How to Scale Information Dissemination to the Virtual Digital Workspace
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This document discusses how organizations can provide a unified search experience for remote workforces. It outlines how COVID-19 has increased remote work and impacted productivity. It then discusses how to transform workforces to the "new normal" by providing a single source of information through search. The rest of the document discusses solutions like Lucidworks Fusion to power search and analytics across structured and unstructured data sources. It also outlines Xenit's capabilities in integrating with Fusion to power search across Alfresco repositories.
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How to Scale Information Dissemination to the Virtual Digital Workspace
- 1. 1 Todays Speakers Simon Taylor VP Global Partners & Alliances Lucidworks Ronny Timmermans CEO & Managing Director Xenit Solutions
- 2. 2 Dissemination of Information to the Virtual Workforce TA K I N G S T E P S T O A C C E L E R AT E R E M O T E W O R K F O R C E P R O D U C T I V I T Y
- 3. How Covid-19 Impacted Organizations Time Steady Workplace Transformation 75% Expanded Work-from-Home Policies 62% Fall in Employee Productivity 31%-52% Current or Anticipated Business Disruption 43% Increased Spending on Information Collaboration 80% Expecting continued remote working and social distancing in office workspace – 12-18 months 1Gartner – Return to the Workplace – May 2020 | 2451 Group – Covid-19 Flash Report, March & May 2020
- 4. Transforming to the “new” normal 75% of organizations have or will be implementing expanded work-from-home polices2 33% of US workers [are] too anxious to come back to a physical location2 38% that think work-from-home policies will become be long-term or made permanent2 62% experienced a fall in employee productivity2 41% report having already felt an internal strain on their IT resources. 51% [of public sector professionals] identified lack of internal coordination as the greatest challenge to government transformation programs1 1EY/Government Business Council | 2451 Group, March & May 2020
- 5. TRANSITIONING TO A VIRTUAL DIGITAL WORKPLACE IS CHALLENGING
- 6. Top Priorities o PROVIDE A UNIFIED SINGLE SOURCE OF INFORMATION [I NC . C USTOM ER DATA] o ENABLE EMPLOYEES & CUSTOMERS TO ACCESS KEY INFORMATION QUICKLY & EASILY o PROVIDE RICHER DATA INSIGHT FOR LINE OF BUSINESS DECISION MAKERS Voice of The Enterprise: Workforce Productivity & Collaboration, Employee Lifecycle | 451 Group Research, Q2 2019
- 7. We need to connect people to data insight? Predictable, data quality, unification & access BEFORE Empowered data consumers seek insight and drive productivity on their own NOW
- 8. Data Scientist Driven Activity Business Alignment Meet the “Last Mile Problem” RawData Velocity Variety Veracity Volume Value DataCollection ETL, Capture, Archive, Move & Manage DataLake Store to HDFS, Azure or AWS, Break down for processing LakesideUnderstanding Select, Describe, Explore, Verify Quality, Parse, Clean, Join & Structure DataRefinery Model, Aggregate & Transform Multi- Structured Data (JSON) Transactions&Interactions Retain runtime & historical models for ongoing refinement ApplicationConstruction Contextual Business Use Cases Cubes for Navigation & Mining Intelligence&Analysis User Specific Visualization, Presentation, Dashboards & Reporting BigDataManagement &Analytics ❶ ❷ ❸ ❹ ❺ ❻ ❼ ❽ Problems with Activating Data for Digital Transformation
- 9. Data Scientist Driven The Last Mile Problem Reduced Time to Value Quantifiable & Faster ROI ManagedData Human Process Application IndexIngestionPipelines Ingest, Verify, Index, Entity, Cluster ML Rules & Algorithms Ontology Enrichment BehavioralEnrichment Insight, Relevancy Ranking, Signals Processing Real-time Learning ApplicationVisualization Dynamic Prototype, Build & Visualization Interactive Dashboarding Search,Discovery& OperationalAI RawData Velocity Variety Veracity Volume Value DataCollection ETL, Capture, Archive, Move & Manage DataLake Store to HDFS, Azure or AWS, Break down for processing LakesideUnderstanding Select, Describe, Explore, Verify Quality, Parse, Clean, Join & Structure DataRefinery Model, Aggregate & Transform Multi- Structured Data (JSON) Transactions&Interactions Retain runtime & historical models for ongoing refinement ApplicationConstruction Contextual Business Use Cases Cubes for Navigation & Mining Intelligence&Analysis User Specific Visualization, Presentation, Dashboards & Reporting BigDataManagement &Analytics ❶ ❷ ❸ ❹ ❺ ❻ ❼ ❽ ❶ ❷ ❸ ❹ Direct Alignment with Business Needs Problems with Activating Data for Digital Transformation
- 10. 10 How do we provide a collaborative unified search in the remote workplace? • Knowledge• Process • People• Technology
- 11. SEARCH FILTER VISUALIZATION NAVIGATION INSIGHT RELEVANCY STATISTICS RANKING CORRELATION ACTIVITY CONTENT INDEX NATURAL LANGUAGE BOOSTED RESULTS MACHINE LEARNING QUERY RULE MATCHING USER SIGNALS FACET, TOPIC & CLUSTER D ATA Human Generated System Generated Application Generated S O L U T I O N Digital Workplace NATURAL LANGUAGE MACHINE LEARNING QUERY RULE MATCHING USER SIGNALS FACET, TOPIC & CLUSTER ENRICHMENT
- 12. The Nature of Personalization for the Virtual Workforce Employee Productivity Distribution of Data Reactionary Deluge Personalized Efficiency
- 13. 13 ENABLING MACHINE LEARNING Go beyond using static rules and profiles. Instead dynamically customize the experience for each data consumer. Use AI and machine learning to predict user intent and give employees the insights they need, when they need them. Maximise the opportunities to tailor content that fits each and every employee’s wants and needs. Explore Curate Integrate Defining Hyper Personalization
- 14. Advanced connectors and AI enrichment, delivered by intuitive applications created with App Studio, deployed on-prem or as a multi-tenant cloud managed service. D ATA Any format, any platform S O L U T I O N Personalised insights for each individual STORAGE & SEARCH INTENT PREDICTION APP CREATION DATA INGEST & PREP F U S I O N P L AT F O R M Human Generated System Generated Application Generated Digital Workplace
- 15. STORAGE & SEARCH INTENT PREDICTION APP CREATIONDATA INGEST & PREP NLP: NER, phrases, POS Document classification Anomaly detection Clustering Topic detection Search engine & data processing Connectors ETL pipelines Scheduling & alerting SQL engine Rules engine Query pipelines Query intent detector Automatic relevancy Signals & query analytics Recommenders A/B testing Modular components Stateless architecture User-focused experience Geospatial mapping Results preview Rapid prototyping S C A L A B L E O P E R AT I O N S SECURITYCDCRCLOUDSCALABLEEXTENSIBLE Data Quality is a Process
- 16. S O L U T I O N A S S E M B L Y Digital Workplace I N F O R M AT I O N M O D E L Any format, any platform Human Generated System Generated Application Generated Index Search Intent BuildApp RULE ENTITY ML NLP BOOST SIGNAL R A P I D A S S E M B LY P L AT F O R M FUSION Call Center FUSION Data Insights FUSION Workplace Search S O LU T I O N T E M P L AT E S PartnerExpertise
- 17. Over 100 Different Workplace Connectors • Knowledge• Process • People• Technology PartnerExpertise Search&InsightastheCore
- 18. Working with Partners I N T R O D U C I N G X E N I T C A PA B I LT I E S F O R L U C I D W O R K S F U S I O N
- 19. 1919 ABOUT XENIT 12 years of experience in Information and Document Management Extensive Apache Solr and ECM know-how Alfred Desktop & Alfred finder 300+ million of documents managed in Alfresco #1 Alfresco integrator in Belgium, and in top 5 system integrators in Europe
- 20. 2020 • S O L R = S E A R C H E N G I N E B E H I N D A L F R E S C O • A D VA N C E D F U L L - T E X T S E A R C H C A PA B I L I T I E S • O P T I M I Z E D F O R H I G H V O L U M E T R A F F I C • N E A R R E A L - T I M E I N D E X I N G • O P E N - S O U R C E X E N I T A N D S O L R T EC H N O LO G Y
- 21. 21 INTELLIGENT INFORMATION MANAGEMENT Expanding ECM into IIM ECM IS A S ERV ICE TO M A NAG E A ND S ECU R E ENT ER P R IS E CO NT ENT • Content is a pre-digital way to look at documents, records, web pages, emails, multi-media INT ELLIGE NT INFO R M AT IO N M A NAG EM ENT • Integrates all structured and unstructured information • Creates a common context = Meta-Model W I S D O M K N O W L E D G E I N F O R M AT I O N D ATA META-DATA META-MODEL
- 22. 22 BU ILD A S H A R ED CO NCEPT UA L INFO R M AT IO N M O DEL • Shared – All (new) employees look at the same map • Conceptual – The model is the map, not the road • Model – The map is abstract stable W I S D O M K N O W L E D G E I N F O R M AT I O N D ATA A META-MODEL MAKES SENSES OF SEARCH Own-Item Search To locate/refind Known-Item Search To Find Exploratory Search To Learn A Path that works today Exhaustive Search To Master Pattern of Shortest Path
- 23. 23 LINKING ALFRESCO WITH FUSION XENIT’S CONNECTOR Xenit Alfresco Fusion The Xenit’s connector: • Allows to index in an incremental manner ( as opposed to “re-crawl”) • Supports Access Control Lists • Achieves an indexing of 130+ meta-data objects per second consistently How can Fusion enrich Alfresco ? • We leverage machine learning to enrich the content • Fusion uses signals to personalize the search experience.
- 24. 24 Use Case : On demand Customer intelligence A T H E M O M E N T O F C U S T O M E R I N T E R A C T I O N , I WA N T T O S E E : - The last interaction in our CRM - What product has the customer? - The contract and last annex - The draft proposal we are preparing - Credit rating of the company Replace customer by patient, partner, designer, product, .. M E TA M O D E L - Concepts: - “Customer”, - “contract”, - “proposal” Your enterprise architect knows. - access rights - Privacy & confidentiality
- 26. 26 MACHINES MAKE THE META MODEL In the future, machine learning technology can create the meta-models • Initial creation of the model from the information à Machine Learning • Supervised Learning à Curation creates Clarity • Continuouos Adaptation to business reality à the agility of information TOP DOWN & BOTTOM UP PROCESS • Curation is crucial in a continuous cyclic process Discover Curate Discover
- 28. 28 Questions? D O N ’ T B E S H Y … .
- 29. 29 Thank You C O N TA C T U S AT S A L E S @ X E N I T. E U