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Best Practices for Creating a Data Science Team (Clair Sullivan)
## Resources
- Slides: https://github.com/cj2001/data_umbrella_2024/blob/main/data%20umbrella%20sullivan%202024.pdf
## About the Event
Most data projects fail, often without reaching production or delivering business outcomes. Many companies, in their pursuit to be data-driven, adopt an "upside-down approach," leading to wasted resources and no return on investment. Success hinges on four key elements: a strong data culture, the right problem, accurate data, and the right people.
This session will define the "upside-down approach," explore what constitutes a good data culture, and discuss identifying the right business problems. Finally, we'll offer strategies for maintaining a robust data culture to ensure long-term success.
This presentation covers:
- Evidence of an unsuccessful data science team
- A “Data Fable” showing an all-to-real fictional creation of a data team
- The “upside-down” approach to creating a data science team
- Suggestions for how to right the ship if your data science team was formed using the “upside-down” approach
- Cautionary tales for the near future of data science teams and history repeating
## Timestamps
00:00 Intro to Data Umbrella
02:50 Introduction to the speaker, Dr. Clair Sullivan
06:00 Ugly truths and statistics about data science
06:37 only 40.5% of companies have a Chief Data Officer
07:28 24% of companies consider themselves data-driven
08:04 80% cited cultural issues as the greatest obstacle for delivering business value
08:52 less than 1% of unstructured data is even analyzed or used
10:12 Data fable introduction
16:38 Clair analyzes how the fable went wrong and how to fix it
21:59 Upside down approach
22:11 Start with culture, problem, data then hiring people
25:19 Data culture
28:28 Cultivating a data culture
29:41 Importance of having a data strategy
31:38 The right problems for data scientists
31:58 Measuring ROI (Return on Investment)
35:46 History repeats with generative AI
40:00 Empower every individual
41:33 Wrap up
42:07 Q&A
43:00 Q: What are some red flags to look for in data science job postings?
45:46 Q: What are the best roles for defining business problems that can be solved by data?
47:02 Q: How can data science managers advocate for data science and manage expectations?
50:16 Q: Do smaller companies use data well?
52:52 Q: What differentiates senior data scientists from junior data scientists?
## About the Speaker
Dr. Clair Sullivan is currently the Founder and CEO of Clair Sullivan and Associates, a company dedicated to providing data science consulting services. Her career spans over 22 years of working with data, with interests in graphs, natural language processing, and generative AI. She has worked in a variety of settings ranging from director-level roles to individual contributor at a variety of startups to academia and the federal government.
- LinkedIn: https://www.linkedin.com/in/dr-clair-sullivan-09914342/
#DataScience #DataCulture #EffectiveTeams #DataDriven