Brandon Wong, Lead Software Engineer, Academy of Motion Picture Arts and Sciences Business Intelligence is a technology-driven process that analyzes data and forms conclusions to help assist workers to make informed business decisions. From collecting to cleaning, to morphing, to displaying we will address the pain points, tips, and tricks on how to navigate this process of converting data from raw material to a final product. You'll learn: From a high level, the process of bringing data from the "back" to the "front". Tools and best practices for cleaning and displaying data. Understanding the foundations of business intelligence to better execute on objectives. The various ways of displaying data depends on circumstance.

1. Demystifying the art of Business Intelligence
and Data Analytics
Brandon Wong, Lead Software Engineer @ AMPAS

2. Brandon Wong
Lead Software Engineer at AMPAS

4. Love the Process!

5. SQL Reports SSRS

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?

28. Example:
Visualization

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!