Panel #25: Unlocking the Value of Acquisition DataNaval Postgraduate SchoolApproved for public release; distribution is unlimited

Drezner, Jeff

Lee, Yang

Wang, Richard

English

Panel #25: Unlocking the Value of Acquisition DataApproved for public release; distribution is unlimited.Naval Postgraduate Schoo

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Calhoun: The NPS Institutional ArchiveDSpace RepositoryReports and Technical Reports Faculty and Researchers' Publications2020-04-27Acquisition Data Practice in the Era ofInterconnected Digital Transformation?Wang, Richard; Lee, Yang; Drezner, JeffMonterey, California. Naval Postgraduate Schoolhttp://hdl.handle.net/10945/64777This publication is a work of the U.S. Government as defined in Title 17, UnitedStates Code, Section 101. Copyright protection is not available for this work in theUnited States.Downloaded from NPS Archive: Calhoun1April 2020Acquisition Data Practicein the Era of Digital TransformationThe Rise of the Chief Data OfficerDr. Yang W. LeeProfessor of Information ManagementFaculty Director of MS in Business Analytics ProgramNortheastern UniversityDeputy Director, MIT CDOIQ ProgramDr. Richard Y. WangExecutive Director, iCDO @ UA Little RockProfessor, UA Little Rock EIT CollegeDirector, MIT CDOIQ ProgramAcquisition processes demandupdated data practiceRealities of Acquisition Processes:• Acquisition calls upon entire supply and demand value chain• Crosses intra- and inter-organizational bounders• Local actions have global implicationsConventional data practice does not meet the challenges of contemporary acquisition processes, policy, and implementation.• Limitations of legacy data practices can yield the death of supply chain management• Data management solutions:• Bottom-up approach• Adopting disruptive IT infrastructureApril 2020 (c) MIT CDOIQ Program 2Acquisition processes demandupdated data practiceRealities of Acquisition Processes:• Acquisition calls upon entire supply and demand value chain• Crosses intra- and inter-organizational bounders• Local actions have global implicationsConventional data practice does not meet the challenges of contemporary acquisition processes, policy, and implementation.• Limitations of legacy data practices can be the death of supply chain management• Data management solutions:• Bottom-up approach• Adopting disruptive IT infrastructureApril 2020 (c) MIT CDOIQ Program 3It is important to appreciate the interconnected nature of acquisition in order to understand and more effectively implem nt acquisition pro esses.Novel data practice model for the interconnected nature of acquisitionApril 2020 (c) MIT CDOIQ Program 4Three fundamental perspectives applied to acquisition data practice1. Practice (Bourdieu, 1990; Schon, 1985) and the practice of data work (Lee et al., 2006; Lee et al., 2014; Kwon et al., 2014)2. Problem identification and problem solving (Von Hippel et al., 2016; Schon 1985; Lee, 2004)3. Organizational strategy (Tanriverdi et al., 2020; Mentzer et al., 2001)1. PracticeFundamental practice theories and literature state that:• There is a critical relationship between knowing & doing, interpreting & using, symbolic mastery & practical mastery• Data quality management significantly influences big data analyticsApril 2020 (c) MIT CDOIQ Program 5Bourdieu, 1990; Kwon et al., 20141. Practice cont’dBased on these studies, we define two fundamental concepts for data practice in acquisition:1. Understand and explicitly communicate the practitioner’s logic, constraints and opportunities.2. Consider the established data practices (e.g. data quality products, data quality practice/governance, data quality management, etc.)Understand the interconnected data & interconnected data contextApril 2020 (c) MIT CDOIQ Program 6April 2020 (c) MIT CDOIQ Program 72. Problem Identification& Problem SolvingFundamental problem solving theories and literature state that:• It is critical to identify the problem in order to solve a problem• Properly identifying the problem allows one to efficiently engaging various stakeholdersVon Hippel et al., 2016; Schon, 1983April 2020 (c) MIT CDOIQ Program 82. Problem Identification& Problem Solving cont’dIn the context of acquisition, we argue that:• It is critical to understand the interconnected nature of the problem in order to find the appropriate solution• Doing so triggers engagement from more diverse resources and divisionsApril 2020 (c) MIT CDOIQ Program 93. Organizational StrategyIn the era of digital transformation & big data, organizations must be able to adapt processes and make data-powered business decisions.To achieve this, several issues to consider:• Bureaucratic control, internal political/cultural constraints, external restrictions (by-products of organizational inertia)• Trust in industry relationships• Supply chain integration issues• IT use in supply-chain relationshipsApril 2020 (c) MIT CDOIQ Program 103. Organizational Strategy cont’dIn the context of acquisition, we argue that:• It is critical to consider the organizational strategy from an interconnected, strategic perspective• Interorganizational boundaries, relationships, trust, integration, politics & constraints• Doing so allows organization to assess alignment between strategic goals and business processes with its data needsNovel data practice model for the interconnected nature of acquisitionApril 2020 (c) MIT CDOIQ Program 11Three fundamental perspectives applied to acquisition data practice1. Practice and the practice of data work2. Problem identification and problem solving3. Organizational strategyApril 2020 (c) MIT CDOIQ Program 12Applying Acquisition Data Practice toCDO PracticeThree common questions from CDOs:1. “How does firm know what data to collect?”2. “What gaps exist in the data to answer questions?”3. “How should the data be organized to support different kinds of analytics?”Assessing these questions from a novel data practice perspectiveallows us to  comprehensively address interconnected acquisition processes, policy and implementationApril 2020 (c) MIT CDOIQ Program 13Applying Acquisition Data Practice toCDO PracticeThree common questions from CDOs:1. “How does firm know what data to collect?”2. “What gaps exist in the data to answer questions?”3. “How should the data be organized to support different kinds of analytics?”➢I Practice➢II Problem identification and problem solving➢III Organizational strategyApril 2020 (c) MIT CDOIQ Program 14“How do we know what data to collect?”I. PracticePractitioner’s logic: The goal is to build IT infrastructure for analytics (example).• By communicating goal explicitly, the answer to question can be directed explicitly towards this goal.• Communicating explicitly allows the organization to consider whether or not this is appropriate goal for the organization, or at this time.Without considering established data practices, one might answer this question with data items, such as customer data, product data, sales data, vendor data, etc.However, from a data quality perspective, for example, this question can be more comprehensively answered by further asking:Is the data relevant?E.g., Did internal and external stakeholders participate in what data should be collected?Is the data complete?E.g., What is the scope of the data beyond short term needs (time horizon)?Does the data add value?E.g., Future organizational goals and strategies, for example for future data consumers?April 2020 (c) MIT CDOIQ Program 15“How do we know what data to collect?”II. Problem identification and solvingThis question would be better served if we first identify a problem which the data collected can address. • For example, what collective inquiry does the organization have?• Furthermore, what mechanism is in place to report and summarize that inquiry?April 2020 (c) MIT CDOIQ Program 16“How do we know what data to collect?”III. Organizational StrategyThis question would be better served by also asking, for example:• Towards what organizational goal, and by what strategy?• Are stakeholders invested in this goal?• What are the interorganizational boundaries that may limit this?• How will this data be shared across these boundaries?• Who controls the data? Who controls the data collection process?April 2020 (c) MIT CDOIQ Program 17“What gaps exist in the data?”I. PracticePractitioner’s logic: More explicit communication of scope and boundary – are these business questions for the organization as a whole, or for the department? Analytic questions? Short-term questions or long-term questions?• From a data quality perspective, is the data complete?E.g., Data complete to answer explicitly communicated questions?• From a data quality practice perspective, who will address the gaps that exist, and is there a process and mechanism to report these gaps?April 2020 (c) MIT CDOIQ Program 18“What gaps exist in the data?”II. Problem identification and solvingThis question would be better served by identifying a specific problem and solution, for example:• Problem identified: Which questions are we asking? Questions about vendors, customers, products, future market, etc.• Solution identified: What kind of gaps exist? Not enough data? Difficulty in use of data?April 2020 (c) MIT CDOIQ Program 19“What gaps exist in the data?”III. Organizational StrategyThis question would be better served by also asking, for example:• Are these questions addressing the organizational goals or operations for specific departments?• Are there known and unknown gaps hidden due to interorganizational boundaries?April 2020 (c) MIT CDOIQ Program 20“How should the data be organizedto support different kinds of analytics?”I. PracticePractitioner’s logic: This type of question typically arises when data collected with originally different purposes are to be used for new purposes, typically for enhanced analysis and analytical purposes.Thus, it is critical for the context of the data and analytics – the logic, constraints, and opportunities – to be explicitly communicated as data gets used and reused.This question inherently focuses on data storage alone, but from a data quality perspective, the organization could also consider data consumer’s retrieval and use of the data:• Ease of manipulation• ConsistencyAdditionally, from a data quality practice perspective, for example, the organization could also consider whether there is a process or mechanism to report on data analytics needs and competency.April 2020 (c) MIT CDOIQ Program 21“How should the data be organizedto support different kinds of analytics?”II. Problem identification and solvingThis question is inherently bounded to a technical problem (supporting different kinds of analytics) for a technical solution (finding ways to organize the data).• As such, it naturally recruits IT/analytics (problem) and data (solution) experts.• Focuses on stored data in a database or a repository.However, data is influenced by the underlying business process and organizational strategy.• Consider the interconnectivity of the data while identifying the problem and solution• Consult with other experts in addition to data, IT and analytics experts• Business experts (who oversee the underlying business process and organizational strategy) • Data consumers (who use the results from analytics and organized data)April 2020 (c) MIT CDOIQ Program 22“How should the data be organizedto support different kinds of analytics?”III. Organizational StrategyThis question would be better served by also asking, for example:• How do different analytic solutions address organizational strategies and goals?• How will different analytic solutions be aligned across interorganizational boundaries?• How will data organization influence how data consumers interact with that data?April 2020 (c) MIT CDOIQ Program 23Understanding acquisition in the context of three data practice concepts embraces the interconnected quality of data, which is at the heart of acquisition business processes and strategies1. Practice and the practice of data work2. Problem identification and problem solving3. Organizational strategyConclusionApril 2020 (c) MIT CDOIQ Program 24Conclusion - Cont’dThis novel data practice for acquisition allows organizations to:✓ Comprehensively address issues across entire supply-and-demand value chain✓ Identify localized acquisition action items and processes toward global intra- and interorganizational strategies✓ Engage and communicate broadly on how acquisition impacts both upstream and downstream activities, resources and personnelApril 2020 (c) MIT CDOIQ Program 25Future Implications• The work described here demonstrates a powerful new approach to data practice for acquisition.• This work paves the way for future studies examining best practices in acquisition processes, policy and implementation.THANK YOU!Yang Leey.lee@northeastern.eduwww.mitcdoiq.orgRichard Wangrwang@mit.eduwww.mitcdoiq.orgApril 2020 (c) MIT CDOIQ Program 26Methods & Data CollectionApril 2020 (c) MIT CDOIQ Program 27• Data collection was done, in part, in a collaborative effort by RAND research team, MIT CDOIQ Program, and Northeastern University.• Analysis, interpretation and theory grounding are by the authors alone.ReferencesApril 2020 (c) MIT CDOIQ Program 28• Bourdieu, P., (1990), The Logic of Practice. Stanford University Press.• Gunasekaran, A., & Ngai, E. W. (2004). Information systems in supply chain integration and management. European journal of operational research, 159(2), 269-295.• Hannan, M. et al. (1977). The population ecology of organizations. American Journal of Sociology, 82(5), 929-964.• Kumar, N. (1996). The power of trust in manufacturer-retailer relationships. Harvard business review, 74(6), 92.• Kwon, O., Lee, N., & Shin, B. (2014). Data quality management, data usage experience and acquisition intention of big data analytics. International journal of information management, 34(3), 387-394.• Lee, Y. (2004) Crafting Rules: Context-reflective Data Quality Problem Solving. Journal of MIS, January. • Lee, Y. et al., (2006) Journey to Data Quality, MIT Press.• Lee, Y. et al. (2014) A cubic Framework for the Chief data Officer: Succeeding in a World of Big Data. MIS Quarterly Executive, March (13:1) • Lyall, A. et al. (2018). The Death of Supply Chain Management. Harvard Business Review Blog, June 15, 2018.• Masli, A., et al. (2016) Senior Executives’ IT Management Responsibilities: Serious IT-related Deficiencies and CEO/CFO Turnover. MIS Quarterly (40;3). • McAfee, A. et al. (2008) Investment in the IT that Makes a Competitive Difference. Harvard Business Review (86:7/8).• Mentzer, J. T. et al. (2001). Defining supply chain management. Journal of Business logistics, 22(2), 1-25.• Porter, M. E. (2008). The five competitive forces that shape strategy. Harvard business review, 86(1), 25-40.• Schon, D. (1983) The Reflective Practitioner: How Professional think in Action, Basic Books.• Subramani, M. (2004). How do suppliers benefit from information technology use in supply chain relationships?. MIS quarterly, 45-73.• Tanriverdi, H. et al. (2020) Corporate Strategy Changes and Information Technology Control Effectiveness in MultibusinessFirms. MIS Quarterly. • Von Hippel, E. et al. (2016) Crossroads -Identifying viable “Need-solution pairs”: problem solving without Problem formulation, Organizational Science 27 (1): 207-221.  • Zhang, H. et al. (2017) Chief Data Officer Appointment and Origin: A theoretical Perspective.  Twenty-third Americas Conference on Information Systems.

Acquisition Data Practice in the Era of Interconnected Digital Transformation?

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Acquisition Data Practice in the Era of Interconnected Digital Transformation?

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