Author: Turnerb

Predictive analysis is made possible from constantly growing raw data, also known as unstructured data, made available every second by the internet. Without this constant data, we wouldn’t be able to learn from it and in turn make real-time calculated predictions from it.

To achieve predictive analysis, we need to explore the technological components required to enable this capability. These components include but are not limited to Data Mining, Predictive Modelling and Machine Learning Algorithms. (Bari, Chaouchi and Jung 2017, p.15).

Firstly, to implement predictive analysis we need to define a data set to apply it to, once defined, we can then begin data mining. Data Mining is the process of managing unstructured data by pre-processing it through what’s called predictive modelling to extract patterns and trends that can then be used to convert data into information with a clear purpose for further analysis.

Predictive modelling is achieved using machine learning, a variety of artificial intelligence that studies mathematical computer algorithms and improves them automatically to learn insights for future predictions from data. For example, machine learning can be applied to discover trends in crime based on what day of the week it is, the weather, traffic conditions and so on.

Accenture (2019) offers a simple explanation to machine learning in video below.

While predictive analytics is regarded as an emerging technology of the 21^st century, it’s played a prominent role throughout history for many years.

While predictive analytics is regarded as an emerging technology of the 21^st century, it’s played a prominent role throughout history for many years.

The Antikythera mechanism constructed in approximately 150 BC was a calendar developed by ancient Greeks to predict eclipses and astronomical positions. During World War II in the 1940s predictive analytics assisted with the invention of the enigma machine by British intelligence to decipher German encryption. From the mid-1970s Earthquake forecasting has used predictive analytics to calculate the magnitude, location and time an earthquake would occur. (Winters 2017, p.9,10).

More recently in 2008 the Los Angeles Police Department (LAPD) successfully engaged in a predictive analytical software trial over six months that allowed police to identify and intervene before a crime could take place. This software also assisted police in solving past crimes using statistical predictions establishing itself as a catalyst for predictive policing. (Perry 2013, p.32).

Demonstrated by the select cases above, there was no single field or industry where predictive analytics was established. What they do all have in common is that predictive analytics was used to study collected data to predict an outcome in the future. The method in which prediction is achieved has changed dramatically from the Antikythera machine to modern-day databases that store millions of gigabytes of data but the overall theory of predictive analytics remains the same – to achieve a solution.

As the impact of predictive analytics varies depending on the industry it’s applied to, I will focus mainly on its influence within the industry of law enforcement.

Predictive analytics is an emerging technology within law enforcement as its continual development and potential capabilities are still largely undefined. At the same time, predictive analytics progressively disrupts policing procedures as the technology pushes the bounds of traditional policing methods into new territories.

There’s no doubt policing practices are already experiencing disruptions from the emerging technology of predictive analytics as demonstrated from the success of the LAPD trials that focused on both crime control and management of the individual officer. This allowed the LAPD police organisation to embrace a precision policing model including; targeted policing decisions, improved budget allocation and improved policing knowledge. (Wilson 2019, p.74).

With police organisations being disrupted by predictive analytics, questions start to arise in society regarding the legitimacy in which impartiality is determined, for example, how do the algorithms that enable this capability operate and who owns the storage platforms that hold this data?

This question alone brings into debate data security, privacy laws, intentional and unintentional biases and social control. With this new technology emerging into law enforcement it’s imperative to understand the intentional and unintentional consequences of machine-learned decisions that can have both a positive and negative affect on society. In particular, it is essential for law enforcement to continually monitor and adapt this technology as it enters into mainstream practises to avoid undermining its original intention – to achieve a solution. (Sarah 2017).

The impact of predictive analytics applied to law enforcement will have a significant effect on police operations and the societies they support. For this reason, I rate predictive analytics a 4.5 out of 5 on the scale of emerging and disruptive technology in 2020 and beyond.

James Stevenson (Using computers to predict crime) discusses the impact of predictive analytics applied to policing in the TED Talk below.