Big Data The Next Phase Lessons from a Decade+ Experiment in Big Data
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1 Big Data The Next Phase Lessons from a Decade+ Experiment in Big Data David Belanger PhD Senior Research Fellow Stevens Institute of Technology dbelange@stevens.edu 1
2 Outline Big Data Overview Thinking about: Technology Strategy Ecosystem Where is it going? DGB 5/2013 2
3 Definition of Big Data Standard Three V s Volume Velocity Variety McKinsey Global Institute (2011) Data Warehouse datasets whose size is beyond the ability of typical database software tools to capture, store, manage, and analyze. These Definitions, and others, don t answer the question: What s really different that matters? For example: How might you use Big Data as it becomes more mainstream? That is, when Big Data becomes Data. DGB 5/2013 3
4 Canonical Examples of Big Data (1) Search <!DOCTYPE HTML <!DOCTYPE HTML PUBLIC "- PUBLIC "-//W3C//DTD //W3C//DTD HTML 4.01//EN" HTML 4.01//EN" " " rict.dtd"> <HTML> <HEAD> 4/strict.dtd"> <HTML> <TITLE>My first HTML <HEAD> <TITLE>My first document</title> </HEAD> HTML document</title> <BODY> <P>Hello world! </HEAD> <BODY> </BODY> </HTML> <P>Hello world! </BODY> </HTML> DGB 5/
5 Canonical Examples of Big Data (2) Fraud DGB 5/2013 5
6 Canonical Examples of Big Data (3) Call Center DGB 5/2013 6
7 Watson, come here, I want you What Does Big Look Like? translate.google.com Native 沃 森, 來 到 這 裡, 我 要 你 沃 森, 过 来, 我 需 要 你 7 Image Source Page: 1,000 Image Source Page: ~C(10^5) DGB 5/2013 7
8 Some Things That Make a Difference Individual Level Granularity Weak vs Strong Signals Latency Population vs Sample Transparency Prediction Learning Behavioral Data DGB 5/2013 8
9 Is it Real? CrunchAnalytics provides an answer based on data from CrunchBase, showing us where VCs are placing their biggest bets: Cloudera (5 rounds) $141 million Apache Hadoop-based software, services and training MuSigma (1) 133 Data-Science-as-a-Service Opera Solutions (1) 84 Data-Science-as-a-Service 10gen (6) 73.4 MongoDB (open-source, document database) Gauvus (3) 70 Big data analytics solutions ParAccel (3) 64 Analytic platform Talend (5) 61.6 Application and business process integration platform GoodData (5) 53.5 Cloud-based platform and big data apps DataXu (3) 45.8 Digital marketing software DataStax (4) 38.7 Apache Cassandra-based big data platform The 40 startups included in the CrunchAnalytics database have raised about $1.2 billion in venture capital. Source: DGB 5/2013 9
10 Organizing for Innovation - Getting Started Then Classical research or exploratory development teams create new products, often in large teams with significant timelines. Careful attention is paid to decision gates to prevent runaway costs. Due to costs, decisions often top down. Now Small, elite teams create prototypes of potential products quickly, trial the prototypes, and, when successful, present for funding to go to market. Go to trial, very quick. Classical research provides technology base to prototyping teams, and partners with them. Innovation Laboratory - InfoLab Data New Applications New Technologies Effective Organization of Innovation Subject Matter Expertise DGB 5/
11 The next crowd - Objects Devices That Can Be Networked & IP Addressable How can we best exploit the billions of devices, many mobile, intelligent, and video enabled as computing, sensing, and communications platforms? Consumer Items & Sensors Pallets and Cases Machinery Home Appliances Vehicles & Handheld Devices Crowd Computers Invisible Computing 1 Consumer 0 Items Pallets and Cases Will Far outnumber current IT Devices and People Home Appliances Machinery Vehicles and Handheld Devices Sensors Machine to Machine Internet Page 11 DGB 5/
12 Applications of Big Data Application Type Service Oriented Retrieval, Individual Precision, Sparse Data Paths, Graphs, Relationships Diversity of Sources Real Time, Predictive, Data in Flight Driving Technology
13 Sparse Targets & Individual Precision Then Precision for many measurements, and most targets, are aggregate. Sampling is often used. Accessing a dataset which is a small subset of a huge dataset is difficult, especially for unstructured data. Surveys used for customer experience. Search Now Analysis is of individual events, and measured against individual metrics. Map/Reduce useful for finding relatively small subsets of very large datasets. Customer behavior/results can be used for customer experience. Misuse Fraud Customer Experience photoblog.nbcnews.com At&t Labs - Research DGB 5/
14 Paths, Graphs, & Relationships Then Most analysis and visualization done on graphs is relatively small scale, and seldom interactive. Now Networks, including explicit, implicit, and inferred, are analyzed and visualized at very large scale. DGB 5/
15 Diversity of Data Types - Variety Then Most large data sets are either combinations of alphanumeric fields, or text. Now Data types range from the traditional, structured alpha-numic fields, to semi-structured (e.g. Web), to unstructured (text, speech, video, image). All of these, and mixtures of them, are analyzed at scale. Vodeo/Image Mining Personal Environment Sensors Speech Mining Customer Experience Customer Interaction Record
16 Data in Flight Then Real time systems are custom engineered and controlled, typically with relatively small data in flight. Data communications expensive at scale. Health - Smart Slippers Now Analysis of individual events, and measured against individual metrics, and at very large scale is becoming relatively common. Internet of Things starting to drive another spike in growth rate. Safety, Gaming Location Based Services DGB 5/
17 Technology Big Data Open Source Tools Source: DGB 5/
18 Data Analysis Lifecycle: Process Control Monitor Analyze Instrument Decide Control 18
19 Then Data Analysis For large datasets, it is usually the case that relatively small samples must be used. Customer studies are often based on surveys. Study results are frequently on aggregate data. Data numeric or text. Now Characterized by analytics on the population of data, though some datasets are still so big that sampling must be used. Customer studies based on behavior, and at extreme detail. Wide scale use of relationships e.g. social networks. Data numeric, text, speech, image. Graphs, networks, and paths Relationships Visualized Recommender Systems Visual Pattern Recognition Machine Learning openclassroom.stanford.edu 19
20 Information Visualization Human in the loop Then Largely descriptive and embedded in reports or dashboards. Aggregate measures most common, and created from a fairly restricted set of models characterized by statistical system. Now Characterized by scale, interactivity, and integration. Usually real time with immediate drill down facilities. Often with powerful new models to express detail Sometimes derived from gaming systems. Graphs, networks, and paths Word Clouds Relationships Visualized Recommender Systems VizGems Transparency, Integration, Control Through Visualization DGB 5/
21 Some Lessons Learned Technology Multidimensional technical expertise is essential: Network Computing Data Analysis Visualization - Domains The Nature of analytics has changed: parallel, streams, predictive, geospatial Data Feed management can scale linearly. That is really bad Tradeoffs: Optimize Speed vs. Accuracy Depth & Volatility: Rules vs. Differences Information Content: Weak & Strong Signals DGB 5/
22 Organizing for Innovation - Maturity Then Classical research or exploratory development teams create new products, often in large teams with significant timelines. Careful attention is paid to decision gates to prevent runaway costs. Due to costs, decisions often top down. Now Small, elite teams create prototypes of potential products quickly, trial the prototypes, and, when successful, present for funding to go to market. Go to trial, very quick. Classical research provides technology base to prototyping teams, and partners with them. Incubation and Production Data OA&M Data Governance DGB 5/
23 Organization Source: DGB 5/
24 Data Governance Policy => Process => Practice Data Modeling Data Quality Risk & Compliance Retention Privacy and Security Chief Data Officer or CIO? Chief Privacy Officer, Chief Security Officer, DGB 5/
25 Some Lessons Learned Strategy A defendable niche provides time to mature scale It s all about the data organic and inorganic The Goal is to Enable Fundamental Process and Product Changes Ask Big Questions: e.g. Can an IP Network Run Itself? DGB 5/2013 Page 25
26 A More Complete Picture Data Analysis Data Management Visualization Applications Sandbox Privacy Data Governance Policy, Process Data Security OA&M Software Sustainability Integrity Semantics Framing Questions Distribution & Ownership of Results DGB 5/2013 Page 26
27 Meta Challenges Then Significant systems containing sensitive data are not easily accessed. Complex semantics and poor integrity often exist, but impact is hidden because data is relatively closed. Integration, outside of joins, uncommon at scale. Now Protection of SPI data a constant problem. Transparency of use, integrity of data a concern. Open data provides much more opportunity for interesting new apps from integration, and semantic confusion. Integration complex. Security Privacy Integrity Semantics Integration DGB 5/
28 Data Security Standards: PCI, HIPAA, FISMA curity101.pdf Encryption Logs / Audits Cloud DGB 5/
29 Data Quality and Integrity Then Much of the burden of quality and integrity lies in the fact that the ACID properties and input rules of transactional systems are strictly enforced. There is a very rich technical ecosystem that has been built around integrity and is made available in most mature data management systems. Now In many systems, at the volume, velocity, latency, and complexity expected, the levels of correctness required of transactional systems are neither possible nor necessary. Analytic techniques must take these changes into account. Given the very diverse nature of potential data sources, and consequent reduction in control over the data, this becomes a very challenging problem. DGB 5/
30 Some Lessons Learned Ecosystem Few corporations can ignore the broader technology world, and none should. Sometimes the most effective way to impress management is to go outside e.g. Netflix, Idol Customer Focus - Choose your partners well, and make them heroes Components of an ecosystemthe above image of an ecosystem includes DGB the 5/2013 following components some of... Page 30
31 Where Information Services Are Going Programmable World Pervasive Monitoring/Control Internet of Things OPEN DATA COSM Xively 1 Traditional Services TP, DW, Analytic Reports 3 Next- Generation Value from Data Immersive, Augmented Reality Interfaces s2 s1 s3 Multiple RT Streams 2 Info in Flight Real Time Stream Mining Mining Unstructured Data Mobility Next Gen Analytics, Prediction Data Stream Mining Speech/Text Mining Anywhere, AnyDevice DGB 5/
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