Big data: generic roadmaps as global solutions for practice

Introduction

The ramifications of big data in practice are critical and countless. This phenomenon will persist through tune, and corporate and non-corporate organisations, businesses and then executives, government agencies, scientists, universities, and the world in general are now starting to adapt, some reluctantly, others proactively. On the one hand, big data provides new possibilities in providing a virtual platform from which to build on, make new discoveries, and push the boundaries of collaboration within, between, and across organisations globally. People, individually and collectively, can now use data to further their learning and personal development, boost the development of businesses and collaborations locally or globally, and simply transform organisations. As Lee and Holt discussed in Chapter 6, those in charge of ensuring the security and protection of others, such as the police, now also benefit from more technological tools to conduct then-work. On the other hand, big data also serves as a platform for those with malicious intentions. It provides individuals with criminal opportunities to also push boundaries in terms of capability and committing harms. Therefore, big data and analytics have the potential to generate innovation in the pursuit of good and the well-being of societies but also cany negative consequences, sometimes new fonns of crimes, or more commonly, pennitting certain crimes or inequities to proliferate. Regardless of the angle we take to approach big data and understand its impact on the world, big data is now an integral part of societies. In the end, it could be argued that the benefits and costs of big data neutralise each other to some extent - we are facing more complex problems, but we can also offer more sophisticated solutions.

The main aim of the cunent volume is to present a forward-thinking approach to the opportunities and challenges for the fields of criminology and criminal justice brought forth by the big data era. When commencing work on this volume, we drew on the business literature as we appreciated that big data, its challenges, and discussions on potential solutions to manage and benefit from it are cunently given more prominent attention in this field than in academia in general, and criminology and criminal justice specifically. Throughout this exercise, most importantly, we also realised that the cunent volume has the power to not only guide the fields of criminology or criminal justice, but also those outside of academia, including both corporate and non-corporate organisations and government agencies. As a result, two roadmaps were developed to address culture and the research process and data analytics. From an organisational perspective, these roadmaps essentially fall under what is considered change management.

In an information era for which a defining characteristic has been described by many as a data deluge, it is without a doubt pertinent to adopt interdisciplinary and multidisciplinary approaches to problem solving. Furthermore, in the context of criminology and criminal justice, industry-academia partnerships are now more relevant than ever. One of the long-standing tensions between academia and industry in the context of criminology can be understood in terms of divergent goals in the two spheres. The goals of criminal justice are numerous, practical, and in some cases complementary, and in others, conflicting (e.g., punishment, rehabilitation, conununity safety, crime prevention, and so on). There is substantial pressure on criminal justice agencies and the professionals and practitioners in those agencies to get the ‘right balance’ on achieving these multiple and heterogeneous goals, and criminal justice agencies operate with increasingly limited budgets and human resources to do so. This context results in the additional pursuit for efficiency, and here, depending on political will and context, certain approaches may be favoured over others (e.g.. crime control, rehabilitation, etc.).

A substantial part of academic criminology has been focused on understanding these contexts independent of engagement with industry to maintain a sense of objectivity or neutrality. For many, long-standing socio-structural inequities that arguably have favoured certain groups in society over others for generations are considered the key source of crime. Furthermore, in this context, criminal justice systems may be considered by many as a tool of colonial and patriarchal oppression that perpetuates inequity. There might be a certain degree of merit to such arguments depending on where one stands: criminal justice systems have not evolved substantially over the course of a couple centuries unlike many other-social institutions. However, what is certain is that neglecting to engage with institutions such as the criminal justice system to work toward addr essing these issues also means that the issues will persist well into the future. Furthermore, despite the substantial influence of politics in framing crime problems and criminal justice approaches, in the era of big data, we argue that it is becoming increasingly difficult, moreso than in the past, to overlook evidence on the factual basis for crime problems. Therefore, it is now more important than ever that criminology as an academic discipline strategically engages with the criminal justice system in order to improve efficiency, reduce inequity, and contribute to a just society. The key question remains as to how this can be done most effectively in the era of big data.

Drawing on the challenges related to big data articulated at the beginning of this volume, this concluding chapter is divided into two parts, each offering a generic roadmap to provide a systematic step-by-step process that can be adapted to any organisation and used as part of the solution for moving forward with big data. The first part provides a roadmap for creating or shaping the culture of

Big data: generic roadmaps for practice 119 an organisation toward facilitating a transition into big data. It is now becoming clear that big data and technological advances such as artificial intelligence do not represent the biggest challenges to organisations, but that culture does (Fountaine, McCarthy, & Saleh, 2019). The second part offers a roadmap to implement a systematic research process to collect relevant data, accumulate evidencebased knowledge, and translate the findings into practices that could improve organisations - the process that would typically follow transforming culture noting that many organisations may be gathering data without having necessarily a culture receptive to big data in the first place. With reason, the entry point and exit point of such a process have already been noted as critical in the busmess literature (Davenport, 2013). Once again, it is important to point out that the proposed roadmaps are generic and flexible in nature, and thus can be applied (and adapted if need be) to any organisation. While offering generic roadmaps applicable to any organisation, our discussion of these roadmaps will focus most closely on big data in criminology and criminal justice.