How can data create economic growth?
16 January 2013
This is the third in a series of blogs that provide an introduction to data, and consider its uses and its impact on the economy. You can find the two previous pieces here and here.
Data has a huge number of potential uses, for businesses, governments and individual people. Indeed, so broad and so fast-moving is this range of uses that it is pointless to try to identify or predict all of them. However, there is value in considering what impact data might have on the economy. How can data improve the way businesses operate, and how can it change our lives for the better? How can this innovation translate into growth and jobs?
In broad terms, there are four ways in which data can feed into economic growth . These are:
- Optimising business processes and boosting efficiency – whether by managing their stock in real time or taking better informed decisions, there are a host of ways in which companies can use data to reduce costs and become more efficient;
- Reducing labour requirements for routine tasks – many routine information-based tasks, from data entry to compiling reports, may be automated or require fewer workers. Such automation would raise productivity, but may cause short-term job losses;
- Enabling firms to better understand customers – data and digital technologies provide new ways for companies to learn about and interact with their customers, making it much easier for them to meet their needs;
- Enabling new types of product and service – transformative new technologies often make new products and services possible, generating growth and jobs. Data could support a wide range of such new things, from smart systems to personal monitoring services.
The first two points are primarily to do with raising productivity, enabling firms to increase out put per person. In the long term, this is the key determinant of economic growth, although productivity increases without new products or services can lead to job losses in the short term. The latter two points relate more to the demand side, about how businesses engage their customers and create new things that people want. This should create new jobs, new businesses and grow the economy.
Let’s look at each of those four points in a bit more detail.
Optimising business processes and boosting efficiency
Data is already making many companies far more efficient and productive, and there is significant potential to magnify this effect.
One aspect of this involves businesses (and governments) using data to make better-informed decisions. By accessing more complete and more timely information on how different parts of a business work, data savvy managers should be able to make decisions which save time or money and make processes run more smoothly. For instance, managers in a logistics firm may be more easily able to identify bottle necks in their operations, and take action to solve them. Local governments may be able to identify particular locations or services that are costing too much money, and then monitor interventions to solve this using data.
Research by Nesta on “Datavores” found that only 18% of their sample of online-active companies systematically used data to drive decision-making . Given that this sample only featured businesses involved in the internet economy and with more than 50 employees, this figure seems surprisingly low. It suggests there is significant scope for more companies and public organisations to make better use of data in their decision-making, and to become more effective and productive businesses as a result.
Another emerging way in which firms can use data to boost operational efficiency is by tracking and managing objects in “smart” systems. Using technologies associated with the “internet of things”, firms have scope to manage their stock, their machines, their supply chains and many other things more effectively. For instance, it is possible for logistics firms and retailers to tag each of their shipments with a sensor, and then manage their stock in real-time, dealing with any problems. Rolls Royce engineers already use data-generating sensors to manage jet engines in-flight, just as Formula 1 teams do with their cars. There is no reason why doctors should not be able to do this with their patients, saving them huge amounts of time.
This device-to-device communication can also be taken a step further, by letting smart networks manage themselves, using algorithms to take basic automated decisions. Storage temperatures for food could be adjusted automatically, trains could be coordinated to run more efficiently together, and traffic lights could adjust their timings as traffic volumes shift. And these are some of the more unimaginative applications of this type of system.
For companies, this type of smart management of data could save time and money, while making operations more reliable. Equally, this type of approach could be applied to reduce resource consumption and environmental impacts, by reducing waste.
Automation of routine tasks
Software and algorithms can often be used to replace – or make substantially easier – jobs that are normally carried out by people. Being able to connect more data to use in these tools could vastly increase this effect. Some roles – such as data entry or record keeping – may be replaced entirely, as more and more data management becomes automated. Other roles – for example, inspectors or quality assurance staff – may not be replaced, but have their productivity vastly enhanced by being able to process information far more quickly with the help of data.
In general, software- or algorithm-based automation is very be good at doing routine, repetitive tasks very quickly, it tends to be much less good at providing insights or more complex tasks . For this reason, many automated processes will still require human input or oversight, even if the nature of that work – and perhaps the skills needed to do it – may change.
Better understanding of customers
Much of the data that is gathered by companies is about their customers. Data about people is different to data about machines or software processes, because human behaviour is much less predictable. This data can provide companies with immensely valuable information on the habits and tastes of their customers, enabling them to better meet their needs and make money from them.
This customer data can operate at many different levels. Many companies now make use of traffic data on their website, to understand how to better reach and communicate with their customers, and target things such as advertising (enabling these types of analytics is Google’s stock in trade). Similarly, companies such as supermarkets gather customer data to optimise their prices, to place their products more effectively, or to spot patterns that help them increase sales. Taking this a step further, there is much scope for companies to personalise goods and services according to each customer, either by gathering their preferences directly from them, or observing their patterns of behaviour.
On top of this personalisation, it may be possible for companies to offer data to their customers as part of their service to them, to help them make more informed decisions. For example, if companies can offer their customers data about the effects of their diet or their financial health, they may be able to greatly enhance the value of the service they provide. However, there is a big challenge here around learning to use data to interact with customers. The majority of people have limited interest in or capacity for absorbing data, and developing user-friendly ways for people to interact with data will be a big challenge.
New products and services
Data is already giving rise to new services and products, which are sold both to consumers and other businesses. One initial effect will be the rise of data analytics companies, along with other firms which help other companies to use data and digital technologies more effectively. Besides companies specialising in data, other more traditional firms, such as consultants and lawyers, are likely to be able to offer new services to help clients use data more effectively. Equally, there may be opportunities for the technology firms which make the chips, sensors and network equipment that helps support the internet of things. All of these new business-to-business services will be driven by companies’ desire to use data more effectively themselves.
Using data to develop new consumer services may be more complex. One potential type of consumer service comes in the form of monitoring services, in which data is provided to consumers to help them manage their everyday lives (as per the previous section). This may be especially prominent in areas like healthcare and transport.
Another type of service could arise from internet of things-based systems. Services which enable people to manage conditions within their home (such as the temperature) or elsewhere could be extremely valuable, although there are likely to be many commercial challenges involved in providing them.
The next blog will look at how to go about creating a market for data, as a new class of asset.