The talk will cover in broad strokes the building blocks, facilitators and challenges for big data based decision making. Using examples from two projects from very dissimilar domains (High tech manufacturing and Public Health) Dr. Vinze will present possibilities for Data Science for both practitioners and academic researchers.

1. Using Data Riches
A tale of two projects
Ajay Vinze
Dean, Robert J. Trulaske, Sr. College of Business
University of Missouri
September 18-19, Belgrade, Serbia

2. What we are up against
The world of today s Manager
1. Innovation, Technology and Data
2. Management and Culture!
3. Globalization!!

3. Big Data (Data Riches)…
everyone is talking about it

4. But not everyone is buying it …
Source: https://hubaisms.com/tag/romney/
A different
perspective

5. Data, the accelerator for change

6. Data generation in 2018
Source: https://www.domo.com/blog/data-never-sleeps-6/

7. Time
Making Decisions is a
Balancing Act
Data Instinct
Why the focus on data

8. Role of Data
Making Decisions is a
Balance
Data
Opinion
(aka Best Professional
Judgment)
In the absence of data, business decisions are often made by the HiPPO

9. The Trifecta
A convergence of 3 technologies
Data
Collection
Computing
Power
AI &
Machine
Learning.
DA
- Processing speeds
- Powerful workstations
- Parallel computing
- Specialized data
gathering techniques
-Data Warehousing
(in Fortune 500 companies)
1993 - 8%; 2000 – 54%
by 2015 - 100%
Technology
is finally
catching up
with the power of
the AI/Machine Learning techniques

10. Exponential growth of computing power
Source: Time magazine – February 10, 2011

11. AI and Machine Learning
Source: https://www.cargroup.org/behind-headlines-artificial-intelligence-challenges-
using-ai-automotive-industry/ai_machinelearning/

12. Data is at the heart of Business
• Databases today can range in size into the Exabytes
(and more) i.e., 1,000,000,000,000,000,000 bytes of
data and more …
– My strong hunch is that within these masses of data lies
hidden information of strategic importance
• Jacob Bernoulli recognized this in 1713 and published
the Law of Large Numbers in Ars Conjectandi (Art of
Conjecture)
– He described LLN as a principle so simple that even the
stupidest man instinctively knows it is true. It however took
him 20 years to develop a rigorous mathematical proof
published

13. Law of large numbers
Source: Wikipedia

14. Data Riches
Tech and Managerial Issues
§Volume: Enterprises are awash with ever-growing data of all types,
easily amassing terabytes—even petabytes—of information.
§ Turn 12 terabytes of Tweets (each day) into improved product sentiment
analysis
§ Convert 350 billion annual meter readings to better predict power
consumption
§Velocity: Sometimes 2 minutes is too late. For time-sensitive
processes such as catching fraud, big data must be used as it
streams into your enterprise in order to maximize its value.
§ Scrutinize 5 million trade events created each day to identify potential fraud
§ Analyze 500 million daily call detail records in real-time to predict customer
churn faster

15. §Variety: Big data is any type of data - structured and unstructured
data such as text, sensor data, audio, video, click streams, log files
and more. New insights are found when analyzing these data types
together
§ Monitor 100’s of live video feeds from surveillance cameras to target points of
interest
§ Exploit the 80% data growth in images, video and documents to improve
customer satisfaction
§Veracity (or Value): 1 in 3 business leaders don’t trust the information
they use to make decisions. How can you act upon information if you
don’t trust it? Establishing trust in big data presents a huge challenge
as the variety and number of sources grows.
Data Riches
Tech and Managerial Issues

16. To Get Data is EASY
To Get the Right Data is HARD
To Get Insights is EASY
To Translate Insights into Decisions and Action is Hard
Tale of two projects

17. Tale of two projects
Extracting value from Data Riches
• FA/FI
• Public Health

18. The FA/FI Project – Scale of Operation
Image source: http://cnt.canon.com/technology/

19. FA/FI - Data generation

20. FA/FI project – Decisional orientation
The Data Problem: How you
organize (store) and how you find
(retrieve) are dissimilar processes!

21. FA/FI Project – the approach

22. Public Health project – the situation

23. Public Health project – data sourcing

24. Public Health project – EpiPlanz

25. Data Riches
driving the move to Enterprise 4.0?

26. Broader perspective - Observations
Enterprise 1.0 to 3.0 … 4.0
ENTERPRISE 4.0

27. Mentally select a card and concentrate on it.
However, do not reveal your selection to the
person next to you.
Final thoughts …
When dealing with Data Riches
you need to be careful

28. And now, whisper the name of your card to
yourself NOT out loud.
Don't skip this part, it is important
Tap your fingers on the table when you are ready.

29. Voila!
Is your card missing?

30. Thank you!
Хвала и најбоље жеље!