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Understanding Analytics

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Presentation on theme: "Understanding Analytics"— Presentation transcript:

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 Ability to store vast amounts of data.
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 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 Enterprise Leadership Targets Communication
‘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
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

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