Big Data Analytics: The Art of the Data Scientist

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3 No More Managing from Scarcity 3

4 My Generation This Generation Control Security Stability Manage from Scarcity Single Version of Truth Experience Engagement Gamification Open Source Context Copyright 2013 Neil Raden and Hired Brains Research LLC 4

7 Even Big Data Doesn t Speak for Itself Incomplete Behaviors underrepresented Anonymizing disasters Single source of data inadequate Harmonization Not a crystal ball Copyright 2013 Neil Raden and Hired Brains Research LLC 7

8 Missing in Big Data: Abstraction The 1971 Audi 100 had an accelerator cable connected to the gas pedal and operated the carburetor. The 2013 Audi S8 is purely fly-by-wire. Sensors in the accelerator pedal communicate with the engine management system, which determines how much or how little fuel to allow the fuel injectors to shoot into the engine. Stepping on the gas in 2013 is an abstraction. A 2013 Audi S8 has more aggregate computing power than 1971 IBM mainframe If you had to manage all of these systems yourself, you wouldn t get out of the driveway. Their function is abstracted Copyright 2013 Neil Raden and Hired Brains Research LLC 8

9 What Is Data Science? Discovering what we don t know from data Getting predictive and/or actionable insight Development of data products that have clear business value Providing value to the organization through sharing and learning Using techniques like storytelling and metaphor to explain concepts Building confidence in decisions Copyright 2013 Neil Raden and Hired Brains Research LLC 9

12 Euler Gave Us the Tools Contribution Graph Theory Infinitesimal Calculus Topology Number Theory Example Graph & Ontology Databases Everything Topological Data Analysis Encryption Nothing we do in Big Data would be possible without Euler Copyright 2013 Neil Raden and Hired Brains Research LLC 12

13 But Euler Got One Thing Wrong Tobias Mayer A contemporary of Euler Famous for his observations of the libration of the moon TONS of observations Figured out how to group them Famous quote: Because these observation were derived from nine times as many observations, one can therefore conclude that they are nine times more more accurate Copyright 2013 Neil Raden and Hired Brains Research LLC 13

14 Euler Not a Data Scientist Euler: By the combination of two or more equations, the errors of the combinations and the calculations multiply themselves. The greatest mathematician of all time pre-dated the concept of statistical error Copyright 2013 Neil Raden and Hired Brains Research LLC 14

15 Why Does This Matter? Because Data Science is not the realm of the most brilliant mathematicians It s for people who know how to do it and who have the correct training and tools to do it themselves Copyright 2013 Neil Raden and Hired Brains Research LLC 15

16 The Data Scientist Term invented by Yahoo Super-tech, super-quant Business expert too Orientation: Search and Web We used to call them quants Few and far between How do you find/train them? Hint: like actuaries Copyright 2013 Neil Raden and Hired Brains Research LLC 16

17 A Typical Day Basic data manipulations to wrangle data and fit a variety of standard models -40% Translate a business problem into the design of a data analysis strategy - 5% Graphically explore data to motivate modeling choices and improvements 10% Interpret and critically examine standard model output 5% Test the performance of models on holdout data - 10% Go to meetings 30% 70% is not Data Scientist work Copyright 2013 Neil Raden and Hired Brains Research LLC 17

19 Data Scientist Job Listing A PhD, a Master's Degree with at least five years of data mining experience. A degree in one of the following fields: computer science, computational biology, statistics, or another computational area with emphasis on the use of machine learning/data mining to build predictive models. Extensive hands on experience working with very large data sets, including statistical analyses, data visualization, data mining, and data cleansing/transformation. Under-the-hood knowledge machine learning: supervised/unsupervised,loss functions,regularization,feature selection,regression/classification,cross-validation bagging kernel methods, sampling, probability distributions Experience prototyping and developing data mining solutions using statistical software (R, Matlab, etc). Strong ability to communicate deep analytical results in forms that resonate with scientific and/or business collaborators, highlighting actionable insights. Entrepreneurial inclination to discover novel opportunities for applying analytical techniques to business/scientific problems across the company. Strong Unix/Linux scripting skills (Perl, Python, etc). Familiarity with writing SQL queries and working with databases. Object oriented programming experience (Java, C++, etc). Capacity to motivate and train junior scientists and offer counsel to peers. Basic understanding underlying scientific problems/processes and to facilitate effective communication with scientific collaborators is a plus. Previous experience working with common R&D data streams is a plus. Copyright 2013 Neil Raden and Hired Brains Research LLC 19

20 Who Remembers Distributions? The equations are the Moment Generating Function and Fourier transform of a normal distribution f with mean μ and deviation σ This is one of the most common formulas in statistics. How many of you remember it? Copyright 2013 Neil Raden and Hired Brains Research LLC 20

21 From IBM: Looking for Unicorns There is not a clear definition of the kind of profile you need for a top analytic performer. IBM is conducting research with various organizations to interview leaders and analytic experts to develop this kind of profile. What s bubbling out of this is that top performers: Have good communication skills Have good math skills (not typically associated with #1) Good individual problem-solver Collaborative (again, a bit of a contradiction with #3) Intellectually curious Competitive Copyright 2013 Neil Raden and Hired Brains Research LLC 21

22 Not Invented Here Danger Very few industries start with rawest materials and finish through complete product Mining Big Data for insight and action can be considered the same way Even digital giants like Twitter don t Why would you? Copyright 2013 Neil Raden and Hired Brains Research LLC 22

24 Types of Analysis Analytic Types Descriptive Title Quantitative Sophistication/Numeracy Sample Roles Type I Quantitative R&D PhD or equivalent Creation of theory, development of algorithms. Academic /research. Work in business/government for very specialized roles Type II Data Scientist or Quantitative Analyst Advanced Math/Stat, not necessarily PhD Type III Operational Analytics Good business domain, background in statistics optional Type IV Business Intelligence/ Discovery Data and numbers oriented, but no special advanced statistical skills Internal expert in statistical and mathematical modelling and development, with solid business domain knowledge. Running and managing analytical models. Strong skills in and/or project management of analytical systems implementation Reporting, dashboard, OLAP and visualization, some design, posterior analysis of results from quantitative methods. Spreadsheets, business discovery tools Copyright 2013 Neil Raden and Hired Brains Research LLC 24

25 Types of Shifting Analysis Analytic Types Descriptive Title Quantitative Sophistication/Numeracy Sample Roles Type I Quantitative R&D PhD or equivalent Creation of theory, development of algorithms. Academic /research. Work in business/government for very specialized roles Type II 3rd Party Services Data Scientist or Quantitative Analyst Advanced Math/Stat, not necessarily PhD Type III Operational Analytics Good business domain, background in statistics optional Type IV Type V Better BI/Viz/Disco Training/Mentoring/Apps Training/Mentoring/Apps Business Intelligence/ Discovery Data and numbers oriented, but no special advanced statistical skills Internal expert in statistical and mathematical modelling and development, with solid business domain knowledge. Running and managing analytical models. Strong skills in and/or project management of analytical systems implementation Reporting, dashboard, OLAP and visualization, some design, posterior analysis of results from quantitative methods. Spreadsheets, business discovery tools Copyright 2013 Neil Raden and Hired Brains Research LLC 25

26 Type Shifting As much as 80% of Data Scientist work can be done by others Data gathering, cleansing, profiling, parsing and loading Data and process stewardship Platform availability Providing organizational and market domain expertise Creation of presentation material Copyright 2013 Neil Raden and Hired Brains Research LLC 26

27 Types of Analytics Business Intelligence Data Mining Predictive Analytics Optimization X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X How do I use data to learn about my customers? What has been happening in my business? Who are my best/worst customers? How do I turn my data into rules for better decisions? How are those customers likely to behave in the future? How do they react to the myriad ways I can touch them? How do make the best possible decisions given my constraints? Knowledge - Description Action - Prescription

28 Descriptive Analytics - Improve Rules High Income High income, low-moderate education Low-moderate income, young Moderate-high education low-moderate income Education High Moderate education, low income, middle-aged High Low education, low income Copyright 2013 Neil Raden and Hired Brains Research LLC 28

31 Stat Tools Can Be Dangerous Tests are not the event Tests are flawed Tests detect things that don t exist Tests give test probabilities not the real probabilities False positives skew results People prefer natural numbers Even Science is a test Copyright 2013 Neil Raden and Hired Brains Research LLC 31

32 John Tukey The combination of some data and an aching desire for an answer does not ensure that a reasonable answer can be extracted from a given body of data.

34 Analytics Is Hard Analytics is hard Analytics takes resources Analytics takes effort to create and assimilate You need to focus your analytics at the key leverage points of your business UPS focuses on where the package is Marriott focuses on yield management If you try to do everything, you won t do anything well. Copyright 2013 Neil Raden and Hired Brains Research LLC 34

35 Analytics is hard. Analytics takes resources. It takes effort for an organization to create and assimilate learnings from analytics. You need to focus your analytics at the key leverage points of your business. UPS focuses their analytics on knowing where packages are, Marriott focuses on yield management. If you try to do everything, you won t do anything well. Copyright 2013 Neil Raden and Hired Brains Research LLC 35

36 Decisions: A Miracle Happens? Will Data Science Lead Us to Better Decision Processes? The Jordan river Problem: 40 years wandering with BI; how do we get across? Getting to a culture of decision making requires your business to have real, solid wins using analytics to make people care from top to bottom. Copyright 2013 Neil Raden and Hired Brains Research LLC 36

37 Amazon Marriott Honda Intel Novartis Wal-Mart UPS Verizon P & G Progressive Capital One Yahoo Dell Barclays A Final Thought About Analytics The challenge of analytics is communication and creating a shared understanding. It s about focusing on high impact areas, moving forward one step at a time, being skeptical, being creative, searching for the truth. 120% 80% 40% 0% Stock Market Returns for the Competing on Analytics Cohort Average Stock Market Return Any company can Compete on Analytics. But not like this -40% -80% Copyright 2013 Neil Raden and Hired Brains Research LLC 37

38 Five Things to Remember Data is an asset, people make it valuable Your data scientists may well be a team Communication, insight and reason more important than math You have lurking data scientists in your firm Start with what matters, build confidence Copyright 2013 Neil Raden and Hired Brains Research LLC 38