Author: The Guerrilla Economist. Adapted and updated from the original blog published in April 2020 via Miller Research
Data is defined as individual units of information. It is collected everywhere, from what items we purchase at the supermarket, to how long we spend at the hospital. The desire to collect more data about more aspects of our lives has led to the emergence of various forms of data technologies, such as big data, data science, artificial intelligence (AI) and machine learning, to name a few. These technologies have helped unlock the power of data, developing detailed insights with endless social and economic value. The impact of the revolution has created a data-driven society, which has influenced governments’, industries’, and individuals’ behaviours and reshape how they interact with the world.
How has data been used?
Modern technological infrastructure has enabled us to capture large amounts of data, process that into information, and then transform that information into actionable insights. This process has enabled data to be used in many innovative ways across different industries and professions.
The development of Netflix is an interesting example of how data has disrupted an industry. In the early 2000s, before Netflix was a household name, Blockbuster was a leading film and gaming rental service. At the time Netflix was an innovative company introducing DVD sales and rental by post. Blockbuster took little interest and felt ‘too popular to fail’ and even turned down the opportunity to buy Netflix. Netflix went on to reinvent the way we watch TV and films, pioneering the online streaming service. Online streaming service technology enables data to be transmitted using a computer and mobile devices across the internet. Netflix uses data to understand customer behaviour, collecting millions of data points from user interaction, and using this information to tailor their streaming service to individual preferences. Blockbuster on the other hand made a series of poor decisions and eventually filed for bankruptcy in 2010. Netflix has since become one of the most valued companies in the world, thanks to big data and analytics.
How is it changing?
Data has played an important role in the rise of disruptive technologies, which are rapidly reshaping the economy, driving innovation across industries, and developing valuable analytical tools to unlock invaluable insights. These developments are helping economic agents make better-informed decisions and improve the overall human experience.
The emergence of ‘Big Data’ (defined by 3 Vs – Volume, Velocity and Variety) is an example of how data is changing the world. Big data is simply larger and more comprehensive, varied and complex data sets processed at high speed. Big data infrastructure can be used to capture microdata and reduce the use of manual processes such as census surveys to capture particular data, which are costly and time-consuming.
The healthcare sector is an area that is rapidly being revolutionised by big data. Hospitals are adopting specialised cloud technology systems designed to capture and store large amounts of health data at a lower cost; improving both efficiency and accuracy. Data technologies such as AI, robotics and data science methods use health-related data in some way shape or form to enhance the healthcare service. Some examples of the way data is changing the healthcare sector, include the following:
- AI and Robotics is harnessing data to streamline operational procedures, improve accuracy of diagnosis and provide target driven medicine. Examples range from deep learning methods enhancing the scanning and analysis of x-ray images to the development of prosthetic limbs. It is also providing more accurate prediction and the multiplier effects of diagnosis and treatments using big healthcare data.
- Genomics involves the reading and writing of genome or DNA data. This technology is influencing diagnosis of disease and more patient-specific treatment.
- Digital Medicine is playing a massive role in the development of the ‘digital hospital’. Technologies such as smartphone apps, wearable technologies and remote monitoring are creating an environment for patients to self-manage and monitor their health. They can also receive virtual consultations without having to use front line NHS services.
This digital evolution is enabling the healthcare system to provide more effective and personalised care, become more economically efficient and transform the world of medicine for the better. Further examples and prospects of the future of technology in the healthcare sector can be found in the Topol Review.
Challenges facing this data disruption
Technology has enabled researchers to gather greater social and economic insights from data but the data revolution has also brought about many challenges.
Firstly, industries are becoming more and more technology and data-focused and many existing jobs are fast becoming redundant, creating a knowledge gap within the existing workforce. The problems that exist within the current workforce are exacerbated by the lack of supply to meet the demand. According to the Royal Society, over the last five years, the demand for data scientists and engineers has tripled in the UK.
Accessibility is also a problem. The digitisation of information and cloud technologies has led to the globalisation of data, enabling data sources to be accessible globally. Access to data sources from social media activity, admin records, and internet activity can be used to unlock endless insights that were not possible before. Tech companies such as Facebook can acquire user data and build individual data profiles. The idea that it is ‘your data’ has led to uncertainties around data rights and ownership. This ambiguity has been exploited in recent times, an example being the Facebook Cambridge Analytica scandal, where personal data of Facebook users was sold without consent.
Online virtual data storage in the proverbial cloud has also raised privacy and security concerns and exposure to problems such as hacking. Nonetheless, new data technologies such as blockchain are helping mitigate security issues through cryptographic decentralised encryption. It is also important to highlight the issue of data quality and integrity. Many existing data sets contain unreliable data or lack thereof, which diminishes the value of its use.
The data economy is still in its infancy, the social and economic potential is still massively under realised and many challenges still limit the way data can be collected and used. As these conditions improve, from an economics perspective this will only enhance the ability of policymakers to design and implement better policy programmes and economists to deliver more informed research and predictions.