Big Data for Arts Industry: Creating a Data Literacy Curriculum

1. Activity-Work with a Partner-Compare 2 Graphs and Answer Questions • What time period is covered? • What is being measured by the data? • How is the data displayed (real number? Percent?) • Can the data in the two graphs be compared? • What story does the data in each graph tell, and does that story agree with the other graph?

2. Big Data for the Arts Industry Creating a Data Literacy Curriculum April Levy Jason Stephens

3. Agenda • Why data literacy? • Data literacy unit • In class activity with librarian • In class graphing • Graphing assignment • Big Data unit • Library Fellows grant • In class instructor-selected case studies • Student-selected business case studies assignment • Post-Assessment Results

4. Why data literacy? • Students had difficulty • reading long numbers • interpreting data in charts and graphs • awareness of data sources • Business & Entrepreneurship Department no longer taught a data course

5. Data Literacy Unit: In-class activity National Arts Index, 2014 How a Nation Engages with Art, 2012

6. Data Literacy Unit In-Class Assignment: Converting and Comparing Data • Objectives: • Understand “indexing” • Convert data sets into Excel • Create a comparative graph in Excel from multiple outside data sets • Identify correlating and deviating patterns between data sets • Propose hypotheses that explain data variations • Research and supply evidence that supports or refutes a hypothesis • Example: 2 data sets taken from National Arts Index

7. Undergraduate Students Example • Hypothesis: • A rise in the ratio of BA’s to higher degrees between 2009 and 2012 in the Arts field led to a reduction in wages within those occupations, despite a rise in the number of degrees awarded. • Evidence: • https://www.census.gov/prod/2012pubs/acs-18.pdf • http://www.census.gov/library/infographics/visual_art_majors.html • Conclusion: • Evidence refutes hypothesis, which shows the ratio of BA’s to higher degrees fell by 3% between 2009 and 2012.

8. Adapting “Information Has Value” Threshold Concept for Data Literacy

9. Big Data Unit • Expand upon Data Literacy unit • We received a Library Fellows grant to develop new unit • Importance of Big Data for small, arts-focused businesses

10. Developing a Big Data Curriculum • Case Studies of Instructor-selected Businesses using Big Data • Assignment Objectives: • Know the difference between qualitative and quantitative data • Understand where, how, why an existing business collects data • Identify the impact of big data on decision-making • Propose improvements to a company’s data collection and usage

11. Big Data Questions Students Answered • What Data is Collected • How Data is Collected • Why Data is Collected • How Data is Utilized • Potentialfor Data Collected

12. Big Data Case Study Assignment Example: Starbucks • Articles assigned: • Thau, Barbara. 2014, April 24. "How Big Data Helps Retailers like Starbucks Pick Store Locations - An (Unsung) Key to Retail Success." http://www.forbes.com/sites/barbarathau/2014/04/24/how-big-data-helps-retailers-like-starbucks-pick-store-locations-an-unsung-key-to-retail-success/#11630e9221c3 • Kaye, Kate. 2013, March 22. "At Starbucks, Data Pours In. But What to Do With It?” http://adage.com/article/datadriven-marketing/starbucks-data-pours/240502/

13. Starbucks Big Data Summary Example Undergraduate Students Introduction There is no denying that Starbucks is a powerhouse company when it comes to the coffee shop industry. With the introduction of digital marketing, digital sales integration and a massive increase in loyal customers,they have an enormous amount of Big Data from new media to sift through. This summary purviews the concerns and potentials with Big Data for Starbucks. What Data is Collected The data that Starbucks collects is mostlyquantitative data….from their loyalty card and social media... This data includes:specific items ordered, how often the customer uses their loyalty card, customer’s birthday, number of visits in a given time period, time of day visits occur, and email address.We believe that Starbucks [should] also collect qualitative data in order to better their company and customer experience, and we wonder how specifically they could gather qualitative data from the membership program, in order for their company to grow.

14. Starbucks Big Data Summary Example, continued How Data is Collected According to the articles Starbucks is currently collecting data through all means possible. Traditional data is coming from store transactions, traffic volume by location, etc. Digitally, the company has seen a spike in mobile app involvement and loyalty membershiprising which provides data about the customers. Also digitally, the company is collecting data throughsocial media. In addition Starbucks is implementing new customer experiences, eventually opening the door for more media data and other digital forms. Why Data is Collected Starbucks is collecting the data for a…number of reasons, all centered around customer and company growth.The company would like to find ways to quickly and more effectively provideone on one marketing and communications to not only expand but strengthen the consumer and company relationship.The company can use data to accuratelypredict location expansion accelerating Starbuck’s global position. Starbucks also wishes to use data collection to strengthen internal operationswith in-store partners, an area of current untapped potential….

15. Starbucks Big Data Summary Example, continued How Data is Utilized Starbucks is converting [customers] into loyalists.Currently, there are over 10 million active My Starbucks Rewardsmembers in the US alone, 27% higher than statistics…have shown last year (Forbes). This data gathered from registered customers is being used for birthday rewards, seasonal drink announcements, mobile pay and peak consumption periods analysis to promote visits during lull periods. Potential for Data Collected Starbucks hasn’t included any form of qualitative datain their business model or customer experience. Starbucks could use this as a marketing and advertising model. Imagine if they could use data to determine what kind of person purchases Starbuckscoffee. Perhaps through this study of data, they would see how to better market themselves to specific psychographics.Furthermore, they would be able to see how they can appeal more to demo/psychographics that they aren’t currently reaching. Going forward, implementing more enjoyable customer experiences such as the “mobile order and pay” function and adding the reward points for customers who purchase in grocery stores will be very rewarding for the company….

16. Big Data Final Assignment Case Study of Student-Selected Business Entity Objectives: • Research a small, arts-oriented business • Identify what data the business uses, and what data it may have access to that it doesn’t yet use • Determine how the company can use data to improve its business • Understand the opportunity costs, limitations and ethical concerns associated with data collection and usage • Present the findings as an organized data-plan for the company

17. Big Data Final Assignment Example Undergraduate Students • Example: Old Town School of Folk Music • (excerpt from examination of opportunity costs and ethical concerns) • Collecting data on competitors could require additional investment in order to participate in potentially high-cost, industry-wide researches. OTS could also approach this through designating a research positionin the organization. This might reduce capital that would be otherwise utilized in other activities beneficial to the organization. Furthermore, an ethical issue to keep in mind is the temptation to slip into know-how theft....OTS would need to be ethically responsible to ensure that theydo not steal competitors’ strategies and/or IP and directly apply them to their organization.

18. Big Data Final Assignment Example • Graduate Students • (summary of one recommendation from data plan related to branding) • Assessment: • [Via wordpress and Google analytics] they collect blog traffic (activity), number of visitors, blog interaction, number of shares, demographics and psychographics. • Content is too broad and isn’t created with active users’ interests first based on the data. • Data has shown that… interview videos are the most popular posts, yet most content doesn’t adhere to this. • Recommendation: • Use the data to make all content better match the interests of active viewers in order to gain more subscribers like them. • Then, gradually, and singularly, introduce more diverse content to better assess where content growth can successfully happen.

19. Adapting “Information Has Value” Threshold Concept for Big Data

20. Post-Assessment Results Undergrads

21. Post-AssessmentResults, continued When thinking about what we've studied regarding data in this class, I feel I've:

22. Post-AssessmentResults, continued Undergrads

23. Post-AssessmentResults, continued Undergrads

24. Post-AssessmentResults, continued Before taking this class, I considered data to be:

25. Post-AssessmentResults, continued Now, after taking this class, I consider data to be: Grads

26. Post-AssessmentResults, continued Undergrads

27. Post-AssessmentResults, continued

28. Post-AssessmentResults, continued Before taking this class, when it comes to the collection and use of data:

29. Post-AssessmentResults, continued After taking this class, when it comes to the collection and use of data: Grads

30. Post-Assessment Results, continued Undergrads

31. Post-AssessmentResults, continued Overall, I feel the lessons in the class spent on data:

32. Links to Resources: http://libguides.colum.edu/bigdataforarts April Levy – alevy@colum.edu Jason Stephens – jstephens@colum.edu Questions