Retail Data Innovation Collider
Work alongside leading industry experts and faculty to develop big data strategies and recommendations for retailers big and small. A $5000 cash prize is available! Limited to 10 students. This is a rare opportunity to learn valuable industry information and judgement about what really works for firms who are considering new uses of (big) data.
Submit your resumes for consideration no later than March 7, 2016
Submit resumes here:
Data for Competitive Advantage in Retail
Big data is a topic that is widely talked about. While a few firms are well known for setting the standard on how data can be used for advantage (i.e. Google, Facebook, Amazon, Netflix, etc.), most firms do not have the resources, expertise or strategy to monetize their data.
Students will work alongside industry experts and faculty at Berkeley, gaining valuable industry insight and experience into what really works for retail firms considering new uses of (big) data. Mike Olson, Chief Strategy Office at Cloudera will be leading this project with Shomit Ghose of Onset Ventures, Greg LeBlanc of Haas Business School and Ikhlaq Sidhu of the Sutardja Center at Berkeley.
A cash prize is available to the winning team and course credit (2 units) is available to interested students. This project will last 6-8 weeks. Requirements: Undergrad or Graduate students interested in big data and the retail sector must submit their resumes for consideration no later than March 7, 2016.
For this project we are recruiting up to 10 students to conduct research to understand what "normal retail firms" can do to leverage data for their businesses. The questions below are representative of common challenges:
- What data can a retail firm practically collect, how do they do it.
- What can they actually do with this data given the skills and tools they have?
- Why would they do it from a Business Model and ROI perspective?
- What tools are actually available?
- What services are available, what is the cost, and do they actually help?
For a CIO of a smaller or mid-sized retail firm, what would be the best recommendation on how to best deploy the following budgetary amounts to optimize return on investment from the firm's data:
- $100K annually
- $1M annually
- $10M annually
Students will present their finding in slide format and will guided by a panel of industry experts and a UC Berkeley faculty member. Students should validate their findings by talking to existing retail firms throughout the process.