Speaker Bio:Blake Burch is a self-taught data expert whose journey began in marketing campaign management for various brands. He found himself constantly performing repetitive tasks such as downloading data, making changes, and uploading it back into the system. Determined to find a more efficient method, Blake collaborated with the development team to set up databases and taught himself SQL to access data daily.

He discovered he could write queries and apply changes automatically, which prompted him to learn Python and API integration to automate the process further. This led to the creation of a data team at the agency where he worked. Realising the potential for standardised datasets and innovative data solutions, Blake co-founded Shipyard, a company that focuses on helping data teams easily transfer and automate actions within systems.

Their success has been remarkable, enabling clients to handle data in just minutes instead of weeks of engineering time. Blake's journey showcases his dedication to streamlining data processes and empowering businesses with efficient automation solutions.

Connect with Blake: * LinkedIn * Twitter * Shipyard (Blake's company)

Episode Topics:
1. The Journey of a Self-Taught Data Analyst:
- Blake started as a marketing campaign manager and experienced the frustration of manually managing and analysing data.
- He taught himself SQL to access data daily and learned Python and API skills to automate tasks.
- Blake built a data team and focused on data innovation to stay ahead in the industry.

1. Shipyard: Empowering Data Teams:
2. Blake spun out the technology they developed to create Shipyard, a platform that helps data teams move and automate their data quickly.

3. Shipyard has experienced great success in saving time and effort in data management and automation.

4. Owning Models and Data Security:

5. Blake emphasises the importance of owning models when working with real-world data.
6. Data availability should be restricted within organisations to limit access to certain data sets.

7. Data access should be restricted at a column level to avoid granting everyone access to all data.

8. Clean and Consistent Data Sets:

9. Clean and consistent data sets are crucial for effective machine learning.

10. Hiring a data engineer before a data scientist ensures proper data setup.

11. Evaluating the Impact of Machine Learning:

12. The quantity and quality of data play a significant role in machine learning outcomes.

13. Continuous evaluation of machine learning models is crucial to ensure consistent performance.

14. AI-Driven Tools and Applications:

15. AI-driven tools like code interpreters can empower non-technical users to manipulate and validate data without coding knowledge.

16. Generative AI can provide approximations of likely outcomes, useful for experimentation and testing.

17. Data Orchestration and Building Trust:

18. Orchestration is crucial for connecting data processes and ensuring data flow.

19. Proactive alerting and notifications build trust with business users.

20. Understanding the Work and Goals of Different Departments:

21. Embedding data analysts into different departments helps them understand day-to-day problems and drive specific metrics.

22. Building empathy and understanding is key to effectively utilising data insights.

23. Scoping Projects and Conversations:

24. Conversations with requesters to understand goals and outcomes are essential for effective data projects.

25. Creating fake datasets and simulating scenarios can help clarify desired information.

26. Leveraging Logs and Increasing Data Usage:

27. Logs are an underutilised area that can unlock significant value for businesses.

28. Tracking queries and measuring usage are crucial for validating ROI and measuring revenue change attributed to datasets.

29. Building a Modern Data Stack:

30. Critically assessing business needs is important for building a relevant data stack.
31. Real-time processing versus batch processing should be considered.
32. A centralised analytics database, cloud-based preferably, and efficient data loading tools are essential.

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