Keeping up with the Quants & Lifting
the mist.
Dr Andrew McCarren







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?




Leads the data analysis/ Data capture
Interprets the needs of the organisation
Understands the data and the analysis
Can speak a common language




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.

+ 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

- 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.






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

Faster computers
◦ Processing power

Ability to store vast amounts of data.
◦ Cloud, hadoop

Better visual analytics
◦ Dashboards
◦ Graphics
◦ More user friendly solutions (Excel, SAS, Cognos
etc)

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?




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.

Data
◦ ‘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








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

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?

Stage 1
◦ 1. Problem recognition
◦ 2. Review of previous findings

Stage 2
◦ 3. Modelling
◦ 4. Data Collection
◦ 5. Data Analysis

Stage 3
◦ 6. Results presentation


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?


3. Modelling/ Variable selection
4. Data Collection.
◦ Precision/ measurement capability
◦ Qualitative/ Quantitative
◦ Structured/unstructured

5. Data analysis
◦ Types of stories-descriptive vs Inferential analysis

6. Results
◦ Presentation and Action
◦ Academic not equal to ‘Normal’ Interpretation
◦ A Picture Tells a thousand Words
Total
50
40
30
20
10
0
Total

Results presentation and action
◦ Not normally focused on by academics. But
beginning to change. Need to tell the story with
narrative and pictures.

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






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)

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?





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





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



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

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.

Assumptions about the data?

Failures to adapt models
◦ Proctor and Gamble run 5000 models a day
Wrong interpretation of the models


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.