Successfully reported this slideshow.

Big Data Analytics: INSEAD eLab Pre-Study Results

1,522 views

Published on

In January 2014, INSEAD eLab surveyed INSEAD alumni in order to learn how companies are exploiting big data

Published in: Technology
  • Be the first to comment

Big Data Analytics: INSEAD eLab Pre-Study Results

  1. 1. © INSEAD eLab 2014 INSEAD eLab Theodoros Evgeniou, Professor of Decision Sciences and Academic Director Joerg Niessing, Affiliate Professor of Marketing and Executive Director Contact: elab@insead.edu Big Data Analytics: INSEAD eLab Pre-Study Results
  2. 2. © INSEAD eLab 2014 http://centres.insead.edu/elab/ elab@insead.edu 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) 2
  3. 3. © INSEAD eLab 2014 http://centres.insead.edu/elab/ elab@insead.edu 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 3
  4. 4. © INSEAD eLab 2014 http://centres.insead.edu/elab/ elab@insead.edu 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
  5. 5. © INSEAD eLab 2014 http://centres.insead.edu/elab/ elab@insead.edu 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
  6. 6. © INSEAD eLab 2014 http://centres.insead.edu/elab/ elab@insead.edu Organization’s innovation performance average well abovewell below Organization’s client intimacy performance average well abovewell below Organization’s operational performance average well abovewell 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
  7. 7. © INSEAD eLab 2014 http://centres.insead.edu/elab/ elab@insead.edu …and companies that are also using big data more efficiently are outperforming their peers even more Organization’s innovation performance average well abovewell below Organization’s client intimacy performance average well abovewell below Organization’s operational performance average well abovewell 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? 7 Why
  8. 8. © INSEAD eLab 2014 http://centres.insead.edu/elab/ elab@insead.edu … 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 8 How
  9. 9. © INSEAD eLab 2014 http://centres.insead.edu/elab/ elab@insead.edu 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 9 How
  10. 10. © INSEAD eLab 2014 http://centres.insead.edu/elab/ elab@insead.edu …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
  11. 11. © INSEAD eLab 2014 http://centres.insead.edu/elab/ elab@insead.edu 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
  12. 12. © INSEAD eLab 2014 http://centres.insead.edu/elab/ elab@insead.edu 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% 12 Next
  13. 13. © INSEAD eLab 2014 http://centres.insead.edu/elab/ elab@insead.edu 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
  14. 14. © INSEAD eLab 2014 http://centres.insead.edu/elab/ elab@insead.edu 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
  15. 15. © INSEAD eLab 2014 http://centres.insead.edu/elab/ elab@insead.edu 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% 15 Next
  16. 16. © INSEAD eLab 2014 http://centres.insead.edu/elab/ elab@insead.edu 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
  17. 17. © INSEAD eLab 2014 http://centres.insead.edu/elab/ elab@insead.edu 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 17
  18. 18. elab@insead.edu

×