Gregory Richards - Big Data and Analytics Applications in Government: Current Practices and Future Opportunities

Big Data and Analytics Applications in Government

Data Analytics Applications

Series Editor: Jay Liebowitz

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Actionable Intelligence for Healthcare

by Jay Liebowitz, Amanda Dawson

ISBN: 978-1-4987-6665-4

Data Analytics Applications in Latin America and Emerging Economies

by Eduardo Rodriguez

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Sport Business Analytics: Using Data to Increase Revenue and Improve Operational Efficiency

by C. Keith Harrison, Scott Bukstein

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Big Data and Analytics Applications in Government: Current Practices and Future Opportunities

by Gregory Richards

ISBN: 978-1-4987-6434-6

Data Analytics Applications in Education

by Jan Vanthienen and Kristoff De Witte

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Big Data Analytics in Cybersecurity and IT Management

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Data Analytics Applications in Law

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Data Analytics for Marketing and CRM

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Data Analytics in Institutional Trading

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Big Data and Analytics Applications in Government

Current Practices and Future Opportunities

Edited by

Gregory Richards

CRC Press

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Contents

MATTHEW CHEGUS

SWEE C. GOH, CATHERINE ELLIOTT, AND GREGORY RICHARDS

KHALED KHATTAB AND RAJESH K. TYAGI

ANDREW VALLERAND, ANTHONY J. MASYS, AND GARY GELING

EDUARDO RODRIGUEZ

NIKHIL VARMA AND RAJESH K. TYAGI

JULIO J. VALDES

GREGORY RICHARDS, CATHERINE ELLIOTT, AND SWEE C. GOH

OKHAIDE AKHIGBE AND DANIEL AMYOT

SEAN GEDDES AND KEVIN LAI

Why Government Analytics? Why Now?

Editors Introduction to This Volume

The Big Data phenomenon started out of necessity because of the large amounts of data generated by the Internet companies (primarily Yahoo and Google). These organizations needed to find a way to manage data continually generated by users of their search engines and so in 2004, Jeffrey Dean and Sanjay Ghemawat of Google released a paper in which they described techniques for distributed processing of large data sets. Since then, the field has grown in several directions: newer technologies that improve data capture, transformation, and dissemination have been invented as has new techniques for analyzing and generating insights. In addition, new structural models in organizations, for example, the creation of chief data officers, are being adopted to better manage data as a corporate asset.

In contrast, analytics has long been a staple of public sector organizations. Scientists working in fields such as space engineering, protection of waterways, prediction of the impact of policies, or in gathering and analyzing demographic information have for many years relied on statistical techniques to improve decision-making. Practical examples such as the United States Federal Drug Administration use of analytics for adopting a risk-based approach to inspecting manufacturing facilities, the Bureau of Indian Affairs Crime Analytics program, the use of advanced statistics in many countries for enhanced border control, and the continued growth of Compstat-style approaches pioneered in New York City attest to the widespread adoption of analytics programs within the public sector.

In many cases, however, these examples are point solutions focused on one specific area within an organization. The Big Data phenomenon has encouraged a democratization of analytics across organizations as managers learn that analytic techniques can be applied outside of strict scientific or financial contexts to improve program delivery. It is for this reason I used the term Big Data Analytics (BDA). Some analytic techniques require large data sets, but others use smaller data sets to deliver insights to program managers. In each case, it is the application of analytic techniques to data that helps to improve program delivery, not the fact that the data exists.

With these observations in mind, the first question: why government analytics? can be answered by noting that government organizations are no different to any other organization when it comes to ensuring the delivery of value for money. Managers and politicians alike seek to do the best they can often do with limited budgets working in an environment characterized by rapidly changing external conditions. Where government organizations differ from those in the private sector is in the level of complexity and ambiguity that is part and parcel of managing in public sector organizations. Within this context, BDA can be an important tool given that many analytic techniques within the Big Data world have been created specifically to deal with complexity and rapidly changing conditions. The important task for public sector organizations is to liberate analytics from narrow scientific silos and expand it across the organization to reap maximum benefit across the portfolio of programs.

The second question: why now? can be answered by realizing that up until a few years ago, a significant amount of attention was focused on simply being able to gather and process data. The tools are now available to do so. We need to turn our attention to the