Big Data

Benoit Leclerc, Jesse Cale, Benoit Leclerc, Jesse Cale

• Publisher: Taylor & Francis
• Year: 2020
• eBook ISBN: 9781351029681
• Edition: 1
• Topic: Computer Science
• Subtopic: Data Modelling & Design
• Index: Computer Science

The internet has launched the world into an era into which enormous amounts of data are

generated every day through technologies with both positive and negative consequences.

This often refers to big data. This book explores big data in organisations operating in the

criminology and criminal justice fields.

Big data entails a major disruption in the ways we think about and do things, which

certainly applies to most organisations including those operating in the criminology and

criminal justice fields. Big data is currently disrupting processes in most organisations – how

different organisations collaborate with one another, how organisations develop products

or services, how organisations can identify, recruit, and evaluate talent, how organisations

can make better decisions based on empirical evidence rather than intuition, and how

organisations can quickly implement any transformation plan, to name a few.

All these processes are important to tap into, but two underlying processes are critical

to establish a foundation that will permit organisations to flourish and thrive in the era of

big data – creating a culture more receptive to big data and implementing a systematic data

analytics-driven process within the organisation.

Written in a clear and direct style, this book will appeal to students and scholars in

criminology, criminal justice, sociology, and cultural studies but also to government

agencies, corporate and non-corporate organisations, or virtually any other institution

impacted by big data.

1
Big data in criminology and criminal justice through the lens of the business literature

Jesse Cale, Benoit Leclerc, and Francis Gil

Introduction

Whether in government agencies, corporate and non-corporate organisations, large firms, academia, politics, and so on, the internet has launched the world into an era into which enormous amounts of data are generated every day through technologies with both positive and negative consequences. This often refers to big data. But what is big data, how is it relevant in general, and, more specifically, for criminology and criminal justice? Speculations on the implications of big data for the field have begun, but there is no solid consensus in the crimino-logical literature about what exactly big data refers to, and how it will impact theory, research, and most importantly, practice. Big data typically implies an abundance of data and new processes, such as data mining and data analytics, which provides virtually anyone using data with the opportunity to generate new discoveries or upscale an organisation’s capabilities. Most importantly, big data brings challenges to organisations and innovative embedded systematic solutions are much needed to overcome these challenges so that organisations can thrive and prosper in a safe environment.

This chapter is divided into two major sections. In the first section we primarily revisit how big data is defined while trying to make sense of the concept for clarification and ‘industry’ consensus purposes. We then discuss two critical challenges that cut across organisations and industries. One fundamental challenge is culture. In any organisation, culture is a dimension that has been examined in depth because of the benefits associated with ‘getting it right’ and the costs of ‘getting it wrong’. Any organisation should typically be seeking to align its mission with its people and culture and vice versa. Therefore, if the culture of one organisation is not receptive to big data, making the best of data for that organisation will be twice as challenging. Another relevant challenge is related to the applications of big data and data analytics. Suffice it to say that having access to an enormous amount of data is meaningless for an organisation unless these data are of a certain quality, can be utilised effectively, and that the ‘entry point’ (i.e., formulating questions to address) and ‘exit point’ (i.e., interpretation and dissemination of findings) of the process of data analytics are completed with diligence.

After discussing the importance of culture and the process of big data and data analytics in the first section, the second section of this chapter consists of looking essentially at the challenges noted earlier but with a specific focus on the fields of criminology and criminal justice. Indeed, this discussion likely carries much relevance for other academic disciplines and industries as well. We examine big data and its challenges in the context of criminology and criminal justice while referring at different points to each of the six contributions included as part of this volume on big data.

Big data – what is it?

Big data is a concept that has been ‘defined’ in various ways and may be understood differently by people working in different industries or even within the same industry. More so, it is often understood differently within the same organisation especially by people who have had very little exposure to data. Big data generally concerns the abundance of data generated through technology and the internet of things – social media, mobile phone usage, any personal information held by banks, the government or any organisation in the public sector, and so on. In this sense, it also refers to an era dominated by easy access to information and disruptive technology. Big data may also refer to the movement taken by large organisations toward using data to become more productive and profitable. McAfee and Brynjolfsson (2012) specifically speak of a movement, which uses three key characteristics to define big data, that is, volume, velocity, and variety. Volume implies the massive amount of data generated every day while velocity refers to how fast the data can be created from various sources of information. Variety concerns the number of infinite potential sources of data ranging from simple messages posted on social media to GPS signals produced from mobile phones. In the end, how we define big data is significant to the extent that people across organisations and industries should be able to discuss the challenges and solutions on how to make the best of data. Importantly, defining big data may assist in educating people not generally exposed to data and make them aware of not only the risks and challenges associated with an approach favouring the use of data but also its benefits.

To us, it is important to approach big data as referring not only to the abundance of data in our societies but also to its immense potential for improving knowledge in a broad sense for the evolution, safety and well-being of societies – to help societies function better individually and in synergy in the context of a constantly evolving world. We offer a deliberately optimistic definition with the mindset that data should be used for the good of all. Of course, we do remain aware of the challenges and risks posed by ‘too easy’ access to sensitive data, for instance, or flawed analytics, in particular, that could lead to decisions with negative outcomes. Simply think of criminology and criminal justice. Child exploitation, fraud, money laundering, bullying, identity theft, human trafficking, and so on can have disastrous consequences that are facilitated by technology. Big data also provide new opportunities for people with ill intentions. How can we get the best of data while neutralising its worst potential then? This question is beyond the scope of this volume but helps us frame the volume in a more constructive manner for readers. It makes us remember and appreciate the numerous ramifications generated by big data. In our view, it is wiser to engage with data rather than resist it, especially given the nature and extent of problems and crimes that can emerge from it. In the next section we discuss two major challenges at the foundation of big data – two challenges that cut across organisations and industries and into which solutions for addressing crime problems should be embedded. These two challenges represent a fundamental starting point for any organisation regardless of their mission, objectives, and values.

Big data challenges

Shaping the culture

Culture is one of the most critical dimensions for success in any organisation. If the people within one organisation are not working as a group (or a team ideally), and in a similar ‘headspace’, that organisation will not progress as effectively as it could otherwise. Arguably, if the culture of one organisation is flexible and receptive to transformation then it should be relatively straightforward to take on novel directions. However, in any organisation, some people are commonly reluctant to changes as it puts them out of their comfort zone – they may simply be intimidated by the changes for fear of new role responsibilities, losing privileges, or even losing their job. A first and common challenge across organisations and industries in relation to big data and data analytics is also the culture of an organisation (e.g., Diaz, Rowshankish, & Saleh, 2018; McAfee & Brynjolfsson, 2012). McAfee and Brynjolfsson (2012) argue that many organisations need to stop pretending to be more data-driven than they actually are. Other organisations may be simply intimidated by technology to get into the data space or fear some negative consequences from adopting data-driven approaches, such as intrusions to privacy and risks related to sensitive information leakage. Some organisations may simply not understand the potential of an approach that encompasses the use of empirical data. Perhaps most importantly, people must be educated about the advantages, challenges, and risks related to using data so that they do not feel intimidated by it and understand its potential. This process brings the importance of shaping culture and bringing everyone together for a common purpose on how to make the best of data. From this point in time on, big data can only evolve and it is critical to accept this reality for making positive contributions to society but also better addressing challenges and problems that big data will continue to generate.

Diaz et al. (2018) point out that a data culture is what is in fact lacking in most organisations in the first place. Diaz and his colleagues present several principles that favour a healthy data culture in organisations. One principle refers to ‘culture catalysts’, that is, people who have the ability to bridge data capabilities and day-to-day knowledge to the operations of an organisation. The idea is that these people should lead the way as they can understand how an organisation could benefit from data. This principle taps into two important problems that lead organ-isations to failure when trying to incorporate data analytics into their operations (Fleming, Fountaine, Henke, & Saleh, 2018). The first problem emerges when an organisation lacks what they called ‘analytics translators’. Essentially these are the people who represent ‘culture catalysts’ – those who have both business and data knowledge and can translate organisational needs to data analysts. There is an absolute need to have these people in the organisation and they should be recruited from within and outside the organisation (see the fourth principle in the next paragraph). The second problem taps into the importance of integrating analytics into the core of the organisation. If analytics works in isolation (i.e., not in synergy with other departments, divisions, or business functions) – the potential benefits of big data will decrease dramatically, and the risks of negatives consequences, such as errors in decision-making processes will be more likely.

A second related principle noted by Diaz et al. (2018) is data democratisation, which implies that people should get excited about the prospects of using data before embarking into the journey. This can be achieved through providing people with easy access and opportunities to interact with data and eventually, with time, people will start understanding and appreciating the benefits of it and believing in it – this speaks of practical and repeated exposure to data. The third principle raises awareness on the risk of data analytics of not generating positive outcomes for the organisation. The risk of getting analytics wrong always exists, which requires ongoing monitoring to check whether what has been generated actually makes sense for the organisation before decisions are made. A fourth principle involves marrying talent and culture. This principle refers to integrating the right talent (what is needed to embed a culture that favours the utilisation of data) through recruiting new people and upskilling people already employed by the organisation. Choosing the right people to lead data initiatives will be crucial to set up the foundation for a sustainable direction. In addition, analytics roles should be clearly defined within the organisation – people in the organisation should be aware of how the needs in analytics are covered and by whom (Fleming et al., 2018). A fifth principle – one that has been raised as critical by most authors in the data space – involves getting the ongoing support from the C-suites, board or other decision makers in the organisation. Conversations between the decision makers and the people leading data initiatives are essential to obtain a high level of commitment from decision makers, which in turn can generate a greater level of awareness to avoid negative consequences in the process. In the end, one ultimate objective of using empirical data is to enhance the capacity of decision makers to make more informed decisions for the organisation – making decisions driven by evidence rather than intuition (McAfee & Brynjolfsson, 2012). The principles outlined here are all relevant and interconnected. For instance, getting the right people – for example the ‘culture catalysts’ – will favour data democratisation, which obviously will be greatly facilitated if the decision makers clearly understand and support data initiatives. Furthermore, shaping the culture of the organisation requires these elements to work in synergy for success.

Groysberg, Lee, Price, and Yo-Jud Cheng (2018) outline a three-step process to adopt in order to set up culture targets for shaping cultures. The first step refers to understanding the current culture through its history and heritage as well as its strengths. This step is critical for an organisation because a solid historical foundation could be used for positive reinforcement and serves as evidence that its people are capable of embarking on new initiatives. The second step involves considering the strategy and the environment in which the strategy is being executed. This refers to shaping the culture according to the strategy chosen by the organisation to make the best of big data. For example, if the strategy is to build evidence-based knowledge on how an organisation is performing in attracting new and retaining current customers, the people should be aware of how the data will assist in understanding how the organisation is performing with customers and then, in turn, accept and support the strategy. The third step requires framing this transformation as an organisational priority to solve problems and create value within the organisation, not as a ‘culture change’ initiative as such. This step is highly valuable as ‘transforming’ the culture of one organisation could be perceived as a threat by some of its people because it will put them at the fore-front of the new direction that the organisation is seeking to take. However, if the focus rather is put on the value that will be created by changing direction, that is, on positive and tangible outcomes, people may prove to be more receptive to it. As part of such a program, hiring new employees that fit with a culture and its mission in which big data is embraced is another strategy that organisations often adopt (Kell & Carrott, 2005) – a principle also discussed by Diaz et al. (2018). As pointed out by Bernik (2001), if one needs to fix an organisation and guide it toward a more successful path, the focus should arguably be put on the people first, which we believe is absolutely critical.

Providing that culture is receptive to big data and data analytics, data can provide an exceptional competitive advantage to organisations dedicated to it (Boudreau & Ramstad, 2003; Davenport, Harris, & Shapiro, 2010). As an illustration, applied to talent management within an organisation, Davenport et al. (2010) identify six relevant areas for the application of talent analytics, such as human-capital investments, analytical HR, and workforce forecasts. First and foremost, instead of relying on intuition, executives can use evidence-based knowledge from talent analytics to make important decisions about people in the organisation. Data are likely to lead to more optimal decisions and thus better outcomes for organisations (McAfee & Brynjolfsson, 2012). For instance, data analytics can assist in identifying the skills and interests of staff and link those to high performance. Then staff can be moved accordingly into roles that maximise their own success and as a result, generate better outcomes for the organisation (Davenport et al., 2010). Too often, organisations put skilled staff into the wrong role for convenience purposes or due to a lack of general competence into making critical decisions or understanding the mission of the organisation. Data on motivation and personal competencies could also help design strategies to improve workplace climate and, indirectly, staff performance. Such data can provide deep insights into the ‘headspace’ of staff and how well they are working as a ‘team’. With data analytics skills, human resources management may also have a better chance to gain a seat at the executives table to actively participate in the organisation as a strategic/transformational partner (Lawler, Levenson, & Boudreau, 2004; McAfee & Brynjolfsson, 2012). However, once again, Davenport et al. (2010) indicate that executives themselves are often sceptical of examining human behaviour (probably because the execution of strategies in this regard is often poor in organisations), which brings us back to the importance of examining culture in the first place. In this context, leaders who support the use of human-capital insights must foster a...

Table of contents

• Cover
• Half Title
• Series Page
• Title Page
• Copyright Page
• Dedication Page
• Contents
• List of contributors
• Foreword
• 1 Big data in criminology and criminal justice through the lens of the business literature
• 2 The data are everywhere: integrating criminology and epidemiology and improving criminal justice
• 3 Big data and criminology from an AI perspective
• 4 Future applications of big data in environmental criminology
• 5 Leveraging police incident data for intelligence-led policing
• 6 The challenges and concerns of using big data to understand cybercrime
• 7 Genetics, bioethics, and big data
• 8 Big data: generic roadmaps as global solutions for practice
• Index