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Thomas Vavra | New Ways of Handling Old Data

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http://2016.semantics.cc/thomas-vavra

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Thomas Vavra | New Ways of Handling Old Data

  1. 1. New Ways of Handling Old Data Tom Vavra AVP Software & Industry Insights and Analysis IDC
  2. 2. Top Trends © IDC Visit us at IDC.com and follow us on Twitter: @IDC 2 People Centric Networks Intersection of People to People to Data Cognitive Computing & Assistive Technology Work Context & flow Organizational Dynamics Sales, marketing, service not “working” Shifting workforce dynamics (for the 1st time Millennials are the largest % of the workforce) Disruptive new connected business models Applications are Changing Social: Inherent Ability to Connect Underlying Platform Services Distributed Information Access – Decision Support
  3. 3. Unstructured Content: Value Waiting to be Delivered © IDC Visit us at IDC.com and follow us on Twitter: @IDC 3 Unstructured content – email, video, instant messages, documents, and other formats – accounts for of all digital information Unlocking value from this content should be the goal of every organization, but very few are actually getting all the value they should be. THIS CONTENT IS LOCKED IN A VARIETY LOCATIONS AND APPLICATIONS MADE UP OF SEPARATE REPOSITORIES THAT DON’T TALK TO EACH OTHER – E.G., EMC DOCUMENTUM, SALESFORCE.COM, GOOGLE DRIVE, SHAREPOINT, ET AL. 90%
  4. 4. IDC’s Big Data and Analytics Predictions (1) © IDC Visit us at IDC.com and follow us on Twitter: @IDC 4 1. Through 2020, spending on cloud-based BDA technology will grow 4.5x faster than spending for on-premises solutions; open source technology will represent the core of this new architecture. 2. By 2020, 50% of all business analytics software will incorporate prescriptive analytics built on cognitive computing functionality. 3. Shortage of skilled staff will persist and extend from data scientists to architects and experts in data management; big data–related professional services will have a 23% CAGR by 2020. 4. By 2020, 90% of databases (relational and non-relational) will be based on memory-optimized technology. 5. By 2020, distributed micro analytics and data manipulation will be part of all big data and analytics deployments.
  5. 5. IDC’s Big Data and Analytics Predictions (2) © IDC Visit us at IDC.com and follow us on Twitter: @IDC 5 5. Through 2020, spending on self-service visual discovery and data preparation market will grow 2.5x faster than traditional IT-controlled tools for similar functionality. 6. By 2020, data monetization efforts will result in enterprises pursuing digital transformation initiatives increasing the marketplace’s consumption of their own data by 100-fold or more. 7. By 2020, the high-value data part of the digital universe that is worth analyzing to achieve actionable intelligence will double. 8. By 2020, 60% of information delivered to decision makers will be considered by them always actionable, doubling the current rate. 9. By 2020, organizations able to analyze all relevant data and deliver actionable information will achieve an extra $430 billion in productivity benefits over their less analytically oriented peers.
  6. 6. Why Do So Few Organizations Find Value in Their Information? © IDC Visit us at IDC.com and follow us on Twitter: @IDC 6 of knowledge workers regularly access 4 or more systems to get the information they need to do their jobs of a typical knowledge worker’s day is spent looking for and consolidation information spread across a variety of systems 61% 36% Nearly 15% access 11 or more systems These workers find the information required to do their jobs only 56% of the time
  7. 7. Cognitive Software Attributes • Performs deep natural language processing and analysis both for information ingestion and research as well as to provide human style communication (usually posed as questions and answers) • Conducts learning in real time as data arrives • Has the ability to identify similar past experiences and use learning to current situation • Predicts and recommends possible outcomes • Score those outcomes with evidence for human analysis • Cycle back to the start so that the continuous learning is practiced, making the system better over time © IDC Visit us at IDC.com and follow us on Twitter: @IDC 7 Cognitive software support human decision-making with more accuracy, confidence, speed, and agility based on broader and deeper bodies of evidence applied to a more comprehensive view of pertinent conditions without bias.
  8. 8. The Content Analytics, Discovery & Cognitive Systems Market Defined  Content Analytics • Text Analytics, Video Analytics • Categorizers and clustering engines • Speech Recognition, Language analyzers  Discovery • Enterprise search engines, information access platforms, and applications for browsing and navigation • Knowledge Base/Graph Generation • Rich media search  Cognitive Systems • Digital assistants • Automated advisors • Artificial intelligence, deep learning and machine learning • Automated recommendation systems © IDC Visit us at IDC.com and follow us on Twitter: @IDC 8 CADCS software analyzes, organizes, accesses, and provides advisory services based on a range of unstructured information and provides a platform for the development of analytic and cognitive applications.
  9. 9. 9 Cognitive Solutions Ecosystem Source: IDC Behavioral Interactional Performance Long form Geolocation News Personal data Healthcare Location Sports & Entertainment Social Corporate Logistics Financial Marketing Sales Procurement Asset mgmt. R&D Logistics HR Anti money laundering Retail pricing Patient outcomes Telco churn IT performance mgt. Retail Travel Media Healthcare Insurance Investment Commercial leasing Advertising Legal Driverless cars Smart home devices Self-flying drones Robotic systems Text analysis Video analysis Image analysis Predictive analytics NLP APIs ConnectorsData stores Hypotheses generation Machine learning Speech Recognition Dialogue Mgt. Finance Risk mgmt. Weather
  10. 10. Cognitive Systems Use Cases  Healthcare • Diagnosis and Treatment Systems • Education and Training Systems • Pharmaceutical Research and Discovery  Retail • Expert Shopping Advisors & Product Recommendations • Automated Customer Service Agents • Automated Training Systems  Finance/Insurance • Automated Financial Advisors • Policy Advisors & Question and Answer Systems • M&A Investigation and Recommendations  Government • Police Investigation Systems • Program Advisors and Recommendation Systems  Manufacturing • Operational Improvement Systems • Asset Maintenance Systems 10
  11. 11. 11 Source: IDC, 2015 2014–2019 Revenue ($M) with Growth (%) ($M) (%) 827 1075 1419 1916 2644 3683 0 5 10 15 20 25 30 35 40 45 0 500 1,000 1,500 2,000 2,500 3,000 3,500 4,000 2014 2015 2016 2017 2018 2019 Cognitive Total growth (%) Game Changer Commercial cognitive software platforms have just begun to emerge on the market scene. This category of software used to build “smart” applications and expert advisors will grow rapidly over the next five years enabling a multi-billion dollar intelligent applications market. 2014–2019 Revenue ($M) with Growth (%) ($M) (%) Game Changer Commercial cognitive software platforms have just begun to emerge on the market scene. This category of software used to build “smart” applications and expert advisors will grow rapidly over the next five years enabling a multi-billion dollar intelligent applications market. Worldwide Cognitive Software Platform Forecast
  12. 12. Worldwide Cognitive Market by Industry, 2015
  13. 13. WW Cognitive Systems Spending (US$M) by Use Case, 2015 © IDC Visit us at IDC.com and follow us on Twitter: @IDC 13 $- $ 100 $ 200 $ 300 $ 400 $ 500 $ 600 $ 700 Adaptive Learning Asset/Fleet Management Automated Claims Processing Automated Customer Service Agents Automated Threat Intelligence and Prevention Systems Defense, Terrorism, Investigation and Government Intelligence Systems Diagnosis and Treatment Systems Expert Shopping Advisors & Product Recommendations Fraud Analysis and Investigation Freight Management Merchandising for Omni Channel Operations Others Pharmaceutical Research and Discovery Program Advisors and Recommendation Systems Public Safety and Emergency Response Quality Management Investigation and Recommendation Systems Regulatory Intelligence Sales Process Recommendation and Automation Value (USD M)
  14. 14. European Cognitive Systems Spending (US$M) by Use Case, 2015 © IDC Visit us at IDC.com and follow us on Twitter: @IDC 14 $- $ 20 $ 40 $ 60 $ 80 $ 100 $ 120 Adaptive Learning Asset/Fleet Management Automated Claims Processing Automated Customer Service Agents Automated Threat Intelligence and Prevention Systems Defense, Terrorism, Investigation and Government Intelligence Systems Diagnosis and Treatment Systems Expert Shopping Advisors & Product Recommendations Fraud Analysis and Investigation Freight Management Merchandising for Omni Channel Operations Others Pharmaceutical Research and Discovery Program Advisors and Recommendation Systems Public Safety and Emergency Response Quality Management Investigation and Recommendation Systems Regulatory Intelligence Sales Process Recommendation and Automation Supply and Logistics 2015 Value (USD M) 2 3 9 11 12 1
  15. 15. Source: IDC, 2016 Practices to Implement Cognitive Systems Initiatives
  16. 16. Source: IDC, 2016 1 - Setting Expectations
  17. 17. Be Realistic Issues  Business and IT both assume Cognitive will replace humans  Cognitive can only assist  Outputs are never a “sure bet”  Requires collaboration between IT and LOBs  Relevant data is needed, not just more of it © IDC Visit us at IDC.com and follow us on Twitter: @IDC 17
  18. 18. Real World #1: Bankers vs. Robots © IDC Visit us at IDC.com and follow us on Twitter: @IDC 18
  19. 19. Source: IDC, 2016 2 – Leverage Cloud Services
  20. 20. Cloud as a Facilitator and Problem Solver Issues  Cognitive systems require vast data and processing power  On premise investment can be expensive and time consuming  Cloud services can do “heavy lifting” and alleviate up front costs and time…  … but, not all Cognitive solutions are Cloud- ready or appropriate © IDC Visit us at IDC.com and follow us on Twitter: @IDC 20
  21. 21. Real World #2 :When Planes Love Clouds © IDC Visit us at IDC.com and follow us on Twitter: @IDC 21 We Cloud
  22. 22. Source: IDC, 2016 3 – Identify Repetitive Routine Actions
  23. 23. Choosing a Starting Point Issues  Identifying the right use case takes time and thought  Need to start by documenting current business processes to identify resource-intensive tasks  Narrow down list for cognitive applications  Free up human resources to analyze exceptions and outliers © IDC Visit us at IDC.com and follow us on Twitter: @IDC 23
  24. 24. Real World #3 : Letting Doctors be Doctors © IDC Visit us at IDC.com and follow us on Twitter: @IDC 24
  25. 25. Source: IDC, 2016 4 – Validate Outputs
  26. 26. Cognitive systems don’t fix bad inputs and untrained users Issues  The lack of quality inputs and expert trained users will, necessarily, result in mistakes and bad outputs  Surprises are common in the early stages  Constant validation is required to minimize erroneous results  Feedback on errors must be part of the regular workflow © IDC Visit us at IDC.com and follow us on Twitter: @IDC 26
  27. 27. Real World #4 : Matching Clients with Hotels © IDC Visit us at IDC.com and follow us on Twitter: @IDC 27
  28. 28. Source: IDC, 2016 5 – Manage Data to Avoid Inaccurate Results
  29. 29. Data management is always central and key to any initiative Issues  Data can have many issues: inconsistency, varied formats, ownership, (lack of) governance  Need to map data: Where is it? Who owns it? Is it connected/integrated?  Third-part data is often required to complement existing sources  Value of data increases exponentially when different types and sources are combined © IDC Visit us at IDC.com and follow us on Twitter: @IDC 29
  30. 30. Real World #5 : IT Mfg + Internal IT + External provider © IDC Visit us at IDC.com and follow us on Twitter: @IDC 30
  31. 31. Thank you! Tom Vavra Tel: + 420 221 423 140 tvavra@idc.com Associate Vice President IDC CEMA Malé naměstí 13 110 00 Praha 1 Czech Republic www.idc-cema.com www.idc.com CEMA Region

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