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A Statistician's View on Big Data and Data Science in Pharmaceutical Development (Version 4)

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Presentation given by Dr. Diego Kuonen, CStat PStat CSci, on October 13, 2014, at `F. Hoffmann-La Roche' in Basel, Switzerland.

ABSTRACT

There is no question that big data have hit the business, government and scientific sectors, as well as the pharmaceutical industry. The demand for skills in data science is unprecedented in sectors where value, competitiveness and efficiency are driven by data. However, there is plenty of misleading hype around the terms `big data' and `data science'. This presentation gives a professional statistician's view on these terms in pharmaceutical development, illustrates the connection between data science and statistics, and highlights some challenges and opportunities from a statistical perspective.


The presentation is also available at http://www.statoo.com/BigDataDataScience/.

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A Statistician's View on Big Data and Data Science in Pharmaceutical Development (Version 4)

  1. 1. A Statistician's View on Big Data and Data Science in Pharmaceutical Development (Version 4 as of 10.10.2014) Dr. Diego Kuonen, CStat PStat CSci Statoo Consulting Statistical Consulting + Data Analysis + Data Mining Services Morgenstrasse 129, 3018 Berne, Switzerland @DiegoKuonen + kuonen@statoo.com + www.statoo.info F. Homann-La Roche, Basel, Switzerland | October 13, 2014
  2. 2. Abstract There is no question that big data have hit the business, gov- ernment and scienti
  3. 3. c sectors, as well as the pharmaceutical in- dustry. The demand for skills in data science is unprecedented in sectors where value, competitiveness and eciency are driven by data. However, there is plenty of misleading hype around the terms `big data' and `data science'. This presentation gives a professional statistician's view on these terms in pharmaceutical development, illustrates the connection between data science and statistics, and highlights some challenges and opportunities from a statistical perspective.
  4. 4. About myself (about.me/DiegoKuonen) PhD in Statistics, Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland. MSc in Mathematics, EPFL, Lausanne, Switzerland. CStat (`Chartered Statistician'), Royal Statistical Society, United Kingdom. PStat (`Accredited Professional Statistician'), American Statistical Association, United States of America. CSci (`Chartered Scientist'), Science Council, United Kingdom. Elected Member, International Statistical Institute, Netherlands. CEO, Statoo Consulting, Switzerland. Senior Lecturer in Statistics, Geneva School of Economics and Management, University of Geneva, Switzerland. President of the Swiss Statistical Society. Copyright c 2001{2014, Statoo Consulting, Switzerland. All rights reserved. 2
  5. 5. About Statoo Consulting (www.statoo.info) Founded Statoo Consulting in 2001. Statoo Consulting is a software-vendor independent Swiss consulting
  6. 6. rm spe- cialised in statistical consulting and training, data analysis and data mining ser- vices. Statoo Consulting oers consulting and training in statistical thinking, statistics and data mining in English, French and German. Are you drowning in uncertainty and starving for knowledge? Have you ever been Statooed? Copyright c 2001{2014, Statoo Consulting, Switzerland. All rights reserved. 3
  7. 7. Who has already been Statooed? Statoo Consulting's clients include Swiss and international companies like ABB; Alcan Aluminium Valais; Alstom; Arkema, France; AstraZeneca, United Kingdom; Barry Callebaut, Belgium; Bayer Consumer Care; Berna Biotech; BMW, Germany; Bosch, Germany; Cargill; Credit Lyonnais, United Kingdom; CSL Behring; F. Homann-La Roche; GfK Telecontrol; GlaxoSmithKline, United Kingdom; H. Lundbeck, Denmark; Lombard Odier Darier Hentsch; Lonza; McNeil, Sweden; Merck Serono; Nagra Kudelski Group; Nestle; Nestle Frisco Findus; Nestle Research Center; Novartis Pharma; Novelis; P
  8. 8. zer, United Kingdom; Philip Morris International; Pioneer Hi-Bred, Germany; PostFinance; Procter Gamble Manufacturing, Germany; publisuisse; Rodenstock, Germany; Sano
  9. 9. -Aventis, France; SAP France and SAP Ireland; Saudi Arabian Oil Company, Saudi Arabia; Smith Nephew Orthopaedics; Swisscom Innovations; Swissmedic; The Baloise Insurance Company; tl | Public Transport for the Lausanne Region; Wacker Chemie, Germany; upc cablecom; Copyright c 2001{2014, Statoo Consulting, Switzerland. All rights reserved. 4
  10. 10. as well as Swiss and international government agencies, nonpro
  11. 11. t organisa- tions, oces, research institutes and universities like King Saud University, Saudi Arabia; Lausanne Hotel School; Institute for International Research, Dubai, United Arab Emirates; Paul Scherrer Institute (PSI); Statistical Oce of the City of Berne; Swiss Armed Forces; Swiss Federal Institute for Forest, Snow and Landscape Research; Swiss Federal Institutes of Technology Lausanne and Zurich; Swiss Federal Oce for Migration; Swiss Federal Research Stations Agroscope Changins{Wadenswil and Reckenholz{ Tanikon; Swiss Federal Statistical Oce; Swiss Institute of Bioinformatics; Swiss State Secretariat for Economic Aairs (SECO); The Gold Standard Foundation; Unit of Education Research of the Department of Education, Canton of Geneva; Universities of Applied Sciences of Northwestern Switzerland, Technology Buchs NTB and Western Switzerland; Universities of Berne, Fribourg, Geneva, Lausanne, Neuchatel and Zurich. Copyright c 2001{2014, Statoo Consulting, Switzerland. All rights reserved. 5
  12. 12. Copyright c 2001{2014, Statoo Consulting, Switzerland. All rights reserved. 6
  13. 13. `Normality is a myth; there never was, and never will be, a normal distribution.' Robert C. Geary, 1947 Copyright c 2001{2014, Statoo Consulting, Switzerland. All rights reserved. 7
  14. 14. Contents Contents 8 1. Demystifying the `big data' hype 10 2. Demystifying the `data science' hype 21 3. What distinguishes data science from statistics? 41 4. Conclusion and opportunities (not only for statisticians!) 44 Copyright c 2001{2014, Statoo Consulting, Switzerland. All rights reserved. 8
  15. 15. `Data is arguably the most important natural resource of this century. ... Big data is big news just about ev- erywhere you go these days. Here in Texas, everything is big, so we just call it data.' Michael Dell, 2014 Copyright c 2001{2014, Statoo Consulting, Switzerland. All rights reserved. 9
  16. 16. 1. Demystifying the `big data' hype There is no question that `big data' have hit the business, government and scientific sectors. Indeed, the term `big data' has acquired the trappings of religion! However, there are a lot of examples of companies that were into `big data' before they were called `big data' | a term coined in 1997 by two researchers at the NASA. But, what exactly are `big data'? In short, the term `big data' applies to an accumulation of data that can not be processed or handled using traditional data management processes or tools. Big data are a data management architecture and most related challenges are IT focused! Copyright c 2001{2014, Statoo Consulting, Switzerland. All rights reserved. 10
  17. 17. The following characteristics | known as `the four Vs' or `V4' | provide one standard de
  18. 18. nition of big data: { `Volume' : `data at rest', i.e. the amount of data ( `data explosion problem'), with respect to the number of observations ( `size' of the data), but also with respect to the number of variables ( `dimensionality' of the data); { `Variety' : `data in many forms' or `mixed data', i.e. dierent types of data (e.g. structured, semi-structured and unstructured, e.g. log
  19. 19. les, text, web or multimedia data such as images, videos, audio), data sources (e.g. internal, external, open, public) and data resolutions; { `Velocity' : `data in motion', i.e. the speed by which data are generated and need to be handled (e.g. streaming data from machines, sensors and social data); { `Veracity' : `data in doubt', i.e. the varying levels of noise and processing errors. Copyright c 2001{2014, Statoo Consulting, Switzerland. All rights reserved. 11
  20. 20. What do big data look like? Source: The Association of the British Pharmaceutical Industry (2013). Big Data Road Map (www.abpi.org.uk/our-work/library/industry/Pages/big-data-road-map.aspx). Copyright c 2001{2014, Statoo Consulting, Switzerland. All rights reserved. 12
  21. 21. Where are the opportunities? Source: The Association of the British Pharmaceutical Industry (2013). Big Data Road Map (www.abpi.org.uk/our-work/library/industry/Pages/big-data-road-map.aspx). Copyright c 2001{2014, Statoo Consulting, Switzerland. All rights reserved. 13
  22. 22. `While its size receives all the attention, the most di- cult aspect of big data really involves its lack of struc- ture.' Thomas H. Davenport, 2014 Source: Davenport, T. H. (2014). Big Data at Work: Dispelling the Myths, Uncovering the Opportunities. Boston, MA: Harvard Business School Press. Copyright c 2001{2014, Statoo Consulting, Switzerland. All rights reserved. 14
  23. 23. `Volume' is often the least important issue: it is de
  24. 24. nitely not a requirement to have a minimum of a petabyte of data, say. Bigger challenges are `variety' and `velocity', and possibly most important is `ve- racity' and the related quality and correctness of the data. Especially the combination of dierent data sources (such as combining omics data generated by various high-throughput technologies) | resulting from `variety' | provides a lot of insights and this can also happen with `smaller' data sets. The above standard de
  25. 25. nition of big data is vulnerable to the criticism of sceptics that these four Vs have always been there. Copyright c 2001{2014, Statoo Consulting, Switzerland. All rights reserved. 15
  26. 26. `The only thing really new about big data is the cool new name.' Daniel J. Solove, September 16, 2014 Nevertheless, the above definition of big data provides a clear and concise `business' framework to communicate about how to solve different data processing challenges. But, what is new? Copyright c 2001{2014, Statoo Consulting, Switzerland. All rights reserved. 16
  27. 27. Big Data' ... is the simple yet seemingly revolutionary belief that data are valuable. ... I believe that `big' actually means important (think big deal). Scientists have long known that data could create new knowledge but now the rest of the world, including government and management in particular, has realised that data can create value.' Source: interview with Sean Patrick Murphy, a former senior scientist at Johns Hopkins University Applied Physics Laboratory, in the Big Data Innovation Magazine, September 2013. Copyright c 2001{2014, Statoo Consulting, Switzerland. All rights reserved. 17
  28. 28. `Suddenly it makes economic sense to try to extract value from all this data out there.' Sean Owen, 2014 Source: interview with Sean Owen, Cloudera's director of data science, in Research Live, May 19, 2014. Copyright c 2001{2014, Statoo Consulting, Switzerland. All rights reserved. 18
  29. 29. `The term `big data' is going to disappear in the next two years, to become just `data' or `any data'. ' Donald Feinberg, 2014 `In 2015, we exchange the term `big data' for `all data'.' Thomas H. Davenport, June 26, 2014 Copyright c 2001{2014, Statoo Consulting, Switzerland. All rights reserved. 19
  30. 30. `Data are not taken for museum purposes; they are taken as a basis for doing something. If nothing is to be done with the data, then there is no use in collecting any. The ultimate purpose of taking data is to pro- vide a basis for action or a recommendation for action.' W. Edwards Deming, 1942 Copyright c 2001{2014, Statoo Consulting, Switzerland. All rights reserved. 20
  31. 31. 2. Demystifying the `data science' hype The demand for `data scientists' | the `magicians of the big data era' | is unprecedented in sectors where value, competitiveness and eciency are driven by data. The Data Science Association de
  32. 32. ned in October 2013 the terms `data science' and `data scientist' within their Data Science Code of Professional Conduct as follows (see www.datascienceassn.org/code-of-conduct.html): { Data science is the scientific study of the creation, validation and transformation of data to create meaning. { A data scientist is a professional who uses scienti
  33. 33. c methods to liberate and create meaning from raw data. Copyright c 2001{2014, Statoo Consulting, Switzerland. All rights reserved. 21
  34. 34. `Data-Driven Decision making' (DDD) refers to the practice of basing decisions on data, rather than purely on intuition: Source: Provost, F. Fawcett, T. (2013). Data Science for Business. Sebastopol, CA: O'Reilly Media. Copyright c 2001{2014, Statoo Consulting, Switzerland. All rights reserved. 22
  35. 35. Copyright c 2001{2014, Statoo Consulting, Switzerland. All rights reserved. 23
  36. 36. Data science has been dubbed by the Harvard Business Review (Thomas H. Dav- enport and D. J. Patil, October 2012) as `the sexiest job in the 21st century ' and by the New York Times (April 11, 2013) as a ` hot new
  37. 37. eld [that] promises to revolutionise industries from business to government, health care to academia'. But, is data science really new and `sexy'? Copyright c 2001{2014, Statoo Consulting, Switzerland. All rights reserved. 24
  38. 38. The term `data science' was originally coined in 1998 by the statistician Chien-Fu Jeff Wu when he gave his inaugural lecture at the University of Michigan. Wu argued that statisticians should be renamed data scientists since they spent most of their time manipulating and experimenting with data. In 2001, the statistician William S. Cleveland introduced the notion of `data science' as an independent discipline. Cleveland extended the field of statistics to incorporate `advances in computing with data' in his article `Data science: an action plan for expanding the technical areas of the
  39. 39. eld of statistics' (International Statistical Review, 69, 21{26). Copyright c 2001{2014, Statoo Consulting, Switzerland. All rights reserved. 25
  40. 40. Although the term `data scientist' may be relatively new, this profession has existed for a long time! For example, Johannes Kepler published his first two laws of planetary motion, which describe the motion of planets around the sun, in 1609. Kepler found them by analysing the astronomical observations of Tycho Brahe. Kepler was clearly a data scientist! Or, for example, Napoleon Bonaparte (`Napoleon I') used mathematical models to help make decisions on battlefields. These models were developed by mathematicians | Napoleon's own data scien- tists! Copyright c 2001{2014, Statoo Consulting, Switzerland. All rights reserved. 26
  41. 41. Another (famous) example of that same time period is the following map (`carte figurative') drawn by the French engineer Charles Joseph Minard in 1861 to show the tremendous losses of Napoleon's army during his Russian campaign in 1812-1813, where more than 97% of the soldiers died. Sources: Van der Lans, R. (2013). The data scientist at work. BeyeNETWORK, October 24, 2013 (www.b-eye-network.com/view/17102). Tufte, E. R. (2001). The Visual Display of Quantitative Information (2nd edition). Cheshire, CT: Graphics Press. Copyright c 2001{2014, Statoo Consulting, Switzerland. All rights reserved. 27
  42. 42. Minard was clearly a data scientist! Copyright c 2001{2014, Statoo Consulting, Switzerland. All rights reserved. 28
  43. 43. `Data science in a way is much older than Kepler | I sometimes say that data science is the `second oldest' occupation.' Gregory Piatetsky-Shapiro, November 26, 2013 Copyright c 2001{2014, Statoo Consulting, Switzerland. All rights reserved. 29
  44. 44. `I keep saying the sexy job in the next ten years will be statisticians.' Hal Varian, 2009 Source: interview with Google's chief economist in the McKinsey Quarterly, January 2009. Copyright c 2001{2014, Statoo Consulting, Switzerland. All rights reserved. 30
  45. 45. `And with ongoing advances in high-performance com- puting and the explosion of data, ... I would venture to say that statistician could be the sexy job of the century.' James (Jim) Goodnight, 2010 Copyright c 2001{2014, Statoo Consulting, Switzerland. All rights reserved. 31
  46. 46. `I think he [Hal Varian] is behind | using statistics has been the sexy job of the last 30 years. It has just taken awhile for organisations to catch on.' James (Jim) Goodnight, 2011 Copyright c 2001{2014, Statoo Consulting, Switzerland. All rights reserved. 32
  47. 47. Looking at all the `crazy' hype around the terms `big data' and `data science', it seems that `data scientist' is just a `sexed up' term for `statistician'. It looks like statisticians just needed a good marketing campaign! Copyright c 2001{2014, Statoo Consulting, Switzerland. All rights reserved. 33
  48. 48. But, what is statistics ? { Statistics can be de
  49. 49. ned as the science of `learning from data' (or of making sense out of data), and of measuring, controlling and communicating uncertainty. It includes everything from planning for the collection of data and subsequent data management to end-of-the-line activities such as drawing conclusions of numerical facts called data and presentation of results. { Uncertainty is measured in units of probability, which is the currency of statistics. { Statistics is concerned with the study of uncertainty and with the study of deci- sion making in the face of uncertainty. Copyright c 2001{2014, Statoo Consulting, Switzerland. All rights reserved. 34
  50. 50. However, data science is not just a rebranding of statistics , large-scale statistics or statistical science! Data science is rather a rebranding of `data mining' ! `The terms `data science' and `data mining' often are used interchangeably, and the former has taken a life of its own as various individuals and organisations try to capitalise on the current hype surrounding it.' Foster Provost and Tom Fawcett, 2013 Copyright c 2001{2014, Statoo Consulting, Switzerland. All rights reserved. 35
  51. 51. `Data science is a train that is going 99% faster than the rails its on can support. It could derail, go o a cli, and become nothing but a failed fad.' Mark A. Biernbaum, 2013 Copyright c 2001{2014, Statoo Consulting, Switzerland. All rights reserved. 36
  52. 52. `It is already time to kill the `data scientist' title. ... The data scientist term has come to mean almost any- thing.' Thomas H. Davenport, 2014 Source: Thomas H. Davenport's article `It is already time to kill the `data scientist' title' in the Wall Street Journal, April 30, 2014. Copyright c 2001{2014, Statoo Consulting, Switzerland. All rights reserved. 37
  53. 53. But, what is data mining? Data mining is the non-trivial process of identifying valid, novel, poten- tially useful, and ultimately understandable patterns or structures or models or trends or relationships in data to enable data-driven decision making. `Non-trivial': it is not a straightforward computation of prede
  54. 54. ned quantities like computing the average value of a set of numbers. `Valid': the patterns hold in general, i.e. being valid on new data in the face of uncertainty. `Novel': the patterns were not known beforehand. `Potentially useful': lead to some bene
  55. 55. t to the user. `Understandable': the patterns are interpretable and comprehensible | if not immediately then after some postprocessing. Copyright c 2001{2014, Statoo Consulting, Switzerland. All rights reserved. 38
  56. 56. The data science (or data mining) Venn diagram Source: Drew Conway, September 2010 (drewconway.com/zia/2013/3/26/the-data-science-venn-diagram). Copyright c 2001{2014, Statoo Consulting, Switzerland. All rights reserved. 39
  57. 57. `Statistics has been the most successful information science. Those who ignore statistics are condemned to re-invent it.' Brad Efron, 1997 Copyright c 2001{2014, Statoo Consulting, Switzerland. All rights reserved. 40
  58. 58. 3. What distinguishes data science from statistics? Statistics traditionally is concerned with analysing primary (e.g. experimental) data that have been collected to explain and check the validity of specific existing ideas (hypotheses). Primary data analysis or top-down (explanatory and confirmatory) analysis. `Idea (hypothesis) evaluation or testing' . Data science (or data mining), on the other hand, typically is concerned with analysing secondary (e.g. observational) data that have been collected for other reasons (and not `under control' of the investigator) to create new ideas (hypotheses). Secondary data analysis or bottom-up (exploratory and predictive) analysis. `Idea (hypothesis) generation' . Knowledge discovery. Copyright c 2001{2014, Statoo Consulting, Switzerland. All rights reserved. 41
  59. 59. The two approaches of `learning from data' are complementary and should proceed side by side | in order to enable proper data-driven decision making. Example. When historical data are available the idea to be generated from a bottom- up analysis (e.g. using a mixture of so-called `ensemble techniques') could be `which are the most important (from a predictive point of view) factors (among a `large' list of candidate factors) that impact a given output?'. Mixed with subject-matter knowledge this idea could result in a list of a `small' number of factors (i.e. `the critical ones'). The con
  60. 60. rmatory tools of top-down analysis (statistical `Design Of Experiments', DOE, in most of the cases) could then be used to con
  61. 61. rm and evaluate this idea. By doing this, new data will be collected (about `all' factors) and a bottom-up analysis could be applied again | letting the data suggest new ideas to test. Copyright c 2001{2014, Statoo Consulting, Switzerland. All rights reserved. 42
  62. 62. `Neither exploratory nor confirmatory is sufficient alone. To try to replace either by the other is madness. We need them both.' John W. Tukey, 1980 Copyright c 2001{2014, Statoo Consulting, Switzerland. All rights reserved. 43
  63. 63. 4. Conclusion and opportunities (not only for statisticians!) Data, and the capability to extract useful knowledge from data, should be regarded as key strategic assets! Decision making that was once based on hunches and intuition should be driven by data and knowledge ( data-driven decision making). Extracting useful knowledge from data to solve `business' problems must be treated systematically by following a process with reasonably well-de
  64. 64. ned stages. Like statistics, data science (or data mining) is not only modelling and prediction, nor a product that can be bought, but a whole iterative problem solving cycle/process that must be mastered through interdisciplinary team effort. Copyright c 2001{2014, Statoo Consulting, Switzerland. All rights reserved. 44
  65. 65. Phases of the reference model of the methodology called CRISP-DM (`CRoss Industry Standard Process for Data Mining'; see www.statoo.com/CRISP-DM.pdf): Copyright c 2001{2014, Statoo Consulting, Switzerland. All rights reserved. 45
  66. 66. `If I had only one hour to save the world, I would spend
  67. 67. fty-
  68. 68. ve minutes de
  69. 69. ning the problem, and only
  70. 70. ve minutes
  71. 71. nding the solution.' Albert Einstein Copyright c 2001{2014, Statoo Consulting, Switzerland. All rights reserved. 46
  72. 72. One might rightly be sceptical about big data because the idea is still fuzzy and there is an awful lot of marketing hype around. However, big data are here to stay and will, over time, impact every single `busi- ness', including the pharmaceutical industry and the pharmaceutical development! The `age of big data' will (hopefully) be a golden era for statistics. There is convincing `evidence' that data-driven decision making and big data tech- nologies will substantially improve `business' performance. Statistical rigour is necessary to justify the inferential leap from data to knowledge. Lack of expertise in statistics can lead (and has already led) to fundamental errors! Copyright c 2001{2014, Statoo Consulting, Switzerland. All rights reserved. 47
  73. 73. `To have any hope of extracting anything useful from big data, ... , eective inferential skills are vital. That is, at the heart of extracting value from big data lies statistics.' David J. Hand, 2014 Copyright c 2001{2014, Statoo Consulting, Switzerland. All rights reserved. 48
  74. 74. `We are in the era of big data, and big data needs statisticians to make sense of it.' Eric Schmidt and Jonathan Rosenberg, 2014 Source: Eric Schmidt, Google's chairman and former CEO, and Jonathan Rosenberg, Google's former senior vice president of product, in their 2014 book How Google Works (New York, NY: Grand Central Publishing | HowGoogleWorks.net). Copyright c 2001{2014, Statoo Consulting, Switzerland. All rights reserved. 49
  75. 75. `Statisticians have spent the past 200 years figuring out what traps lie in wait when we try to understand the world through data. The data are bigger, faster and cheaper these days | but we must not pretend that the traps have all been made safe. They have not.' Tim Harford, 2014 Source: Tim Harford's article `Big data: are we making a big mistake?' in the Financial Times Magazine, March 28, 2014. Copyright c 2001{2014, Statoo Consulting, Switzerland. All rights reserved. 50
  76. 76. `There are a lot of small data problems that occur in big data. They do not disappear because you have got lots of the stuff. They get worse.' David Spiegelhalter, 2014 Copyright c 2001{2014, Statoo Consulting, Switzerland. All rights reserved. 51
  77. 77. `In reality, although each individual aw has a much smaller impact on the whole dataset than it did when there was less data, there are more flaws than before because there is more data.' Ted Friedman, September 25, 2014 Copyright c 2001{2014, Statoo Consulting, Switzerland. All rights reserved. 52
  78. 78. `Big data believers ignore the boundaries and limita- tions of traditional statistical techniques.' Gil Press, 2014 Source: Gil Press's article `The government-academia complex and big data religion' in Forbes, September 9, 2014. Copyright c 2001{2014, Statoo Consulting, Switzerland. All rights reserved. 53
  79. 79. The challenges from a statistical perspective include { the need to think really hard about a problem and to understand the underlying mechanism that drive the processes that generate the data ( ask the `right' question(s)!); { the provenance of the data, e.g. the quality of the data | including issues like omissions, data linkage errors, measurement errors, censoring, missing observa- tions, atypical observations, missing variables ( `omitted variable bias'), the characteristics and heterogeneity of the sample | big data being `only' a sample (at a particular time) of a population of interest ( `sampling/selection bias', i.e. is the sample representative to the population it was designed for?); Copyright c 2001{2014, Statoo Consulting, Switzerland. All rights reserved. 54
  80. 80. { the ethics of using and linking (big) data, particularly in relation to personal data, i.e. ethical issues related to privacy ( `information rules' need to be de
  81. 81. ned), confidentiality (of shared private information), transparency (e.g. of data uses and data users) and identity (i.e. data should not compromise identity); { the visualisation of the data; { spurious (false) associations ( `coincidence' increases, i.e. it becomes more likely, as sample size increases) versus valid causal relationships ( `con
  82. 82. rmation bias'); { the identi
  83. 83. cation of (and the controlling for) confounding factors; Copyright c 2001{2014, Statoo Consulting, Switzerland. All rights reserved. 55
  84. 84. { multiple statistical hypothesis testing with tens of thousands or even millions of tests performed simultaneously using high-throughput technology advances, often with complex dependencies between tests (e.g. spatial or temporal de- pendence), and the development of further statistically valid methods to solve large-scale simultaneous hypothesis testing problems; { the dimensionality of the data ( `curse of dimensionality', i.e. data become more `sparse' or spread out as the dimensionality increases), and the related usage and development of statistically valid strategies for dimensionality reduction, e.g. using `embedded' variable subset selection methods like `ensemble techniques'; Copyright c 2001{2014, Statoo Consulting, Switzerland. All rights reserved. 56
  85. 85. { the validity of generalisation ( avoid `over
  86. 86. tting'), and the replicability and reproducibility of findings; { the nature of uncertainty (both random and systematic); { the balance of humans and computers. `Data and algorithms alone will not ful
  87. 87. l the promises of big data. Instead, it is creative humans who need to think very hard about a problem and the underlying mechanisms that drive those processes.' David Park, 2014 Copyright c 2001{2014, Statoo Consulting, Switzerland. All rights reserved. 57
  88. 88. `We give numbers their voice, draw inferences from them, and define their meaning through our interpreta- tions. Hidden biases in both the collection and analysis stages present considerable risks, and are as important to the big data equation as the numbers themselves.' Kate Crawford, 2013 Copyright c 2001{2014, Statoo Consulting, Switzerland. All rights reserved. 58
  89. 89. `The numbers have no way of speaking for themselves. We speak for them. We imbue them with meaning. ... Data-driven predictions can succeed | and they can fail. It is when we deny our role in the process that the odds of failure rise. Before we demand more of our data, we need to demand more of ourselves.' Nate Silver, 2012 Source: Silver, N. (2012). The Signal and The Noise: Why Most Predictions Fail but Some Do Not. New York, NY: The Penguin Press. Copyright c 2001{2014, Statoo Consulting, Switzerland. All rights reserved. 59
  90. 90. `Big data promises to collect large sets of data and find associations between genes and diseases. There's de
  91. 91. nitely something useful in the data collected, but the danger is that we have no clue how to interpret it. Also, you must remember that all statistically sig- ni
  92. 92. cant things are not biologically signi
  93. 93. cant. So, it is definitely not a panacea.' Walter Gilbert, September 23, 2014 Source: interview with Walter Gilbert, Nobel Laureate 1980 in Chemistry for his contribution to sequence DNA (theconversation.com/nobel-laureate-big-data-and-full-genome-analysis-not-all-theyre-cracked-up-to-be-31992). Copyright c 2001{2014, Statoo Consulting, Switzerland. All rights reserved. 60
  94. 94. `As you plan your big data analytics journey, make sure you know your business goals, understand the data life- cycle and have a plan for delivering insights to every- one who needs them. Taking a holistic approach to big data analytics will be an important step in addressing technical challenges and reaching your business goals.' John Whittaker, 2014 Copyright c 2001{2014, Statoo Consulting, Switzerland. All rights reserved. 61
  95. 95. `Most of my life I went to parties and heard a little groan when people heard what I did. Now they are all excited to meet me.' Robert Tibshirani, 2012 Source: interview with Robert Tibshirani, a statistics professor at Stanford University, in the New York Times, January 26, 2012. Copyright c 2001{2014, Statoo Consulting, Switzerland. All rights reserved. 62
  96. 96. Have you been Statooed? Dr. Diego Kuonen, CStat PStat CSci Statoo Consulting Morgenstrasse 129 3018 Berne Switzerland email kuonen@statoo.com @DiegoKuonen web www.statoo.info /Statoo.Consulting
  97. 97. Copyright c 2001{2014 by Statoo Consulting, Switzerland. All rights reserved. No part of this presentation may be reprinted, reproduced, stored in, or introduced into a retrieval system or transmitted, in any form or by any means (electronic, me- chanical, photocopying, recording, scanning or otherwise), without the prior written permission of Statoo Consulting, Switzerland. Warranty: none. Trademarks: Statoo is a registered trademark of Statoo Consulting, Switzerland. Other product names, company names, marks, logos and symbols referenced herein may be trademarks or registered trademarks of their respective owners. Presentation code: `Roche/BDDS.October.2014'. Typesetting: LATEX, version 2. PDF producer: pdfTEX, version 3.141592-1.40.3-2.2 (Web2C 7.5.6). Compilation date: 10.10.2014.

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