Big Data Priorities: January 24, 2013 Webinar
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Transcript of Big Data Priorities: January 24, 2013 Webinar
January 24 2013, Webinar Panel Discussion
Featured Speakers
Hilary Mason Chief Scientist bitly, inc
David Boyle SVP Insights EMI Music Group
Ken Wincko Senior Marketing Director Dun & Bradstreet
Carol Krol Managing Editor, Custom Content CBS Interactive
Angus Macaskill Industry Analyst CBS Interactive
Agenda
Overview of findings from ZDNet’s Big Data Priorities
2013 Research
Panel discussion of key findings
Panel response to questions from audience
Wrap-up
Project Scope, Timeline, Respondents
The business imperatives of Analytics and Big Data
Fieldwork in October and November 2012
Respondent profile:
45.1%
38.9%
15.9%
N=596
<100
>100
Not Disclosed
15.9%
15.1%
2.7%
3.9%
4.5%
5.7%
6.7%
7.0%
9.2%
13.4%
15.8%
0% 5% 10% 15% 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
5
50.3%
37.1%
23.8%
0% 10% 20% 30% 40% 50% 60%
2014
2013
2012
Percentage of organizations saying Analytics/Big Data has high potential, n=596
Tim
e P
erio
d
Organizations say the business potential
of Analytics/Big Data will grow rapidly
Audience Poll
What is the potential for Data Analytics/Big Data to have a major
influence on your organization’s business performance this year?
Is it:
Low
Moderate
High
8
41.1%
40.5%
25.4%
34.7%
22.1%
18.6%
15.9%
18.1%
16.8%
21.9%
25.4%
22.5%
13.7%
8.6%
21.6%
14.4%
2.1%
4.1%
7.3%
5.0%
4.2%
6.3%
4.3%
5.2%
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Not Disclosed
<100
>100
ALL RESPONDENTS
Percentage currently using/not using Analytics/Big Data daily, n=596
Around one-half of businesses use
Analytics in everyday decision-making
9
16.3%
29.6%
15.9%
13.0%
9.3%
9.3%
9.6%
7.8%
6.7%
7.4%
7.0%
28.9%
18.9%
17.8%
15.6%
15.9%
14.4%
13.7%
13.3%
12.6%
8.5%
8.9%
28.5%
17.0%
31.1%
31.5%
29.6%
28.1%
23.7%
24.8%
24.8%
26.7%
21.5%
16.3%
21.9%
24.8%
24.1%
27.8%
26.7%
28.9%
28.5%
29.3%
27.8%
37.8%
10.0%
12.6%
10.4%
15.9%
17.4%
21.5%
24.1%
25.6%
26.7%
29.6%
24.8%
0% 20% 40% 60% 80% 100%
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 of customers / prospects to acquire new business
Markets, marketing analysis: Create deeper understanding of markets, campaign effectiveness analysis etc.
Productivity gains, cost 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.
Percentage using Analytics/Big Data, n=270
Not a Priority Low Priority Medium Priority Major Priority Top Priority
For most businesses, Analytics/Big Data is all about
outcomes in revenue, customers, productivity and markets
10
12.9%
25.8%
38.6%
22.7%
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
Analytics/Big Data ROI expectations are high
11
6.4%
19.3%
32.2%
32.2%
9.9%
0% 10% 20% 30% 40%
To a great extent
To a major extent
To a medium extent
To a minor extent
Not at all
Percentage of organizations using Analytics/Big Data, n=233
Almost all organizations have achieved some measurable
financial benefit, and 25% have achieved major financial benefit
12
Businesses use a variety of data sources, especially
in-house and online, for day-to-day decision-making
6.3%
23.7%
26.7%
27.4%
28.9%
34.4%
44.8%
77.4%
0% 20% 40% 60% 80%
None of the above
Mobile Devices, location data e.g. smartphones, tablets
Data as a Service (DaaS) i.e.the aggregation,integration, automation and dissemination of 3rd party information from suppliers such as StrikeIron, Experian,TheWebService, …
Internet Clickstream data e.g. analysing where visitors go on your web site
Networked Devices and Sensors – e.g. electronic devices such as IT hardware, smart energy meters, temperature
sensors, chips in products etc.
Social Networking and Media e.g. tracking and analysing social media updates, tweets, blog posts
Internet transactions data e.g. from purchases, enquiries, requests etc.
Operational Data e.g. from Finance, ERP, CRM and other internal applications
%age of organizations disclosing, n=209
13
21.3%
11.9%
19.8%
19.3%
27.7%
4.7%
5.4%
18.6%
23.0%
48.3%
0% 10% 20% 30% 40% 50%
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
Percentage of organizations, n=596
End of 2012
End of 2013
Deployment of Analytics and/or Big Data
platforms will gather pace in 2013
14
2.2%
2.6%
2.6%
6.7%
9.6%
13.7%
13.7%
22.6%
26.3%
0% 5% 10% 15% 20% 25% 30%
Manufacturing / production Leader
Chief Marketing Officer (CMO)
Data Science Team or Team Leader
Chief Operating Officer (COO)
Business Intelligence (BI) Team or Team Leader
Chief Financial Officer (CFO)
No-one has the responsibility – we don’t have a strategy/plan
CEO
Chief Information Officer (CIO)
Percentage of organizations using analytics and/or Big Data, n=270
Primary responsibility for budget, strategy
and plans for Analytics/Big Data
15
13.7%
8.1%
8.9%
8.9%
11.5%
12.6%
16.3%
20.0%
0% 5% 10% 15% 20%
None of the above
Inability to demonstrate the return on investment
Inability to agree ownership of data across the organization
Inability to prioritise funding for big data
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
Percentage of organizations using analytics and/or Big Data, n=270
The major obstacles to deriving maximum benefit from Analytics:
lack of an analytics culture, data skills and executive support
Audience Poll
Which of the following (if any) are the biggest obstacles to your
organization deriving maximum benefits from analytics
Lack of an analytics culture in the organization
Lack of senior executive leadership and support
Inability to agree ownership of data across the organization
Inability to prioritize funding for big data
Lack of skills in the organization in the areas of analytics / data /
data science
Inability to demonstrate the return on investment
17
13.8%
22.4%
29.8%
34.0%
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 data but 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 data
Percentage of organizations not using analytics and/or Big Data, n=362
Why have organizations not embraced Analytics/Big Data? They
don’t have much data, they just don’t see a return, lack of skills
Q&A
Wrap-up
Respondents see big potential in analytics/big data –over one-
half say it will have high impact on the business by 2014
The targeted business outcomes are improvements in revenue,
customers, productivity and markets
Deployment of advanced analytics/big data platforms is in its
infancy, but will grow rapidly in 2013
Lack of analytics culture, data skills, executive support, and
policy on data are barriers – businesses need to find solutions
Data is sourced form internal and external sources, and ue of
mobile data and DaaS is growing
January 24 2013, Webinar Panel Discussion
THANK YOU FOR JOINING US