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

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Presentation on theme: "Keeping up with the Quants & Lifting the mist. Dr Andrew McCarren."— Presentation transcript:

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



4  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  Leads the data analysis/ Data capture  Interprets the needs of the organisation  Understands the data and the analysis  Can speak a common language

6  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  + 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  - 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  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  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)

11  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  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  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

14  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  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  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

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

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

21  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  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  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  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  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  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  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  Assumptions about the data?  Failures to adapt models ◦ Proctor and Gamble run 5000 models a day Wrong interpretation of the models

29  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.


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