Critical Literature Review
Introduction
This critical literature review attempts to identify how much of an impact innovation has had on the UK economy by bringing together and assessing research regarding both the benefits it brings through the introduction of technology including Big Data Analytics and Application Programming interfaces, as well as observing the impact on employment.
Big Data Analytics & Internet of Things (IOT)
Big data is the collection of large amounts of data from traditional and digital sources that represents a source for analysis in order to understand it more usefully (Arthur, L. 2013). Big data is transforming how policymakers, governments and businesses make decisions. The amount of data being collected is increasing daily (SAS UK, 2018). But, in recent times it has risen exponentially since technological innovations including self-driving cars and intelligent personal assistants rely heavily on the flow of data (IDC, 2018). This has opened up many streams of technological innovation that were previously untapped, these are based mainly in the field of mobile, social, cloud and big data & analytics and is described as the four pillars of the new ‘3rd platform technologies’. These are suggested to be the main elements pushing the new Digital innovation-focused economy (IDC, 2018). This age has been termed as the Fourth Industrial Revolution (FIC) and marks the transformation of products and services through the application of technological innovations including: Artificial intelligence, machine learning and Internet of Things (IOT); the IOT allows for advances such as the interconnection of objects through the Internet (IDC, 2018). This refers to the network of connecting devices with automated systems which collect data and are able to interpret it in order to produce a relative response (Burgess, 2018). Moreover, Big Data Analytics is vital for reasons including: 1) cost reductions, 2) lead-time reductions, 3) new product development and optimised offerings, and 4) smart decision making (SAS UK, 2018). Examples of these include: finding the exact reasons for failures and problems in near-real time, vouchers tailored to suit customer’s purchasing habits and spotting fraudulent activity before it affects the organisation (SAS UK, 2018). The value of Big Data Analytics is evident but recent research has shown that 87% of organisations are classified as having low business intelligence (BI) and analytics maturity. This means the majority of organisations are actually in a weak position to capitalise on the wealth of data using machine learning. This is a major drawback because according to Reinsel, Gantz and Rydning (2018) data creation is set to rise drastically from 33 Zettabytes(ZB) in 2018 and reaching 175 zettabytes (ZB) by 2025.
Impact of automation on employment
Technological innovation has often been associated with the stigma of replacing workers (Mokyr et al., 2015). Automation and digitalisation has been making vast advancement in replacing tasks that previously needed human reasoning to make decisions and this has led to the driverless car, predominately autonomous factories, service robots and 3D printing (Brynjolfsson & McAfee, 2014). Indeed, this displaces many workers and can make it very difficult for certain companies to survive due to the comparative advantage gained by companies that have evolved more technologically i.e. Uber replacing traditional cab companies and even to smaller businesses such as a car wash which have been largely replaced by robotic technology. Brynjolfsson & McAfee (2011) find that in a long series of cognitive tasks, computers are increasingly representing a challenge to human labour as many low-medium skilled jobs are being displaced by algorithms and robots. This can result in a polarisation of the labour force with highly skilled and highly paid workers on one side and low skilled workers on relatively low pay due to the reduction in skill level required. Indeed, this can lead to a rise in the levels of inequality within the economy, however the OECD comparative analysis of ‘The Risk of Automation for jobs in OECD countries’ found that automation and digitalisation was unlikely to get rid of large numbers of jobs (Arntz, Gregory and Zierahn, 2016). Whilst, the number of jobs at risk over the coming decades are suggested to be from the mid-40% up to over 60% (Degryse, 2016), and there has been evidence from OECD analysis that investments in ICTs has had a negative impact on employment at periods but these effects are predicted to diminish over time as there is a global restructuring in business models (Spiezia, 2017). Interestingly, Degryse (2016) found from his research that the higher up a country’s position in the Digital Economy and Society Index (DESI), the less its jobs will be affected digitalisation. However, this restructuring is still likely to benefit the skilled workers but OECD estimates shows, fewer than 40% of those using software every day have the necessary skills to utilise digital technologies most effectively (Spiezia, 2017). Indeed, this will likely have a negative impact by enlarging the inequality gap and the trend has been most recognisable in English-speaking countries where rapid growth in the finance services sector has influenced a shift in income from wages to bonuses which has pushed income disparity levels up (Hollinger, 2012).
Healthcare
Technological innovation has had a profound impact on healthcare services since new technology saves lives but also because many medical processes and practice has changed (Banova, 2018). One of the biggest reforms in the industry has been the digitalisation of health records and the use of IT systems to schedule appointments, update patient records using Electronic Health Record technology (Banova, 2018). Using the vast amount of data available in EHRs, it can be analysed by an expert which can create many benefits including reducing healthcare costs, predicting epidemics, improving quality of life and reducing wastage (Banova,2018). In hospitals doctors attempt to predict heart attacks with a success rate of 30%, however a machine learning algorithm that achieves a successful prediction rate of 80% has been invented by Sriram Somanchi (Economist, 2016). Health care spending has risen significantly since 1950 in the UK but since 2010 the healthcare spending in the UK as a percentage of its GDP has been falling and this is predicted to continue to 2020. There are many reasons for increasing health costs, including beneficial medical advances that come with expensive treatments and increased life expectancy, leading to high costs in healthcare associated with the elderly (Pettinger, 2017).
Application Programming Interfaces (API)
APIs are the link between business processes, services and data to channel partners, internal developers, customers and the world generally thus making it a key driving force behind the digital revolution. APIs also provide an opportunity to help structure software deployments (Willmott and Balas, 2018). With software being at the core of the digital revolution, there comes the issue of connecting different software interfaces in order to create useful networks for communication of data. API is a solution as it provides an interface that allows multi-directional communication of information between applications (Willmott and Balas, 2018). APIs enable organisations to provide programmable access to their systems and processes using uniform data interfaces in order to allow easy software integration between separate entities (Jisc, 2012).
To conclude, this literature review looks at the impact of innovation and how it has been achieved through looking at the effect on the healthcare, on inequality levels and 2 forms of innovation driving the digital revolution including Big Data Analytics and Application Programming Interfaces.