Data, business & the economy 1: The modern factory is a data factory

     Economics = Land, Labour, Capital               [foundation of economics]

                  Economics = Land, Labour, Capital, Data      [...in the information age?]

Data has value; that we know from the exploits of new economy firms Google, Facebook, Baidu et al.

Such startups, primarily data firms (revenue derived from data), are increasing in numbers.

And with machine learning conventional firms are increasing the role of data in their business.  They now see value in data, once discarded from their daily operations.

This is the beginning.

As society digitises, more data will be tapped and monetised to benefit businesses, governments, citizens.  The latter in ways that is unthinkable a decade ago, even now!

What will industry look like as the data economy enters mainstream?  What is the effect on society? Will the suppliers, we, be compensated if data as The Economist suggest is the most valuable resource?  Will governments offer up their massive data (privacy contained and managed) - now sitting fallow, for a fee?  Can this compensate for the tax we pay?  This post ponders.

Once superfluous, data is becoming important for business in the information age

Alibaba rose with a business model that unlike traditional retailers holds no stocks.  Instead it hosts a digital platform for sellers to list their products.  That is, Alibaba makes money by providing data for buyers and sellers to transact.   The foundation of Alibaba business is data.  Likewise, AirBnB’s and Grab’s revenue derives from data, not physical assets.

Most internet startups are either based on data or data forms a crucial part of their business.  This is similar to the early stage of the industrial revolution.  Just as the new firms (Ford, US Steel, Standard Oil, JP Morgan) then created businesses around commodities such as oil, steel and industrial machines, this one is on data, computers and the internet (Uber, Google, Facebook, Ant Financial).

And while there have always been firms profiting from data, it was never at this scale.  The way data is collected is different too.  Unlike the labouring of traditional data firms, these next-gen firms employ crowdsourcing (a method to source from the crowd, individuals, to participate in an activity, paid or unpaid, but of mutual benefit, over the internet (see crowdsourcing; a tool for business), us, largely based on day-to-day usage of services, itself unusual.

Is data or rather the revaluation of data pointing to new dynamics in the economy?

One effect of data on industry – see Datarisation and its transformative impact on industries

Anything that can be digitised, turned into data – products (music, books, software, media), services (voice phone calls, recruitment, photography, computing), transactions (money) -  and delivered over the internet will have its value reduced, some to zero, transforming industry…messaging apps (telephony and telcos), Amazon (books and retailing), Uber (transportation and taxis), Paypal (money and finance industry), Netflix/YouTube/Spotify (video/music and movie industry).

If your industry involves products that can be digitised, it’s a candidate for transformation.

Yes. 

We have bandied the term ‘information age’ about for decades, but never quite figured out what it all means.  We now have an instance, data has value.  It affects the way business is done.

The modern factory is a data factory

The process is similar to the factories we are familiar with.  Instead of raw materials as input and workers turning them into physical products, these new forms of factories take as input data produced from the usage of services (and other sources) using computers to ‘manufacture’ informational and quasi-informational products.  These ‘finished products’ – specific, tailored information are used to support the sales of ads. They also add value to physical products or to services or to businesses while nuggets of information, refined data and data itself have a market.

The difference is that instead of a factory, this modern version is a digital platform.  Instead of miners, consumers produce the raw material.  Instead of the assembly line, networks of computers use analytics, machine learning and AI to assemble insights of information. 

This suggests that a new form of production is emerging just as moving assembly lines did in the industrial one. 

Prof. Yochai Benkler of Yale in his treatise “Coase's Penguin, or Linux and the Nature of

the Firm” suggest a new resource in the production of goods and services through the open source model” …he is referring to a form of crowdsourcing.  With the open source model, participants from all walks of life partake, out of interest, on a project, be it software, an encyclopaedia (Wikipedia) or even shoe designs.  See Open source business model

This also suggests that personal effort, personal data has value.

In the second post here, we ask if the ‘miners’ that is you and I, should be paid and if this solves the issue of personal data privacy.  Further, we’ll argue that governments could monetise the data throve they have, using the proceeds to benefit society, perhaps reduce tax.

Data, business & the economy 2: Should the ‘miners’, that is you and I be paid?

In the prior first part here the value of data is explored, suggesting that the modern factory is a data factory.

Should the ‘miners’, that is you and I be paid?

There are undercurrents.

Jaron Lanier first wrote about payments in his 2013 book, Who owns the future? suggesting that we get paid for the data we provide.  The rationale was that data tech firms treat data as material, which comes to them at no cost. 

Governments have started considering ownership of data, “Ownership of personal data underpins these issues, which is why debates on introducing the concept of ownership of data as a legal right have recently emerged at the EU level and beyond. In 2017, issues concerning ownership of data occupied regulators’ minds in the United Kingdom, Australia, India, as well as the European Union.”…..regulation is probably the only way the data firms could lose control of the data they make their money, if,… and obviously huge battles lies ahead with this approach.  A more amenable way is for the firm to share ownership with the ‘miners’.  Or an indirect way is for the data firm to allow the user to make available his data to, say, insurance firms, so his premiums are lowered.

Berners-Lee of W3C, a consortium that determines Web technology standards, launched in 2018 a development platform called "Solid" aimed at giving users control of their data.

Judging from their high-profit margins, there may be a case. 

And, I think, inevitable that some payments would be made to the ‘miners’ when society adjusts to the new economy, in the form of tokens (exchange for services), or what I call nanopay, tiny amount, say, 0.01 cent or fractions of a cryptocurrency.  Tiny, because consumer’s data in isolation has little value, aggregated it has.

Payment solves the issue of ownership of data and privacy.  If ‘miners’ choose to monetise all or some of it, privacy becomes a non-issue since now they make the decision.  Laws should, however, be enacted to protect the minors and regulations against unscrupulous use of data.

“The majority of people between 18 and 34 would be willing to let insurance companies dig through their digital data from social media to health devices if it meant lowering their premiums, a survey shows. – june ’18

Data related to insurance could be in their personal data wallets that should include compartment for the car (how they drive), health, data from usage of online services, and so on.

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If so, data wallets aplenty

If personal data has value, we will need somewhere to store it.

Data wallet is a contender.

Beginning with basic data such as sites visited, over time this wallet could store our buying habits, usage data, online behaviour and meta-data.

The consumer can then use it to trade for services eg. bus ride (an instance in the US), make a purchase, reduce insurance fees or watch a paid YouTube channel.  It’s bartering all over again, re-imagined and likely through an online exchange.

A retailer may want to be in that exchange to sell, perhaps at a discount, for consumers’ data.  Or pay consumers access to their data for insights.

Data analytics firms will compete to process personal data; for a fee, a digital token, an exchange or even pro bono.  Processed data adds value to the owner.  They will aggregate data from selected consumers based on clients’ requirements.

Privacy, data, ownership & nano-payments

Is there conflict with the digital model of monetising consumer data exchanging it for using a service? The Cambridge Analytica case where Facebook provided the data implies yes.  But that is a case of abuse.  It’s clear now that personal data should not be sloshed about. After the storm settles an appropriate model will result together with new laws.

Will the service providers stop providing free service? Unlikely because they depend on consumer data in the first place.  What’s more likely is a compromise.

Considering the massive profitability of such firms, there could be space to share it with consumers through nano-payments.  The fines being imposed by EU and others to follow could be the starting point to determine the value.

The issue of privacy vanishes if it is the consumer who decides.  Obviously like money, there should be regulations and a legal framework to protect against the unscrupulous.

If this scenario pans out, we determine if we want to share our data.  We determine our own privacy or more specifically the level of privacy and how it is shared; all, some or specific forms for a price.  It recognises that data has value.

Businesses will have their own wallets and similarly used for trade and for leasing.  Perhaps even the government.

And what of governments?

Governments have a lot of data. 

In an era where data has value, would it be allowed to lie fallow in their data vaults?

This data can benefit society.  And businesses, for which they will pay for.

Government data does not have to be released raw to the buyer.  Instead, they could be processed in-house.  Only the results are provided.  Other ways are to supply anonymized data or partially processed data, stripped of consumer names.

Monetised, the government will now have a new source of revenue, businesses get insights to better their businesses, the tax burden on citizens perhaps reduced.

In this scenario, probably shocking to some officials only because it’s radical presently actually benefits the economy and in particular the citizens.  Patient data from government hospitals can help produce better medicine or lower insurance prices.  Municipal data can reduce crime.

However, governments cannot simply provide data but what if this is treated like a municipal license, say, an annual license to access specific slivers of anonymised data under specific terms?  Obviously, privacy must be respected but there are ways to handle this as discussed earlier.  Data regulations and a legal framework should also be in place.

In a digital economy, it would be a waste if data is left idle when it could be optimised to benefit the country.  As economists say, money needs to move around to improve the economy.  If data now has monetary value, wouldn’t this apply?  It should, so the first data regulation, on sovereignty is puzzling.

In the concluding post here, we ask if businesses could monetise their data besides using them to improve business.  And what could the data ecosystem look like as it grows from its beginnings today.

Data, business & the economy 3: What can businesses take from this?

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.

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What is digitisation and how does it affect businesses?

The first part covers digitisation (6 minutes), the rest are pointers for execution (12 minutes).  ‘Misconceptions that can derail execution’ may be useful.

Digitisation is simply how business is carried out over the internet. 

Or to be more complete, business operations; ranging from the sell-side (marketing, sales) to product design, business development, customer services ..….and to tasks like monitoring trends, gauging customers’ wants, customer relationship, many of these in situ.  To be clear, it mostly realigns the processes while also introducing new methods to operate in the digital space.

Digitisation uses the internet or rather its machinery - business models, rules, methods, tools, engagement platforms – and technology (tech), to do that.  It is a new way to carry out business operations, brought about by the reach, borderless nature and scale the internet creates.

Think of digital as another channel to carry out your business, alongside conventional means and digitisation as the mechanism.

Applying digitisation…open culture, externalisation, customer-centric, data-centric

Whether a goal, scheme or simply a task, craft a (digital) plan over it.  Tool it using tech.

In planning, assess the different internet models, digitisation mechanisms that can play a role (eg. co-creation, a form of crowdsourcing), engagement tools (OpenAPI, a tool to link up partners) and digital techniques (machine learning), applying the relevant aspects.  Weigh the plan against the influence of digitisation and effects of tech. That’ll probably modify it. 

The internet can be described as a communications utility with wide global reach, borderless, characterised by being cheap, fast and easy-to-use, allowing services built on it to be provided 24x7 and with immediacy.  These change behaviour. 

Example.  The internet connects buyers directly to sellers, and to information.  Customers now have access to information when once it was restricted.  Such democratisation of information moves the power closer to the buyer.  Consumers are now more comfortable buying directly.  They may buy from sources or places they were once hesitant to.  This is changing commerce.  The middleman model that until now rules commerce is threatened.  This is also why customer experience is now so important.  The internet introduces scale and thus a lot more competitors.

 

Keep an open mind when crafting the plan.  Be wary of old rules, ditch the old ways if they hinder.  Most do.  Think agile, do not over develop it.  Start basic, with a MVP (minimum viable product).  Use data science (collect data, apply algorithms to analyse and derive insights) to monitor and if relevant ask the community (customers, public).  You will get a better idea of the areas to be further developed.  A/B testing (versions of the same product/features/ideas are tested in the market simultaneously) may help here.  Bear in mind the execution should be a dynamic process - pivot the entire plan, a part of the plan or continue with the plan according to the data gathered.

In tooling it, with tech, bear in mind traditional IT.  It may unwittingly hinder execution.

IT, all these while have been dealing with internal operations.  Digitisation is about externalisation, in particular the sell-side.  Different thinking is required, different approaches. See the breakout box below. 

Even corporate websites that embodies digitisation efforts are still mostly version 1.0, designed to be read and made to please senior management with emphasis on good graphic design.  In the digital era, it should be for engagement and made to please customers, with emphasis on speedy interactions, minimising glitzy designs.  The best websites 2.0 are conversational, emphasizing interacting with the audience.  Content should be written for SOE (search engine optimization) so that your site can be found more easily.  Website 1.0 content is taken from their brochures!  Website 2.0 turned into a platform may include private channels like blogs, messaging and perhaps bots (for automated response).  It may link to public digital channels such social media and public blogs for outreach.

 

Digitisation is realised through a digital platform, mobile apps, tech and the public digital channels.  For the latter, besides social media, YouTube and pubic blogs, do not forget the less obvious ones like maps, location-based services like Foursquare and review sites like Glassdoor.  How and which to use obviously depends on the task.

As you execute the plan, continue to think agile.  Listen to your customers then adjust.  We once think we know what’s best for the customers but we don’t, not fully.  The good thing is, now we can, expansively, carefully by listening with intent or better by having a deliberate conversation (community model).

Be open.

If it is a business strategy, have a look at this – how do I create a digital strategy

Some pointers….

Pay particular attention to externalisation aspects.  Digitisation after all is really about external dynamics or a better way to put it - externalisation of business. AirBnB uses external resources, the public and their homes instead of owning them.  The public designs t-shirts for Threadless, saving the costs of designers (see later).

Say, you make and sell specialty mountain bikes.  You may ask…can the customers be engaged to gauge interest in these new features?  Would it be useful for them to play a role in the product life cycle?  Can consumers (read – would-be customers) be involved to design a new bike?  The answer is yes but how can these be built into the plan?  My previous posts, referenced further below, offer some ideas.

Let’s move on to another important topic - the effects of culture.  Digital culture is unlike what we are used to, something to know when executing digital plans.  The culture is open and it is this openness that has led to externalisation, opening up the traditionally closed organisations. 

"Digital culture is all about cooperation, partnerships. Old culture is about internalisation (tasks mostly done in-house) while new culture adds a huge dose of externalisation (tasks carried out through partners, customers, would-be customers, public) to reduce costs but mostly to improve effectiveness." 

Since culture shapes behaviour and affects decision, the team executing digitisation could embrace it.  Internet startups do.

Culture by definition reflects the environment.  Digital culture (it should really be referred to as internet culture) evolve from the environment the internet operates in.  Its scale and borderless nature induces a heightened sense of competitiveness, leading to a customer-centric ethos.  Its wide reach makes us think more in terms of dealing directly, prioritising the direct model in our minds, when pre-internet we simply left it to the middlemen to bridge the gaps.  Similarly pre-internet, we paid little heed to data.  Today, we see value in it. Data in isolation has little value as is small measure of it. The internet ecosystem produces prodigious amount of data while facilitating cross links.

As we apply digitisation, put the customer in the centre.  Digital culture is customer-centric.  Prioritise their engagement experience, iron out the kinks to make it end-to-end.  Minimise friction to buy, for queries, to complain.  Turn them into conversations.  Don’t forget to collect data – it helps fine tune customer experience, continuously.

Organisations tend to favour staff over customers.  Multiple-form filling is an example, forcing customers to replicate the same information.  This culture has to change from making it easier for staff to making it easier for customers.  The aim, always, is to reduce customer effort. Internet startups instinctively get this, birthed into a borderless environment of intense competition.

Always have data in mind.  Collection should begin at the start.  Think through the type of information useful for the business scheme.   Come up with a data plan. 

Finally, develop a community plan.  The community model is especially impactful.  Most internet startups have such a plan in place.  In the case of Brian Chesky, Airbnb co-founder, he is CEO and Head of Community.

When?

Home entrepreneurs already take to social media or marketplaces to sell as a given.  As they grow into small businesses, they could build on their client base, catalyse them into discussions and then open it up by encouraging the public to participate, ie. applying the community model.  Engagement increases sales.  Getting their opinion, say, on brands reinforces relationship and the brands.  Conversation produces data….further aiding sales.  They’ll think in terms of would-be customers rather than the pesky public.

They’ll use the data turning them into insights; preferences, likes/dislikes, trends, etc.  Data science is the term used to do this, using clever algorithms to mine the data.  If you are a small firm, don’t be distracted by such big terms, there are simpler ways to do this.

As engagement increase, producing more and better data, they may find the use of social media and other public platforms limiting.  The now mid-sized businesses could decide to build their own platform to have full control over the process and data. This would be the open platform, designed for engagement, with tools to collect and analyse data.  Algorithms reflecting business processes designed to meet the firm’s objectives are constructed. They also look for specific data.  Besides information, the platform can be tapped for business operations eg. to find proven staff (the existing recruitment process can be a bit hit-and-miss) or experts to help fix a difficult issue.

“No matter who you are, most of the smartest people work for someone else’

        - Bill Joy, co-founder of Sun Microsystems

The community model has been emphasized because businesses hardly use it.  They should.  It’s a powerful digitisation tool that can improve business.  It helps make better decisions by tapping customer insights since they are always right!

“Amazon is letting viewers help choose its new lineup of TV shows, scuttling a secretive, wasteful process once reserved for Hollywood taste-makers. The online retailing giant will let visitors from the U.S, U.K. and Germany watch, rate and critique 14 pilot episodes the company has bankrolled. Viewer comments will help the company decide which shows, if any, get the green light” - 17 April 2013, shootonline.com

“Threadless, an online merchant sells T-shirts but it does not have its own designers.  Instead it runs design competitions online.  Members submit their ideas and then voted on the one they liked best.  Hundreds of thousands of people use the site blogging and chatting about designs and socialising with their fellow enthusiasts.  They also buy a lot of shirts.”

There are obviously many other ways to apply digitisation.  Like science, the foundation is the first step.  Some topics, blog-posted are listed below.  They include numerous use cases.

internet culture - We’re experiencing a shift in culture today, top down command-and-control to
one that is more open & inclusive, thus the explosion of creativity we are witnessing today and the new ways to do things. Since culture shapes behaviour, therefore execution, it’s useful for those charged with digitisation to be familiar with it.

externalisation - Single most significant topic to grasp in order to execute digitisation well

crowdsourcing - A method to tap previously unused resources – consumers, public.

consumer economy - The digital economy has redefined the consumer – once they only consume, now they also produce.  Realise the impact of this change on business as AirBnB have ie. a source of productive resource

community model - Digital engagement method (clients, would-be clients, partners, public) and a method to tap consumers to assist the business

datarisation - Spot industries that are likely to be transformed first.

the.simple model - Internet startups live & breathe this approach as they carry out their plans.

open source model - Lean management, agile, mvp, pivot…source of modern mgt  principles.

free now has value - Free as a business tool has moved from the periphery to mainstream.

peer-to-peer model  -The basis of the sharing economy (Uber, AirBnB), marketplace model (Agoda, Alibaba) and fintech that firms can adapt for their business.

websites 2.0 = Website 1.0, common today is not effective.  Digitisation emanates from website 2.0, reshaped into an open platform, for business.

 

Misconceptions that can derail execution

Digitisation, as with anything new can be confusing.

  Digitisation ≠ tech, tech is one of its tools

Many wrongly associate digitisation with tech as though digitisation is tech and tech is digitisation.   Business is always first, then tech.  Sure, there is interplay that likely remake the business model/process but tech is used to then tool it for the digital channel.  The term ‘digital economy’ which is often spoken in the same breath as digitisation sums it up - it is about the economy.

Digitisation ≠ social, ≠ marketing & sales, it is much more

Another common misconception is that digitisation is equated to marketing, sales and social media.  The sell-side dominates our perception.  Perhaps this is because the marketing folks were among the first to see the sway the internet can do for marketing.  But companies could do better if they look at it from the angle of business operations, of which, marketing is part of.

Digitisation ≠ replace, it is another channel for business

Many think in terms of digital versus traditional business, a monumental distraction.  It’s best to keep ‘versus’ in perspective and be clear that digital is only another channel of business, alongside existing means as newsprint is for newspapers or stores are for retailers.  Instead, and this is important, focus on the core business.  For media, it is content.  Depending on the audience, they could use a combination of newsprint, digital site and third party aggregators (Google news) and the public channels like social media (Twitter for breaking news that feeds into their digital site) or messaging platforms (WeChat).  Strategies must centre on content and their readers, not the channels, a common error.  Similarly a small retailer could use digital to draw customers to its shop, its original sales channel on the high street and at the same time expand the customer base to other towns and beyond.  For a large retailer, digital can be used to engage customers like a small shop owner can, personalised, friendly, knowledgeable (about the customer) and to a far wider client base.  They are selling products.  They should use any channel that helps this core business.

Concluding

We have entered the information age.  Like the preceding industrial era, this changes the way business is done.  And the reason staffing at new economy firms like Amazon (566,000, 2017) are a fraction of the traditional equivalent Walmart (2.3 million, 2017).

After computerisation (1960’s) that improved internal operations efficiency, the introduction of the internet further altered business dynamics.  The internet function as an efficient connector, linking businesses to customers, partners and to the public in ways that alters the relationship, bringing an external element into business operations.  It is these external resources, now tap-able, that changed things.  Crowdsourcing is one method, engaging the public to participate directly and indirectly in an organisation’s operations; in Uber’s case as drivers, with Quora the knowledge, with Amazon their operations.

‘Early on the company hired a lot of editors to write book and music reviews—and then ­decided to use customers’ critiques instead. ‘ - Jeff Bezos's Top 10 Leadership Lessons, Forbes, 4 April 2012.

It is the combination of the internet, computing, clever (new) business models and new culture that brought all these about.  This is digitisation…the way business is carried out over a new channel, the internet.

The groundwork is laid here.  Pay particular attention to externalisation, business by definition is outward facing.

Finally, execute with an open mindset, openness is a critical success factor for digitisation.