At the core of SMU DataArts’ operations lies a wealth of information from various sources, notably the “Cultural Data Profile,” a historical repository that is available on the SMU DataArts website. This invaluable dataset is meticulously curated to support grantmakers and cultural leaders, encompassing a comprehensive spectrum of data from organizations. Ranging from ticket sales, financial records, performance analytics, leadership insights, to venue specifics, this amalgamation of diverse data serves as a valuable resource for decision-makers.
Expanding beyond the Cultural Data Profile, SMU DataArts has implemented an innovative approach to include additional datasets from sources like the IRS, ticketing agencies, the Census Bureau, COVID-related information, and more. Through the adept work of the SMU DataArts teams, this amalgamation of data has been instrumental in identifying trends within the cultural art communities. Despite the abundance of data, however, SMU DataArts encountered a challenge: data fragmentation across various formats and platforms. Retrieving specific information became an arduous task, hindering operational efficiency.
Recognizing the need to streamline their process, the team embarked on a quest for a robust data warehousing solution. Bob Louderback, Director of Technology for SMU DataArts, considered multiple vendors and solutions and eventually decided to leverage Snowflake, a Data warehouse solution. Snowflake offered not just a centralized data warehouse but also provided key performance indicators (KPIs), user-friendly dashboards, and increased operational efficiencies. Notably, the cost-effectiveness of storage and its flexible processing time, dependent on monthly usage, made it an ideal fit for SMU DataArts’ objectives.
In order to synthesize the data, Bob worked closely with Lane Duncan, Assistant Director of Integration Services, and Ganesh Varhineedi, our Developer, who focused on the Extract, Transform, and Load (ETL) processes to ingest IRS data. “That part was a heavy lift, but we were able to get it done,” said Bob. “I’m so grateful for the team support that we have in OIT.”
Dr. Jennifer Benoit-Bryan further explained, “Integrating our analysis across many large datasets is critical to the kinds of fieldwide insights into patterns across the arts & culture sector that we provide at DataArts. Snowflake allowed us to build an organized data warehouse quickly and efficiently, extending the power of our existing Consolidated Data Set. This critical tool makes our work more efficient, allows easy updates to data access privileges, and provides valuable flexibility to integrate with new datasets in the future.”
SMU DataArts hopes to create public-facing dashboards, thus extending access to cultural insights for non-profits, advocacy groups, and university researchers. These tools will serve as essential resources for examining trends and utilizing AI predictions.
