Big Data Analysis and Business Intelligence

Big Data Analysis and Business Intelligence

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  Daqing Zhao, PhD Founderand Principal, Eureka Analytics Business Intelligence Innovation Summit, Chicago 5/23/2013 ©Daqing Zhao All rights reserved Frontiers of Big Data Business Analytics, Patterns and Cases in Online Marketing
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  Agenda • Overview ofbig data analytics • Insights of big data and analysis • BI process on big data • Lessons of model building • Cases for behavioral profiles for predictive models – Yahoo network segmentation – Tribal Fusion display ads impression optimization – University of Phoenix student retention and lead optimization • Case of Ask.com SEM algorithms 2
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  Daqing Zhao, PhD •Big Data scientist with deep domain knowledge • Academic training – Analyzed molecular spectra on Cray supercomputers – Determined, modeled, simulated molecular motions in 3D space • Enjoy working with large data and large scale computing • Worked on computational Internet marketing since 1999 3
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  New Book onBig Data Analytics • In the book: • Daqing Zhao: • Frontiers of Big Data Business Analytics: Patterns and Cases in Online Marketing 4
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  Big data, BigOpportunities • Thanks to Moore’s law, on CPU, storage, network connections • Too much data, too little knowledge • Data, analytics changed every field many times over • From science, government, to commerce 5
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  Big data characteristics •Amount of data too big to handle using normal technology, most data collected are dormant • Raw data are stored, appended but not updated • Formatted or free format data • No aggregation for purpose of data reduction • Individual customer level and individual event level data • Sensor data • Complete 360 degree view • Process from raw data to get insights and build models • Some business uses of big data: customer profile, event prediction, automated decision machine, risk management, wisdom of crowd 6
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  Things computers goodat • Computers have perfect memory – Every page view, click, transaction, every event,… • Good at finding a needle in a haystack – E.g., target abandoned shopping carts with promotions – Clickers of this page in the last week • Good at trade offs among large number of factors – Female, 25-34, with child < 5, Asian, earning $30K, rent, divorced, live in Calif., some college, Walmart, Coupons.com, Monster.com, drive Camry, … – Buyer of X or not? 7
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  Things computers onInternet are good at • Platforms of cloud sourcing – Google PageRank, Adwords, Picasa, Translate, … • Data not previously looked at in aggregate – Google PageRank/Translate, Amazon Find Product • Data not previously created, or accumulated – Social network data at LinkedIn, Facebook – Amazon Customer Review, Yelp – Twitter, Flickr – Wikipedia, Youtube/Khan Academy, eHow, Udemy, Yahoo/Answers 8
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  Computers make itpossible • Given data, find models and parameters – Identify reproducible patterns in the data – Provide simple picture of a large number of events – Predict events in the future • Simulations generate future events, given assumptions, and current state – Given a set of models, how future scenario will look like, under given set of conditions, “what ifs” • Robots, and agents – Make decisions based on environment and goals, self driving cars 9
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  Computers can’t doeverything • Data often have issues before being well analyzed • Data often have no taxonomy and context • Free format data, relevant information need to be extracted • Analyst has to define targets, construct predictors • Analyst has to include critical predictive factors • Analyst need to add common sense 10
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  Every wrong datais wrong in its own way • Some data are not collected, “too big” or “useless”, as in flood control, purged log data • Some data feeds to warehouse are incomplete • Multiple definitions and inconsistent business rules, no documentation • Data incomplete due to business nature – Sparse data – Separate log in and log out data – Credit card purchases versus cash • Some flaws are easy to catch, such as missing, constant • Some flaws hard to find, partially missing or incorrect 11
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  Best practices ofanalyst • Understand how the data are collected, what data can and cannot be collected • Balance cost of collecting data and optimize modeling • Use feedback loop to test hypotheses • Do simulations to see if changes are reasonable • Good ideas are not necessarily complicated ideas • Focus on domain knowledge, not just data mining tools 12
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  Best Practices ofAnalytics Managers • Well versed on analytics, understand analyst, their behavior, the tests, their work and value • Focus on domain knowledge, not just data mining tools • Focus on impact, not elegance in modeling • Big Data Analytics are different from small sample statistics, and need to learn on the job • As activities become more technical, it is hard to recognize values and identify issues – 2008: Financial crisis and credit derivatives – Principal-agent problem 13
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  New Information Explosions •Before ~1450, only nobilities had a few books • After Gutenberg, information was limited by paper and printing capacities – People cried out loud there was too much information – Then we had libraries, index, abstract, book reviews,… • Now information is limited by disks & cloud storage – A person’s lifetime spoken words stored in a thumb drive – Soon everything can be stored • Now: how do we make use of all the information? – Search, crows sourcing, Twitter, Wikipedia, YouTube, big data and analysis algorithms, … 14
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  Paradigm Shift inData Organization • Mathematics is a way to efficiently use brain resources – With pen and paper, only simple problems solvable – Crude approximations, and samples for complicated ones – Unreasonable effectiveness of mathematics – E. Wigner • Now, algorithms are ways to efficiently use computing resources – Numerical solutions of complex equations – Large scale simulations, full population databases – Unreasonable effectiveness of data – P. Norvig • Elegant, over simplified models are less useful 15
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  Paradigm Shift inKnowledge • Knowledge is power, by Francis Bacon • Past: Drowning in information, starving for knowledge, by John Naisbitt • Now, Knowing how to extract knowledge is power • Soon: There is abundance of knowledge, seeking for relevance – Incl. personal finance, medical, political decisions • Innovations are about connecting the dots – Distances between the dots are getting smaller – Leverage knowledge to make decisions, manage risks 16
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  Big Data problem •Data size larger than what databases can handle • Terabytes of data may take hours just to scan it • Solution requires a cloud of servers with local storage – Read, process and write intermediate results in parallel – Aggregate at the end • Cloud computing can build models in scale • Cloud often scales linearly as number of servers 17
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  Modeling need toscale • Traditional predictive models take long time to build – Small data sets, samples expensive to collect • Now data are cheap and models may degrade in weeks – Dimension of predictors are very large – Number of categories are large • Human interactive model building not scalable • Reasons for target events are complex • Without detailed analysis, it is unclear what drives the event • We need to rely on “out of sample testing” and “off the shelf” modeling 18
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  Cloud computing • Webuilt a SAS cloud at University of Phoenix – I have an invited SAS talk available at SAS web site – Can process billions of impressions in minutes • Hadoop clouds are used widely – Open source software, Hive, Impala, Mahout – Commodity servers and storage • Clouds may have 100Ks of servers – Find needle in a haystack in milliseconds – Model computations usually would take years to compute now finishes in minutes 19
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  Big Data Centers 20 Facebookand Google data centers use commodity servers Google uses 260 million watts can power 200K Homes – NY Times Data centers near Columbia River At Dalles, Oregon
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  Traditional BI pyramid •Defines a sequence of efforts • Most companies never get beyond reporting and simple analysis • No full analysis and predictive modeling ever done • Some data issues may not be caught • Limited insights hinder optimal extraction of knowledge 21 Multidimensional Report Standard Report Segmentation Predictive Modeling Knowledge Discovery Datamaturity Baseline Pyramid
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  Hadoop Analysis leads tobetter data quality 22 Raw data Algorithms Analysis Reports Business Rules Algorithms Predictive Models
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  More analysis leadsto better quality 23 Data Collection Exploratory Analysis Predictive Modeling Decision Algorithms Better data quality
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  Data most important •In modeling, find key data most important – Identify the smoking gun • Data transformations – PageRank is a game changing data transformation – Wine.com case, wineRank – Social graph is a key data transformation for credit card fraud detection 24
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  Modeling can gowrong • Leakage in lead scoring model – For example, use lead source to predict conversion, when certain values of the field were populated only for converters • Display ads conversion model – Construct data set by taking all converters and a sample of non-converters – Predict on page view profiles – Problem: sample of non-converters included customers who had no impressions of the ad 25
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  Modeling lessons • YahooDSL subscribers, one year contract • If you try to model month to month retention, you find high retention rate – Because of contracts and penalties • The correct way is to model retention at contract expiry, only on 1/12 of the customers • For Yahoo email, if you look at quarter by quarter retention, you find that those acquired early in the first quarter have lower retention rate – Because those customers have more time to churn • A correct way is to use survival analysis 26
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  Conclusions • For optimalmodeling, domain knowledge is most important • May require Big Data solutions to scale • Identify key data and transformations • Data are not reliable until after seriously analyzed • Conduct deep analysis, before develop BI reports • Test and optimize in real market is crucial • Focus on customer experience not model complexity or predictive accuracy • “The best way to get good ideas to have a lot of them” – Linus Pauling • Use a lot of common sense 27