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Trends and Predictions for 2019

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John and Kelle reflect back on 2018 and suggest how it may inform new opportunities and challenges in 2019.

This webinar, which will include a longer Q&A session, will also focus on:

Industry learnings from 2018
Forecasting the analytics opportunities in the areas of people, technology, data, and processes
Real-world case studies to inform and illuminate

Publicado en: Datos y análisis
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Trends and Predictions for 2019

  1. 1. The First Step in Information Management Produced by: MONTHLY SERIES In partnership with: Big Data as a Gateway to Knowledge Management November 1, 2018
  2. 2. Welcome to Today’s Discussion ▪ Overview of knowledge management ▪ Scope of current knowledge management technologies ▪ Analytics and big data use cases ▪ Knowledge management and future usage ▪ Best practices and key takeaways ▪ Q&A pg 2© 2018 First San Francisco Partners
  3. 3. Overview of Knowledge Management pg 3© 2018 First San Francisco Partners Late 90s We don’t know what we don’t know Orgs need to be self-learning Davenport/Prusac Ikujiro Nonaka Business Drivers Overwhelming wave of data volume Unstructured data Loss of organization knowledge and wisdom via aging workforce. Stop expertise "walking out of the door." Reuse valuable knowledge, and stop "reinventing the wheel.” Use best practice to improve consistency and quality.
  4. 4. Overview of Knowledge Management pg 4© 2018 First San Francisco Partners Solution Areas Human Capital Management Organizational Learning Collaboration Knowledge Identification and Dissemination Extending BI capabilities Extending BI Capabilities Unstructured Information Usage Actionable Use of Information Identification/Tracking of Knowledge and Info Assets Closed Loop Agents (AI, ML)
  5. 5. Knowledge Management and Future Usage pg 5© 2018 First San Francisco Partners “ Knowledge Management turns the potential capacity of raw “connected and collaborative intelligence”, i.e. all those brains at the end of the computer, into a “collective know-how” that will improve operations, competitiveness and value. ….. It is a SUM of information assets, …and most importantly, the un- captured, tacit expertise and experience resident in the minds of people.” “ Knowledge management is a discipline that promotes an integrated approach to identifying, capturing, evaluating, retrieving, and sharing all of an enterprise's information assets. ... The one real lacuna of this definition is that it, too, is specifically limited to an organization's own information and knowledge assets. “ ▪ The context, metadata and the relationships are as important as the values of the records. John Ladley Wikipedia
  6. 6. Where Did It Go? pg 6© 2018 First San Francisco Partners It was too hard to change behavior. Everything devolved to technology. The technology that organizations wanted to employ was Microsoft’s SharePoint. It was too time consuming to search for and digest stored knowledge. Google KM never incorporated knowledge derived from data and analytics Source: Tom Davenport, Wall Street Journal, “Whatever Happened to Knowledge Management?” June 24, 2015
  8. 8. Analytics and Big Data Use Cases ▪ Gain visibility across all data categories, classifications, nooks and crannies ▪ Achieve the summit of understanding tacit knowledge ▪ Capture work using AI and related technologies across complicated communities with large volumes of data = a use case for KM pg 8© 2018 First San Francisco Partners John Ladley, Making EIM Work for Business, 2010, Morgan Kaufman
  9. 9. Knowledge Management Factors and Use Cases ▪ Blurs with AI and machine learning ▪ Still retains old challenges that AI needs to take to heart (data quality/data movement/context) ▪ Future − You still need to apply what people ALREADY KNOW − You need to understand what remains tacit − Accessible − Navigable − Contextual pg 9© 2018 First San Francisco Partners FutureAnalytics Knowledge Management Machine Learning Artificial Intelligence Well Managed Data Supply Chain
  10. 10. Analytics and Big Data Use Cases pg 10© 2018 First San Francisco Partners Data Meaning and Context BI & Reports Experience Knowledge Base ----------- Store insights as to what happened in response to information, and enable action and responses Knowledge MapInsight Content Meaning and Context Tagged New Information Big Data Analytics Meaning and Context New Context New Information New Insight Analytics New Information Tagged Experience
  11. 11. Future Uses — Sample Architecture ▪ Graph for knowledge mapping and metadata ▪ Document database for document storage and use ▪ Hadoop or other NoSQL for merging and analyzing varied content ▪ Columnar for handling Vintage area BI and Reporting ▪ Add place to “store” learned behaviors and data supporting AI pg 11© 2018 First San Francisco Partners Contemporary Area 1 Data Life Cycles Data Management Data Usage “WORK” Vintage Area Legacy BI and Reporting Data Warehouse, ODS, Mart ETL, EAI, Msg, Copy Data Lake Advanced Analytics RDBMS, SQL, Columnar, Transactional Metadata Logical DW Data Sources Knowledge Graph BIVisualization Document “Abstraction Engine” “Knowledge Lake” Hadoop Work Collaboration
  12. 12. Knowledge Management “Area” Capture, retain and share knowledge and enable collaboration Knowledge Management and the Operating Framework pg 12© 2018 First San Francisco Partners SUPPORTING PROGRAMS Organizational Change Management Data Governance Human Capital / Workflow / Collaboration Enterprise Architecture Data Operational Areas IT / AppDev Knowledge Bases Collaboration / workflow Support innovative efforts • New Digital content and products • Disruptive technologies (IoT) • Data monetization Support conventional efforts • Content management • ERP • Analytics • Disruptive regulations (GDPR) Other efforts • Bootstrap innovation projects • Manage large initiatives • Content management & tagging • Search • Expertise location Analytics Process Capabilities
  13. 13. Unstructured Tacit Knowledge Management Supports Organizational Learning and Human Capital Development pg 13© 2018 First San Francisco Partners Structured Sources AI / Analytics Models, Knowledge Abstraction Conclusion AI “closed loop” rule Knowledge Graph LEARNING CAPTURED LEARNING ACTION Un structured Explicit ?
  14. 14. Best Practices ▪ Focus on practical applications − It is good to know what you know − All industries can benefit from knowledge management, while some still require it: ▪ Complex manufacturing - Aerospace ▪ High risk, high human interaction – Energy, Healthcare ▪ Service – Help Desk ▪ Balance AI-driven “closed loop” vs. human interactions ▪ Use AI and Big Data as the platform of interactions and activity tracking pg 14© 2018 First San Francisco Partners
  15. 15. Key Takeaways pg 15© 2018 First San Francisco Partners KEEP IN MIND… ▪ Big Data, Analytics and AI allow for a pragmatic gateway to knowledge management-like activity ▪ “Learning organizations” require a lot more than just technology, and are probably a long way off ▪ Understand that AI might be intended to replace, but it should initially supplement and help manage tacit knowledge ▪ Knowledge management, in the academic view, is far away and is a capability rather than a functional area
  16. 16. Please Share Your Questions and Comments MONTHLY SERIES
  17. 17. Thank you for joining us today! Our Thursday, December 6 #DIAnaltyics webinar is: Trends and Predictions for 2019 . John Ladley @jladley Kelle O’Neal @kellezoneal