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KNOWLEDGE ARCHITECTURE: IT’S IMPORTANCE TO AN ORGANIZATION Combining Strategy, Data Science and InformaCon Architecture to Transform Data to Knowledge KM2020 – I V & V May 19, 2016 David Meza Chief Knowledge Architect © NA 20 S 1A 5 IHS Jo . h AL n L s RIG on HT S Sp RE ac SEe RVC ED e . nter

KNOWLEDGE ARCHITECTURE: IT’S IMPORTANCE TO AN … · KNOWLEDGE ARCHITECTURE: IT’S IMPORTANCE TO AN ORGANIZATION. Combining Strategy, Data Science and Informaon Architecture to

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Page 1: KNOWLEDGE ARCHITECTURE: IT’S IMPORTANCE TO AN … · KNOWLEDGE ARCHITECTURE: IT’S IMPORTANCE TO AN ORGANIZATION. Combining Strategy, Data Science and Informaon Architecture to

KNOWLEDGEARCHITECTURE:IT’SIMPORTANCETOANORGANIZATION

CombiningStrategy,DataScienceandInformaConArchitecturetoTransformDatatoKnowledge

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

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AGENDA

• •  OpportuniCes

– Search– Storage– DataDrivenVisualizaCon

•  QuesCons?

KnowledgeArchitecture

2

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“ThemostimportantcontribuConmanagementneedstomakeinthe21stCenturyistoincreasetheproducCvityofknowledgeworkandtheknowledgeworker.”PETERF.DRUCKER,1999

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“Ifknowledgecancreateproblems,itisnotthroughignorancethatwecansolvethem.”ISAACASIMOV

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ToconvertdatatoknowledgeaconvergenceofKnowledgeManagement,InformaConArchitectureandDataScienceisnecessary.

5

KnowledgeManagement

DataScienceInformaConArchitecture

Page 6: KNOWLEDGE ARCHITECTURE: IT’S IMPORTANCE TO AN … · KNOWLEDGE ARCHITECTURE: IT’S IMPORTANCE TO AN ORGANIZATION. Combining Strategy, Data Science and Informaon Architecture to

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

6

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AreasofOpportunity

7

•  Search•  Storage•  DataDrivenVisualizaCon

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

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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

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Google It!

11

CourtesyofSocMedSean.com

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PageRankByTheNumbers

Google 5Billionqueriesperday

Enterprise 1000queriesperday

WhatWeAreLookingFor

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NASA SEARCH EVALUATION

13

•  There is No One Solution

•  Master Data Management Plan is essential

•  Identify Critical Data

•  Develop Standards for Government and Contractor created data

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TOPUSERREQUIREMENTS

14

•  SemanCcsearch

•  Clusteringortopicmodelingalgorithms

•  FaceCng•  Repositoryspecificsearches•  Abilitytosavesearches•  Alerts

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RepositorySpecific

Clustering

Save,Alerts

FacetFilter

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Opportunity2:StorageandAccess

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DocumenttoGraph

18

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PATTERNSEMERGE

19

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LESSON LEARNED DATABASE

20

2031lessonssubmikedacrossNASA.FilterbydateandCenteronly.UsefulinformaConstoredindatabase.

@davidmeza1

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TOPIC MODELING

21

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

LDAModelfromBlei(2011)

@davidmeza1

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GRAPHMODELOFLESSONLEARNEDDATABASE

22

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GRAPHMODELOFLESSONLEARNEDDATABASE

23

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GRAPHMODELOFLESSONLEARNEDDATABASE

24

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OPPORTUNITY3:DATADRIVENVISUALIZATION

25

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31

WHATCOULDYOUACCOMPLISHIFYOUCOULD:

•  Empowerfasterandmoreinformeddecision-making

•  Leveragelessonsofthepasttominimizewaste,rework,re-invenConandredundancy

•  Reducethelearningcurvefornewemployees

•  EnhanceandextendexisCngcontentanddocumentmanagementsystems

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32

JSCKnowledgeArchitectureServices:§  RStudioServer

§  ShinyServer

§  Neo4jandMongoDB

§  VisualizaConServices

§  DataAnalysis

§  GoldfireSearch

§  WikiFarm

§  CodeSharingandProjectcollaboraCon

§  Training

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Contact Information

David Meza – [email protected]

Twitter - @davidmeza1

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

Github – davidmeza1

Blog davidmeza1.github.io

33

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Contents

©2015IHS.ALLRIGHTSRESERVED. 34ReportName/Month2015

QUESTIONS?


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