NCDS Working Groups identify and investigate data science challenges of interest to members. Working groups are designed to be active for approximately one year and are organized around a specific topic in data science. A member representative leads each group and is assisted by facilitators and NCDS staff as appropriate.
Working groups kick off with a meeting to review the topic, establish objectives, and develop a plan for the year. After the initial meeting, groups covene regularly, including at least one in-person meeting, to hear updates, report on research results, provide input, and review progress. Member organizations and their employees’ involvement in working groups varies and depends on the relevance of the topic to each organization’s strategic objectives.
Each working group determines the format of its final products. Options include white papers, position papers, best practice documents, open forum lectures, panel discussions, special events, or any combination of ideas that allow for the further advancement of issues related to the data revolution.
2015 Working Groups
Internet of Things (IoT) – The convergence of wireless communication, sensor technology, and computer power has created excitement for a future where not only do computers and people interact but any object can be fitted to capture event data and transmit information to other objects, to computers, or to people for processing. The commercial opportunities for this “revolution” are many, as are the challenges of managing the data it creates.
Many companies and organizations have started large programs to create products and solutions around IoT, and many conferences and events are being organized around the world on the topic; however, we believe that a systematic understanding of the underlying science and technology for IoT and the data it creates is still not available.
The NCDS IoT Working Group plans to develop a series of three workshops on the IoT to understand the current state of practice, engage with private and public organizations working in IoT, and identify opportunities and open questions in this area with the aim of developing a research and education agenda in data science of relevance to IoT.
Synthetic Data – Privacy and/or security concerns sometimes limit user access to data and complete data sets, reducing the amount of analysis that can be done on that data. Examples of such data sets include population use cases, medical records, and financial transactions. The limitations in techniques for masking data so that privacy is protected but analytics are possible represent a hurdle for achieving the benefits of big data.
The goals of the Synthetic Data Working Group involve directing research and setting standards for synthetic data. This could include outlining use cases for synthetic data, understanding the limits of synthetic data, anonymizing data, and generating data.
Workforce Development – The 2011 report from the McKinsey Global Institute predicted an acute deficit of “deep analytical talent” in the work force by 2018. A quick search finds an eclectic response to this deficit, ranging from university master’s programs, to vender-driven training, to MOOCS and other online resources. Anecdotal conversations with recruiters show that technological advances in tools and techniques are bringing data to the front lines of business but many entry-level employees do not have the analytical skills to take advantage of these advances.
This group will address issues such as:
- The gap between the forecasted data science-related graduates and demand in the workforce.
- The specific skills needed to build the data workforce of the future.
- The pros and cons of developing a data science core curriculum as well as accreditation criteria for data science courses.