Getting Knowledge Transfer Right Enterprise Wide

Transcription

1 Getting Knowledge Transfer Right Enterprise Wide Ken Lemons VP Federal Programs Concept Searching Todd Griffith CTO and Co-Founder Discovery Machine Anna Griffith CEO and Co-Founder Discovery Machine John Challis Founder and CTO/CEO Concept Searching

2 Expert Speakers Ken Lemons VP of Federal Programs at Concept Searching has over 25 years experience in the IT industry, with a track record in consulting, solutions delivery, sales and project management in the federal sector. He has managed Microsoft consulting practices for several US government integrators, latterly as VP of Business Development for Air Force and DoD programs. Ken has provided US DoD agencies with solutions to address a range of challenges, leveraging a combination of Microsoft and third party technology solutions. Todd Griffith CTO and Co-Founder at Discovery Machine has worked in the area of intelligent systems research for more than a decade and is widely published on subjects that include cognitive science, human-computer interaction and intelligent systems. His current research involves building knowledge acquisition tools that allow subject matter experts to encode their own problem-solving strategies. Anna Griffith CEO and Co-Founder at Discovery Machine has more than 10 years experience in the commercialization of AI based software solutions. She developed Discovery Machine s methodology for knowledge capture which leverages cognitive science and AI research to help individual experts in their articulation required for both knowledge capture and automation. This methodology has proven successful in a wide domain for both government and commercial sectors. John Challis Founder and CTO/CEO at Concept Searching is an experienced entrepreneur, and has had success with several ventures involving the management of unstructured data. In 2002 he founded Concept Searching, and under his technical expertise introduced statistical search and classification products that utilize compound term processing to identify concepts within unstructured information.

3 Agenda Introductions Discovery Machine About Us What We Do How We Differentiate Ourselves Demonstration Use Case Examples Concept Searching Approach and Technologies Knowledge Flow Knowledge Transfer Demonstration Next Steps

4 Company founded in 2002 Product launched in 2003 Focus on management of structured and unstructured information Technology Platform Delivered as a web service Automatic concept identification, content tagging, auto-classification, taxonomy management Only statistical vendor that can extract conceptual metadata 2009, 2010, 2011, 2012, Companies that Matter in KM (KMWorld Magazine) and Trend Setting product of 2009, 2010, 2011, 2012, 2013 Authority to Operate enterprise wide US Air Force and enterprise wide NETCON US Army Locations: US, UK, and South Africa Client base: Fortune 500/1000 organizations Managed Partner under Microsoft global ISV Program - go to partner for Microsoft for auto-classification and taxonomy management Smart Content Framework for Information Governance Concept Searching The Industry Leader in Managed Metadata Solutions Product Suite: conceptsearch, concepttaxonomymanager, conceptclassifier, conceptclassifier for SharePoint, concepttaxonomyworkflow, conceptcontenttypeupdater for SharePoint

5 About Discovery Machine Inc. (DMI) Company Founded in 1999 Headquartered: Williamsport, Pennsylvania, USA Awards: Department of Defense SBIR Success Story with the Defense Advanced Research Projects Agency (DARPA) Ben Franklin Technology Partners 2010 Most Technologically Innovative Product Award Winner Finalist for the 2013 Pennsylvania's Small Business Governor's Award US Patents: US Patent 7,257,455 B1 - System & Method for Collecting & Representing Knowledge US Patent 7,757,220 B2 - Computer Interchange of Knowledge Hierarchies US Patent 7,958,073 B2 - Software Methods & Methods for Task Method Hierarchies US Patent 8,019,716 B2 - Reflective Processing of Computer Hierarchies Products: Knowledge Service Modeler Behavior Modeling Console for VBS2 Maritime Console Behavior Creation Toolkit Virtual Training Coaches

6 About Discovery Machine Inc. (DMI) Cont. Capture and Deploy Adaptable Expertise Enable better decision making Take action

7 Classic Lessons Learned System Ontology Based Lessons Learned Deployable Best Practices Enter Text Search DATA Enter Data Search Information Enter Best Practices Search Solutions Unstructured Text Classify Ontology Manage Knowledge Coordinator Formal/ Deployable Models Adaptation via Critics SME

8 1. Expert Domain Scoping - Creating 1. Umbrella umbrella for project DMI Methodology 2. Conceptualize - Tell stories & sketch process 3. Formalize - Map information to process 4. Operationalize - Create knowledge system 5. Test and Deploy - Deploy to the enterprise Word Excel Xml html

9 Combined Solution Differentiators Capture enterprise knowledge and deploy subject matter expertise Effective content identification and classification Create tangible knowledge assets Automatic identification Visual representation complex business and technical processes that promote best practices Real-time decision making through dynamically updated, executable strategies and methods Improved training and maintenance of organizational know-how

10 Toaster Coach Demonstration

11 Uses Uses & Combined Solution DMI Model Knowledge Database Retrieved Information Queries Database to make informed decisions Virtual Coach Knowledge Service Modeler

12 Manufacturing

13 Aerospace

14 Energy

15 Health Care

16 Contact Information Website: Follow us on LinkedIn: Like us on Facebook: YouTube:

17 Manual Tagging is a Behavior Modification Problem conceptclassifier automates the tagging process to remove the behavior modification problem of manual tagging for Governance, Findability and Migration conceptclassifier increases productivity, improves Findability, automates Governance, migrates content Organizes intellectual assets and provides a factor of improvement for end users to find company information with an automated tagging approach for any search platform Improves ability to target content through EMM/TS with 2013 Search and SharePoint 2010/2013 Improves SharePoint Portal Adoption Aligns content with governance polices and federally mandated requirements Intelligently migrates content into and out of SharePoint Mitigates Risk Reduces federal governance, corporate information policy, and personally identifiable information exposures while improving ediscovery audit capabilities and ensuring alignment with content retention policies Lowers Cost of Administration Leverages native integration to Microsoft stack, manages migration of GUIDS Lowers cost of ownership to build out and administer EMM/Term Store Intelligent content migration Intuitive user interface for easy product adoption

18 A Manual Metadata Approach Will Fail 95%+ Of The Time Issue Organizational Impact Manual Approach Leads to Failure Inconsistent Risky Cumbersome - expensive Malicious compliance No perceived value for end user Less than 50% of content is correctly indexed, meta-tagged or efficiently searchable rendering it unusable to the organization. (IDC) 59% of middle managers miss valuable information every day because they can t find it or never see it (Accenture) Average cost of manually tagging one item runs from $4 - $7 per document and does not factor in the accuracy of the meta tags nor the repercussions from mis-tagged content. (Hoovers) End users select first value in list. (Perspectives on Metadata, Sarah Courier) What s in it for me? End user does not see value for organization nor risks associated with litigation and non- conformance to policies. Less than 14% of end users receive training. (AIIM) What have you seen Metadata will continue to be a problem due to inconsistent human behavior. The answer to consistent metadata is an automated approach that can extract the meaning from content eliminating manual metadata generation yet still providing the ability to manage knowledge assets in alignment with the unique corporate knowledge infrastructure.

19 Intelligent Knowledge Flow Expert Knowledge Transfer Discovery Machine and Concept Searching have combined artificial intelligence with expert knowledge, metadata generation, auto-classification, and taxonomy management to deliver expert knowledge transfer conceptclassifier Automatically generates semantic metadata Auto-classifies content Provides the most relevant and accurate information for Discovery Machine's Knowledge Service Modeler

20 Benefits Reduce costs, risks, and time associated with information governance, records management, data privacy, and government mandates Improve information governance - find trusted and relevant information on health and safety, asset maintenance, compliance guidelines Effective content identification and classification, regardless of content location in the enterprise Eliminate the error and cost burdens of identification and management of records Document knowledge in the context of business process to create tangible knowledge assets Increase the quality of information for decision making Automatic migration of content driven by classification of metadata

21 Smart Content Framework Getting It Right Metadata driven application and enforcement of policies - conceptclassifier has been deployed since 2003 to automatically generate metadata and use that metadata to apply and enforce policies. Most clients are using the platform to support their information governance strategy. Proven, mature functionality out of the box - The platform has been deployed in numerous sites and applications across the enterprise, including SharePoint 2007, 2010, 2013, Office 365, Documentum, Hummingbird, SQL Server, Oracle, File Shares, and web sites.

22 Industry Unique Technology Concept Searching s statistical concept identification underpins all technologies Multi-word suggestion is explicitly more valuable than single term suggestion algorithms Concept Searching provides Automatic Concept Term Extraction Triple Baseball Three Heart Organ Center Bypass Highway Avoid conceptclassifier will generate conceptual metadata by extracting multi-word terms that identify triple heart bypass as a concept as opposed to single keywords concepttaxonomymanager uses statistical concept identification to provide real-time feedback during the process of building, testing, refining, and deploying taxonomies Metadata can be used by any search engine index or any application/process that uses metadata.

23 Concept Searching Demonstration

24 What s the End Result? Concept Searching has incorporated automatic semantic metadata generation, auto-classification, and taxonomy management into Discovery Machine s Knowledge Service Modeler Virtual coaches provide guidance and actionable knowledge through the use of artificial intelligence, automatic generation of semantic metadata, auto-classification, and taxonomy management capabilities Combined leading-edge technologies Unique solution Find full details here For a comprehensive demo of the combined solution and discussion of expected ROI, please contact Ken Lemons at Concept Searching or Todd Griffith at Discovery Machine

25 Thank You Ken Lemons VP Federal Programs Concept Searching Todd Griffith CTO and Co-Founder Discovery Machine Anna Griffith CEO and Co-Founder Discovery Machine John Challis Founder and CTO/CEO Concept Searching