Partnerships for the Goals

Living Edition
| Editors: Walter Leal Filho, Anabela Marisa Azul, Luciana Brandli, Pinar Gökcin Özuyar, Tony Wall

Examining the Role of Big Data for Strengthening Multi-stakeholder Partnerships in the SDGs

  • Shalini S. GopalkrishnanEmail author
Living reference work entry
DOI: https://doi.org/10.1007/978-3-319-71067-9_1-1

Never again should it be possible to say, “we didn’t know”.

No one should be invisible. This is the world we want – a world that counts.

(2014 A World That Counts)

Definitions

Big Data is a set of hardware, software, and algorithmic tools using large sets of data from a myriad of sources, from social media to satellite imagery, in real time to make insightful decisions. Big Data for Sustainable Development Goals applies these tools to the 17 SDGs to enable nations to make better assessments of their progress.

Background

The “2030 Agenda for Sustainable Development” (SDG) was approved in September 2015 and launched in January 2016. It encompasses 17 goals to include economic growth, social inclusion, and environmental protection. These cover Poverty removal (Goal 1) to gender equality, climate change, etc. Big Data has the potential to accelerate the assessment as well as get quicker feedback to enable policy makers to achieve these goals.

This chapter covers the role of Big Data in...

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References

  1. Barboza L, Li B, Tingley MP, Viens FG (2014) Reconstructing past temperatures from natural proxies and estimated climate forcings using short- and long-memory models. Ann Appl Stat 8(4):1966–2001CrossRefGoogle Scholar
  2. Baron S, Russell-Bennett R (2016) Editorial: the changing nature of data. J Serv Mark 30(7):673–675CrossRefGoogle Scholar
  3. Biesdorf S, Court D, Willmott P (2013) Big data: What’s your plan. McKinsey Quarterly 2:40–51Google Scholar
  4. De Mauro A, Greco M, Grimaldi M (2016) A formal definition of Big Data based on its essential features. Libr Rev 65(3):122–135CrossRefGoogle Scholar
  5. Deininger K, Xia F (2018) Assessing the long-term performance of large-scale land transfers: challenges and opportunities in Malawi’s estate sector. World Dev 104:281–296CrossRefGoogle Scholar
  6. Gantz J, Remsel D (2012) The digital universe in 2020: big data, bigger digital shadows and biggest growth in the far east. In: Proc. IDC iView, IDC Anal. FutureGoogle Scholar
  7. Harrison T, Pardo T, Gasco-Hernandez M, Canestraro D (2018) The salience and urgency of enterprise data management in the public sector. In: Proceedings of the 51st Hawaii international conference on system sciencesGoogle Scholar
  8. Hilbert M (2016) Big data for development: a review of promises and challenges. Dev Policy Rev 34:135–174CrossRefGoogle Scholar
  9. Huber BR (2014) ‘Mosaic Effect’ paints vivid pictures of tech users’ lives, Felten tells privacy boardGoogle Scholar
  10. Kagermann H, Wahlster W (2013) Securing the future of German manufacturing industry: recommendations for implementing the strategic initiative INDUSTRIE 4.0. Working Group, AcatechVNational Academy of Science and Engineering, Germany, Final Report of the Industrie 4.0Google Scholar
  11. Kaisler S, Armour F, Espinosa JA, Money W (2013) Big Data: issues and challenges moving forward. System sciences (HICSS), 2013 46th Hawaii international conference. IEEE, p 995–1004. [Online] Available at: http://www.academia.edu/download/43949080/BigDataPaper-Final_09042012.doc
  12. Krishnan G (2018 under review) Marketers, big data and intuition – implications for strategy and decision-makingGoogle Scholar
  13. Laney D (2001) 3-D data management: controlling data volume, velocity and variety. META Group Res Note 6. 6. Available at http://blogs.gartner.com/doug-laney/files/2012/01/ad949-3D-Data-Management-Controlling-DataVolume-Velocity-and-Variety.pdf
  14. Lee Y, Madnick SE, Wang RY, Wang F, Zhang H (2014) A cubic framework for the chief data officer: succeeding in a world of big data. MIT:ESD. ESD-WP-2014-34Google Scholar
  15. Letouzé E (2015) Big data and development: an overview. Pop Alliance White Paper Series. Data-Pop Alliance, World Bank Group, Harvard Humanitarian Initiative. Available at http://datapopalliance.org/wpcontent/uploads/2015/12/Big-Data-Dev-Overview.pdf
  16. Liedtke CA (2016) Quality, analytics, and big data. Research Report. [Online]. Available at: http://strategicimprovementsystems.com/wp-content/uploads/2016/01/LIEDTKE_SIS_QABD_SHARE_VERSION.pdf
  17. Manyika J, Chui M, Brown B, Bughin J, Dobbs R, Roxburgh C, Byers AH (2011) Big data: the next frontier for innovation, competition, and productivity. McKinsey Global Institute ReportGoogle Scholar
  18. Metcalf J, Keller EF, Boyd D (2016) Perspectives on big data, ethics, and society. Council for Big Data, Ethics, and Society. Accessed 7 Dec 2017. http://bdes.datasociety.net/council-output/perspectives-on-big-data-ethics-and-society/
  19. Reese CS, Tolley HD, Keith J, Burlingame G (2014) Unraveling the mystery of grade inflation and student ratings: a BYU case study. Technical Report, TR–14–021. Department of Statistics, Brigham Young UniversityGoogle Scholar
  20. Salado-Cid R, Ramírez A, Romero JR (2018) On the need of opening the big data landscape to everyone: challenges and new trends. In: Linnhoff-Popien C, Schneider R, Zaddach M (eds) Digital marketplaces unleashed. Springer, BerlinGoogle Scholar
  21. Samuel A (1959) Some studies in machine learning using the game of checkers. IBM J Res Develop 3(3):210–29CrossRefGoogle Scholar
  22. Schwab K (2017) The fourth industrial revolution. Crown BusinessGoogle Scholar
  23. Tatevossian RA (2013) Blog; new introductory guide on big data for developmentGoogle Scholar
  24. Thinyane M, Goldkind L, Lam HI (2018) Data collaboration and participation for sustainable development goals – a case for engaging community-based organizations. J Hum Rights Soc Work 3:44.  https://doi.org/10.1007/s41134-018-0047-6CrossRefGoogle Scholar
  25. United Nations Global Pulse (2013) Big Data for Development: A primerGoogle Scholar
  26. Vaitla B et al (2017) Big data and the well-being of women and girls: applications on the social scientific frontierGoogle Scholar
  27. Vassakis K, Petrakis E, Kopanakis I (2018) Big data analytics: applications, prospects and challenges. In: Skourletopoulos G, Mastorakis G, Mavromoustakis C, Dobre C, Pallis E (eds) Mobile big data. Lecture notes on data engineering and communications technologies, vol 10. Springer, ChamGoogle Scholar
  28. Verhoef PC, Kooge E, Walk N (2016) Creating value with big data analytics: making smarter marketing decisions. RoutledgeGoogle Scholar
  29. Wang Y, Kung L, Byrd TA (2018) Big data analytics: understanding its capabilities and potential benefits for healthcare organizations. Technol Forecast Soc Chang 126:3–13CrossRefGoogle Scholar
  30. Yayboke E et al (2017) Harnessing the data revolution to achieve the sustainable development goals: enabling frogs to leapGoogle Scholar
  31. Yin S, Kaynak O (2015) Big data for modern industry: challenges and trends [point of view]. Proc IEEE 103(2):143–146CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Middlebury Institute of International AffairsMontereyUSA
  2. 2.Menlo CollegeAthertonUSA

Section editors and affiliations

  • Monica Thiel
    • 1
  1. 1.School of Public Administration and School of Business AdministrationUniversity of International Business and Economics & China University of PetroleumBeijingChina