Analytics Process Management: A New Challenge for the BPM Community

  • Fenno F. (Terry) HeathIII
  • Richard HullEmail author
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 202)


Today, essentially all industry sectors are developing and applying “big data analytics” to gain new business insights and new operational efficiencies. Essentially two forms of analytics processing support these business-targeted applications: (i) “analytics explorations” that search for business-relevant insights in support of description, prediction, and prescription; and (ii) “analytics flows” that are deployed and executed repeatedly to apply such insights to support reporting and enhance existing business processes. The human environment that surrounds business-targeted analytics involves a multitude of stake-holder roles, and a number of distinct processes are required for the development, deployment, maintanence, and governance of these analytics. This short paper presents preliminary work on a framework for Analytics Process Management (APM), a new branch of Business Process Management (BPM) that is intended to address the central challenges managing analytics flows at scale. APM is focused on the processes that manage the overall lifecycle of analytics flows and their executions, and their integration into “operational” business processes that have been the traditional domain of BPM. The paper identifies key meta-data that should be maintained for analytics flows and their executions, and identifies the core business processes that are needed to create, apply, compare, and maintain such flows. The paper also raises key research questions that need to be addressed in the emerging area of APM.


Business Process Customer Satisfaction Analytic Flow Business Process Management Entity Resolution 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The perspective described here developed through discussions and projects with many people, including: Matt Callery, Richard Goodwin, Elham Kabhiri, Mark Linehan, Pietro Mazzoleni, Danny Oppenheim, Krishna Ratakondra, Jeff Robinson, Anshul Sheopuri, Piwadee (Noi) Sukaviriya, Roman Vaculín, Chitra Venkatramani, and Dashun Wang.


  1. 1.
    Callahan, S.P., Freire, J., Santos, E., Scheidegger, C.E., Silva, C.T., Vo, H.T.: Vistrails: visualization meets data management. In: Proceedings of the ACM SIGMOD International Conference on Management of Data, Chicago, Illinois, USA, 27–29 June 2006, pp. 745–747 (2006)Google Scholar
  2. 2.
    Callery, M., et al.: Towards a plug-and-play B2B marketing tool based on time-sensitive information extraction. In: IEEE International Conference on Services Computing, SCC 2014, Anchorage, AK, USA, June 27–July 2 2014, pp. 821–828 (2014)Google Scholar
  3. 3.
    Chaudhuri, S., Dayal, U., Narasayya, V.R.: An overview of business intelligence technology. Commun. ACM 54(8), 88–98 (2011)CrossRefGoogle Scholar
  4. 4.
    Davidson, S.B., Freire, J.: Provenance and scientific workflows: challenges and opportunities. In: Proceedings of the ACM SIGMOD International Conference on Management of Data, pp. 1345–1350 (2008)Google Scholar
  5. 5.
    Jagadish, H.V., Gehrke, J., Labrinidis, A., Papakonstantinou, Y., Patel, J.M., Ramakrishnan, R., Shahabi, C.: Big data and its technical challenges. Commun. ACM 57(7), 86–94 (2014)CrossRefGoogle Scholar
  6. 6.
    Krishnamurthy, R., et al.: Systemt: a system for declarative information extraction. SIGMOD Rec. 37(4), 7–13 (2008)CrossRefMathSciNetGoogle Scholar
  7. 7.
    Lohr, S.: For Big-Data Scientists, ‘Janitor Work’ is Key Hurdle to Insights, 17 August 2014.
  8. 8.
    Manyika, J., et al.: Big data: the next frontier for innovation, competition, and productivity, May 2011. McKinsey Global Institute report.
  9. 9.
    Marin, M., Hull, R., Vaculín, R.: Data centric BPM and the emerging case management standard: a short survey. In: La Rosa, M., Soffer, P. (eds.) BPM Workshops 2012. LNBIP, vol. 132, pp. 24–30. Springer, Heidelberg (2013) CrossRefGoogle Scholar
  10. 10.
    RapidMiner. RapidMiner Studio Manual.
  11. 11.
    RapidMiner Opensource Development Team. RapidMiner - Data Mining, ETL, OLAP, BI.
  12. 12.
    Shearer, C.: The CRISP-DM model: the new blueprint for data mining. J. Data Warehous. 5(4), 13–22 (2000)Google Scholar
  13. 13.
    Swenson, K.D.: Mastering the Unpredictable: How Adaptive Case Management will Revolutionize the Way that Knowledge Workers Get Things Done. Meghan-Kiffer Press, Tampa (2010) Google Scholar
  14. 14.
    Truong, H.L., Dustdar, S.: A survey on cloud-based sustainability governance systems. IJWIS 8(3), 278–295 (2012)Google Scholar
  15. 15.
    Wang, T., Wang, D., Wang, F.: Quantifying herding effects in crowd wisdom. In: The 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2014, New York, NY, USA - 24–27 August 2014, pp. 1087–1096 (2014)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.IBM T.J. Watson Research CenterYorktown HeightsUSA

Personalised recommendations