Understanding Analytics

Keeping up with the Quants & 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 don’t always give

us the same answers) Vast amounts data. Is it clean? How do we present our inferences?

Lifting the Mist

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

What is an analyst?

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 Gut

+ 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

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.

Problem solving with 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

History of Analytics

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?

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?

Problems

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.

Culture

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?

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

Leadership

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 box

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

Quantitative Analysis 3 stages-6 steps: T. Davenport

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 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

Solve the problem

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

Results

1 2 3 4 5 6 7 8 9 11(blank)05

1015202530354045

Total

Total

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 Results

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 & failure

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)

Types of analytical stories

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?

Measurement Problems

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

What non-Quants (Deciders) should expect of Quants

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)

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

The future?

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.

It takes time

Assumptions about the data?

Failures to adapt models◦ Proctor and Gamble run 5000 models a day

Wrong interpretation of the models

Mistakes

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.

Conclusion

Thank You