A recent article in the Financial Times doesn’t at first look like it has anything to do with data. It’s about a new ‘ethical’ watch brand, Baume, spun off from Swiss watchmaker Baume & Mercier, owned by in turn by Swiss luxury goods conglomerate Richemont. The new brand is features vegan straps in place of the traditional leather, is constructed from the minimum number of parts possible, meaning that the watches can be disassembled for reuse and has a socially and environmental supply and production chain.
However, several paragraphs into the article comes the telling sentence that reveals just how valuable data has become to the economy and why it is now often referred to as having evolved into a form of ‘currency’:
“Millennials – now the world’s biggest global generation and powerful spenders – are willing to pay more for responsibly derived goods, according to Nielson, a data analytics company”.
Ten years ago, the kind of commercial decision that might lead to a new brand specifically targeting millennials would have been made on a combination of empirical evidence, sales figures from inside the company or its wider group and the output of focus groups. In 2018 these decisions are made based on the output of algorithms crunching vast reams of consumer data.
On Nielsen’s home page, the company sets out its big data sales pitch under the heading ‘The Science Behind What’s Next’:
“The right data can mean the difference between guessing and knowing. Nielsen’s data is backed by real science, so you won’t have to wonder what’s next—you’ll know”.
If one thing moves economies and financial markets more than anything, it is the perceived ratio in the balance between certainty and uncertainty. And it is this ratio that ‘big data’ analysis promises to swing in the direction businesses want – towards certainty. We have now definitively entered the first stage in the evolution of the data economy.
“Ninety percent of the data in the world today was created in the last two years. Between now and 2020, the global volume of digital data is expected to multiply another 40 times or more”.
– Deloitte, Data as the New Currency.
How Data Is Driving the Contemporary Economy
Amazon recently came to the decision to establish 2 new HQs, one in the Virginia suburbs of Washington D.C. and one on New York’s Long Island has been held up as an example of ‘data-driven’ business strategy decision. Aaron Cheris of Bain & Company’s Americas retail practise provided some insight into the decision:
“Jeff (Bezos – Amazon’s CEO) is a data-oriented dude,” They set up an algorithm for where they were going to go [with HQ2], they plug in cities’ offers and data, and what the algorithm says is where they’re going to lean toward”.
So the data was fed through the algorithm and put two locations on an equal pegging but for different reasons. They both had diverging strengths and weaknesses. So based on data, instead of one new HQ, Amazon decided to split the investment and go for two. And it’s not just office locations that Amazon now relies on data to inform decision. The company has taken the responsibility for forecasting demand and negotiating prices away from human analysts and executives, handing it over to data-fuelled algorithms.
For years the game of football (or soccer depending upon where you might be based), poo-pooed the fixation the North American sports of baseball, basketball, ice hockey and American football had on data. The argument was that whatever merits the data-centric approach may have within the context of these sports, they did not, at least in a complete enough way, translate into the context of football. It was ‘too free-flowing’, ‘too complex’ and ‘too chaotic’ for data analysis to be able to provide real insight into the effectiveness of systems or individual player contribution to the team’s overall performance. Respected pundits still routinely add a ‘context’ disclaimer to data points.
Now every major football club makes their recruitment decisions in a way that is heavily influenced by, if not entirely, data. The consultancy 21st Club has developed a tool that, using data, analyses what players do on the pitch in terms of isolated actions, and then places that within the context of the team’s performance over the course of a game.
Each player is assigned a rating that can broken down into granular details such as winning back possession for their team, initiating an attack and many more specific actions. Clubs’ scouting systems can then see if a particular player would, based on their historical data in comparison to the players they currently have on their books, make a difference to their team’s overall level. Current and expected salary demands are even integrated to provide a ‘bang for the buck’ assessment. Only at the point all of the data indicates the players would likely be a positive addition, do the club’s scouts usually actually go and watch the player live.
Source: 21st Club
Data, and even more importantly how it is processed into actionable insights, has become the single biggest driver of developments in the latest technology in the world and how business strategy decisions are being taken.
The next generation of medicine and health care, one that is expected to have the most profound impact on the quality and span of human life since Alexander Fleming 1928 discovery of penicillin, is being developed based on big data. Driverless cars will soon be a reality as a direct result of big data. Digital business models from ‘big tech’ like Facebook and Google to start-ups are almost exclusively data-driven or derive their USP from the ‘smarter’ way they are able to gather and process data.
Thermostats like those of Nest have been in some homes for a few years now but the pace of the IoT (internet of things) revolution is now picking up. The next smoke detector, security system or even fridge, oven, kettle or toaster many of us buy are likely to be new IoT versions of these traditional technologies. The principal of IoT devices is that they constantly transmit data on their own performance and condition, as well as how and when they are used, back to the cloud.
IoT devices should be able to self-diagnose and alert owners to technical faults before they happen, such as the need for a component or filter to be changed or cleaned as well as, in some cases, recommending more energy efficient or lifespan lengthening settings or regimes based on how they are being used. All of these transmissions, processed to the cloud and combined as ‘big data’ sets, will also be used to inform the manufacturer decision on future improvements, both technical and commercial that can be incorporated into future models: in theory, win-win.
How Data Became Currency
Bank of England governor Mark Carney has referred to data as the ‘new oil’. Perhaps ‘commodity’ is a more accurate term than currency when referring to data? Currency is how we create and exchange economic value across geography and through time. It is anything that can serve as a medium of exchange and swapped for goods and services, used to pay debt or to store value for future use. It can be strongly argued that data now embodies each of these characteristics.
Whether data is best defined as a currency or commodity can be put down to a preference in semantics but the core point is encapsulated by one statement in a recent Deloitte report entitled Data as the New Currency:
“…data has an economic value that can be bought, sold, and traded”.
The definition referred specifically to personal data but holds true across the different commercially relevant, which is most, categories of data.
The data economy has rapidly turned into the new reality as the result of the convergence of two technology developments – ‘cloud’ storage and the new generation of CPU processors. Cloud computing allows for the cost-efficient and easy-to-access storage of the huge volumes of data connected devices now generate. And modern CPU processors, extremely powerful and also cheap, mean AI algorithms are able to quickly and accurately process those huge volumes of data, finding patterns within it that offer actionable technological and commercial insight.
Deloitte’s report splits the participants in the new data marketplace into 4 main categories:
Open Data Providers: many organisations, particularly government departments and agencies, collect huge volumes of data as a natural by-product of their core activities. Until recently, most of this data simply sat gathering dust. Now, the combination of open public data policies and new data processing and analysis capabilities, mean this data, fresh and historical, can be put to use.
A perfect example of this is the UK’s Ordnance Survey mapping agency. It has even established a start-up accelerator, Geovation Hub, offering office space and other resources, as well as sometimes even funding, to new companies that analyse and apply its huge repository of geographical data. Ordnance Survey also licenses its data to other government departments and more established companies.
A department founded in 1791 to create maps with primarily military utility, such as where to best place defence units, doesn’t sound like it would be at the vanguard of the data economy revolution. But it holds a two-petabyte trove of geographical data that maps, to within a metre accuracy, the position of every fixed physical object in the whole of the UK. 200 surveyors and a flying unit update it daily with 20,000 new additions and ‘tweaks’.
Innovative use cases for this data mean that the historical agency’s data could well be used by young tech-savvy upstarts to disrupt major industries. Government agencies across the world and across a huge variety of activities hold similar data treasure troves that are just now being opened and their immensely valuable contents sifted through by CPU-powered algorithms.
Data Aggregators: some companies, having spotted the economic value of data early, have made it their business to aggregate and sort data, selling on the processed commodity to other companies, governments or organisations for whom it has commercial or operational value.
Marketing companies, or large retail concerns, are the most willing buyers of this kind of aggregated data. By combining public records data with other records such as email addresses and smartphone and digital footprint generated data such as consumer behaviour transactions and geographical movement, advertisers can gain hugely valuable insight into target audiences.
Data for Service Barterers: ‘free’ services such as social media platforms are, as we all now know, not free at all. We barter our personal data in exchange for use of the service. This is then sold on to the platform’s advertisers, allowing them to create more targeted and successful native social media marketing strategies, which means the original service provider can then charge more for advertising packages.
Data Protectors: the fourth category of actors in the data marketplace is that of the data protectors. Data protectors can be government or intra-governmental regulators and legislators that create and enforce legal frameworks. They can also be private enterprises. A number of companies, such as Personal.com and Reputation.com offer commercial products that allow individuals to control access to their personal data as well as track who is gathering it, what is being collected and how it is then used.
How Can I Invest In the Data Economy?
For anyone investing online who wants early exposure to the development of the data economy, there are a number of options.
Early Adopters – Data-Centric Digital Economy Companies
One is simply to invest in the companies that are the earliest adopters of exploiting the economic value of data such as big tech. This list of companies would include Amazon, Alphabet, Facebook and most other big digital-economy companies. Amazon is a particularly exposed to the data economy. In addition to it’s huge e-commerce and digital content streaming units, its fastest growing unit, and one that many analysts expect to one day in the not so distant future reach a similar scale to that of its retail-facing businesses is Amazon Web Services (AWS). AWS is the company’s cloud computing unit and its growth, close to 50% quarter-on-quarter over 2018, is being fuelled by the data economy, for which it is the market leading storage and processing power provider.
Other smaller companies also make commercial use of data, often in hugely innovative ways. But they either have to pay for access to third party data sets or work from relatively small data sets they have started gathering themselves. The big boys of the digital economy that already own data on hundreds of millions of users are way out ahead. However, that doesn’t mean it is not worth investing in strong companies in the sectors that stand to benefit most from the data economy. Health care providers, pharmaceuticals companies, biotech and payment processors such as Visa are just a handful of examples.
Platforms For Data Mining and Monetising
The most ‘pure’ investment play on the growth of the data economy and data as a currency or commodity are platforms that help their clients mine and monetise data. Amazon’s AWS unit, already mentioned, is one such example. Another is IBM, who were one of the fastest movers in terms of moving to take market share of the data economy. It’s ahead of the competition when it comes to ‘bundling’ its cloud and analytics capabilities as a commercial product. Other traditional hardware and software companies moving quickly to capitalise on the new demand for data services include Oracle and Microsoft.
Data Processing Hardware Manufacturers
All of this processing of big data means the future demand for powerful microchips and processors will be huge. Companies such as Nvidia and the UK’s Arm, acquired last year by Softbank’s Vision Fund, the world’s biggest tech investor, who develop and manufacture chips and processors can be considered investment opportunities that offer strong direct exposure to the data economy. There are plenty of others apart from the two mentioned, such as Intel, that are also stock exchange listed and offer investors choice.
Data Analytics Companies
At the beginning this article we quoted Nielsen, the data analytics company. The company also happens to be listed on the New York stock exchange. There are several other public companies in this space. Some, like Kantar, are part of bigger groups (in this case London-listed WPP) while others are stand alone and can be considered more of a ‘pure’ play. Ipsos and Comscore are two of these.
Most of these companies come from a background of more traditional market research and intelligence and are now moving into big data analytics so potential investors should do their homework on which are strongest in this area.
Data Economy ETFs
There are a number of ETFs that focus on the digital economy and have heavy exposure to the categories of public companies listed above. While, as far as we are aware, there are no ETFs that currently specifically market themselves as indices for the data economy (that is certain to change in the near future), there are several which can be considered reasonable proxies. Some of those worth exploring further are:
- iShares Exponential Technologies ETF
- SPDR Kensho New Economies Composite ETF
- ARK Genomic Revolution Multi-Sector ETF
- The Global X Robotics & Artificial Intelligence ETF
- iShares PHLX Semiconductor ETF
Private Investment – Start-Ups
Finally, high net worth individuals and sophisticated investors might consider investing in a promising start-up with direct exposure to the data economy through the UK government tax-incentivised SEIS or EIS schemes. These allow investors to deduct part of their initial investment from their taxable income from the year as well as providing a tax shelter for future profits, which are exempt of capital gains tax.
And if the investment doesn’t turn out to be successful, investment in start-ups is always high risk regardless of the prospects of the sector, an EIS and SEIS investment that realises a loss can also be partly written off against taxes. All-in-all, up to 65% of the initial capital invested can be tax deductible, hugely increasing the risk level investors take on.
Conclusion
The data economy, and status of data as a currency, is only in its infancy. There is likely to be exponential growth ahead. This means smart investors who gain exposure to the right vehicles have the opportunity to profit. However, as always, a quickly growing sector doesn’t mean every company with exposure to it will be successful. There will be winners and losers. Fast growth also pushes up valuations, which often move ahead of the current market reality on sentiment around future revenue potential. Bubbles can form.
As such, those investing online in publically listed companies in the data economy sector should assess investment opportunities as carefully as they always should and judge each stock on its individual merits and strength as a company. In the case of private investments in data economy-centric start-ups, strong due diligence should be conducted as well as a full understanding that such investments are always high risk and there is a significant chance that the entirety of the investment will be lost.
