Embracing digital transformation to piece together the UK’s productivity puzzle
According to the latest Office for National Statistics (ONS) figures, UK productivity has suffered its worst fall in five years. In fact, the Q2 stats show that we’re still yet to fully recover from the 2008 financial crash. But digital transformation could play a leading role in improving these statistics. Cloud technology and machine learning in particular should be the centrepiece of the UK wide aim of accelerating productivity.
In need of a boost
The UK has been in a productivity slump for more than a decade. The recent Office for National Statistics figures showed no signs of recovery, with productivity levels dropping 0.5 per cent between April and June 2019 compared with the same quarter last year.
Leaders across Government have tried introducing initiatives and schemes to address the issue. For example, the Industrial Strategy, published in November 2017, was created to improve the UK economy and address productivity performance. However, figures have remained stagnant ever since its introduction.
So how can the UK address its longstanding productivity woes?
As leaders look for solutions, the potential for transformational change through cloud services, data science and nascent machine learning (ML) technology could be one important piece of the puzzle.
While there may be preconceptions that technologies like machine learning are complex or hard to implement, they have a real and practical application in businesses today and are actually much more accessible than many decision makers may think. They are already proving to be a real driver for productivity, not just a pipe dream for the future, and can be surprisingly straightforward to establish.
A second opinion
Google in particular has made the technology underpinning machine learning surprisingly quick to implement and swift results can be achieved once organisations have identified their objectives. We only need to look at some of our neighbours in Europe for inspiration on how tangible results can be delivered at pace.
For example, the German Cancer Consortium used machine learning to create a new classification method for head and neck cancer based on chemical DNA changes. In some cases of head and neck cancer, patients can develop lung cancers. However, it can often be very difficult to tell whether these represent an existing cancer or a separate primary lung cancer.
To overcome this, researchers began testing tissue samples for a chemical alteration known as DNA methylation. Taking DNA methylation data from hundreds of head and neck cancers, the German Cancer Consortium trained a complex network to be able to distinguish between lung cancer and head and neck cancer. This created a computer program which was able to distinguish between the two cancers, with a 99 per cent degree of accuracy.
Elsewhere in Europe, Leiden University Medical Centre in the Netherlands has an excellent example of machine learning that has dramatically reduced time spent on administration for clinical practitioners. The research centre found that its clinicians were spending almost as much time on administrative tasks – in particular, typing up patient notes – as on patient care, which placed significant pressure on resources.
We helped them to develop a system using Google Cloud Platform (GCP) that recorded the conversation between patient and clinician, eliminating the need to take notes. The program then transcribed the recording of the conversation and used machine learning to provide instant analysis of the medical terms used. This facilitated the creation of an accurate patient case history, with physicians able to submit the notes of the appointment to the system with the click of a button.
Using machine learning in this way also helps clinicians provide a much quicker diagnosis and can, in some instances, remove the need for a patient to make a second appointment.
There are lots of great examples of technology adoption in Europe. The UK Government too has started taking steps to utilise digital to improve operational efficiency. For example, in 2017, it launched a Government Transformation Strategy spearheaded by the Government Digital Service.
The strategy set out a vision for how digital transformation across Government can help boost its productivity, and address the productivity gap that exists between the UK and its neighbouring countries. Since its implementation, some significant productivity and efficiency benefits have been realised across Government departments. For example, we recently worked with the Department for Transport (DfT) to migrate its in house application onto Google Cloud Platform (GCP).
When the DfT needed to execute a data query on its ticket sales and franchise, its old system would take several hours to source the figures. However, migrating to the GCP means the DfT can now execute the same data query in 20 seconds. The scalability of GCP means the system is also able to run multiple data queries simultaneously, significantly improving productivity and efficiency.
Adapting technology to suit your organisation’s needs
Working with partners that understand how technology can impact an organisation’s needs can go some way towards helping to boost productivity through technology. And, in many respects, the benefits that cloud technology and machine learning can offer are endless. This presents a huge opportunity for businesses.
Firms collect tonnes of data every day – and the ability to rapidly analyse this data can generate business insights that can be hugely valuable. Taking the finance sector as an example, payment fraud prevention startup Ravelin harnessed the potential of machine learning to become a major player in online fraud prevention.
The FinTech firm uses an ML model to analyse terabytes of historic and real time payment data, and then produce a graphical database which can be used to spot anomalies. The ability to rapidly analyse such vast quantities of data has allowed the firm to stop more than £100m of fraudulent transactions in the past three years. This shows the tangible benefits machine learning can deliver.
Machine learning, more automation
Machine learning technology is also instrumental in the automation of processes, which can help businesses improve productivity.
The prospect of automation has raised some questions over the last few years about how it will impact the jobs market, and whether it will result in some roles and skills being made redundant. But automation’s key benefit is to free up employees’ time, helping them to become more productive.
This was the case with luxury hotel group, Kempinski Hotels, which manages more than 100 hotels across 30 countries. As the business grew, the amount of time spent on administrative tasks increased. To address this, the hotel group moved its email system to a cloud based solution which completed some of the underlying administrative tasks automatically. This allowed hotel staff to focus on other tasks within the organisation.
Migrating onto the cloud also helped the business, which is spread out across multiple geographies, streamline processes by enabling all employees to work from a central system instead of remotely.
Improving UK productivity
As these examples show, ML can help address several challenges and help boost productivity for public and private sector organisations, from startups to multinationals. Yet while some progress has been made, there are many organisations that could still benefit from harnessing the power of ML.
Encouraging increasingly widespread adoption of the technology would be a huge leap forward for productivity nationwide, whilst being much less onerous than many businesses and organisations believe.
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