It was maybe two years ago when I was first in discussions about data becoming a liability to companies. Until that time it had only been seen as an asset. This thinking is becoming mainstream and is really changing the behavior of companies. The masterminds that were devising models to get more data five years ago are now concentrating on how to make services that come without ‘data liability’ or are simply creating entirely new data models.

A Google search now reveals several articles about data as a liability and I have raised the subject with many significant tech and consumer companies in Silicon Valley. One very significant company has even told me how they are systematically deleting information that is not directly linked to their core business. Some other companies have mentioned how they had earlier offered data as a bargaining chip to get good deals with other companies. But they have now seen this no longer working for them and many are avoiding collecting data because of the potential liability attached to it.

We are still in a watershed moment; some businesses and business leaders are still in the old paradigm that they want to get more data and believe it is key to their business. But the most advanced companies are finding new ways to get value and new customer relationship models so that they can minimize their liability yet still get value from customer knowledge and also find a fair data relationship with their customers.

These changes and new models are not always easy to explain to the old paradigm people. They might think that the only way to use the data is to have it in their own hands. Of course, new data regulations first in the EU and later, for example, in California or New York will also accelerate this change and understanding. However, it is not easy to understand different models that utilize data if you don’t have the basic knowledge of data science and an understanding of software business models and how software is written, used and distributed nowadays.

Data traders and brokers are the first ones to really suffer from this change. It is not only that companies have become less willing to buy data generally, but the reputation and image of the data trading business has suffered significantly. There are many good reasons for this and we can say that not all of those companies have been ethical or transparent in their business – sometimes with operations in a ‘grey’ area.

There are at least three models to handle data in a new way:

1. Consumers and customers own their own data and it can be utilized in the customer relationship with customer’s consent;
2. Data is kept in distributed solutions (e.g. distributed ledgers) and it can be public or semi-public, depending on its implementation;
3. Relevant data is collected and requested only when needed and it is not stored and specifically not distributed to 3rd parties.

Many new solutions can also combine components of models #1 and #2. Model #3 can also utilize the model #1 approach to get data from the customer. We are still in the early phases of these models, but it is clear there are significant development resources in place to get these to happen with forward-looking businesses actively looking for new solutions.

Some ICO companies have introduced models where people could own their data and then sell it in return for some tokens. This model’s most relevant point is that people could get value from their own data, but in reality, it is very hard to get this kind of market to actually work. The idea of data exchanges and markets is looking quite dead. During the 25-year history of Internet services, we have seen many market place ideas that have not worked in real life. The personal data exchange model is probably one of those.

It is relevant that people get fair value from their own data, but it most probably comes in other formats not necessarily suited for sale in an open market place. And how do you price your data? Can you sell it for one-time use only? Some have compared it to selling one’s own organs, and I can see the point in the comparison.

The key for new data models is to find new customer relationship models. How people can get value from their data in daily situations. The value can be better experiences, better prices and more relevant services. The company must be able to serve the customer better, if the customer shares data in the transactions. Technology, including AI, offers many new ways to achieve this.
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Changes take time. Most automobile companies are still making combustion engine cars that are driven by human beings, although everyone knows the future belongs to self-driving electric cars. No serious carmaker can ignore this future and they must also invest in these future cars. It is the same in the data business, many companies must still manage their old model data, but they must prepare for the future of the data business that is much more distributed and customer driven.

One could easily think that IT and digitization are somehow the same thing – or at least support one other. The corporate reality is that it is sometimes the opposite. It is often the legacy IT and the IT department that are the obstacle to new digital models. Is there any way to get traditional IT and digitization to work together or do we need total disruption to change things?

Legacy IT systems have been built to support processes and operating models that were dominant when the original systems or architectures were designed. It generally happened before truly digital companies began to emerge. By digital companies I mean companies that are built on digital data, data-oriented processes and models built on digital customer experience. We can see that companies such as Google, Uber and Amazon are examples of really digital companies.

A former bank executive said to me recently that “he hasn’t invested in bank shares for years and at the bank he felt like he was sitting on a time bomb with core legacy IT systems.” He said that everyone knows they cannot continue like that for long, but it is scary to start to replace systems where most people have their money. New systems might offer better services for lower costs but he is not brave enough to take those steps, because something might go wrong.

New regulation, for example GDPR and PSD2 in Europe, have demonstrated how hard it is to live in the digital era with legacy IT systems. For example, banks should be able to provide data to their customers, but how they do it is not very modern. An executive from another bank told me how they employ someone to manually collect data on an Excel sheet, when someone asks to get his or her data, and then email it to the client.

This is very different from the big public talks about open API banking.

In practice we have also seen that IT departments are typically very skeptical about accepting any new systems, even though top management and business leaders would like them to. Someone could say they are conservative and against change but there are also very practical reasons for this. They have a hard time managing the existing systems and typically it has been hard to get the legacy systems to talk to each other. Each new system has meant expensive system integration projects.

Generally, it is hard for incumbent companies to change and change their operating models. That’s why disruption has happened in many industries and new companies have emerged to kill the old companies. In some cases, the old companies have survived, but most of the new business has gone to the new players (for example media companies, telco carriers and bricks and mortar retailers).

But are there some ways to make the transition. There are no simple solutions, certainly no miracles, but we can suggest some things that can help:

1. The top management must understand technology, digital business models and be brave enough to make mandatory changes early enough,
2. The starting point for new services and processes must be the customer value and customer experience, not technology or internal processes,
3. There are technology solutions, e.g. RPA (robotic process automation) that help to make the transition and enable digital processes on the top of legacy systems, thus getting old and new systems to talk each other and at the same time enable significant information work automatization.
4. Everyone in the organization must be ready and open minded to look for better solutions to serve the customers and offer them value. It means people must go outside their comfort zone and admit their old routines don’t always make sense. It is better to proactive than reactive.
5. Find real facts and not just trust beliefs you like. For example, cloud, automation tools and Machine Learning are not security risks compared to the old in-house system. All systems have risks, and it is important to implement and manage them right. The devil is in the detail.

We will see more significant digitization in most industries. It will kill many big companies and create new significant companies. We will definitely see significant changes in the models to manage and use customer data, build digital processes on that customer data and make all operations be based on that data. Management must have the courage to drive those changes, including a complete transition from legacy IT to totally new systems and models.

That said, there are some softer ways to handle the technological change, but even with those models it is fundamental to keep the focus on customer value, not on internal development.
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Your focus must not be to develop IT, but your customer value and experience.

“The West monetizes web services with the advertising model, and the East monetizes them with FinTech.”

I heard this comment at a think tank event I recently participated in. It is a very interesting observation. Alibaba, WeChat and many other online and mobile services, especially in China, have integrated payments and finance into their services, and it is a fundamental part of monetization for them.

I wrote earlier about how finance is becoming an important component to many other services. People can transfer money in chat and social media services; lending applications are integrated to e-commerce; and real estate services will include finance services and flexible models to adjust ownership based on cash positions.

Compared to many other Internet services, the positive aspect of new finance (FinTech) services is that money is the crucial component, and it is easy to incorporate commissions and other practices as earnings models, compared to many other web services like media and music.

Having said all this, the comment about West vs East is still a very good way to crystalize and illustrate the situation.

At the same event, I was engaged in a discussion about the future of banking services, and it was easy to see that people still live in two very different realities. We have western and some other market banks that see they still dominate the business – they look at their heavy IT and regulation barriers and think no one can threaten them. Then we heard a comment from a young FinTech entrepreneur from China, who told the audience that he hasn’t used cash for months, and actually he doesn’t even like to talk about ‘mobile banking’ – it is simply ‘mobile’. You don’t have to go to specific finance or banking services to handle money transactions  – those transactions are integrated into services you already want to use. Payment and finance is just an enabler in using them.

Some banking and finance sector services also have a role in the future. We will probably have investment banking, funds and many finance instruments in the future, although data analytics and AI will change those services too. But if we think of our daily activities in which we pay and use money, those will be integrated more into the actual services. An important part of this change is the user experience – it is most convenient for the customer when it is not based on the bank’s processes, IT and earning models.

Why the East is leading the way

Why then do we see these differences geographically – i.e. between East and West? Probably there many reasons, but one important reason is how services have been able to leapfrog ahead in emerging markets, where many people have never had or really used traditional banking services, and have gone directly to FinTech services,. We must remember there are 2 billion adults that have no bank account in the world.

Probably it also depends on how willing people are to adopt new services. In some places, people still might like to use cash. But as already old examples like M-Pesa in Kenya demonstrate, when an easy service can fulfill a relevant need, people start to use it. The fundamental factor is to develop these services for real use cases and needs, not simply to implement a fancy technology.

Of course, we have also regulation and safety aspects. These are the components the incumbent actors want to emphasize, basically to calm their own concerns about whether they should really do something or just hope everything is easy for them in the future too. But we have seen that new solutions can also offer even better solutions for regulatory needs. We can have better (and much more convenient) e-KYC solutions; we can build trust in new ways with blockchain; we can provide much better and real time data for regulators, and also have better tools to detect money laundering.

The West also needs better models to monetize new digital services. FinTech is one solution for this globally. It can be financing for your purchases, convenient payments, effective KYC or token-based digital rights management. Maybe the East is now leading the way, but sooner or later it will be a global phenomenon – not just for the unbanked people, but also for Western bank customers,.

Since Web 2.0 became important, many companies have wanted and claimed to create Web 3.0. The label has mainly been artificial. For example, the semantic web has been a candidate for this role, but we haven’t really seen it or what it might mean in practice. Now we again have a strong candidate for this role:  a blockchain-based distributed web.

Web 2.0 means especially more interactive web services, user generated content and social media. It changed internet services significantly from the broadcast model to real interaction between people. Those interactive social media type services now make up a significant part of web services usage time. We can really say Web 2.0 was a change and it was easy to notice this change, although Web 2.0 hasn’t really had an official specification.
The problem with Web 3.0 has been that many companies and people have tried to use it for marketing purposes. It is nice to include it into a business plan covering how to disrupt internet services and pave the way into a new phase. Despite its wide use in marketing, users and service providers haven’t been able to see these changes.

The Web 3.0 label has been put on Semantic Web where computers can understand content, always-on mobile internet, or virtual world web services. The World Wide Web Consortium, W3C, has even created a Semantic Web standard. But it is probably based more on technological dreams than what the users really see and can use today.

Together with blockchain we now see more services and, at least plans, to offer more distributed services. Cryptocurrencies are, of course, an example of these. They are based on models that don’t require a centralized organization or technology to manage and authorize transactions.

Now we see more evidence that these models are not only for cryptocurrencies. Smart contracts are bringing distributed models for many kinds of transactions from buying real estate to managing digital rights for movies and songs. These services are not only going to change web services, but also the role of central ‘authorities’ like notaries, banks and rights owners. We can even see they might challenge governmental services and the role of governments.

At the same time, we see development towards more distributed data on two levels, physically and logically. Physically distributed data means, for example, a local device with AI functionality keeping data locally for several reasons like availability, latency and privacy (read more on MWC2018 on distributed models). An example is self-driving cars that must be independent enough. The logically distributed data means that, for example, users can own their own data, although it is physically in centralized clouds.

This year privacy issues and the rise of blockchain have made distributed data models more relevant. We don’t necessarily need a centralized social media that keeps our data, we can have a service that only shows the data we wish to share to our friends, but we keep it on our own servers (that can be on our account in a cloud). We don’t need a bank or hospital to retain our data, if we can keep our own verified data and use it in services when needed, granting and revoking access on a need-to-know basis.

Timing is always the difficult part to predict. We can be quite sure; the distributed Web is coming. But it is hard to give an exact timetable for it. A breakthrough always requires that several things click at the same time, like availability of technology, the price of technology and user experience. The final breakthrough then might need some lucky coincidences, like one very successful service. After that changes can happen truly rapidly.
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It is more difficult to say if the distributed web is the Web 3.0. And does it really matter? Logically, Web 3.0 should be any big change in the internet services that comes next and really changes the user experience, business models and dominating internet services. In that way, the distributed web is the most promising candidate at the moment for that role.

Changes take time, until they suddenly seem to appear out of nowhere. Financial services are at the cusp of a remarkable change that few bankers realize. The decentralization of technology, new regulations, increasing competitiveness and ecosystem strategies – all these trends will mark the rise of a new era of finance services. This era will be fundamental to end-user value, and those who provide it will thrive.

Grow VC Group has prepared a report to cover the main changes and drivers in finance services. This includes especially FinTech services, which have impacted not only the whole finance industry, but also and on Internet services and business models.

This report covers much more than just the most predominant trends in financial services – it also discusses analogies of data to the oil business, and how new models miust truly spawn the rise of new ecosystems. We discuss the rise of financial institutions as safeguards of your money (as opposed to hiding money under your mattress), as well as their failings (where instead of a mattress, you have an offline wallet in a decentralized ledger system).

Some key questions considered in this report include:

1. Are blockchain and crypto finance real things, or a bad bubble?
2. Why will cloud-based solutions make banking IT obsolete?
3. Why there are so many misleading ideas about AI and robotics in finance?
4. Can banks survive disruption of the finance sector?
5. Data is the new black gold, but what are the right models to utilize it?

No one has explicit answers, but this report offers new insights and angles to find answers. Finance services are a complex combination of services, instruments and technology. A lot of competence is required to develop new services, but it also requires challenging the old models and thinking.

The disruption of finance services is not driven by technology – it is driven by customer needs, and enabled by FinTech. Financial services as they stand today cannot truly meet customer expectations in the current and especially future era of global Internet and mobile connectivity. Financial services firms are also competing with a breadth of services, and they cannot expect to be key contenders in all of them. As startups and technology companies start to offer better services and really compete, the whole financial services industry must react.

The three key technology drivers are:

1. Cloud based open API solutions
2. Better data utilization, including AI
3. Distributed solutions including different forms of blockchain.

Currently the real influence of these components is in the order above, although if you were to examine the public discussion, the order would seem to be the opposite. In reality, cloud-based services have already begun to significantly change finance services development and costs. Data analytics is already very important, whereas AI is more like a nice keyword.

Key transformations to be seen:

1. Customer-centricity in offering design
2. Cultural change in financial services firms
3. Specialization of financial services functions and long-term positions.

The report covers many aspects of the ongoing disruption in finance and Internet services. It cannot cover all aspects, but it is one of the most comprehensive summaries of FinTech, distributed finance models, and finance data services. The report can help identify the key drivers and changes that will impact digital finance services and Internet services in the coming years.

Read the full report here.