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Industrial Data Science


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A three hour lecture I gave at the Jyväskylä Summer School. The talk goes through important details about the use of data science in real businesses. These include data deployment, data processing, practical issues with data solutions and arising trends in data science.

See also Part 1 of the lecture: Introduction Data Science. You can find it in my profile (click the face)

Published in: Technology, Education
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Industrial Data Science

  1. 1. INDUSTRIAL DATA SCIENCE Tuesday 20 August 2013
  4. 4. BIG DATA CHANGES PEOPLE AND TECHNOLOGY • Data changes the management mindset to expect having supporting data available for all decisions • Decision making then creates its own data stream that can be analyzed • Data is an asset: What is its return? Net value? Depreciation? Future investment plan?
  7. 7. DATA STORAGE FOR LEARNING • Efficient storage is critical for modeling feasibility • What is efficient storage depends on data, algorithms and environment • Memory: working sets, small data, online learning, fast iterations needed • Disk: M-estimation, local context sufficient • Data warehouse: simple models in enterprises, complex input generation • Distributed: stochastic/ensemble methods, large and complex production models • Cloud: variable workloads, very massive data
  8. 8. UNSUPERVISED LEARNING IN USE Modeling Significance testing Decision making As input into other modeling Know-how Selection of useful pattern types
  9. 9. DEPLOYMENT OF A COMMON MODEL Modeling tool DatabaseService Prediction request and answer Datasets periodically for learning Predictions written to DB
  10. 10. DEPLOYMENT OF A LOCALIZED MODEL Modeling tool DatabaseService Prediction request and answer Datasets periodically for learning Predictions Data builder Input construction Query input
  11. 11. DEPLOYMENT OF ONLINE LEARNING Modeling tool Database Incoming data stream Service Data and/or labels Requests with data Predictions Data and/or labels
  12. 12. EVALUATING RESULTS AND QUALITY • Properly evaluating the quality of modeling results depends on project objectives, error costs and data specifics • Classification error makes no sense for skewed class sizes, ranks and ROC curves do • Operational improvements evaluated as lift and incremental $$$ over previous • Uneven error costs: • earthquake risk estimation • medical research, molecule potential VS patient safety • Upsetting recommendations to an e-commerce customer
  13. 13. WHAT IS REAL-TIME? • Real-time can mean very different things to different people • Analyst: “What’s the user count today? By source? Now? From France?” • Sysadmin: “Network traffic up 5x in 5 seconds! What’s going on?” • Google: “Make a bid for these placements. You have 50 ms”
  15. 15. EXAMPLES OF DATA SIZE Human-generated • 5K tweets/s • 25K events/s from a mobile game (that’s 200 GB / day) • 40K Google searches/s Machine-generated • 5M quotes/s in the US options market • 120 MB/s of diagnostics from a single gas turbine • 1 PB/s peaking from CERN LHC
  16. 16. HUMAN AND MACHINE GENERATED DATA • Human-generated data will get more detailed • … but won’t grow much faster than the underlying userbase • It will become small eventually • Machine-generated data will grow by the Moore’s law • … and it’s already massive
  17. 17. PROCESSING DATA THE OLD WAY • User actions modify the current state in a transaction DB • Single events go to an offline audit log for re-running • Snapshots of data are exported for modeling • Production models take exports of snapshots, write back snapshot versioned results Events Snapshot Snapshot Snapshot
  18. 18. PROCESSING DATA IN STATUS QUO • Data from operational databases is constantly copied over to a data warehouse or an analytic database • This is idealistically a one-stop-shop for all analytics and data science • Production models preferably work inside the database, providing high performance and data integrity • Model learning can try pushing back some operations to the database, but complex models will need an external tool • Expensive modeling may require a separate testing database
  19. 19. PROCESSING DATA IN THE CLOUD • Cloud allows endless scale • No fixed limits on CPU and data usage, but everything is I/O-bound • Enterprise hybrid clouds allow testing environments and “cloud bursting” • Large datasets may require specialized algorithms or retrofits to MapReduce • Combining stochastic learning, online learning and ensemble methods has proven itself for the task
  21. 21. REAL WORLD DATA IS RIDDLED WITH PROBLEMS • Corrupted incoming data • Corrupted IDs • Transient IDs • Multiple transient IDs without match • Crazy timestamps • Data types mixed up • New variables emerge • Old variables disappear • Changes in variable definitions • And much, much more … You Garbage Great insights
  22. 22. AND WITH MORE PROBLEMS • Collected data is enriched with many operationally attainable sources ⇒ varying schemas and complicated ID soup • Analytic data often developed by frontline instead of IT waterfall ⇒ faster process, but volatile data definition • Data scientists asking for more data ⇒ temporary kludges • Data is big and growing ⇒ risks of unnoticed discontinuity
  23. 23. NO, I’M NOT FINISHED YET • The data is not a CSV file sitting in your disk • It’s coming in every second of the year, often gigabytes per hour • Availability of this data is a business critical issue • Availability of modeling results is a business critical issue • Robustness of modeling results is a business critical issue
  24. 24. DATA DRIFT • Real-world data is rarely stationary • Equipment ages, people’s preferences change • Quality of old data models decay • Training and testing data may need to be specially designed • Prefer recent data with weights or online learning
  25. 25. ROBUST RESULTS? • Inputs to a decision making process must be assessed for significance “Can I trust these numbers? Is my decision justified?” • Ad-hoc analyses can freely employ complex and bleeding edge modeling • In operations stability and robustness overrides everything else • Sanity checks and fallbacks can be used to avoid failures and errors
  26. 26. POWER LAWS Number of users Revenue per user
  27. 27. POWER LAWS • Power laws are ubiquitous in the real world • Follows from principle: “Whoever has will be given more” • Example: new links emerge to web pages in proportion to their popularity • Product improvements can be tracked through changes in the power law curve • Examples • Power laws often have a cut-off in the beginning, not enough mass to fill the lowest ranks • User engagement and value • Social network activity • Brain activity • Wealth distribution
  28. 28. CONSEQUENCES OF POWER LAWS • Power laws imply extremely skewed distributions ⇒ most models assume Gaussian or generally more balanced distribution • Huge mass at the bottom ladder breaks most traditional analyses • Different parts of the curve have complex real world interaction • On the other hand it is relatively easy to segment power laws ⇒ separately designed treatment for different target groups • Bringing new users as part of the power law lifts the whole curve as new entries slowly diffuse along the curve
  29. 29. THE IMPORTANCE OF PRESENTATION • Operations or not, visualization is critical for acceptance • Challenger shuttle disaster linked to poor visualization of O-ring failure risks • Requires attention from business concept to implementation • What information do these users want to see ? • How does this information support decision making ? • How to visualize it with clarity yet powerfully ?
  30. 30. DATA SCIENCE IN BUSINESS • Data analysis in business is not the sole task of the data scientist • The whole organization must gradually mature and engage data • This is not a technical barrier, it is a human barrier • How to design business and social processes to employ data? • Average business has tons of low-hanging data fruit • Developing and automating all that takes years (and years) • No use for advanced modeling without visibility to the underlying
  31. 31. WHAT’S COMING UP
  32. 32. PROCESSING DATA IN THE FUTURE • The event stream itself is increasingly becoming the master input data for analytics and data solutions • This is a big sea change, requiring new designs of storage and processing • Seeing the full timeline and interactions of each object is a mixed blessing PROS Huge opportunity for discovering significant value CONS A very complex haystack, needs additional processing, how can a human focus on the essential?
  33. 33. STREAM PROCESSING • Instead of handling static states of the data, the data is processed as it enters the system • Tables turn: the internal state of the stream persisted to a database becomes now the backup for failure occasions • Obvious fit for quickly reactive online learning solutions • The whole domain was spearheaded by computer trading • Another example: credit card transaction processing and fraud prevention
  34. 34. HADOOP AND DATA SCIENCE • Hadoop is a general service platform, not just a MapReduce engine • HBase is already becoming a hugely popular service backend • In the long run Hadoop will also host a successful analytic database • A wide selection of very different approaches to analytics and data science exists already: Hive and Pig, Impala, Mahout, Vowpal Wabbit, DataFu, Cloudera ML, Giraph, RHadoop, …
  35. 35. REARRANGING THE MAP • Change is not driven by replacing current bad solutions, but by innovating around their shortcomings • Stream processing of data will capture a large corner, driven by a sweeping push closer to real-time • High-level functional interfaces to data another winner • Examples: Cascading for batch processing, Trident for stream processing • Further innovation in fixing MapReduce shortcomings • Examples: Spark and Shark for iterative tasks, Impala for analytics
  36. 36. THE END