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001 9780429434891
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006 m o d
007 cr cnu---unuuu
008 181103s2018 xx ob 001 0 eng d
040 _aOCoLC-P
_beng
_epn
_cOCoLC-P
020 _a9780429786365
020 _a0429786360
020 _a9780429434891
_q(electronic bk.)
020 _a0429434898
_q(electronic bk.)
020 _a9780429786358
_q(electronic bk. ;
_qEPUB)
020 _a0429786352
_q(electronic bk. ;
_qEPUB)
020 _a9780429786341
_q(electronic bk. ;
_qMobipocket)
020 _a0429786344
_q(electronic bk. ;
_qMobipocket)
020 _z9781138307285
_q(hbk.)
024 7 _a10.1201/9780429434891
_2doi
035 _a(OCoLC)1061126473
035 _a(OCoLC-P)1061126473
050 4 _aHD69.P75
_bD374 2019eb
072 7 _aBUS
_x101000
_2bisacsh
072 7 _aCOM
_x021030
_2bisacsh
072 7 _aMAT
_x029000
_2bisacsh
072 7 _aUN
_2bicssc
082 0 4 _a658.4/040285
100 1 _aSpalek, Seweryn.
245 1 0 _aData analytics in project management /
_cSeweryn Spalek.
260 _aMilton :
_bAuerbach Publications,
_c2018.
300 _a1 online resource (235 pages).
336 _atext
_btxt
_2rdacontent
336 _astill image
_bsti
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
490 1 _aData analytics applications
505 8 _aCover; Half Title; Series Page; Title Page; Copyright Page; Contents; About the Editor; Contributors; Chapter 1 Introduction; Chapter 2 Why Data Analytics in Project Management?; Taking Root; The View from 10,000 Meters; Data Warehousing; Project Manager; Project Office; Chief Operating Officer (COO); Executive Committee; Descriptive, Predictive, and Prescriptive Data Analytics; Data Analytics 3.0?; Once Again: Why Data Analytics in Project Management?; References; Chapter 3 Data Analytics Risk: Lost in Translation?; The Risk Management Process; Establishing Tolerance.
505 8 _aRisk and Data Analytics LanguageData Collection Risk; What Data Do We Need and Why? (Risk #1-The Need); Is the Data Properly Sourced? (Risk #2-The Source); Is the Data Consistent (Risk #3-Consistency Risks); Risk Collection; Engage Meaningful Stakeholders; Gather Risk Insight Consistently; Focus on the Mission; Exploratory vs. Confirmatory vs. Predictive; Exploratory Risk in Data Analytics; Availability; Integrity; Exploratory Trends; Confirmatory Analytics Risks; Data Availability; Data Integrity; Data Trends; Past Performance Is Not Inherently Indicative of Future Results.
505 8 _aPredictive Risk in Data AnalyticsThe Future Is Unknown; The Future Environment Is Only Partially Knowable; Predictive Analysis and Consequences; Risk in Communicating Results; When to Share; How to Share; With Whom to Share the Message; Solving and Resolving Our Data Analytics Risks; Will It Work Consistently?; Does It Generate More Harm than Good?; Does It Allow for the Same Outputs as Other Data in the Analysis?; Success!; Chapter 4 Analytical Challenges of a Modern PMO; Toward an Analytically Matured PMO; Methods and Tools; Human Resources; Project Environment; Project Knowledge Management.
505 8 _aThe PMO as the Multilevel Data Analysis CenterProjects, Portfolio, and Organization; Project Level; Portfolio Level; Organization Level; Operations, Tactics, and Strategy; Operational Level; Tactical Level; Strategic Level; The Challenge of Multi-Sourced Data; References; Chapter 5 Data Analytics and Project Portfolio Management; Introduction; Project Portfolio Management and Data Analytics; Levels of Analysis; Descriptive analysis-this helps answer the question, "What has happened?"; Predictive analysis-this helps answer a more important question, "What will happen?"
505 8 _aPrescriptive analysis-this helps answer a more difficult question, "What we should do?"Approach; Portfolio Reports: Portfolio Bubble Charts; Benefits of Portfolio Bubble Charts; Data Needed; PPM and Decision-Making; Project Portfolio Management as a Rational Decision- Making Process; Project Portfolio Management: Practice and Context; Main Roles in Data Analytics; Role; Responsibilities; Requirements; Data Analytics and Project Portfolio Performance; Conclusions; References; Chapter 6 Earned Value Method; Introduction; EVM Methods; Descriptive EVM; Predictive EVM; Earned Value Graphs.
500 _aInterpreting Earned Value Results.
520 _aThis book aims to help the reader better understand the importance of data analysis in project management. Moreover, it provides guidance by showing tools, methods, techniques and lessons learned on how to better utilize the data gathered from the projects. First and foremost, insight into the bridge between data analytics and project management aids practitioners looking for ways to maximize the practical value of data procured. The book equips organizations with the know-how necessary to adapt to a changing workplace dynamic through key lessons learned from past ventures. The book's integrated approach to investigating both fields enhances the value of research findings.
588 _aOCLC-licensed vendor bibliographic record.
650 0 _aProject management
_xData processing.
650 0 _aProject management
_xStatistical methods.
650 7 _aBUSINESS & ECONOMICS
_xProject Management.
_2bisacsh
650 7 _aCOMPUTERS
_xDatabase Management
_xData Mining.
_2bisacsh
650 7 _aMATHEMATICS
_xProbability & Statistics
_xGeneral.
_2bisacsh
856 4 0 _3Taylor & Francis
_uhttps://www.taylorfrancis.com/books/9780429434891
856 4 2 _3OCLC metadata license agreement
_uhttp://www.oclc.org/content/dam/oclc/forms/terms/vbrl-201703.pdf
999 _c74650
_d74650