KNOWLEDGEARCHITECTURE:IT’SIMPORTANCETOANORGANIZATION

CombiningStrategy,DataScienceandInformaConArchitecturetoTransformDatatoKnowledge

KM2020–IV&VMay19,2016DavidMezaChiefKnowledgeArchitect©NA20S1A5IHSJo.hALnLsRIGonHTSSpREacSEeRVCEDe.nter

AGENDA

• •  OpportuniCes

– Search– Storage– DataDrivenVisualizaCon

•  QuesCons?

KnowledgeArchitecture

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“ThemostimportantcontribuConmanagementneedstomakeinthe21stCenturyistoincreasetheproducCvityofknowledgeworkandtheknowledgeworker.”PETERF.DRUCKER,1999

“Ifknowledgecancreateproblems,itisnotthroughignorancethatwecansolvethem.”ISAACASIMOV

ToconvertdatatoknowledgeaconvergenceofKnowledgeManagement,InformaConArchitectureandDataScienceisnecessary.

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KnowledgeManagement

DataScienceInformaConArchitecture

KnowledgeArchitecture•  Thepeople,processes,andtechnologyofdesigning,implemenCng,andapplying

theintellectualinfrastructureoforganizaCons.

•  Whatisanintellectualinfrastructure?

•  ThesetofacCviCestocreate,capture,organize,analyze,visualize,present,

anduClizetheinformaConpartoftheinformaConage..

•  InformaCon+Contexts=Knowledge

•  InformaConArchitecture+KnowledgeManagement+DataScience=Knowledge

Architecture

•  KMwithoutapplicaConsisempty(StrategyOnly)

•  ApplicaConswithoutKAareblind(ITbasedKM)

•  DataSciencetransformyourdatatoknowledge

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AreasofOpportunity

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•  Search•  Storage•  DataDrivenVisualizaCon

“Wehaveanopportunityforeveryoneintheworldtohaveaccesstoalltheworld’sinformaCon.Thishasneverbeforebeenpossible.WhyisubiquitousinformaConsoprofound?Itisatremendousequalizer.InformaConispower.”ERICSCHMIDT(FORMERCEOOFGOOGLE)

Opportunity1:Search

46%Workerscan’tfindthe

30%oftotalR&Dspendis

54%ofdecisionsaremade

informaContheyneed wastedduplicaCng withincomplete,almosthalftheCme. researchandwork inconsistentand previouslydone. inadequateinformaConSource:IDC

Source:Na+onalBoardofPatentsand Source:InfoCentricResearchRegistra+on(PRH),WIPO,IFA

Google It!

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CourtesyofSocMedSean.com

PageRankByTheNumbers

Google 5Billionqueriesperday

Enterprise 1000queriesperday

WhatWeAreLookingFor

NASA SEARCH EVALUATION

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•  There is No One Solution

•  Master Data Management Plan is essential

•  Identify Critical Data

•  Develop Standards for Government and Contractor created data

TOPUSERREQUIREMENTS

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•  SemanCcsearch

•  Clusteringortopicmodelingalgorithms

•  FaceCng•  Repositoryspecificsearches•  Abilitytosavesearches•  Alerts

RepositorySpecific

Clustering

Save,Alerts

FacetFilter

Opportunity2:StorageandAccess

DocumenttoGraph

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PATTERNSEMERGE

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LESSON LEARNED DATABASE

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2031lessonssubmikedacrossNASA.FilterbydateandCenteronly.UsefulinformaConstoredindatabase.

@davidmeza1

TOPIC MODELING

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Topicmodelsarebasedupontheideathatdocumentsaremixturesoftopics,whereatopicisaprobabilitydistribuConoverwords.Blei,DavidM.2011.“IntroducContoProbabilisCcTopicModels.”Communica+onsoftheACM. DavidBleihomepage-hkp://www.cs.columbia.edu/~blei/topicmodeling.html

LDAModelfromBlei(2011)

@davidmeza1

GRAPHMODELOFLESSONLEARNEDDATABASE

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GRAPHMODELOFLESSONLEARNEDDATABASE

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GRAPHMODELOFLESSONLEARNEDDATABASE

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OPPORTUNITY3:DATADRIVENVISUALIZATION

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WHATCOULDYOUACCOMPLISHIFYOUCOULD:

•  Empowerfasterandmoreinformeddecision-making

•  Leveragelessonsofthepasttominimizewaste,rework,re-invenConandredundancy

•  Reducethelearningcurvefornewemployees

•  EnhanceandextendexisCngcontentanddocumentmanagementsystems

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JSCKnowledgeArchitectureServices:§  RStudioServer

§  ShinyServer

§  Neo4jandMongoDB

§  VisualizaConServices

§  DataAnalysis

§  GoldfireSearch

§  WikiFarm

§  CodeSharingandProjectcollaboraCon

§  Training

Contact Information

David Meza – david.meza-1@nasa.gov

Twitter - @davidmeza1

Linkedin - hkps://www.linkedin.com/pub/david-meza/16/543/50b

Github – davidmeza1

Blog davidmeza1.github.io

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Contents

©2015IHS.ALLRIGHTSRESERVED. 34ReportName/Month2015

QUESTIONS?