1,957
Views
9
CrossRef citations to date
0
Altmetric
Research Article

Data-driven Begins with DATA; Potential of Data Assets

ORCID Icon, , &

References

  • Laney D, Jain A. 2017. 100 data and analytics predictions through 2021. Gartner report ID: G00332376. Gartner;
  • Redman TC. Data driven: profiting from your most important business asset. Boston(Mass): Harvard Business Press; 2008.
  • Fisher T. The data asset: how smart companies govern their data for business success. Hoboken (NJ): John Wiley & Sons; 2009.
  • Ackoff RL. From data to wisdom. J Appl Syst Anal. 1989;16:3–9.
  • Rowley JE. The wisdom hierarchy: representations of the DIKW hierarchy. J Inf Sci. 2007;33(2):163–80. doi:https://doi.org/10.1177/0165551506070706.
  • Reinsel D, Gantz J, Rydining J. The digitization of the world. From edge to core. An IDC White Paper - #US44413318. Framingham (MA): Seagate; 2018. 2019.
  • Devakunchari R. Analysis on big data over the years. Int J Sci Res Publ. 2014;4:1–7.
  • Aiken P, Billings J. Monetizing data management. Unlocking the value in your organization’s most important asset. Basking Ridge (NJ): Technics Publications; 2013.
  • Chaki S. The expert’s voice in information management. Enterprise information management in practice. New York (NY): Springer; 2015.
  • Davenport TH, Patil DJ. Data scientist: the sexiest job of the 21st century. Harv Bus Rev. 2012;90:70–76.
  • Knapp M, Hasibether F. Material master data quality. Proceedings of the 17th International Conference on Concurrent Engineering, IEEE; 2011; Aachen, Germany; 1–8.
  • Schaffer T, Leyh C. Master data quality in the era of digitalization – toward inter-organizational master data quality in value networks: a problem identification. In: Piazolo F, Geist V, Brehm L, Schmidt R. (Eds.) Innovations in Enterprise Information Systems Management and Engineering. ERP Future 2016. Lecture Notes in Bus. Inf. Proc; 2017; Hagenberg, Austria; 285:99–113.
  • Silvola R, Jaaskelainen O, Kropsu-Vehkapera H, Haapasalo H. Managing one master data – challenges and preconditions. Ind Manage Data Syst. 2011;111(1):146–62. doi:https://doi.org/10.1108/02635571111099776.
  • Silvola R, Tolonen A, Harkonen J, Haapasalo H. Defining one product data for a product. Int J Bus Inf Syst. 2019;30:489–520.
  • Allen M, Cervo D. Multi-domain master data management. Advanced MDM and data governance. Waltham (MA): Elsevier; 2015.
  • Hannila H, Tolonen A, Harkonen J, Haapasalo H. 2019. Product and supply chain related data, processes and information systems for product portfolio management. Int J Prod Lifecyc Manage. In press.
  • Smith HA, McKeen JD. Developments in practice XXX: master data management: salvation or snake oil? Comm Assoc Inf Syst. 2008;23:63–72.
  • Walker S, Moran M. Create powerful customer experiences with a 360-degree view of your products. Gartner report ID G00328027. Gartner; 2017.
  • Porter ME, Heppelmann JE. How smart, connected products are transforming competition. Harv Bus Rev. 2014;92:64–88.
  • Cooper RG, Edgett SJ, Kleinschmidt EJ. New product portfolio management: practises and performance. J Prod Innov Manage. 1999;16(4):333–51. doi:https://doi.org/10.1111/1540-5885.1640333.
  • Lahtinen N, Mustonen E, Harkonen J. Commercial and technical productization for fact-based product portfolio management over lifecycle. IEEE Trans Eng Manage. 2019; 1–13. in press. doi:https://doi.org/10.1109/TEM.17.
  • Laney D. 3D data management: Controlling data volume, velocity and variety. Stamford (CT): META Group Research Note 6; 2001.
  • Tolonen A, Harkonen J, Verkasalo M, Haapasalo H. Product portfolio management process over horizontal and vertical portfolios. Int J Prod Life Cycle Manage. 2015a;8(3):189–215. doi:https://doi.org/10.1504/IJPLM.2015.074132.
  • Tolonen A, Shahmarichatghieh M, Harkonen J, Haapasalo H. Product portfolio management – targets and key performance indicators for product portfolio renewal over the life cycle. Int J Prod Econ. 2015b;170:468–77. doi:https://doi.org/10.1016/j.ijpe.2015.05.034.
  • Wang S, Krisch U. A foundation for building a data-driven culture. Appl Marketing Analyt. 2019;4:238–52.
  • Pugna IB, Dutescu A, Stanila OG. Corporate attitudes towards big data and its impact on performance management: A qualitative study. Sustain (Switzerland). 2019;11:1–26.
  • Aiken P. Experience: succeeding at data management-BigCo attempts to leverage data. J Data Inform Q. 2016;7(1–2):1–35. doi:https://doi.org/10.1145/2893482.
  • Porter ME. What is a strategy. Harv Bus Rev. 1996;74:61–78.
  • Gandomi A, Haider M. Beyond the hype: big data concepts, methods, and analytics. Int J Inform Manage. 2015;35:137–44. doi:https://doi.org/10.1016/j.ijinfomgt.2014.10.007.
  • LaValle S, Lesser E, Shockley R, Hopkins MS, Kruschwitz N. Big data, analytics and the path from insights to value. MITSloan Manage Rev. 2011;52:21–31.
  • Thusoo A, Sarma JS. Creating a data-driven enterprise with DataOps. Insights from facebook, uber, linkedin, twitter, and eBay. Sebastopol (CA): O’Reilly Media; 2017.
  • Beal W. Webopedia. [accessed 2019 May 15]. https://www.webopedia.com/TERM/S/structured_data.html & https://www.webopedia.com/TERM/U/unstructured_data.html.
  • Kumar Das T, Mishra MR. A study on challenges and opportunities in master data management. Int J Database Manage Syst. 2011;3(2):129–39. doi:https://doi.org/10.5121/ijdms.2011.3209.
  • Myung S Master data management in PLM for the enterprise scope. In Bouras A, Eynard B, Foufou S, Thoben K.-D. (Eds.) Product Lifecycle Management in the Era of Internet of Things. PLM 2015. IFIP Adv. in Inform. and Comm. Tech; 2016; Doha, Qatar; 467:771–79.
  • Vilminko-Heikkinen R, Pekkola S. Master data management and its organizational implementation: an ethnographical study within the public sector. J Enterp Infor Manage. 2017;30(3):454–75. doi:https://doi.org/10.1108/JEIM-07-2015-0070.
  • Kropsu-Vehkapera H. Enhancing understanding of company-wide product data management in ICT companies. Acta Universitatis Ouluensis C Technica 418. University of Oulu; 2012; Oulu, Finland.
  • Waddington D. Adoption of data governance by business. DM Review. 2008;18:32–32.
  • Haug A, Arlbjorn JS, Zachariassen F, Schlichter J. Master data quality barriers: an empirical investigation. Indust Manage Data Syst. 2013;113(2):234–49. doi:https://doi.org/10.1108/02635571311303550.
  • Terzi S, Bouras A, Dutta D, Garetti M, Kiritsis D. Product lifecycle management – from its history to its new role. Int J Prod Lifecycle Manag. 2010;4(4):360–89. doi:https://doi.org/10.1504/IJPLM.2010.036489.
  • Thornthwaite W. Dealing with data quality: don’t just sit there, do something. In: Kimball R, Ross M, Becker B, Mundy J, Thornthwaite W. (Eds). The Kimball group reader: relentlessly practical tools for data warehousing and business intelligence. Second ed. John Wiley & Sons; 2015. Indianapolis (IN); p. 2009.
  • Borgia E. The internet of things vision: key features, applications and open issues. Comp Comm. 2014;54:1–31. doi:https://doi.org/10.1016/j.comcom.2014.09.008.
  • Kiritsis D. Closed-loop PLM for intelligent products in the era of the internet of things. Comp-Aided Design. 2011;43(2011):479–501. doi:https://doi.org/10.1016/j.cad.2010.03.002.
  • Li J, Tao F, Cheng Y, Zhao L. Big data in product lifecycle management. Int J Adv Manuf Tech. 2015;81:667–84. doi:https://doi.org/10.1007/s00170-015-7151-x.
  • EIU. 2017. Economist intelligence unit. The internet of things business index 2017 – transformation in motion. The economist. [accessed 2019 May 20]. https://perspectives.eiu.com/technology-innovation/iot-business-index-2017-transformation-motion.
  • Borgia O, Fanciullacci N, Franchi S, Tucci M. The use of product information along its entire lifecycle: A practical framework for continuous development. Iss Ind Syst Eng. 2015;2015:177–82.
  • Jetson J, Nelis J. Business process management: Practical guidelines to successful implementations. Amsterdam, Netherlands: Elsevier; 2008.
  • Kimball R. The evolving role of the enterprise data warehouse in the era of big data analytics. In: Kimball, R, Ross M, Becker B, Mundy J, Thornthwaite W. editor. The Kimball group reader: relentlessly practical tools for data warehousing and business intelligence. Second ed. John Wiley & Sons; 2015. Indianapolis (IN); p. 2011.
  • Lans RF. Data virtualization for business intelligence systems. Revolutionizing data integration for data warehouses. Waltham (MA): Elsevier; 2012.
  • Marchetta G, Mayer F, Forradellas R. A reference framework following a proactive approach for product lifecycle management. Comp Indust. 2011;62:672–83. doi:https://doi.org/10.1016/j.compind.2011.04.004.
  • Sriti MF, Assouroko I, Ducellier G, Boutunaud P, Eynard B. Ontology-based approach for product information exchange. Int J Prod Lifecycle Manage. 2015;8(1):1–23. doi:https://doi.org/10.1504/IJPLM.2015.068011.
  • Anderson C. Creating a data-driven organization: practical advice from the trenches. Sebastopol (CA): O’Reilly Media; 2015.
  • Baghi E, Schlosser S, Ebner V, Otto B, Oesterle H. Towards a decision model for master data application architecture. IEEE Computer Society. 47th Hawaii International Conference on System Science; 2014; Waikoloa, HI, USA.
  • Emmanouilidis C, Beroncelj L, Bevilacqua M, Tedeschi S, Ruiz-Carcel C. Internet of things – enabled visual analytics for linked maintenance and product lifecycle management. IFAC PapersOnLine. 2018;51(11):435–40. doi:https://doi.org/10.1016/j.ifacol.2018.08.339.
  • Bonnet P. Enterprise data governance: reference and master data management semantic. Hoboken (NJ): John Wiley & Sons; 2010.
  • Kropsu-Vehkapera H, Haapasalo H. Defining product data views for different stakeholders. J Comp Inf Syst. 2011;52:61–72.
  • Ofner MH, Straub K, Otto B, Oesterle H. Management of the master data lifecycle: a framework for analysis J. Ent Inf Manage. 2013;26(4):472–91. doi:https://doi.org/10.1108/JEIM-05-2013-0026.
  • Tolonen A. Product portfolio management over horizontal and vertical portfolios. Acta Universitatis Ouluensis C Technica 574. University of Oulu; 2016
  • Cooper RG, Edgett SJ, Kleinschmidt EJ. Portfolio management for new product development: results of an industry practices study. R&D Manage. 2001;31(4):361–80. doi:https://doi.org/10.1111/1467-9310.00225.
  • Saaksvuori A, Immonen A. Product lifecycle management. 3rd ed. Berlin Heidelberg, Germany: Springer; 2008.
  • Kropsu-Vehkapera H, Haapasalo H, Harkonen J, Silvola R. Product data management practices in high-tech companies. Ind Manage Data Syst. 2009;109(6):758–74. doi:https://doi.org/10.1108/02635570910968027.
  • Kropsu-Vehkapera H, Haapasalo H, Jaaskelainen O, Phusavat K. Product configuration management in ICT companies: the practitioners’ perspective. Techn Invest. 2011;2(4):273–85. doi:https://doi.org/10.4236/ti.2011.24028.
  • Crnkovic I, Asklund U, Dahlqvist AP. Implementing and integrating product data management and software configuration management. Artech House; Norwood (MA): 2003.
  • Maitlis S, Ozcelik H. Toxic decision processes: a study of emotion and organizational decision making org. Sci. 2004;15:375–93.
  • McAfee A, Brynjolfsson E. Big data: the management revolution. Harv Bus Rev. 2012;90:61–68.
  • Brous P, Janssen M, Vilminko-Heikkinen R. Coordinating decision-making in data management activities: a systematic review of data governance principles. 5th International Conference on Electronic Government and the Information Systems Perspective (EGOV); 2016; Porto, Portugal. in Lect. Notes Comp. Sci. 2016;9820. 115–25.
  • Kwon O, Lee N, Shin B. Data quality management, data usage experience and acquisition intention of big data analytics. Int J Inform Manage. 2014;34(3):387–94. doi:https://doi.org/10.1016/j.ijinfomgt.2014.02.002.
  • Muntean M, Surcel T. Agile BI – the future of BI. Inform Econom. 2013;17(3):114–24. doi:https://doi.org/10.12948/issn14531305/17.3.2013.10.
  • Storey VC, Dewan RM, Freimer M. Data quality: setting organizational policies dec. Supp Syst. 2012;54:434–42. doi:https://doi.org/10.1016/j.dss.2012.06.004.
  • Otto B, Wende K, Schmidt A, Osl P. Towards a framework for corporate data quality management. Proceedings of 18th Australasian Conference on Information Systems ACIS; 2007; Toowoomba, Australia; 916–26.
  • Patton MQ. Qualitative research and evaluation methods. 3rd ed. Thousand Oaks (CA): Sage; 2002.
  • Baxter P, Jack S. Qualitative case study methodology: study design and implementation for novice researchers. The Qual Report. 2008;3:544–59.
  • Merton R, Fiske M, Kendall P. The focused interview: a manual of problems and procedures. 2nd ed. New York, NY: The Free Press; 1990.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.