A demo presentation on Data Analytics Services.

1. Data Science
Service
Zero-Nada

2. CRISP-DM
Methodology
CRoss-Industry Standard Process
for Data Mining:
The leading methodology used by
industry data miners
[KDNuggets]

3. Client
Zero-Nada
What’s on the Table?
Business Understanding
Data Understanding
Data Preparation
Modeling
Evaluation
Deployment

4. Drawback of Purchasing Ready-made Predictive
Analytics Packages from Vendors
- Vendor Lockdown
- Black-Box Approach
- Once you purchase … That’s it!
- It does only what it was designed for...
- Whatever they decided to automate/analyze is what it’ll do!
- May or maynot be what you need!
- You are not buying any “Secret Sauce” from the vendor
- You are actually buying what THEY pulled off-the-shelf and put together.

5. What’s on the Table?
- Guided Analytics
- Keeping the Experts in the Loop
- Automated AI and ML
- Build/test/optimize hundreds of
prediction models
- Select the most optimized and
appropriate for your problem
- Measurable Results

6. Case Studies

7. Chemical Concentration Prediction (PetroChem)
Problem
● Quality Control Process: Identification of
chemical and compound concentration
in a product.
● Cost of chemical analysis of individual
chemical and compound varies.
○ Measurement of some chemicals is
expensive.
Objective
● Reduce cost of chemical analysis
Solution
● Automated AI and ML
○ Build hundreds of predictive models
○ Individual and ensembles
○ Optimize models
Result
An optimized model that predicts
concentration of “expensive” chemicals based
on the concentration of “cheap” chemicals
Accuracy ~ 94.2%

8. Chemical Concentration Prediction (PetroChem)
Chem 1 Comp 1 ... Chem 3 Comp 3 Chem 4 Chem 5
Cost $$
Chem 1 Comp 1 Chem 2 Chem 3 Comp 3 Chem 4 Chem 5
Cost $$
Before
After
Cost Reduction
Data Understanding Data Preparation Modeling Evaluation

9. ER Patient Waiting Time Prediction (Healthcare)
Problem
● Patients registering at ER desk in a
hospital get frustrated from waiting
times.
● Frequent questioning of counter staff
poses hurdles in ER work
Objective
● Improve client experience while waiting
Solution
● Process historic data of ER visits, patient
history and resource utilization.
● Automated AI and ML
○ Build hundreds of predictive models
○ Individual and ensembles
○ Optimize models
Result
An optimized ensemble of models that
predicts waiting time for a patient
Accuracy ~ 86%

10. ER Patient Waiting Time Prediction (Healthcare)
Data Understanding Data Preparation Modeling Evaluation
BeforeAfter
Better User Experience

11. Customer Segmentation (Marketing)
Problem
● Company has over 10M customers
● Company has a large bag of products
and services
● Company wants to conduct targeted
advertising campaign to sell a specific
product
Objective
● Identify top 10,000 potential customers
● Instruct the telemarketing department
to promote a chosen product
Solution
● Analyze customer usage history
● Capitalize on demography data and
survey results (provided by client)
● Automated Data Mining
○ Identify association patterns
○ Cluster search
Result
Provide list of potential customers
Acceptance Rate ~ 72% (reported by client)

12. Variable Toll (Traffic)
Problem
● Traffic varies on the Saudi-Bahrain
causeway depending on peak/off-peak
hours
● Waiting time at the border increases
during peak time
Objective
● Try to distribute traffic evenly
throughout the entire day by linking toll
price with predicted traffic.
● Optimize booth operations
Solution
● Analyze traffic and demographic data of
travellers.
● Automated AI and ML
○ Build hundreds of predictive models
○ Individual and ensembles
○ Optimize models
Result
An optimized prediction model to predicts
traffic on the causeway
Theoretical Accuracy ~ 91.2%

13. Operation Overhead Reduction (Monitoring)
Problem
● Client has grid of sensing stations over a
vast geographic area
● As the grid grows, so does the
operation, monitoring and maintenance
costs
Objective
● Reduce grid operation cost by
eliminating grid points
Solution
● Automated AI and ML
○ Build hundreds of predictive models for
each grid point
● Identify grid points for elimination
● Replace eliminated grid point readings
with predictive model
Result
Propose candidate grid points for
replacement with prediction models
Cost reduction ~ 37%

14. What’s on the Table?
- Guided Analytics
- Keeping the Experts in the Loop
- Automated AI and ML
- Build/test/optimize hundreds of
prediction models
- Select the most optimized and
appropriate for your problem
- Measurable Results
Zero-Nada