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Making a Systematic Business Case for Analytics

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This is my presentation that I made at SES Singapore on November 28, 2012.

This is my presentation that I made at SES Singapore on November 28, 2012.

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  • 1. Making a Systematic Business Case For Analytics SES 2012 Prof. Ashwin Malshe
  • 2. Why We Need to Make a Case for Analytics?• What’s the net present value (NPV) of business analytics?• No academic large-scale study exists that links analytics to firm performance – Brynjolffson et al. (2011) make an attempt• Most of current evidence is based on case studies – Most cases are published by analytics consultant/vendors/solution providers • IBM, SAS, SAP, Oracle, Teradata, etc.
  • 3. The Circle of Mistrust Marketing Top Mgmt IT Finance HR
  • 4. Obstacles to Analytics Adoption• Culture• Lack of business sponsor• Personal vs. organizational goals (short-/long-term)• Few employees who question the data and make judgments• Analytics skills are with too few employees• Poor information management• Lack of behavioral and anthropological training to IT
  • 5. Types of Analytics• Descriptive – E.g., Dashboards• Predictive – E.g., Trend analysis• Prescriptive – E.g., Mathematical programming
  • 6. Analytics Usage and Organizational Type Analytics Usage Descriptive and All three types High Predictive Descriptive Descriptive and Low Predictive Low High Data Driven
  • 7. Analytics Usage and Organizational Type Analytics Usage Descriptive and All three types High Predictive Identify Descriptive Descriptive and the Low Predictive hindrance Low High Data Driven
  • 8. Convincing Marketing Department• What are the benefits you are looking for? – Tracking customer satisfaction – Assessing and increasing ad effectiveness – Media planning – Social media metrics – Detecting trends – Segmentation and positioning – Something else…• E.g., Wal-Mart and 9/11
  • 9. Convincing Marketing Department• Descriptive analytics – Use external vendors on a small scale for demonstrations• Predictive analytics – Work with academic institutions to build models• Run targeted experiments – Exploit insights from predictive analytics – Generate measurements for sales, market share, revenue growth, customer satisfaction, churn rate, repeat purchase, awareness, etc.• Evaluate the effectiveness of analytics insights
  • 10. Managing Human Resources• Should you have an in-house analytics division? – Corporate or SBU division?• There are pitfalls to doing analytics in-house – Demand for skilled analytics labor is extremely high – Supply of skilled labor, unfortunately, is limited• Other options – Outsourcing – Hiring young graduates and training them – Training your existing employees
  • 11. Outsourcing Analytics• Outsourcing poses problems – Data are sensitive • Privacy issues • Proprietary trade information • Legal barriers – Control on the analytics • Quality • Alignment of the objectives • Coordination
  • 12. Hiring and Training• Hire young graduates from – Engineering – Economics – Statistics – Business management• Train them on data analysis and/or business management – Several online courses are available (e.g., Coursera) – Tie up with local business schools (e.g., ESSEC, SMU)
  • 13. Training Existing Employees• Locate talent inside the organization – Organization-wide search – May have to overcome the departmental politics – There may be a large variance in the skill levels• Training alternatives – Using in-house facilities for training • Getting consultants and business schools to offer structured workshops – Part-time business analytics programs
  • 14. Getting to the ROI• Analytics ROI at a staggering 10.66x (Nucleus Research 2011) – Does it make sense? • Survivorship bias (dolphins and 1,000 sailors), selection bias – If that’s true, what’s stopping everyone from using analytics?• ROI calculations are not straightforward – Attributing cost savings, incremental profits, etc. – What about the risk? – More difficult with intangible benefits
  • 15. NPV Rules•
  • 16. Business Success Barriers – IT, BI, etc. Source: Information Week
  • 17. Working with the IT• Main challenges influenced by the culture – Data capture/collection (e.g., MeritTrac) – Data accessibility/sharing – Organization-wide data integration – Using real-time data dissemination• In the initial stages – Stick to available data formats – Avoid merging multiple databases – Avoid using too much unstructured data
  • 18. Summary• Making a case for analytics needs systematic approach• In a non data-driven organization, there are many hurdles to overcome – ROI of analytics is one of the toughest one• Each function (HR, marketing, etc.) may have their own concerns for taking analytics route
  • 19. Thank You Prof. Ashwin Malshe ESSEC Business School malshe@essec.eduTwitter: @ashwinmalshe