Understanding Analytics

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 • Processing power • Ability to store vast amounts of data. • Cloud, hadoop • Better visual analytics • Dashboards • Graphics • More user friendly solutions (Excel, SAS, Cognosetc)

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 • ‘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 • 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. 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