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Big data-analytics-changing-way-organizations-conducting-business

Hi Friends ,

There is an interesting post on how to leveraging Big data analytics in an Integrated GRC Environment in an Organize to have visibility in core enterprises issues on real time basis . This presentation is from Metric stream -an international and Global GRC soloutioning providers in association with Dr. Kirk. D. Borne - Big data consultant and Adviser .Hope you like it and enjoy as well.

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Big data-analytics-changing-way-organizations-conducting-business

  1. 1. 1 © 2015 MetricStream, Inc. All Rights Reserved. Big Data and Analytics: Changing the Way Organizations are Conducting Business Dr. Kirk D. Borne Data Scientist & Advisor Big Data Consultant Vibhav Agarwal Sr. Manager of Product Marketing MetricStream
  2. 2. 2 © 2015 MetricStream, Inc. All Rights Reserved. Today’s Agenda  Data science for driving business innovation  Knowledge discovery and data mining systems for better governance  Analytics automation for just-in-time insights for mitigating risks  Decision science-as-a-service for marketing, retail, financial, security, and other sectors  Question & Answer
  3. 3. 3 DecreasedCost IncreasedRisk
  4. 4. Big Data and the fundamental business conflict: RISK versus REWARD http://www.telegraph.co.uk/news/worldnews/europe/russia/10061780/Russian-convicts-beat-Americans-in-cyber-chess-battle.html
  5. 5. Challenges in Digital Business Demands on Data Analysts Multiple data sources, stakeholders, and constituencies require business analysts to extract insights across a variety of digital user communities and portals (internal employee self-service, internal IT and cybersecurity systems, your business customer interaction channels, B2B “customers”). 5 Lack of True Automation Lack of automated processes prevents analysts from achieving targeted end-user digital content delivery and performing in-depth analytics on massive digital data streams. © SYNTASA 2014
  6. 6. Big Data: What is it good for? The 3 D2D’s  Knowledge Discovery – Data-to-Discovery (knowledge insights)  Data-driven Decision Support – Data-to-Decisions (decisioning insights)  Big ROI (Return On Innovation) !!! – Data-to-Dividends (innovation insights) – Data-to-Dollars (business ROI) 6
  7. 7. 1) Correlation Discovery  Finding patterns, trends, and dependencies, which might reveal new principles of behavior 2) Novelty Discovery  Finding new, rare, one-in-a-[million / billion / trillion] objects and events 3) Class Discovery  Finding new classes of objects, events, and behaviors  Learning the rules that constrain class boundaries 4) Association Discovery  Finding unusual (improbable) co-occurring associations Data Science in 4 easy steps (achieving the 3 D2D’s from your Big Data)
  8. 8. 1) Correlation Discovery  Finding patterns, trends, and dependencies, which might reveal new principles of behavior 2) Novelty Discovery  Finding new, rare, one-in-a-[million / billion / trillion] objects and events 3) Class Discovery  Finding new classes of objects, events, and behaviors  Learning the rules that constrain class boundaries 4) Association Discovery  Finding unusual (improbable) co-occurring associations Data Science in 4 easy steps (achieving the 3 D2D’s from your Big Data)
  9. 9. 4 Business Examples: Association Discovery (for recommender engines)
  10. 10.  Classic Textbook Example of Data Mining (Legend?): Data mining of grocery store logs indicated that men who buy diapers also tend to buy beer at the same time. Business Example #1
  11. 11.  Amazon.com mines its customers’ purchase logs to recommend books to you: “People who bought this book also bought this other one.” Business Example #2
  12. 12.  Netflix mines its video rental history database to recommend rentals to you based upon other customers who rented similar movies as you. Business Example #3
  13. 13.  Wal-Mart studied product sales in their Florida stores in 2004 when several hurricanes passed through Florida.  Wal-Mart found that, before the hurricanes arrived, people purchased 7 times as many of {one particular product} compared to everything else. Business Example #4
  14. 14.  Wal-Mart studied product sales in their Florida stores in 2004 when several hurricanes passed through Florida.  Wal-Mart found that, before the hurricanes arrived, people purchased 7 times as many strawberry pop tarts compared to everything else. Business Example #4
  15. 15. Strawberry pop tarts??? http://www.nytimes.com/2004/11/14/business/yourmoney/14wal.html http://www.hurricaneville.com/pop_tarts.html http://bit.ly/1gHZddA
  16. 16. Knowledge Discovery for multi-source Data: Heterogeneous data collections are the new normal New Knowledge on correlations, causal connections, and interdependencies between events, objects, processes within any application domain Data to Information to Knowledge
  17. 17. Knowledge Discovery for multi-source Data: Heterogeneous data collections are the new normal New Knowledge on correlations, causal connections, and interdependencies between events, objects, processes within any application domain The “first mile” challenge: integrating multi-source data The “first mile” challenge: integrating multi-source data
  18. 18. Knowledge Discovery for multi-source Data: Heterogeneous data collections are the new normal New Knowledge on correlations, causal connections, and interdependencies between events, objects, processes within any application domain The “last mile” challenge: deriving Actionable Intelligence from all of your data sources.
  19. 19. The MIPS model for Dynamic Data-Driven Application Systems (DDDAS) • MIPS = – Measurement – Inference – Prediction – Steering • This applies to any Network of Sensors: – Web user interactions & actions (web analytics data), Cyber network usage logs, Social network sentiment, Machine logs (of any kind), Manufacturing sensors, Health & Epidemic monitoring systems, Financial transactions, National Security, Utilities and Energy, Remote Sensing, Tsunami warnings, Weather/Climate events, Astronomical sky events, … • Machine Learning enables the “IP” part of MIPS: – Autonomous (or semi-autonomous) Classification – Intelligent Data Understanding – Rule-based – Model-based – Neural Networks – Markov Models – Bayes Inference Engines Alert & Response systems: • Actionable insights from streaming business data • Automation of any data- driven operational system http://dddas.org
  20. 20. The MIPS model for Dynamic Data-Driven Application Systems (DDDAS) • MIPS = – Measurement – Inference – Prediction – Steering • This applies to any Network of Sensors: – Web user interactions & actions (web analytics data), Cyber network usage logs, Social network sentiment, Machine logs (of any kind), Manufacturing sensors, Health & Epidemic monitoring systems, Financial transactions, National Security, Utilities and Energy, Remote Sensing, Tsunami warnings, Weather/Climate events, Astronomical sky events, … • Machine Learning enables the “IP” part of MIPS: – Autonomous (or semi-autonomous) Classification – Intelligent Data Understanding – Rule-based – Model-based – Neural Networks – Markov Models – Bayes Inference Engines http://dddas.org Alert & Response systems: • Actionable insights from streaming business data • Automation of any data- driven operational system
  21. 21. From Sensors to Sentinels to Sense: Take Data to Information to Knowledge to Insights (and Action!)  From Sensors (Measurement & Data Collection)…  … to Sentinels (Monitoring & Alerts) …  … to Sense-making (Data Science) …  … to Cents-making (Business ROI) … Actionizing and Productizing Big Data 21
  22. 22. Smart Engines for Data-Driven Discovery and Decision Support • New knowledge and insights are acquired by mining actionable data from all digital inputs (Sensors!) • Decisions are based on the new knowledge mined, prior experience, and your “business” decisioning rules embedded within the pipeline (Sentinels!) • “Smart Sensors” act autonomously in real-time, without human intervention = actionable intelligence (Sense!) http://legacy.samsi.info/200506/astro/presentations/tut1loredo-7.pdf 22
  23. 23. Decision Analytics – based on massive amounts of information (Big Data – What is it good for? …Decision Support and Innovation!) From Devices…… … Intentions… … Location, weather, and other geographic attributes… … Demographics… 23
  24. 24. Automating Analytics as-as-Service (AaaS) • Based on SYNTASA’s Marketing Analytics-as-a-ServiceTM (MAaaS) • “Smart Sentinel in a box” – Your business rules determine the goals, decision points, alerts, and responses. – Moving beyond historical hindsight and oversight (Descriptive & Diagnostic Analytics) to new world of insight and foresight (Predictive & Prescriptive AaaS), eventually achieving right sight (Cognitive Analytics = the 360 view, enabling the right action, for the right web user, at the right place, at the right time). • Mining multi-portal big data streams (across the organization’s departments) • Personalization and Customization (“segment of one”) • Decision Automation in a rich content (Big Data) environment 24Based on Marketing Analytics-as-a-ServiceTM (MAaaS) from http://www.syntasa.com/ Digital user Behavior Modeling
  25. 25. The New Digital Business: Big Data Analytics Challenge = Risk Mitigation • General example of streaming data analytics:  Real-Time Event Mining for Actionable Intelligence:  Identifying, characterizing, & responding to millions of events in real-time streaming data  Deciding which events (out of millions) need investigation and/or response • Web Analytics example:  Web Behavior Modeling and Automated System Response (from online interactions & web browse patterns, personalization, user segmentation, 1-to-1 marketing, advanced analytics discovery,…) • Many other examples:  Health alerts (from EHRs and national health systems)  Tsunami alerts (from geo sensors everywhere)  Cybersecurity alerts (from network logs)  Social event alerts or early warnings (from social media)  Preventive Fraud alerts (from financial applications)  Predictive Maintenance alerts (from machine / engine sensors) RiskMitigation
  26. 26. The New Digital Business: Big Data Analytics Rewards = Innovation & Value • Learning from Data (Data Science)… – Clustering (= New Class discovery, Segmentation) – Correlation & Association discovery – Classification, Diagnosis, Prediction – Outlier / Anomaly / Novelty / Surprise detection • … to conquer the 3 D2D challenges: – Data-to-Discoveries – Data-to-Decisions – Data-to-Dividends (big ROI = Return on Innovation) 26 Rewards!
  27. 27. 27 © 2015 MetricStream, Inc. All Rights Reserved. Leveraging Big Data Analytics in GRC Vibhav Agarwal Sr. Manager of Product Marketing MetricStream
  28. 28. 28 © 2015 MetricStream, Inc. All Rights Reserved. From Integrated to Pervasive GRC Widespread and rapid adoption of new technologies (e.g., mobile, social) Increasing regulatory pressures and Board / Management accountability Represents internally developed solutions Represents vendor solutions First Generation Second Generation Third Generation Fourth Generation ExpandingGRCApplications 2003 2013 ? Sarbanes-Oxley (SOX) enacted following series of accounting scandals (Enron, Tyco, WorldCom) Global financial crisis Siloes lead to greater risk and inefficient use of resources Audit / Finance (Sarbanes-Oxley) Audit / Finance IT GRC Audit / Finance IT GRC Legal Quality Management Compliance Management IT GRC Legal Quality Management Notable disasters include Deepwater Horizon and Fukushima Risk Management Standalone, largely ad hoc, internally developed solutions Siloed vendor and internally developed point solutions Integrated GRC platform solutions Pervasive GRC Audit / Finance IT GRC Legal Quality Management Compliance Management Risk Management Vendor Risk Management Social GRC Long-Tail Apps Comprehensive&UnifiedAnalytics Cloud GRC CommonDataModel;CustomizablePlatform
  29. 29. 29 © 2015 MetricStream, Inc. All Rights Reserved. Big Data: Imperative for Pervasive GRC
  30. 30. 30 © 2015 MetricStream, Inc. All Rights Reserved. 5 Mega Trends Driving this Big Data requirements Globalization – Explosion of rules, policies, data, and regulations as organizations extend across countries Virtualization – Transfer of critical data on cloud for scalability and efficiency to drive the TCO of IT systems lower Mobility – Ubiquitous Access to data across devices for employees, customers and partners Social Media – New set of imperfect data for Real time approximate Risk intelligence. Extensive sharing of internal data. Blurring of traditional organization boundaries Hyper-Connectivity – Expansion of employee, vendor and supply chain ecosystem into a real-time collaborative network
  31. 31. 31 © 2015 MetricStream, Inc. All Rights Reserved. GRC: A Big Data Problem Multiple GRC Data Sources, Event Co-relations Content and Standards Library ERP, SCM, Content Management applications Network Frontiers/UCF, NIST NVD, Cloud Security Alliance, SharedAssessments.org SAP, Oracle, i2, Ariba, JD Edwards, EMC, Documentum, OpenText, Sharepoint Threat , Vulnerability, Logs, SIEMS, Operations and Asset Management nCircle, Nessus , Qualys, Symantec, McAfee, Arcsight, Splunk, BigFix, eEye HP Asset Manager, BMC Remedy SIEM, Log Management, Application Intelligence Risk Models LogLogic, ArcSight, Splunk Market and Credit Risk Models, RiskMetrics, RMA Segregation of Duties, CCM, Transaction Monitoring Risk and Framework Content CrossIdeas , Engiweb Security, Greenlight, Mantaz, Actimize, MES systems ORX, Gold; American Banking, OCEG, IIA, ISO, D&B, Configuration Management Regulatory Content sources Qualys, nCircle Configuration Compliance Manager (CCM), eEye Retina CS Lexis, Factiva, Complinet, Reuters, FDA, State Regs ComplianceOnline - > 1000 sources Data Loss, EndPoint, Mobile, Application Security Smart Grid and Green Data centers Verdasys, Sophos, Veracode, Lookout, Symantec Cisco, SilverSpring Social Media Sources News Feeds
  32. 32. 32 © 2015 MetricStream, Inc. All Rights Reserved. Big Data: Solving the Key Challenge How to channelize the data to right stakeholder? How can the situation be mitigated in real-time? How to filter Voice from Noise in the Social Media? •Hadoop DFS based framework to allow aggregation of content across data sources •Ability to handle both structured and unstructured content Aggregate data across Social Media sources •Advanced text analytics based on custom rules to identify text patterns and indicators of risk. •Sentiment analysis and scoring mechanism to prioritize the identified data. Advanced Text analytics for Sentiment Identification •Create custom dashboards and workflows to channelize the information to right stakeholder. •Identify any risk or gap in the content and channelize through custom workflow. Configuration of custom workflow and dashboards
  33. 33. 33 © 2015 MetricStream, Inc. All Rights Reserved. Big Data: A Effective Risk Management Tool Trends predict Super Cyclone in India 90% of Manufacturing Plants impacted No supply till plants restored Anticipate, Counter supply disruption with remedial plan and publish it Stock stable Super Cyclone in India 90% of Manufacturing Plants impacted No supply till plants restored News of disruption in supply Stock volatile 10.13 10.30 10.35 14.35 10.10 10.30 10.35Next day
  34. 34. 34 © 2015 MetricStream, Inc. All Rights Reserved. Situational Awareness for BCP • Track Social Media platforms like: ─ Twitter ─ Facebook ─ Pinterest ─ Google (Google +, Youtube, Crisis Map etc.) • Correlate Information with Organizational Assets / Facilities / Risks • Trigger / Update Incident Management Workflows & Notifications • Real-Time Reports & Dashboards • Leverage Social Media for Communications During Emergencies
  35. 35. 35 © 2015 MetricStream, Inc. All Rights Reserved. Big Data Risk Analysis – A Product Reputation Use Case Social Media site Postings Call center transcripts Customer Support Emails Internal data & reports Identify the key data sources for gathering the Product reputation and quality feedback Aggregate & Process the data using Hadoop DFS and MapReduce framework Detect the risks using natural language processing based rules, keywords and author profiles and influence Inform the relevant stakeholders through trend analysis reports and dashboards Hadoop DFS Store the complete data in a Distributed File system Create risk detection rules based on key words, repetition frequency & Author influence Analyze the product feedback data based on the rules on a real-time basis Reduce the data to highlight the key product & brand reputation risks and their causes Create trend analysis dashboards to highlight key product feedback categories and risks and causes highlighted based on the analysis
  36. 36. 36 © 2015 MetricStream, Inc. All Rights Reserved. Big Data Risk Analysis– Vendor Due Diligence Use Case Big Data Analytics Unstructured data sets : News feeds, Social Media comments External databases: Exports registry, PEP Database , Rating Agency Databases Internal databases: Vendor information, Credit and Payment information  Aggregate Real time and Up-to-date Vendor Due diligence and Assessment information  Correlate the vendor data against key identified risks for accurate risk scoring and assessment  Manage compliance to FCPA, UK Bribery Act & OECD Convention etc.
  37. 37. 37 © 2015 MetricStream, Inc. All Rights Reserved. Big Data Risk Analysis– IT-GRC Use Case Aggregate the vulnerability bulletins across websites e.g. www.xssed.com, www.iss.net etc… Analyze the feeds based on the text analytics based rules and IT Asset library Highlight the risks & vulnerabilities based on the asset library as well as the rules engine Correlate the Product and CVE details with the internal IT asset libraries and highlight potential risks and vulnerabilities
  38. 38. 38 © 2015 MetricStream, Inc. All Rights Reserved. Correlate & Improve Product Information Aggregate the product information across websites Analyze the feeds using text analytics to look to Text Patterns Highlight any risks & issues based on the patterns and correlation with internal databases Aggregate Analyze Correlate
  39. 39. 39 © 2015 MetricStream, Inc. All Rights Reserved. About MetricStream Vision Integrated Governance, Risk and Compliance for Better Business Performance Solutions • Policy & Compliance Management • Risk Management • Business Continuity Management • IT GRC • Audit Management • Supplier Governance • Quality Management • EHS & Sustainability • Governance & Ethics • Content and Training • Over 1,400+ employees • Headquarters in Palo Alto, California with offices worldwide • Over 350 enterprise customers • Privately held – Goldman Sachs minority owner Differentiators • Technology - GRC Platform – 9 Patents • Breadth of Solutions – Single Vendor for all GRC needs • Cross-industry Best Practices and Domain Knowledge • ComplianceOnline.com - Largest Compliance Portal on the Web Organization
  40. 40. 40 © 2015 MetricStream, Inc. All Rights Reserved. Q&A Please submit your questions to the host by typing into the chat box on the lower right-hand portion of your screen. Thank you for participating! A copy of this presentation will be made available to all participants in next 48 working hours. For more details on upcoming MetricStream webinars: http://www.metricstream.com/webinars/index.htm Dr. Kirk D. Borne Data Scientist & Advisor Big Data Consultant Email: kborne@gmu.edu Vibhav Agarwal Sr. Manager of Product Marketing MetricStream Email: vibhav.agarwal@metricstream.com
  41. 41. 41 © 2015 MetricStream, Inc. All Rights Reserved. Thank You Contact Us: Website: www.metricstream.com | Email: webinar@metricstream.com Phone: USA +1-650-620-2955 | UAE +971-5072-17139 | UK +44-203-318-8554

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  • DMTISpatial

    Apr. 13, 2015

Hi Friends , There is an interesting post on how to leveraging Big data analytics in an Integrated GRC Environment in an Organize to have visibility in core enterprises issues on real time basis . This presentation is from Metric stream -an international and Global GRC soloutioning providers in association with Dr. Kirk. D. Borne - Big data consultant and Adviser .Hope you like it and enjoy as well.

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