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© INSEAD eLab 2014
INSEAD eLab Theodoros Evgeniou, Professor of Decision Sciences and Academic Director
Joerg Niessing, Affiliate Professor of Marketing and Executive Director
Contact: [email protected]
Big Data Analytics:
INSEAD eLab Pre-Study Results
© INSEAD eLab 2014
http://centres.insead.edu/elab/ [email protected]
INSEAD eLab is the research and analytics center of INSEAD that focuses on [big] data analytics for businesses
Connecting research sponsors and external collaborators interested in the area of (big) data and data analytics with INSEAD's expertise in this broad area
Key goal is to develop novel data analytics methodologies, tools, frameworks, and find research insights that can help academics and practitioners better capitalize on the vast opportunities the "world of data" creates
Enhancing organization performance amidst digitization
• Building business competitiveness with new ICTs
(funded by AT&T)
• Social technologies readiness project
(funded by Cognizant)
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© INSEAD eLab 2014
http://centres.insead.edu/elab/ [email protected]
In January 2014, INSEAD eLab surveyed INSEAD alumni in
order to learn how companies are exploiting big data
Pre-study focused on the following topics:
• Where: Business processes and activities affected
• Why: Benefits of using (big) data
• How: Approaches and challenges when using (big) data
• What’s next: Future trends
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© INSEAD eLab 2014
http://centres.insead.edu/elab/ [email protected]
Marketing and sales are the key areas for big data…
Where do organizations plan to use big
data in 1-3 years?
0 10 20 30 40 50 60 70
Marketing
Sales
R&D
IT
Production
Controlling
Supply Chain
% of respondents
4
Where
© INSEAD eLab 2014
http://centres.insead.edu/elab/ [email protected]
Making better decisions and getting to better customer insights are the main benefits of big data technologies and analysis…
The main benefits of Big Data technologies
0 10 20 30 40 50 60 70
Better ability to make strategic decisions
Better customer Insights
Better targeted marketing
Better steering of operational processes
Improved customer service
Better customer retention
Better insight into the market / competition
Better product- / service-quality
Lower cost
More efficient R&D
% of respondents
5
Why
© INSEAD eLab 2014
http://centres.insead.edu/elab/ [email protected]
Organization’s innovation performance
average well above well below
Organization’s client intimacy performance
average well above well below
Organization’s operational performance
average well above well below
= already using big data to inform business decisions
= currently not using big data to inform business decisions
The business value of big data: companies already using big data to make decisions show a competitive edge…
Which of the following statements best describes your organization’s stage in using big data to help make
business decisions?
6
Why
© INSEAD eLab 2014
http://centres.insead.edu/elab/ [email protected]
…and companies that are also using big data more efficiently are outperforming their peers even more
Organization’s innovation performance
average well above well below
Organization’s client intimacy performance
average well above well below
Organization’s operational performance
average well above well below
= (somewhat) inefficient in sharing and reusing data analytics
= average
= (somewhat) efficient in sharing and reusing data analytics
How efficient is your organization in internally sharing and reusing data analytics knowledge and research?
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Why
© INSEAD eLab 2014
http://centres.insead.edu/elab/ [email protected]
… but 75% of the companies participated in the study are still in early stages…
Companies already using big data
0 10 20 30 40 50 60 70 80 90 100
Total
Already executing
Don’t know
Implementing
Testing
Planning
Considering
Not considering
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How
© INSEAD eLab 2014
http://centres.insead.edu/elab/ [email protected]
Missing capabilities and skills are the key reason why organizations do not use big data…
Key reasons why organizations are not considering or
further exploring the use of big data
0% 10% 20% 30% 40%
Capabilities and skills
Don't understand benefits
Poor data quality
Missing knowledge
Lack of commitment
Costs
Business support
Lack of case studies
% of respondents
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How
© INSEAD eLab 2014
http://centres.insead.edu/elab/ [email protected]
…but analyzing data effectively is also challenging….
The main challenges when using big data
0% 10% 20% 30% 40%
Inadequate technical know-how
Inadequate analytical know-how
Data privacy issues
Can not make big data usable for end users
Technical problems
Lack of compelling business case
Costs
% of respondents
Efficient
Not efficient= (somewhat) inefficient in
using data analytics
= (somewhat) efficient in using
data analytics
10
How
© INSEAD eLab 2014
http://centres.insead.edu/elab/ [email protected]
To bring value, data analytics need organizational enablers: One key enabler is the standardization of data sharing
Quality of data analytics compared to biggest competition
0% 10% 20% 30% 40% 50%
Much better
Somewhat better
Equal
Somewhat worse
Much worse
% of Respondents
High standardization data sharing
Low standardization data sharing
0% 10% 20% 30% 40% 50% 60% 70%
Very satisfied
Somewhat satisfied
Neutral
Somewhat dissatisfied
Very dissatisfied
% of Respondents
High standardization data sharing
Low standardization data sharing
Satisfaction with ROI of big data
11
How
© INSEAD eLab 2014
http://centres.insead.edu/elab/ [email protected]
Companies already using big data are saying that it will become even more important for their business in the future
Companies already USING big data to inform
business decisions
Companies currently NOT USING big data to
inform business decisions
Less
important
than today
2%
More
important
than today
76%
Similar
22%
Relative importance of data analysis and data
reporting in the next 1-3 years for your business
More
important
than today
86%
Similar
14%
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Next
© INSEAD eLab 2014
http://centres.insead.edu/elab/ [email protected]
There is a big potential in understanding unstructured data…
The main types of data analyzed
0% 20% 40% 60% 80%
Transactional data
Customer Relationship management data
Social media data
Log (e.g. internet/web) data
Unstructured data (documents, video, images)
Structured survey data
Sensor data
% of respondents
Efficient
Not efficient= (somewhat) inefficient in
using data analytics
= (somewhat) efficient in using
data analytics
- 30% analysed data from just ONE source
- Over 50% analysed data from TWO source’s
- Less than 20% analysed data from MORE THAN TWO source’s
BUT
13
Next
© INSEAD eLab 2014
http://centres.insead.edu/elab/ [email protected]
The business value of big data: it is not only about having large
amounts of data but mainly about analyzing data fast….
14
Frequency of data analysis… …and innovation performance
0% 10% 20% 30% 40%
Well above
industry average
Somewhat above
industry average
At industry
average
Somewhat below
industry average
Well below
industry average
% of Respondents
Hourly or more frequent
Once a week or less frequent
Every 5
sec
11% Every
minute or
less
14%
Hourly
19%
Once a day
25%
Once a week
12%
Once a
month
19%
Next
© INSEAD eLab 2014
http://centres.insead.edu/elab/ [email protected]
Companies that are already efficient in using big data will leverage new technologies more often in the future
Companies already EFFICIENT in using big data Company currently NOT EFFICIENT in using big data
Yes
49% No
51%
Use of cloud based data analytics technologies
the next 1-3 years
Yes
83%
No
17%
Companies already EFFICIENT in using big data Company currently NOT EFFICIENT in using big data
Yes
31%
No
69%
Use of open source data analytics technologies
the next 1-3 years
Yes
79%
No
21%
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Next
© INSEAD eLab 2014
http://centres.insead.edu/elab/ [email protected]
INSEAD: Data Analytics Course
Cloud Based (individual course participant servers)
Open Source Software (R-based open source libraries for data analytics)
Collaborative (GitHub based sharing and collaboration)
Easy Re-use, Replicability, and Sharing of analysis
For more information, visit:
http://inseaddataanalytics.github.io/INSEADjan2014/
16
Next
© INSEAD eLab 2014
http://centres.insead.edu/elab/ [email protected]
Summary of key pre-study findings
• Companies already using data analytics to make decisions show a
competitive edge and could outperform their peers even more if sharing
and reusing data analytics more efficiently
• Marketing and sales will still be the key areas of use
• 75% of the companies are still in early stages
• Analyzing data effectively is challenging (e.g. lack of analytical of technical
skills, lack of compelling business cases for investing in big data
technologies, still data quality issues, etc.)
• Data analytics need organizational enablers. Two key enabler are skills
and standardization of data sharing
– The combination of high frequency of analysis and high data standardization is good for
knowledge and innovation
• There is a big potential in understanding unstructured data
• Cloud and open source are expected to rise
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