Keeping up with the Quants amp Lifting the mist Dr Andrew McCarren What we start with Getting a clear picture What is the question No exact answers Assumptions Variation the same inputs dont always give us the same answers ID: 133619
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Slide1
Understanding Analytics
Keeping up with the Quants & Lifting the mist.
Dr Andrew McCarrenSlide2
What we start with?Slide3
Getting a clear pictureSlide4
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?
Lifting the MistSlide5
Leads the data analysis/ Data captureInterprets the needs of the organisation
Understands the data and the analysis
Can speak a common language
What is an analyst?Slide6
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.
Analytics VS GutSlide7
+ Cigna health insuranceUsing 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
Problem solving with AnalyticsSlide8
- 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.
Problem solving with AnalyticsSlide9
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
History of Analytics Slide10
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
)
Improvements?Slide11
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?
ProblemsSlide12
People not Knowing what they wantQuants not been given a clear mandate by the organisation
Rapid change in operational and delivery technologies
Lack of standards.
Culture Slide13
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
What’s needed?Slide14
TrainingProfessionalismDefine metrics/KPI
Ask the right question
Pick the right projects
Engage management and get their commitment
Show the benefits
Make the results clear
LeadershipSlide15
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?
Looking Outside the boxSlide16
Stage 11. Problem recognition
2. Review of previous findings
Stage 2
3. Modelling
4. Data Collection
5. Data Analysis
Stage 3
6. Results presentation
Quantitative Analysis
3 stages-6 steps: T. DavenportSlide17
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?
Frame the ProblemSlide18
3. Modelling/ Variable selection4. Data Collection.
Precision/ measurement capability
Qualitative/ Quantitative
Structured/unstructured
5. Data analysis
Types of stories-descriptive
vs Inferential analysis
Solve the problemSlide19
6. Results Presentation and Action
Academic not equal to ‘Normal’ Interpretation
A Picture Tells a thousand Words
ResultsSlide20
Results presentation and action
Not normally focused on by academics. But beginning to change. Need to tell the story with narrative and pictures.
Communicating and Acting on ResultsSlide21
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
Examples of Success & failureSlide22
CSI Solve a problemSolve 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)
Types of analytical storiesSlide23
Choice of measurement device criticalWeigh 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?
Measurement ProblemsSlide24
Learn the business process and problemCommunicate 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
What non-Quants (Deciders) should expect of QuantsSlide25
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
What Quants should expect of Non-Quants (Deciders)Slide26
Machine LearningVoice, 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
The future?Slide27
Building the capability takes a huge amount of time and resourcesBarclays 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.
It takes timeSlide28
Assumptions about the data?Failures to adapt models
Proctor and Gamble run
5000
models a day
Wrong interpretation of the models
MistakesSlide29
Follow the 6 stepsAlways 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.
ConclusionSlide30
Thank You