Data Analytics Using AI part 1 Overview of AI-Enabled Analytics

Data Analytics Using AI part 1

1. BAI3399
Data Analytics Using AI
Overview of AI-Enabled Analytics
Islam Ali

2. Learning Objectives
•Understand the Meaning of AI
•Learning the difference between AI and ML
•Knowing what is involved in ML Projects
•Comprehending the difference between Analysis and Analytics
•Avoiding Bias with the use of AI
•ROI of AI

3. What is AI?
•The ability of Machines to make decisions
that humans usually do.

4. AI is going to be part of all
businesses
When?

5. Technology Adoption
Grant Thornton's 21 May 2019 report
•38% of respondents indicated that they currently
implemented advanced analytics
•29% are planning implementations in the next 12 months
•29% had implemented machine learning
•24% were planning to implement in the next 12 months

6. The Good
•indicative of the priority of and
accelerating trend in the adoption of
analytics and AI throughout business

7. The Bad
•Projects have been highly targeted to only
certain areas of the business
•Projects have been highly targeted to only
certain tasks of these business areas
•Many more failures than successes

8. Good News •AI and Analytics Failures can be avoided

9. Hindrances
•Many Executives do not have clear vision
on how to navigate their business through
the adoption of AI and Analytics
•Some Executives have misconception
about what AI enablement means for a
business.
•A lot of Executives know what they need to
do, but they do not know how to get there.

10. AI vs ML
•Artificial Intelligence (AI) is the
broad concept of creating
machines that can mimic
human intelligence
•Machine Learning (ML) is
asubset of AIthat provides
systems with the ability to
automatically learn and
improve from experience
without explicit programming.

11. all ML is AI, but
not all AI is ML
•AI encompasses various
techniques like robotics and
natural language processing
•ML is a specific approach to
achieving AI by identifying
patterns in data

12. Overview
of Machine
Learning
ML is a technology that
today requires specialized
skills to use and deploy.
ML is an AI engine often
used with other tools to
render the ML output useful
for decisions.

13. Example: Risk Reduction
•Supposeabankwantstoexpandthe
numberofloanswithoutincreasingtherisk
profileofitsloanportfolio.MLcanbeused
tomakepredictionsregardingrisk,and
thentheresultsareimportedto
spreadsheetstoreportthosenew
additionalloanapplicantsthatcannowbe
approved

14. Technical Team
DATA SCIENTISTS PROGRAMMERS DATABASE
ADMINISTRATORS
APPLICATION
DEVELOPERS

15. What else is involved
•Define the problem to be solved
•Large volumesof high-quality,
relevantdata
•…
•ML can be complex, but it is very
useful

16. Next
Session

17. Analytics VS
Analysis

18. BI AND DATA
VISUALIZATION
VS. ANALYTICS

19. BIASED VS.
UNBIASED

20. AI ROI

21. Conclusion

22. Thank You