1. Understanding Analytics
Keeping up with the Quants & Lifting the mist.
Dr Andrew McCarren

2. What we start with?

3. Getting a clear picture

4. Lifting the Mist What is the question? No exact answers? Assumptions?
Variation (the same inputs don’t always give us the same answers)
Vast amounts data.
Is it clean?
How do we present our inferences?

5. What is an analyst? Leads the data analysis/ Data capture
Interprets the needs of the organisation
Understands the data and the analysis
Can speak a common language

6. Analytics VS Gut 40% of decisions are made on gut instinct.
Statistical predictions consistently out perform gut
Extensive evidence that having experts is good but experts using analysis is much better
Expert intuition is better only when there is no data and little time to get the data.

7. Problem solving with Analytics
+ Cigna health insurance
Using phone calls to reduce the amount of time in hospital of its clients
Used analytics to determine which illness had reduced time in hospital through phone call intervention
Saved money by focusing staff on the right strategy with regard to phone calls

8. Problem solving with Analytics
- AIG
Didn’t listen to the quants with regard to the risks the company were taking with over leveraged CDS
Cost AIG billions and effectively put the planet into a tail spin.

9. History of Analytics
Analytics – ‘always’ been around (since 5000BC) - tablets found recording the amount of beer workers were consuming.
WW2 – Focus on supply chain and target optimisation. Advent of Operations Research
UPS created a ‘statistical analysis group’ in 1954
70’s: Intel employ statisticians to develop line optimisation
Howard Dresner at Gartner defines “business intelligence”
2010: Analytics begins to blend with decision management

10. Improvements? Faster computers Ability to store vast amounts of data.
Processing power
Ability to store vast amounts of data.
Cloud, hadoop
Better visual analytics
Dashboards
Graphics
More user friendly solutions (Excel, SAS, Cognos etc)

11. Problems Academic Vs Real World
The interpretation is not always easy to understand or communicate
The world requires data faster and wants real time solutions,
Mathematical Modelling is not intellectually easy.
There is so much data
Which data do we use?
Structured vs non-structured data.
Are our assumptions right?

12. Culture People not Knowing what they want
Quants not been given a clear mandate by the organisation
Rapid change in operational and delivery technologies
Lack of standards.

13. What’s needed? Data Enterprise Leadership Targets Communication
‘Quality’ , clean data
Enterprise
Management approach/systems/software
Leadership
Passion and commitment
Targets
Get the right Key Performance Indicators/metrics
Remember, what gets measured gets managed
Communication
Training/visuals

14. Leadership Training Professionalism Define metrics/KPI
Ask the right question
Pick the right projects
Engage management and get their commitment
Show the benefits
Make the results clear

15. Looking Outside the box
What are other industries doing today that we could do tomorrow
Pharma randomised tests
Retail/online price optimisation
Manufacturing real time yield reporting
Systems
What do we have and can we get data from it?
Is our data on different platforms ?
Can we merge our data?
Can we interrogate our data in an intelligent and efficient manner?

16. Quantitative Analysis 3 stages-6 steps: T. Davenport
1. Problem recognition
2. Review of previous findings
Stage 2
3. Modelling
4. Data Collection
5. Data Analysis
Stage 3
6. Results presentation

17. Frame the Problem
1. Problem Recognition – Usually starts with broad hypothesis – “We are spending to much money on market research”
2. Review previous findings – Research the area. What are others doing?

18. Solve the problem 3. Modelling/ Variable selection 4. Data Collection.
Precision/ measurement capability
Qualitative/ Quantitative
Structured/unstructured
5. Data analysis
Types of stories-descriptive vs Inferential analysis

19. Results 6. Results Presentation and Action
Academic not equal to ‘Normal’ Interpretation
A Picture Tells a thousand Words

20. Communicating and Acting on Results
Results presentation and action
Not normally focused on by academics. But beginning to change. Need to tell the story with narrative and pictures.

21. Examples of Success & failure
Engineer wants to change printers on board manufacturing because boards are being sent wrong way on the line.
Stopped them spending a fortune on replacing printers world wide.
Line installation stopped from going wrong.
Line approval was stopped until machine gave stable results.
Pharmaceutical industry clinical trial on cancer patients and their reaction/adverse events to a drug.
Obsession with significance testing

22. Types of analytical stories
CSI Solve a problem
Solve a long term problem with analytics
MAD Scientist – conducting experiments
Survey the situation
Prediction – use past results to tell the future
What happened –Straight forward reporting, descriptive statistics (accounts, CSO)

23. Measurement Problems Choice of measurement device critical
Weigh up the ROI of the options and the results that can be got from it.
27k simple single measurement device versus 350k for XRAY machine for measuring fat on Pigs.
What are using the data for?
Stability/Accuracy/Consistency and interpretation of Measurement is critical.
Wrong measurement gives wrong conclusions
How does one translate language into numbers?

24. What non-Quants (Deciders) should expect of Quants
Learn the business process and problem
Communicate results in business terms
Seek the truth with no predefined agenda.
Help frame and communicate the problem, not just solve it
Don’t wait to be asked

25. What Quants should expect of Non-Quants (Deciders)
Form a relationship with your quant (Don’t lock them in a room)
Give access to the business process and problem
Focus primarily on framing the problem not solving it
Ask lots of questions, especially on assumptions.
Ask for help with the whole process

26. The future? Machine Learning Voice, Video, text Personalised Analysis
i.e. what is *this particular* consumer likely to buy at this point in time when presented with these particular choices
Automotive Modelling
The models adapt themselves to update analysis

27. It takes time
Building the capability takes a huge amount of time and resources
Barclays 5 year plan on ”Information – based customer management”
The big companies believe in it.
Communication & Culture is key to success.
Every organisation has vast amounts of data they are not using.

28. Mistakes Assumptions about the data? Failures to adapt models
Proctor and Gamble run 5000 models a day
Wrong interpretation of the models

29. Conclusion Follow the 6 steps Always question the data
Where did they come from
How were they measured?
Are the data stable?
Examine outliers/unusual events
Understanding the problem always takes away the mist.
Communication is key to success.
Organisation needs a Culture/ Leadership to succeed in analytics.

30. Thank You