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DI&A Webinar: Big Data Analytics

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The world of data analytics has opened up to include a much broader spectrum of data types than the traditional rows and columns found in relational databases. Text analytics includes whole new classes of tools for search and semantic understanding. Speech and image recognition software have become mainstream. How is data analytics changing in scope and practice in the era of Big Data?
This webinar will answer this question by looking at the following:
New tools for leveraging more data types
Differences in Big Data analytics architecture
New directions in Big Data analytics

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DI&A Webinar: Big Data Analytics

  1. 1. The First Step in Information Management www.firstsanfranciscopartners.com Produced by: MONTHLY SERIES Brought to you in partnership with: Aug. 3, 2017 Big Data Analytics
  2. 2. Topics for Today’s Analytics Webinar  New Directions & Trends in Big Data Analytics − Implications of New Directions  Differences in Big Data Analytics Architecture  New Tools for Leveraging More Data Types  Key Take-Aways  Q&A pg 2© 2017 First San Francisco Partners www.firstsanfranciscopartners.com
  3. 3. Polling Question pg 3© 2017 First San Francisco Partners www.firstsanfranciscopartners.com  What data types are you analyzing? − Row and column − Free-form text − Geospatial − Images − Audio − Video − All of the above
  4. 4. www.firstsanfranciscopartners.com New Directions & Trends in Big Data Analytics
  5. 5. Big Picture Trends pg 5© 2017 First San Francisco Partners www.firstsanfranciscopartners.com We are getting better at Analytics We are still expensive Software and Hardware need to catch up
  6. 6. Different Questions Being Asked of Data  Evolving from what to when to why then how?  Prescriptive and Predictive Analytics are more commonly adopted  Graph Analytics shows relationships across multi-structured data (that are virtually impossible to see with structured data) pg 6© 2017 First San Francisco Partners www.firstsanfranciscopartners.com Implications: − Analytics are Everywhere − Real-time (Analytics)  Decreasing Latencies
  7. 7. Big Data is the New Normal  Big Data has changed the way we view technology and how we respond to innovations  “Big Data” vendors now supporting other types of data; and vice versa  Expanding Sources  Open Data  Algorithm Marketplaces  Crowdsourcing  Consideration of “dark data” pg 7© 2017 First San Francisco Partners www.firstsanfranciscopartners.com Implications: − Expanding Sources drive re-evaluation of data governance concerns − Driving to self-service Data Preparation and Data Catalogs that are truly business created and managed − Outsourcing: Do non-traditional data activity, Outsource “Mode 2”  Additional Concerns: IP protection, Regulatory, Governance, Accountability, etc.
  8. 8. Internet of Things (IoT)  Key component of a digital business  More data, more complexity and more automation  Driving spin-off areas of investment like: − IIOT – Industrial Internet of Things − IoT Edge Analytics − Mobile App Edge Analytics − Event Stream Processing  Shift to a services-based model, not capital-based pg 8© 2017 First San Francisco Partners www.firstsanfranciscopartners.com Implications: − Will drive increased data integration requirements − Data Privacy and Security is still a big concern as the potential amount of sensitive data collected can be large
  9. 9. Artificial Intelligence (AI) is Everywhere  Key component of digital business  Success is based on the data  Availability of data and computing power has fueled AI growth  Types: − Machine Learning − Deep Learning − Natural Language Processing and Generation, Conversational AI Platforms − Computer Vision pg 9© 2017 First San Francisco Partners www.firstsanfranciscopartners.com “AI is the new electricity.” - Andrew Ng Implications: − Be aware of how your data is used − Corroboration of Correlation − IT organizations are leveraging AI to better manage their operations and the growth of data
  10. 10. Bots are Hot  Based on a specific set of predefined rules  Can be a unique implementation of AI when leveraging algorithms − Conversational User Interface (Chatbots)  Can emulate a User or an App  Bots embedded in applications facilitate workflow pg 10© 2017 First San Francisco Partners www.firstsanfranciscopartners.com Implications: − Bots not only use data – they also create it − Data Privacy and Security need to be considered
  11. 11. Edge Computing  Faster, more available analytics, even when you’re offline  Enables the digital enterprise  Flavors: IoT Edge Analytics and Mobile App Edge Analytics, Intelligent Apps pg 11© 2017 First San Francisco Partners www.firstsanfranciscopartners.com Implications: − Architectures need to adapt and stretch to enable edge locations − Lack of network availability drives requirements for data thinning and file compression
  12. 12. www.firstsanfranciscopartners.com Differences in Big Data Analytics Architecture
  13. 13. Recap: “Different” Things pg 13© 2017 First San Francisco Partners www.firstsanfranciscopartners.com Analytics are everywhere Big Data is the new normal Internet of Things AI is everywhere Edge Computing Bots are hot  Unified Strategy  Latency differences  Storage  Processing closer to/ within your device  Integration of capabilities in multiple areas  Data obfuscation New Directions/Trends
  14. 14. FSFP Reference Architecture – Abstract Data Insight Architecture pg 11© 2017 First San Francisco Partners www.firstsanfranciscopartners.com 1 Data Movement/ Logistics Context Monitoring Controls Management Layer Metadata, Lineage, Work Flow, Models, Reference Data, Rules, Canonical Data Data Access Layer Visualization, Prediction, “Closed Loop,” Edge Analytics Vintage Area ERP CRM Finance Traditional Data Collection Contemporary Area Edge Processing Ingestion Business Strategy Smart Machines Social Bots Traditional Stakeholders
  15. 15. FSFP Reference Architecture – Explicit Data Insight and Analytics Architecture pg 12© 2017 First San Francisco Partners www.firstsanfranciscopartners.com 1 Data movement / logistics Cross- generation Abstraction Processes & Mapping Vintage Area Contemporary Area Business Strategy Vintage Views DBMS Future Apps Data Movement/ Logistics Cross- Generation Abstraction Processes & Mapping Web Services Distributed Processing Data Virtual’n $ Monetization EDW RDBMS Bot data Unstr’d Data Edge Vintage Apps Management Layer Metadata, Lineage, Work Flow, Models, Reference Data, Rules, Canonical Data Data Access Layer BI/Reporting, Analytics, Mobile DBMS ETL ETL Data Lake DM IoT
  16. 16. www.firstsanfranciscopartners.com New Tools for Leveraging More Data Types
  17. 17. Vital New Capabilities for Data and Analytics Source: Gartner, “What Big Data Means Today and How to Position Effectively,” Oct 2016, (High Tech Tuesday Webinar by Terilyn Palanca)
  18. 18. Key Take-Aways  Analytics will be everywhere. − Account for it in your architectures and your data governance and management strategies.  Take advantage of new technologies and service providers to expand the use of sophisticated analytics.  Recognize the skills gap that still exists across the Big Data and AI spectrum and plan accordingly.  Privacy will be increasingly important with computing closer to the individual, including location data.  “Data Freedom” will require insight enablers, not data providers.  Don’t rely on regulations to guide how you think you should use your data. pg 18© 2017 First San Francisco Partners www.firstsanfranciscopartners.com
  19. 19. Questions? pg 19© 2017 First San Francisco Partners www.firstsanfranciscopartners.com MONTHLY SERIES
  20. 20. Thank you for dialing in! Please join us Thursday, Sep. 7 for the next webinar: “Analytics, Business Intelligence and Data Science: What's the Progression?” Kelle O’Neal @kellezoneal kelle@firstsanfranciscopartners.com

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