In
the first part here the value of data
is explored, suggesting that the modern factory is a data factory
The second here suggests that the ‘data miners’
that is you and I, should be paid and this could solve the issue of personal
data privacy. Further it argues that
governments could monetise the data throve they have, using the proceeds to
benefit society, perhaps reduce tax.
Data sovereignty at odds with
the digital global economy?
Data
sovereignty, to me, was incongruous when governments first raised the issue.
If data has
economic value and is the basis of the digital economy, shouldn’t the principle
of ‘free trade, free movement of goods’ apply as global economic activity
digitises? In this case cross-border
data flow. Data sovereignty impedes the
flow.
Pharmaceuticals
could improve its R&D if they have easier access to consumer data
globally. Global firms that advertise
online would benefit from consumer data worldwide. Banks aiming to take advantage of
digitisation to offer services globally and fintech firms are affected if they
cannot access customer data in another country quickly. Data sovereignty hinders.
Perhaps when
data sovereignty first appeared bureaucrats were not cognizant of the emerging
digital economy. Or they were distracted
by the major data leaks.
The internet is
borderless. That’s how internet business
models and digital commerce operate, on the borderless premise. Restricting the flow of data will be
detrimental to digital commerce. Data
leaks are related to data storage.
Security is really a separate issue.
With the digital
economy gaining significance I expect the notion of data sovereignty to be
diluted, with strict regulations applied only to data with national security
implications and a light touch to the rest.
Processed anonymised data should have freedom. Permissioned data should too. The exception is geopolitics.
Data commerce and data
ecosystem
As the value of
data increases an ecosystem will revolve around it.
Data, by then permissioned and regulated will be traded, by businesses, consumers, government entities and with that the data ecosystem enlarges:
- Data exchanges, marketplaces and a futures market allow buyers, suppliers and brokers to lease, supply, process and trade data. Data could be anonymised, raw, categorised, packaged. They will be stored in secure data vaults, some offline.
- A pharmaceutical firm may lease data from a specialist heart centre to identify traits to improve their R&D into drugs for treating heart diseases. But they may utilise a broker to package data from 10 heart centres around the world instead. Data brokers could source them directly or from the exchanges instead. Transactions would be done on blockchain-based platforms.
- Face recognition tools are available from a cloud provider today. Projecting this into the near future, myriads of data analytical cloud services could allow anyone including the pharmaceutical firm to use specialised tools instead of having an expensive in-house team.
- Because in isolation, consumer data has no value, platforms will emerge to serve as aggregators. Consumers would upload their data wallets into their secure data vaults. These sites would have the tools to analyse consumer data. Independent data analytics firms can draw specific insights, adding value to the consumers.
- Data consultants will know what’s available in the market. They could also to help companies craft data plans.
- New career categories will emerge: data managers, data architects, chief data officer, data consultants, data brokers, data salespersons, community managers, data regulators
What can businesses take from
this?
Data adds value; from insights to improve
business operations from its day-to-day operations to direct monetisation.
Much of the focus
today is on analytics, machine learning and AI to derive the insights. But what if data is looked upon also as a
revenue source?
Like the digital
data firms today. Facebook’s service
generates data for monetisation and also for partners.
Obviously after the Cambridge Analytica fiasco, passing user data to
partners is controversial but perhaps it is a growing up experience. With regulations, proper procedures in place
and allowing only specific forms of data such as permissioned data or
anonymised data and only to qualified partners, society can benefit. Such as better medicines. Users could take a small share of that
revenue.
Conventional firms can do the same, in a
different way.
Here is one.
If you could listen to a group of customers
and consumers chat freely about a product, wouldn’t that information be useful
to a business selling that product? If
it is about features they like, wouldn’t that take the guesswork out of the new
version of the product? Involving
customers is akin to designing a product the consumer wants.
This example
uses a community model. It’s similar to
Facebook platform engaging consumers.
This method, largely bypassed by businesses
but evident with startups, can if applied properly, bring valuable data from
customers. And in many cases, you don’t
need the complexity of data analytics.
The crowd provides the insights.
Create a digital community, say,
of off-road motorcycle enthusiasts around your brand. Participate and listen in to their
conversations. From time to time,
introduce a topic. If it is on ‘useful
add-ons’, the bikers will bring up what they like. That’s input to the product division. Unlike surveys or study groups, where the
participants may feel compelled, the community model provides a natural setting
and a better flow of authentic ideas. Do this in a transparent manner, so they know
what is going on.
How the community manager engages
the community, in particular, the topics introduced, determines the type of
data generated.
Industry would
find such data useful. They would be
willing to pay for it. So besides using
the data internally, they could lease this data (note that this is processed
data so there’s no personal information) but don’t forget to reward those
contributing ideas.
Data, the digital economy and
economics
I’m no economist but data, I’ve noticed in my
years studying the digital economy, is not valued economically and neither are
its impact (and digital models’ impact) on economic measures
such as GDP and productivity appropriately reflected.
Should they?
Consider the following:
- The primary assets of most online firms are now data
- Data is now being deliberately collected as input to businesses, like raw material
- Online firms have a significantly lower number of workers relative to traditional ones
- The open source mechanism replaces internal manpower with external ones
- The sharing economy demonstrate more efficient allocation of resources
- We are witnessing the data industry as a new sector in the economy
They are a new form of inputs in the
production of services and goods, new forms of production, all made possibly
through the internet, digital technology and mechanisms of the digital
economy. A
common factor in these, crowdsourcing (see crowdsourcing; a tool for business), enabled by the
same, has redefined the role of the consumer – once consumer only consumes, now they also produce. And in doing so, created value in data and
introduced a new form of economic production.
Conclusions
Data now has value. With that data
ownership will move back towards the creator, including the consumers. Online firms that depend on consumer data
will remunerate the consumers for the data.
The
socio-economy will alter; with new form of wages and a corresponding change in
the tax code while the concept of jobs is redefined.
Finally, would the foundation of economics be
adjusted, in the information age?
Economics = Land, Labour, Capital [foundation of economics, formulated in the industrial age]
Economics = Land, Labour, Capital, Data […and in the information age]
I think so.
Thanks for
coming this far in the article and be sure to leave your comments.
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