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How Can Analytics Improve Business?

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Inside Analysis

TechWise with Eric Kavanagh, Dr. Robin Bloor and Dr. Kirk Borne Live Webcast on July 23, 2014 Watch the archive: https://bloorgroup.webex.com/bloorgroup/lsr.php?RCID=59d50a520542ee7ed00a0c38e8319b54 Analytical applications are everywhere these days, and for good reason. Organizations large and small are using analytics to better understand any aspect of their business: customers, processes, behaviors, even competitors. There are several critical success factors for using analytics effectively: 1) know which kind of apps make sense for your company; 2) figure out which data sets you can use, both internal and external; 3) determine optimal roles and responsibilities for your team; 4) identify where you need help, either by hiring new employees or using consultants 5) manage your program effectively over time. Register for this episode of TechWise to learn from two of the most experienced analysts in the business: Dr. Robin Bloor, Chief Analyst of The Bloor Group, and Dr. Kirk Borne, Data Scientist, George Mason University. Each will provide their perspective on how companies can address each of the key success factors in building, refining and using analytics to improve their business. There will then be an extensive Q&A session in which attendees can ask detailed questions of our experts and get answers in real time. Registrants will also receive a consolidated deck of slides, not just from the main presenters, but also from a variety of software vendors who provide targeted solutions. Visit InsideAnlaysis.com for more information.

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Grab some coffee and enjoy the pre-show banter before the top of the hour!
“How Can Analy,cs Improve Business?” TechWise Webcast | July 23, 2014
+ Guests Host: Eric Kavanagh CEO, The Bloor Group Dr. Kirk Borne Data Scientist, George Mason University Dr. Robin Bloor Chief Analyst, The Bloor Group PLUS: Will Gorman Chief Architect, Pentaho Steve Wilkes CTO, WebAction Frank Sanders Technical Director, MarkLogic Hannah Smalltree Director, Treasure Data
Analytics Can Help a Business: • Streamline operations • Improve marketing • Raise revenue • Identify opportunities • Assess plans + Executive Summary
Dr. Kirk Borne Data Scientist, George Mason University +
Big Data Analytics for Data-to-Decisions Support Kirk Borne George Mason University, Fairfax, VA ● www.kirkborne.net @KirkDBorne
Extrac,ng Knowledge, Insights, and Data-­‐to-­‐Decisions (D2D) from Big Data is hard!
The D2D Challenge** 1. Characterize and !me flux Contextualize first. 2. Collect and Curate each entity’s features. …then Come to the data-driven decision! • Data-to-Discoveries • Data-to-Decisions • Data-to-Dollars
Characteriza,on & Contextualiza,on Feature & Context Detection and Extraction: • Identify and characterize features in the data: – Machine-generated – Human-generated – Crowdsourced? (= Tapping the Power of Human Cognition to find patterns and anomalies in massive data!) • Extract the context of the data: the source, the channel, the data user, the use cases, the value, the re-uses … where, when, who, how, what, why = Metadata! • Curate these features for search, re-use, and D2D! • Find other parameters and features from other data sources and databases – integrate all information to help characterize & contextualize (and ultimately make decision regarding) each new event.
Characterization via Tagging & Annotation • Report entity’s features & characteristics back to the database for search, retrieval, sharing, and reuse • Individual (or groups of) entities (objects and/or events) are tagged and annotated ... – with new knowledge discovered – with related data/information of any kind – with common knowledge about those things – with inter-relationships between entities and their properties – with concepts – with context – i.e., assertions (e.g., classifications, interpretations, quality flags, relationships, references, common knowledge, learned knowledge, inter-connectivity with other entities) – with data collection parameters – with sensor channel descriptors Semantics! Data integration Provenance (for data curation)
Characteriza,on & Contextualiza,on Feature & Context Detection and Extraction: • Identify and characterize features in the data: – Machine-generated – Human-generated – Crowdsourced? (= Tapping the Power of Human Cognition to find patterns and anomalies in massive data!) • Extract the context of the data: the source, the channel, the data user, the use cases, the value, the re-uses … where, when, who, how, what, why = Metadata! • Curate these features for search, re-use, and D2D! • Find other parameters and features from other data sources and databases – integrate all information to help characterize & contextualize (and ultimately make decision regarding) each new event.
Then what?
Then what? Get down to business with the Curated Collection of Characterizations and Contextualizations: • Data Analytics: – Outlier / Anomaly / Novelty / Surprise detection – Clustering (= New Class discovery) – Correlation & Association discovery • D2D: – Data-to-Discoveries – Data-to-Decisions – Data-to-Dollars
The Business Analyst-­‐in-­‐the-­‐Loop Tags, annota,ons, features, and context – – These can be … • measured (by observa,on), or • inferred through machine learning, or • provided by human analysts. – The resul,ng synergy yields: • improved or all 3 of these processes simultaneously. training sets, more accurate predic,ve models, fewer false posi,ves/nega,ves, ac,ve learning, efficient human interven,ons – Combining machine learning on Big Data with the power of human cogni,on for discovery (e.g., using Data Visualiza,on, Visual Analy,cs, Immersive Data Environments, or Crowdsourcing) therefore augments and accelerates discovery, insights, and D2D.
Dr. Robin Bloor Chief Analyst, The Bloor Group +
The Data Scientist & The Business Analyst Robin Bloor
The Data Analysis Budget u Data Analysis is Business R&D u The focus is on business process u The outcome of successful R&D is a changed process u Think of manufacturing for a useful example
Big Data Architecture
What is a Data Scientist? u Project manager u Qualified statistician u Domain Business expert u Experienced data architect u Software engineer (IT’S A TEAM)
The Impact of Machine Learning Machine learning is changing the process (for the BUSINESS ANALYST & the DATA SCIENTIST) BUT the analytics team needs to understand IT!!
Take Note! You can know more about a business from its data than by any other means
There are Two Issues for the Business Can you get the Can you get the TECHNOLOGY right? PEOPLE right? &
+ Will Gorman Chief Architect, Pentaho
© 2014, Pentaho. All Rights Reserved. pentaho.com. Worldwide 24 +1 (866) 660-7555 July 2014 Pentaho Business Analytics Architected for the Future of Analytics Will Gorman, Chief Architect
WHAT WE DO We enable the modern, big data-driven business Modern, cohesive data integration and business analytics platform • Full spectrum of advanced analytics for all key roles • Embeddable, cloud-ready analytics • Big data blending for analytics in real-time environments • Broadest and deepest big data integration Innovation through open source • Open, pluggable, purpose-built for the future • Early sustained leadership in big data ecosystem with technology innovation Critical mass achieved • Over 1,500 commercial customers • Over 10,000 production deployments © 2014, Pentaho. All Rights Reserved. pentaho.com. Worldwide 25 +1 (866) 660-7555
Pentaho 5.1 Architected for the Future Simplified analytics @ scale for all users © 2014, Pentaho. All Rights Reserved. pentaho.com. Worldwide 26 +1 (866) 660-7555
Evolving Big Data Architectures © 2014, Pentaho. All Rights Reserved. pentaho.com. Worldwide 27 +1 (866) 660-7555 Existing ETL Tool or PDI EDW Data Marts Analytics Existing ETL Tool or PDI Customer Provisioning Billing Other BI Tools
Evolving Big Data Architectures Existing ETL Tool or PDI P Just-in-Time Integration D I Network © 2014, Pentaho. All Rights Reserved. pentaho.com. Worldwide 28 +1 (866) 660-7555 PDI Analytic DB Location Web Social Media Existing Process or PDI Hadoop Cluster NoSQL EDW Data Marts Analytics Existing ETL Tool or PDI Customer Provisioning Billing Other BI Tools
The strength of Pentaho lies in the power of combination Data integration Big data +Any data © 2014, Pentaho. All Rights Reserved. pentaho.com. Worldwide 29 +1 (866) 660-7555 Business +analytics The IT department Lines of +business Any data. Any environment. Any analytics.
Thank You JOIN THE CONVERSATION. YOU CAN FIND US ON: blog.pentaho.com @Pentaho © 2014, Pentaho. All Rights Reserved. pentaho.com. Worldwide 30 +1 (866) 660-7555 Facebook.com/Pentaho Pentaho Business Analytics
Steve Wilkes CTO, WebAction +
The Future of Data Driven Apps July 2014
WebAction® delivers the leading Real-time App Platform enabling the next generation of Data Driven Apps for the Agile Enterprise
Acquire Store Process Batch Reactive RDBMS EDW BI / Analytics Structured Data Machine Data Click Location Stream Structured Data Machine Data Real-time Proactive Click Location Stream REALTIME BARRIER Data Driven Apps RDBMS Hadoop Acquire Process in Memory Store
Distributed DIM Processor Distributed WAction Cache Metadata High Speed Data Acquisition WActionStore Transaction Data Social Feeds Tungsten Device Data Visualization RDBMS Big Data Infrastructure Industry Data Enterprise Applications Enterprise Data Warehouse Data Driven Apps System/ IT Data
Security Event Processing Cloud Application Control Risk & Fraud Alerting Quality of Service Management Consumer Analytics DataCenter Management
Frank Sanders Technical Director, MarkLogic +
Data Centered Approach is More Flexible PDF SLIDE: 38 © COPYRIGHT 2014 MARKLOGIC CORPORATION. ALL RIGHTS RESERVED. Slide 38 Copyright © 2010 MarkLogic® Corporation. All 2011 rights reserved.
Universal Index Powers Search & Analytics <location> <lat> 37.497075 <long> -122.363319 Unstructured full-text <object> SLIDE: 39 © COPYRIGHT 2014 MARKLOGIC CORPORATION. ALL RIGHTS RESERVED. Slide 39 Copyright © 2010 MarkLogic® Corporation. All 2011 rights reserved. <SAR> <title> Suspicious vehicle… <date> 2012-11-12Z <type> <threat> suspicious activity <category> suspicious vehicle <description> A blue van… <subject> <subject> <predicate> <object> IRIID IRIID isa value license-plate <predicate> ABC 123 observation/surveillance <type> <triple> <triple> Geospatial Va l u e s
Fairfax County Police Events Application SLIDE: 40 © COPYRIGHT 2014 MARKLOGIC CORPORATION. ALL RIGHTS RESERVED. Slide 40 Copyright © 2010 MarkLogic® Corporation. All 2011 rights reserved.
OECD Better Life Index SLIDE: 41 © COPYRIGHT 2014 MARKLOGIC CORPORATION. ALL RIGHTS RESERVED. Slide 41 Copyright © 2010 MarkLogic® Corporation. All 2011 rights reserved.
MarkMail: Search-powered Visualization SLIDE: 42 © COPYRIGHT 2014 MARKLOGIC CORPORATION. ALL RIGHTS RESERVED. Slide 42 Copyright © 2010 MarkLogic® Corporation. All 2011 rights reserved.
Hannah Smalltree Director, Treasure Data +
The Treasure Data Cloud Service Store! Cloud Storage! Managed, Monitored, Scalable, Secure! Web Mgmt. Console! View/query data, Access controls! Collect! Stream ! Logs/Events in Real-time! Bulk Import! from Most Sources! Copyright ©2014 Treasure Data. All Rights Reserved. Analyze! Query with SQL Multiple Query Engines, Ad Hoc! ! ! BI Tool Connectivity! Tableau, Most BI/Viz/ Analytics Tools! Export! Query Results or Datasets! Anytime! Cloud Managed Service (SaaS) || <2 Week Setup || Flat monthly rate!
Specializing in Streaming “BIG” Data Volume Velocity Variety Examples: Clickstream, Web Access Logs, Mobile Data, App Logs, Event Logs, Sensors, Machine Data… Copyright ©2014 Treasure Data. All Rights Reserved.
Big Data Analytics Use Cases Use Case! Key Data Sources! Results! Treasure Example! Copyright ©2014 Treasure Data. All Rights Reserved. Website & " Mobile App " Behavior Analytics" Mobile App Clicks " Web Clickstream" + eComm, POS" Increase sales and retail foot traffic within weeks" Mobile Application Analytics" Mobile Application Logs" Increase Engagement (=Sales) by Iterating Quickly" Product Behavior " & Sensor Analytics" Sensor Data" Improved Product Development" " New Product/Service Development" $216B Global Retailer Video Games
Treasure Data In Your Analytics Environment Collect" Store" Analyze" Copyright ©2014 Treasure Data. All Rights Reserved. Your" Server," Device," Gateway" etc…" SQL" Your BI, Visualization" Adv. Analytics" Your Data Mart" Data Warehouse" DBMS, etc." Streaming" Treasure Data Service" Aggregates" Export/Integrate"
Copyright ©2014 Treasure Data. All Rights Reserved. Resources TreasureData.com! Datasheets, Case Studies, Whitepapers! TDWI, 451, Analyst Whitepapers! Gartner Report: Cool Vendors in Big Data! ! Try the Starter Service For Free! TreasureData.com/TryItNow!
+ Questions? #TechWise or USE THE Q&A
+ THANK YOU! FIND THE ARCHIVE AT InsideAnalysis.com & Techopedia.com

More Related Content

How Can Analytics Improve Business?

  • 1. Grab some coffee and enjoy the pre-show banter before the top of the hour!
  • 2. “How Can Analy,cs Improve Business?” TechWise Webcast | July 23, 2014
  • 3. + Guests Host: Eric Kavanagh CEO, The Bloor Group Dr. Kirk Borne Data Scientist, George Mason University Dr. Robin Bloor Chief Analyst, The Bloor Group PLUS: Will Gorman Chief Architect, Pentaho Steve Wilkes CTO, WebAction Frank Sanders Technical Director, MarkLogic Hannah Smalltree Director, Treasure Data
  • 4. Analytics Can Help a Business: • Streamline operations • Improve marketing • Raise revenue • Identify opportunities • Assess plans + Executive Summary
  • 5. Dr. Kirk Borne Data Scientist, George Mason University +
  • 6. Big Data Analytics for Data-to-Decisions Support Kirk Borne George Mason University, Fairfax, VA ● www.kirkborne.net @KirkDBorne
  • 7. Extrac,ng Knowledge, Insights, and Data-­‐to-­‐Decisions (D2D) from Big Data is hard!
  • 8. The D2D Challenge** 1. Characterize and !me flux Contextualize first. 2. Collect and Curate each entity’s features. …then Come to the data-driven decision! • Data-to-Discoveries • Data-to-Decisions • Data-to-Dollars
  • 9. Characteriza,on & Contextualiza,on Feature & Context Detection and Extraction: • Identify and characterize features in the data: – Machine-generated – Human-generated – Crowdsourced? (= Tapping the Power of Human Cognition to find patterns and anomalies in massive data!) • Extract the context of the data: the source, the channel, the data user, the use cases, the value, the re-uses … where, when, who, how, what, why = Metadata! • Curate these features for search, re-use, and D2D! • Find other parameters and features from other data sources and databases – integrate all information to help characterize & contextualize (and ultimately make decision regarding) each new event.
  • 10. Characterization via Tagging & Annotation • Report entity’s features & characteristics back to the database for search, retrieval, sharing, and reuse • Individual (or groups of) entities (objects and/or events) are tagged and annotated ... – with new knowledge discovered – with related data/information of any kind – with common knowledge about those things – with inter-relationships between entities and their properties – with concepts – with context – i.e., assertions (e.g., classifications, interpretations, quality flags, relationships, references, common knowledge, learned knowledge, inter-connectivity with other entities) – with data collection parameters – with sensor channel descriptors Semantics! Data integration Provenance (for data curation)
  • 11. Characteriza,on & Contextualiza,on Feature & Context Detection and Extraction: • Identify and characterize features in the data: – Machine-generated – Human-generated – Crowdsourced? (= Tapping the Power of Human Cognition to find patterns and anomalies in massive data!) • Extract the context of the data: the source, the channel, the data user, the use cases, the value, the re-uses … where, when, who, how, what, why = Metadata! • Curate these features for search, re-use, and D2D! • Find other parameters and features from other data sources and databases – integrate all information to help characterize & contextualize (and ultimately make decision regarding) each new event.
  • 13. Then what? Get down to business with the Curated Collection of Characterizations and Contextualizations: • Data Analytics: – Outlier / Anomaly / Novelty / Surprise detection – Clustering (= New Class discovery) – Correlation & Association discovery • D2D: – Data-to-Discoveries – Data-to-Decisions – Data-to-Dollars
  • 14. The Business Analyst-­‐in-­‐the-­‐Loop Tags, annota,ons, features, and context – – These can be … • measured (by observa,on), or • inferred through machine learning, or • provided by human analysts. – The resul,ng synergy yields: • improved or all 3 of these processes simultaneously. training sets, more accurate predic,ve models, fewer false posi,ves/nega,ves, ac,ve learning, efficient human interven,ons – Combining machine learning on Big Data with the power of human cogni,on for discovery (e.g., using Data Visualiza,on, Visual Analy,cs, Immersive Data Environments, or Crowdsourcing) therefore augments and accelerates discovery, insights, and D2D.
  • 15. Dr. Robin Bloor Chief Analyst, The Bloor Group +
  • 16. The Data Scientist & The Business Analyst Robin Bloor
  • 17. The Data Analysis Budget u Data Analysis is Business R&D u The focus is on business process u The outcome of successful R&D is a changed process u Think of manufacturing for a useful example
  • 19. What is a Data Scientist? u Project manager u Qualified statistician u Domain Business expert u Experienced data architect u Software engineer (IT’S A TEAM)
  • 20. The Impact of Machine Learning Machine learning is changing the process (for the BUSINESS ANALYST & the DATA SCIENTIST) BUT the analytics team needs to understand IT!!
  • 21. Take Note! You can know more about a business from its data than by any other means
  • 22. There are Two Issues for the Business Can you get the Can you get the TECHNOLOGY right? PEOPLE right? &
  • 23. + Will Gorman Chief Architect, Pentaho
  • 24. © 2014, Pentaho. All Rights Reserved. pentaho.com. Worldwide 24 +1 (866) 660-7555 July 2014 Pentaho Business Analytics Architected for the Future of Analytics Will Gorman, Chief Architect
  • 25. WHAT WE DO We enable the modern, big data-driven business Modern, cohesive data integration and business analytics platform • Full spectrum of advanced analytics for all key roles • Embeddable, cloud-ready analytics • Big data blending for analytics in real-time environments • Broadest and deepest big data integration Innovation through open source • Open, pluggable, purpose-built for the future • Early sustained leadership in big data ecosystem with technology innovation Critical mass achieved • Over 1,500 commercial customers • Over 10,000 production deployments © 2014, Pentaho. All Rights Reserved. pentaho.com. Worldwide 25 +1 (866) 660-7555
  • 26. Pentaho 5.1 Architected for the Future Simplified analytics @ scale for all users © 2014, Pentaho. All Rights Reserved. pentaho.com. Worldwide 26 +1 (866) 660-7555
  • 27. Evolving Big Data Architectures © 2014, Pentaho. All Rights Reserved. pentaho.com. Worldwide 27 +1 (866) 660-7555 Existing ETL Tool or PDI EDW Data Marts Analytics Existing ETL Tool or PDI Customer Provisioning Billing Other BI Tools
  • 28. Evolving Big Data Architectures Existing ETL Tool or PDI P Just-in-Time Integration D I Network © 2014, Pentaho. All Rights Reserved. pentaho.com. Worldwide 28 +1 (866) 660-7555 PDI Analytic DB Location Web Social Media Existing Process or PDI Hadoop Cluster NoSQL EDW Data Marts Analytics Existing ETL Tool or PDI Customer Provisioning Billing Other BI Tools
  • 29. The strength of Pentaho lies in the power of combination Data integration Big data +Any data © 2014, Pentaho. All Rights Reserved. pentaho.com. Worldwide 29 +1 (866) 660-7555 Business +analytics The IT department Lines of +business Any data. Any environment. Any analytics.
  • 30. Thank You JOIN THE CONVERSATION. YOU CAN FIND US ON: blog.pentaho.com @Pentaho © 2014, Pentaho. All Rights Reserved. pentaho.com. Worldwide 30 +1 (866) 660-7555 Facebook.com/Pentaho Pentaho Business Analytics
  • 31. Steve Wilkes CTO, WebAction +
  • 32. The Future of Data Driven Apps July 2014
  • 33. WebAction® delivers the leading Real-time App Platform enabling the next generation of Data Driven Apps for the Agile Enterprise
  • 34. Acquire Store Process Batch Reactive RDBMS EDW BI / Analytics Structured Data Machine Data Click Location Stream Structured Data Machine Data Real-time Proactive Click Location Stream REALTIME BARRIER Data Driven Apps RDBMS Hadoop Acquire Process in Memory Store
  • 35. Distributed DIM Processor Distributed WAction Cache Metadata High Speed Data Acquisition WActionStore Transaction Data Social Feeds Tungsten Device Data Visualization RDBMS Big Data Infrastructure Industry Data Enterprise Applications Enterprise Data Warehouse Data Driven Apps System/ IT Data
  • 36. Security Event Processing Cloud Application Control Risk & Fraud Alerting Quality of Service Management Consumer Analytics DataCenter Management
  • 37. Frank Sanders Technical Director, MarkLogic +
  • 38. Data Centered Approach is More Flexible PDF SLIDE: 38 © COPYRIGHT 2014 MARKLOGIC CORPORATION. ALL RIGHTS RESERVED. Slide 38 Copyright © 2010 MarkLogic® Corporation. All 2011 rights reserved.
  • 39. Universal Index Powers Search & Analytics <location> <lat> 37.497075 <long> -122.363319 Unstructured full-text <object> SLIDE: 39 © COPYRIGHT 2014 MARKLOGIC CORPORATION. ALL RIGHTS RESERVED. Slide 39 Copyright © 2010 MarkLogic® Corporation. All 2011 rights reserved. <SAR> <title> Suspicious vehicle… <date> 2012-11-12Z <type> <threat> suspicious activity <category> suspicious vehicle <description> A blue van… <subject> <subject> <predicate> <object> IRIID IRIID isa value license-plate <predicate> ABC 123 observation/surveillance <type> <triple> <triple> Geospatial Va l u e s
  • 40. Fairfax County Police Events Application SLIDE: 40 © COPYRIGHT 2014 MARKLOGIC CORPORATION. ALL RIGHTS RESERVED. Slide 40 Copyright © 2010 MarkLogic® Corporation. All 2011 rights reserved.
  • 41. OECD Better Life Index SLIDE: 41 © COPYRIGHT 2014 MARKLOGIC CORPORATION. ALL RIGHTS RESERVED. Slide 41 Copyright © 2010 MarkLogic® Corporation. All 2011 rights reserved.
  • 42. MarkMail: Search-powered Visualization SLIDE: 42 © COPYRIGHT 2014 MARKLOGIC CORPORATION. ALL RIGHTS RESERVED. Slide 42 Copyright © 2010 MarkLogic® Corporation. All 2011 rights reserved.
  • 43. Hannah Smalltree Director, Treasure Data +
  • 44. The Treasure Data Cloud Service Store! Cloud Storage! Managed, Monitored, Scalable, Secure! Web Mgmt. Console! View/query data, Access controls! Collect! Stream ! Logs/Events in Real-time! Bulk Import! from Most Sources! Copyright ©2014 Treasure Data. All Rights Reserved. Analyze! Query with SQL Multiple Query Engines, Ad Hoc! ! ! BI Tool Connectivity! Tableau, Most BI/Viz/ Analytics Tools! Export! Query Results or Datasets! Anytime! Cloud Managed Service (SaaS) || <2 Week Setup || Flat monthly rate!
  • 45. Specializing in Streaming “BIG” Data Volume Velocity Variety Examples: Clickstream, Web Access Logs, Mobile Data, App Logs, Event Logs, Sensors, Machine Data… Copyright ©2014 Treasure Data. All Rights Reserved.
  • 46. Big Data Analytics Use Cases Use Case! Key Data Sources! Results! Treasure Example! Copyright ©2014 Treasure Data. All Rights Reserved. Website & " Mobile App " Behavior Analytics" Mobile App Clicks " Web Clickstream" + eComm, POS" Increase sales and retail foot traffic within weeks" Mobile Application Analytics" Mobile Application Logs" Increase Engagement (=Sales) by Iterating Quickly" Product Behavior " & Sensor Analytics" Sensor Data" Improved Product Development" " New Product/Service Development" $216B Global Retailer Video Games
  • 47. Treasure Data In Your Analytics Environment Collect" Store" Analyze" Copyright ©2014 Treasure Data. All Rights Reserved. Your" Server," Device," Gateway" etc…" SQL" Your BI, Visualization" Adv. Analytics" Your Data Mart" Data Warehouse" DBMS, etc." Streaming" Treasure Data Service" Aggregates" Export/Integrate"
  • 48. Copyright ©2014 Treasure Data. All Rights Reserved. Resources TreasureData.com! Datasheets, Case Studies, Whitepapers! TDWI, 451, Analyst Whitepapers! Gartner Report: Cool Vendors in Big Data! ! Try the Starter Service For Free! TreasureData.com/TryItNow!
  • 49. + Questions? #TechWise or USE THE Q&A
  • 50. + THANK YOU! FIND THE ARCHIVE AT InsideAnalysis.com & Techopedia.com