Data Con LA 2022 - Demystifying the Art of Business Intelligence and Data Analytics
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The document outlines the fundamentals of business intelligence and data analytics, distinguishing between the roles of data scientists and data analysts. It covers the reporting lifecycle, the evolution of big data, and offers practical tips for effective data visualization and analysis. Additionally, it provides resources and a brief discussion on the objectives of internal and external data usage in businesses.
Data Con LA 2022 - Demystifying the Art of Business Intelligence and Data Analytics
- 1. Demystifying the art of Business Intelligence and Data Analytics Brandon Wong, Lead Software Engineer @ AMPAS
- 2. Brandon Wong Lead Software Engineer at AMPAS
- 6. Agenda Introduction A Brief History of Big Data Data Scientist vs Data Analyst The Four Parts of the BI/Reporting Lifecycle Tips and Tricks Q & A
- 7. A Brief History of Big Data
- 8. 1999
- 9. The emergence of: - Internet - Web 2.0 - Relational Databases ( SQL) - Cloud (AWS, Azure, GCP) - Machine Learning / AI - Web 3.0 - Blockchain! Then: - Social Media (tik tok, snapchat, facebook, etc…) - Advertising, Marketing
- 10. Data mining the practice of analyzing large databases in order to generate new information. ( Yes, I know this is wheat to flour)
- 12. Unstructured Data Structured Data
- 13. In a nutshell... Data Mining + Computer Science = Data Science Data Science + Recurrent Neural Networks = Machine Learning Analogies + Memes = Optimized Learning!
- 14. Data Scientist vs Data Analyst
- 15. Data scientists design and construct new processes for data modeling and production using prototypes, algorithms, predictive models, and custom analysis.
- 16. Data analysts examine large data sets to identify trends, develop charts, and create visual presentations to help businesses make more strategic decisions.
- 17. Analogy Time!
- 19. Data Analyst Data Scientist
- 20. Normally, Data Analysts will live in the boxed area in a large company. Small - mid sized companies will have a Data Analyst perform most to all tasks.
- 21. Four Parts of the Reporting Lifecycle
- 22. Data Source(s) Where are we getting our data from? Do we have access? How many sources do we need?
- 23. XML JSON
- 24. CSV SQL Table
- 25. Data Set(s) What data do we need? Do we need to clean it or convert it beforehand? Will this info help us achieve our end product?
- 26. Parameter(s) Who is the data for? What tools am I going to be using? How it is going to look?
- 27. UI/UX (look and feel ) Who are my primary users? Do they have permissions? How will they be interfacing with it? Why do they need it?
- 29. Tips and Tricks
- 30. Tips and Tricks ● An experienced chef is comfortable in the kitchen because he/she understands the fundamentals and foundations. ● Be knowledgeable of the tools you have available, be creative and you will always find a solution. ● Building visualizations is 80/20 rule. ○ Like Cooking, the preparation process is 80% ○ And the actual cooking part is only about 20%
- 31. Technical Foundations Solidify Your Technical Foundations: ● Aggregate Functions (SQL) ○ SUM, COUNT, GROUP, ORDER BY, etc… ● Object Oriented Programming ○ Data members, Attributes, Classes, etc... ● Relationships ○ Many to Many, One to Many ○ ERD (entity relationship diagrams).
- 32. Key Takeaways Experience and Stories: ● Worked at a ERP SaaS company. ● They did Billing, Accounting, and Project Management Software for Professional Services. ● Bottomline: How can we find out what our users want, without them telling us? Objectives: ● Internal -> Save Money. (cut costs, wage/salary comparison, profitability, etc…) ● External -> Make Money. (product pricing, markup on billing, etc…)
- 33. Summarized Comparison ● A Data Analyst is an individual who is responsible for bridging the gap of understanding between aggregated data and a assists in building tools the business needs to make the best decisions. ● Business Intelligence is the logic, strategy, and industry specific tools that allow the business to make sense of their own data and formulate decisions.
- 34. Link to Shashank Kalanithi’s channel: https://www.youtube.com/watch?v=pKvWD0f18Pc&t=391s Link to Kaggle: https://www.kaggle.com/competitions Link to Tableau Articles: https://www.tableau.com/learn/articles/data-visualization-tips Helpful Resources
- 35. Thank You! Linkedin: Bwong5995 Twitter: @WongtheRight Instagram: @Wongandwhite Q&A Time!