#1 Data: What's so important about data?
Updated: May 5
As we (hopefully) begin the thaw out of lockdown, one thing is increasingly prevalent, whether we recognise it or not – data. As governments begin to allow citizens to circulate, tracking their movements is at the forefront of many policymakers’ minds. Location-tracking data is being implemented and discussed across many States as a mechanism by which to regulate the spread of the coronavirus. The debate on data-driven policy is not new but has typically been consigned to headlines on antitrust investigations or papers in academic journals. Perhaps never has data so blatantly been flaunted before the general public as a source of value.
Over several blogs, we’ll to explore the value of data, rules attached to it and ways in which it has been used and is being used as a mechanism to leverage value. The below aims to be a brief introduction to data – what it is, where it comes from and some early considerations on what’s so important about data, before we look into more specific contexts in future publications.
What do we mean by data?
Data is a broad term. It could be anything from personal data specific to a single individual, or simply information collected. This is endless. Imagine a business – since they’re the highlight of the week right now, let’s take a supermarket, as an example. “Data” includes financial information (such as stock purchases, rent or mortgage payments, interest paid on loans, employee salaries and pension schemes, distributions to any shareholders etc.) as well as business-related information (for example, popular products and brands across different locations, demographics surrounding different stores, projected spending of customers, popular times for customers to shop etc.) and personal information (for example the card details of customers, names of employees and addresses of suppliers).
The stores of data created, collected and utilised are almost inexhaustible. More is generated with every new customer, supplier, product, employee and branch the business encounters.
What do we mean by personal data?
The GDPR is the leading EU authority on personal data protection and we’ll take its definition of personal data as a result. Personal data is any data which is attributable to an identified or identifiable natural person. For example, names, birthdays, identification numbers, location data or a series of special characteristics which express the genetic, mental, social, economic or cultural identity of an individual. Personal data also includes data which can be attributed to an individual, for example credit card details, telephone numbers, addresses and account data.
How is data collected and created?
Consider our earlier example of a supermarket. Some data will be collected, that is directly requested by the businesses from individuals and entities. For example, employees and suppliers will be asked to hand over their names, addresses and bank details and many customers will hand over their card details upon payment. Other data will be observed, that is data the supermarket can produce and analyse as a biproduct of operating its business. For example, supermarkets are able to analyse consumer demand and either stock popular products in bulk, or reproduce own-brand options which can often be sold at a lower cost.
How is data presented?
Consider the differences between data collected, observed and produced by a supermarket in comparison to a law firm. The data described above can often be quickly tabulated and analysed numerically, this is structured data. Other professions and business models, such as law firms, rely more heavily on unstructured data. Firms can, of course, quickly generate data on their clients, suppliers and employees in much the same way as a supermarket. As a service provider, law firms will be less able to track their output than businesses which sell products. Firms deliver legal advice, often buried in emails, calls, contracts and other documents – this data is important to assess market trends and what clients want, but it is not so easy to track.
This is an important consideration to bear in mind when we think of the business models which most heavily generate and utilise data sets to develop their strategy, and implications for where and how AI will be used across various sectors and professions in future.
What’s so important about data?
The above should have given you ideas on how and why data is valuable. For businesses, data collection and analysis can improve efficacy and utility of services or products. Irrespective of whether this is analysis of the business’ own performance, customers, suppliers, competition or markets in general, data is used to tailor business strategies and improve them.
For markets, data provides a real-time map demonstrating the direction of travel. Think in these terms for a moment. Consider any navigation app or device in your car, pocket, home or on your wrist – this technology is capable of telling you which direction you are moving in and what you are likely to encounter along the way. Data allows us to predict the patterns of markets in a similar way – collated data sets help determine the health of an economy, growth segments in markets and the logic behind risk-weighting. The predictability of financial markets is often up for debate and I am by no means suggesting that data offers such a clear road map as you will get if you search for a route on navigation technology. Having said this, data allows us to observe common trends and familiar patterns which we can analyse and use to predict inter alia the emergence of bubbles in the economy and potential growth segments.
For consumers, we also need and value data. We benefit from more efficienct and effective business, tailored to our needs. We benefit from market analytics which offer insight into economic trends. We also value our own personal data and the bubble of data generated as we go about our lives such as the contributions we make to observed data catches.
Data = value. We will explore this simple formula over a series of blogs, assessing what, how and why data is valuable to different stakeholders in varied contexts.