Sep 012017

With data being collected by organizations at a staggering rate, the demand for analytics to leverage the insight from this data is growing just as fast.

Big data can viewed as a gateway to new opportunities, as a means of managing risks, or as a tool to improve business sustainability. Oftentimes, big data is associated with two keywords: analytics and technologies. These keywords represent an evolving suite of trends – from descriptive, predictive and prescriptive analytics to the application of Machine Learning, and Cloud technologies. Continuously monitoring these trends in analytical approaches and technological breakthroughs in the context of Big Data and applying them to produce business value is the key to survival in this fast-paced digital age. But determining what tools to use to analyze this volume of data can be daunting.

This issue of Cutter Business Technology Journal, with Guest Editor Bhuvan Unhelkar, will explore trends in analytics and other technologies as related to Big Data that can be incorporated into business strategies and processes for better decision making and competitive advantage. What types of strategies and technologies should organizations employ to ensure they are turning their data into oil?

Suggested article ideas include, but are not limited, to the following:

  1. What is the role of Big Data analytics in business and how does it influence business decision making (that is different to the decision making without Big Data analytics)?
  2. What are the different types of analytics (descriptive, predictive, prescriptive) and their key characteristics when applied in practice? What are the risks and challenges in utilizing analytics in practice?
  3. How are predictive models developed and used? (e.g. by creation of a predictive score, a Net Promoter Score, and so on)? Are there methods and frameworks that can be used in developing predictive models?
  4. What types of data analytics are appropriate, and under which circumstances?
  5. How can predictive analytics help optimize operations?
  6. Why is Machine Learning crucial to effective utilization of Big Data?
  7. What are the risks associated with application of Machine Learning in business? (legal, ethical issues?)
  8. How does Machine Learning change business processes?
  9. How do you decide on utilizing Big Data for training versus predicting?
  10. What are the key challenges in realizing the full potential of IoT and the data it generates, and how can we address those challenges?
  11. What are the effective strategies for managing IoT data? Are they different from managing traditional enterprise (big) data, and if so, how?
  12. What is the nexus between Cloud computing and Big Data analytics?
  13. What are the popular Cloud computing platforms and what are their advantages and limitations?
  14. What are the popular and appealing use cases in Big Data?
  15. How can Big Data be integrated with enterprise information systems and organizational operational data?
  16. What are the responsibilities of a Data Scientist in helping an organization realize the benefits of Big Data?
  17. What are possible metrics to measure the success of Big Data?



Please send an email to Christine Generali at cgenerali[at]cutter[dot]com and Guest Editor Bhuvan Unhelkar at bhuvan[dot]unhelkar[at]gmail[dot]com and include an extended abstract highlighting the practical significance of your article to the readers.

Please include an outline showing major discussion points, and a brief bio of the author(s). Articles should be original with a practical orientation and be written in a style accessible to practitioners. Overly complex, purely research-oriented, or theoretical treatments aren’t appropriate for Cutter audience. Once the abstracts are accepted, we plan to work iteratively with you to develop an effective paper.


Christine Generali

Christine Generali is a Group Publisher for Cutter Consortium - responsible for the editorial direction and content management of Cutter's flagship publication, Cutter IT Journal.


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