcing o data rom

government organizations. This is becoming widespread in a number o countries and

is a part o open government initiatives. Some o the data provided by such public

sector organizations (e.g. population and fnancial data) is an important source or

businesses.

Mobile devices are another important data source almost one-quarter o businesses

(23.7%) use data rom mobile devices.

Whichda

taiskey

for

Ana

lytic

s/BigData

? Almost one-quarter obusinesses

(23.7%) use

data rom

mobile devices.




Businesses are tackling a range o data issues

QUESTION: To what extent is your organization prioritizing the ollowing data issues?

Analytics/big data is partly about leveraging whizz-bang technologies like Hadoop, and

data derived rom M2M transactions. But its also about the more mundane, such as the

systematic collection, organization and security o data.

In dealing with data issues, the key priorities are providing usable data to business

decision-makers (65.7% o organizations), ensuring data privacy and security (65.2%),

and managing and processing data more cost efciently (59.6%).

These priorities are all business-ocused issues, and suggest analytics-savvy

organizations are increasingly engaged in the value they provide through data.

By contrast, the more mundane but oundational challenges o defning data standards

across the organization (a top or major priority or 52%), and agreeing on ownership

and governance o organization data (43.4%) have lower priority. This may be

partly due to prior work addressing the basics, and partly due to a growing ocus in

businesses to utilize data or business outcomes.

Whichda

taiskey

for

Ana

lytic

s/BigData

?

Agreeing ownership and

governance of organization data

Embedding data in the application to encourage

business users to regularly use analytics

Agreeing on principles for sharing

organization across organizational silos

Defining standards so that data across the

organization is defined in similar ways

Identifying new data sources that

can improve Analytics outcomes

Automating the data Extraction

/ Transfer / Load (ETL) process

Integrating disparate data from

sources across the organization

Timeliness (freshness ofdata at point of use)

Creating Confidence

in the veracity of the data

Finding ways to manage and process

data more cost-effectively

Ensuring data privacy and security

Making data available to business

decision-makers in usable formats

Top PriorityMajor PriorityMedium PriorityLow PriorityNot a Priority

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%


6.9%

3.4%

2.1%

3.4%

2.6%

3.4%

3.9%

3.4%

3.4%

5.6%

6.9%

6.4%

4.3%

24.9% 33.5% 32.2%

6.9% 24.5% 25.3% 39.9%

12.4% 25.8% 36.9% 22.7%

10.7% 28.3% 32.6% 24.9%

9.9% 30.9% 31.8%

7.3% 34.3% 36.9% 18.0%

9.9% 33.0% 33.5% 19.7%

15.5% 28.8% 30.9% 21.5%

12.0% 31.8% 30.5% 21.5%

14.2% 33.0% 32.2% 15.0%

14.6% 32.6% 29.2% 16.7%

17.6% 32.6% 24.9% 18.5%

24.0%




Data-as-a-Service Providers (DaaS)

QUESTION: Do you use, or do you plan to use services rom any o the ollowing Data-as-

a-Service (DaaS) vendors to help solve your big data challenges?

Dun and Bradstreet is the go-to source or DaaS, ollowed by Experian and InoUSA.

The one-third (34.4%) o organizations that use DaaS providers employ a range

o sources. Currently, their number one supplier is Dun and Bradstreet (18% o

organizations use this source), ollowed by Experian (15%) and InoUSA (9.9%).

Respondents say they will grow their use o DaaS over the next two years, and Dun

and Bradstreet, Experian and Data.com will increase their client base at similar levels.

StrikeIron

TheWebService

Data.com

InfoUSA

Experian

Dun and Bradstreet

Already useIn next 12 monthsIn 1 to 2 yearsNo plans

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Percentage of those using Analytics/Big Data, n=233

84.1% 5.6% 5.6%

4.7%

79.4% 6.0% 9.4%

76.0% 8.6% 7.3% 8.2%

77.3% 7.3% 5.6% 9.9%

70.0% 6.0% 9.0% 15.0%

67.0% 8.2% 6.9% 18.0%

5.2%

Whichda

taiskey

for

Ana

lytic

s/BigData

?




The CIO and CEO are the key players

QUESTION: Which senior role in your organization has the primary responsibility and budget or

developing and implementing a data analytic/big data strategy or plan?

The CIO has the primary responsibility or analytics/big data.

In 26.3% o organizations that use analytics/big data in day-to-day business, the

CIO has the primary responsibility and budget or developing and implementing the

analytics/big data strategy or plan.

The only other key player is the CEO (who owns analytics/big data in 22% o organizations).

Next is the CFO, or no-one.

In around one-eighth o organizations (13.7%) there is no strategy or plan, and no

leader, and at a similar number o organizations the CFO owns analytics/big data plans

and budgets.

The business intelligence (BI) leader also has a role, but the chie marketing ocer (CMO)

is conspicuous only by his/her absence.

The BI team (or team leader)

owns analytics/big data in

9.6% o organizations.

Despite requent suggestion

the CMO is a leading player in

client-ocused technology, thisis not reected in this survey.

The CMO owns strategy and

budget or analytics/big data

in 2.6% o organizations. This

doesnt mean the CMO isnt

involved in aspects o strategy

and design, but she rarely

leads the initiatives.0% 10% 20% 30%

Manufacturing / production Leader

Data Science Team or Team Leader

Chief Marketing Officer (CMO)

Chief Operating Officer (COO)

Business Intelligence (BI) Team

or Team Leader

No-one has the responsibility -we don't have a strategy/plan

Chief Financial Officer (CFO)

CEO

Chief Information Officer (CIO)


Using Analytics and/or Big Data, n=270

26.3%

22.6%

13.7%

13.7%

9.6%

6.7%

2.6%

2.6%

2.2%

The CIO has the primary responsibility

or analytics/big data.

BigDa

taPro

jec

towne

rsan

dbudge

tho

lders




Analytics/big data produces measurable fnancial returns

QUESTION: To what extent has your organization achieved measurable fnancial benefts rom

your data analytics/big data initiatives?

Almost all businesses say they derive measurable nancial benets rom analytics/

big data.

All but one-tenth (9.9%) report achieving measurable fnancial benefts rom analytics/

big data initiatives, but the extent varies. One-third report theyve achieved fnancial

benefts to a minor extent, and a urther one-third to a medium extent.

Around one-fth (19.3%) have achieved major fnancial benefts, and 6.4% report

achieving fnancial benefts to a great extent.

T

he

Finan

cialsof

Ana

lytics/BigData

0% 10% 20% 30% 40%

To a great extent

To a major extent

To a medium extent

To a minor extent

Not at all


Using Analytics/Big Data, n=233

9.9%

6.4%

19.3%

32.2%

32.2%




Organizations seek aggressive payback on

analytics/big data investmentsQUESTION: Within what period, approximately, do you expect to achieve payback on your

data analytics/big data investments?

A majority o businesses expect to see payback on analytics/big data investments sooner

rather than later.

Almost two-thirds (61.4%) expect payback within two years or ewer (the sum o the

frst two bars). Roughly one-quarter (22.7%) are looking or payback within one year.

A urther one-quarter (25.8%) expect a return on their investment within two to three

years, while just 12.9% expect the payback period to exceed three years.

0% 10% 20% 30% 40%

More than 3 years

Within 2 to 3 years

Within 1 to 2 years

Within 1 year

Percentage Of Organizations Disclosing, n=233

22.7%

38.6%

25.8%

12.9%

T

he

Finan

cialsof

Ana

lytics/BigData




Types o technologies used in 2012

QUESTION: Which o the ollowing best describes the current deployment o your

data analytics/big data capabilities, and how will that change one year rom now?

The 2012 analytics/big data technology landscape is in its relative inancy.

Almost one-hal o organizations (48.3%) have neither an analytics nor a big data

inrastructure

Almost one-quarter (23%) perorm analytics using data directly rom operational

databases

Big data and even data warehouse inrastructure is not yet widely employed.

18.8% perorm analytics on data sourced rom a data warehouse

Just 5.4% use a big data inrastructure based on Hadoop, MapReduce toolsets

4.7% perorm analytics with data sourced rom data warehouse and

big data environments

Approachto

Ana

lytics/BigDa

tain

frastructur

e

n=596

48.3%

23.0%

18.6%

5.4%

4.7%

We have neither an analytics nor big data capability in place

We have an analytics capability that sources data directly

from transactions/operational databases (i.e. no data warehouse)

We have an analytics capability that sources data from a data warehouse

We have an analytics capability that sources data from a big data platform

(e.g. Hadoop, or next generation columnar data warehouse, or similar technologies)

We have an analytics capability that sources data from a data warehouse and a big

data platform (e.g. Hadoop, or next generation columnar data warehouse, or similar technologies)




Implementation o analytics/big data technologies will

grow sharply in 20132013 is the year the majority o businesses will utilize analytics and/or big data

capabilities.

The proportion o businesses using analytics will grow rom 51.7% in 2012 to

72.3% in 2013.

Most businesses will rapidly develop their analytics/big data capabilities in 2013.

By the end o 2013, 21.3% o respondents will be using a combination o data

warehouse and big data platorms. This amounts to a our-old growth in one year.

A urther 11.9% will be using a big data platorm, doubling the proportion doing

so this year.

The use o data warehouse environments will remain relatively stable most o the

growth in data analytics environments will be in the big data space.

Approachto

Ana

lytics/BigDa

tain

frastructur

e

0% 10% 20% 30% 40% 50%

End of 2013End of 2012

We have an analytics capability that sources data from a

data warehouse and a big data platform (e.g. Hadoop, or next

generation columnar data warehouse, or similar technologies)

We have an analytics capability that sources data from a

big data platform (e.g. Hadoop, or next generation columnar

data warehouse, or similar technologies)

We have an analytics capability that sources

data from a data warehouse

We have an analytics capability that sources data

directly from transactions/operational

databases (i.e. no data warehouse)

We have neither an analytics

nor big data capability in place

48.3%

23%

18.6%

11.9%

21.3%

4.7%

19.3%

5.4%

19.8%

27.7%





Perspectives on big data is it just hype?

QUESTION: Which o these statements best reect your organizations view o big data

(as opposed to data analytics generally)?

Organizations are evenly split about the importance and urgency o big data.

Almost one-hal say its key (comprising the 39.1% who say its a core competency,

and the 10% who say its the most important capability or their business).

The remainder see big data as having possibilities but not a priority (18.9%),

or important but not transormational (27.8%).

Some people think big data is just marketing hype but they are the minority.

Only 4.3% o respondents see big data as hype.

Approachto

Ana

lytics/BigDa

tain

frastructur

e

0% 10% 20% 30% 40%

Big data is the most important capability for our

business now and in the immediate future

Big data is a core competency that we must

leverage in order to create real competitive advantage

Big data is important, and we will apply it,

but we don't see it as a transformational capability

Big data has possibilities,

but it's not a priority for us

Big data is mostly just marketing hype,

it doesn't offer anything new

Percentage Of Organizations Using

Analytics in Everyday Business, n=281

10%

39.1%

27.8%

18.9%

4.3%




Traditional tools rule in 2012

QUESTION: Which o the ollowing toolsets do you currently use or plan to use or data analyticsbig data in your business?

Traditional analytics tools are three times more commonly used than big data tools

In October 2012, when this survey was felded, more than one-hal o businesses

(58.4%) used traditional analytics technologies such as OLAP tools, relational database

management systems (RDBMS) and data warehouses to support and achieve their

analytics objectives.

A urther 14.2% plan to implement traditional technologies by the end o 2012, and

another 16.7% will deploy by the end o 2013 by which time a shade under 90%

o analytics-using business will have deployed traditional OLAP/data warehouse

technologies.

Big data technologies will grow aster, but rom a much smaller base

Packaged big data appliances i.e. pre-confgured combinations o hardware, data

warehouse tools, integration tools and versions o the Hadoop distributed fle system

(DFS) are currently the most widely deployed big data toolset. 16.3% o respondents

have already deployed these appliances, and predicted deployment will grow to 47.2%

o businesses by the end o 2013, a actor o 2.9.

The astest growth in deployment will be o open source distributed tools such as

Hadoop, which respondents indicate will grow by a actor o six by the end o 2013

by which time 50.2% o organizations will be utilizing that approach.

Next generation data warehouse oerings are already used by 13.3% o respondents,

and that will grow by a actor o 4.7 to 63% by the end o 2013.

No plansBy end 2013By end 2012Now

58.4% 14.2% 16.7% 10.7%

16.3% 5.6% 25.3% 52.8%

13.3% 13.7% 36.1% 36.9%

8.2% 10.3% 31.8% 49.8%

Open source distributed

approach (e.g. Hadoop /

MapReduce framework etc.)

Next Generation Data Warehouse

(e.g. massively parallel

processing, data compression,

columnar architectures etc.)

A packaged big dataAppliance from vendors

such as Oracle, EMC, etc.

Traditional analytics

tools (e.g. business analysis

software, OLAP tools, traditional

RDBMS, Data Warehouse etc.)

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100

Percentage of organizations currently using Analytics/Big Data, n=233

Tec

hnolog

ydirec

tions

for

Analyt

ics/

BigData

The astest growth in deployment will

be o open source distributed tools.




OLAP is the leading technology or analysis

QUESTION: Have you deployed or do you have plans to deploy the ollowing

analytics technologies to support your data analytics/big data initiatives?

Almost 40% o businesses use OLAP tools, growing to 63.1% by the end o 2014

Traditional OLAP tools such as Cognos and Sybase have been widely deployed in

businesses using analytics, and that will continue.

Predictive analytics tools are switly becoming common, and by 2014 these will be the

most widely deployed analysis tools.

These tools, rom businesses like SAS, SPSS, Oracle etc., are currently deployed at

31.3% o businesses, and by 2014 will be deployed in more that 70% o organizations.

The use o data visualization will grow to rival OLAP tools by the end o 2014

(58% o businesses will use such tools). Statistical analysis tools are also becoming

more common, as 53.2% o organizations will use these by the end o 2014.

Machine learning technologies are currently used by only 7.7% o analytics-using

businesses, but usage is expected to grow by a actor o ve in the next two years.

Machine Learning (e.g. Pybrain, Elefant,

Mahout, Mechanical Turk etc.)

Statistical analysis toolsets

(e.g. R, CRAN etc.)

Data Visualisation tools (e.g. GnuPlot,

Processing, IBM Many Eyes, Tableau etc.)

Predictive Analytics(e.g. SAS, SPSS, Oracle etc.)

Online Analytical Processing (OLAP)

Tools (e.g. Cognos, Sybase etc. .)

No plansIn 1 to 2 yearsIn next 12 monthsAlready deployed

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Percentage of organizations using Analytics/Big Data, n=233

39.9% 12.4% 10.7% 36.9%

31.3% 18.0% 22.3% 28.3%

22.7% 13.3% 22.3% 41.6%

21.0% 14.2% 18.0% 46.8%

7.7% 12.9% 16.7% 62.7%

Tec

hnolog

ydirec

tions

for

Analyt

ics/

BigData




Approach and sample

This is a quantitative survey based on a sample o US and Canadian organizations. The sample is

principally drawn rom the database o opt-in registered members o CBSis leading business tech

media sites ZDNet and TechRepublic. A portion o the sample was generated rom advertisements

on ZDNet and TechRepublic to capture the views on the audience who are not registered members.

The feldwork or this report was conducted in the US and Canada.

The survey generated 1268 responses. Ater quality assurance and removal o incomplete surveys,

596 responses were sufciently complete and included in the analysis. The sample size or each

chart in this report is displayed, and sample size varies due to the conditional path respondents

took through the survey. The initial questions about strategic perspectives on analytics/big data are

reported based on the ull sample o 596 responses.

A substantial number o respondents answered all key questions in the survey, but declined

to disclose details o their organization size, industry sector, and other inormation that could

uniquely identiy them. This is understandable given many analytics/big data projects are critical

components in building competitive advantage. These responses are included in the survey, and the

organizations are identifed as Not Disclosed in results reported by organization size and industry

sector.

The frst branch in the survey separated respondents into those who use analytics in everyday

decision-making (281 respondents), and those who dont (315). A second branch distinguished

between organizations using analytics/big data technologies and those who dont. The latter group

were served one urther question about why they do not yet use analytics/big data. The ormer

group were served a range o questions on the ollowing topics: their current use o analytics,

including targeted business outcomes; the roles that manage analytics/big data plans and budgets;

the data types and data sources they utilize; and the technology inrastructure theyve deployed to

support their analytics/big data initiatives.

Scope and Terminology

The Big Data Priorities 2013 survey is ocused both on analytics and big data. This was a deliberate

attempt to better understand the overall scope and direction o all analytic initiatives in businesses.

In most cases, questions asked broadly about approaches to analytics/big data, but in limited cases

questions were asked more narrowly about analytics or about big data.

Survey

Met

ho

do

logy




Timeline

The project spanned the ollowing major activities:

RespondentsThe questionnaire was targeted at organizations with a minimum o 100 employees, but a

substantial quantity o responses came rom smaller businesses via the advertisements on ZDNet

and TechRepublic. Due to the obvious interest in the topic rom these organizations, and their

initiatives to utilize analytics/big data, these responses are included in this report. In most cases,

their answers are reported separately rom larger organizations and those that did not disclose

organization size (which ZDNet interprets rom answers to the survey are a mix o small and large

businesses).

Respondent Demographics

Respondent Organizations by Size

Survey

Met

ho

do

logy

Project design, including scope

and questionnaire designAugust/September 2012

Fieldwork October/November 2012

Analysis and reporting December 2012

Webcast to launch the ndings January 24, 2013


35/35


Respondent Organizations by industry sector

Survey

Met

ho

do

logy

0% 10% 20%

Not Disclosed

Other

Transportation/Aerospace

Engineering/Construction/R&D

Media/Entertainment/Design

Retail/Distribution/Wholesale

Manufacturing

Banking/Financial Services/Insurance etc

IT and Communications

Business Services/Consulting

Education/Health Care/Government

Percentage Of Organizations, n=596

15.8%

2.7%

3.9%

4.5%

15.1%

15.9%

5.7%

6.7%

7.0%

9.2%

13.4%

ZDNet Big Data Priorities 2013 PDF

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