People networks shape the world. Niall Ferguson’s book The Square and the Tower gives an excellent introduction to their history. Networks have played an important role in politics, business and daily life. They can be very public and transparent networks, or secret societies, or even fictional like parts of the Illuminati network. 

Official organizations can be very different from real networks. We all know companies where the organization chart tells one story about who makes decisions and the actual network of people that make decisions are very different. Networks can also be more dynamic than official organizations, and they can survive changes.

Companies try to become more dynamic and agile. Often organizational structures create friction to be dynamic, react rapidly or to be proactive in business. Organizations themselves could be more dynamic but then comes IT. Processes are applied to complex IT systems, but it is tough to change tools and IT solutions quickly. We have heard stories on how a CEO can use his or her network inside the organization at different levels when some quick changes or new activities are needed, and the organization is too slow to implement them.

Many organization structures and management practices have their history in military organizations. Nowadays, many people hesitate with military management styles in business, because they are seen as old-fashioned, command-and-control models. But it is important to remember that military and security environments can still also offer examples and lessons to very modern organizations.

For example, military organizations have traditionally operated with very formal models. When armies fight against each other, they have front lines, concentrate troops at points where they can make breakthroughs and defend borders. This is no longer the reality. Guerrillas, terrorists, activist cells, unofficial troops (like in the Ukraine) and dynamic networks are a more significant risk to many countries than traditional forces. Fundamental new models are now required to operate and manage military and security organizations. 

Wars in Afghanistan, Iraq, Ukraine and Syria have not been about fighting between official armies, and many countries have seen attacks from local terrorists, and independent cells or individuals that have are often associated with global networks. This has forced military and security organizations to find new models for fighting against these enemies. It also means their own organizations need to be more dynamic. 

Military organizations have traditionally had very hierarchical structures. Their operations and technologies were built to support those models; command chains, rights based on organizational position and limited communications between parallel organizations. Now they have been forced to rethink their existing models. At the same time, consumerization is coming to armies too; people are using mobile phones, social networks and messaging apps during operations. Military organizations can either ignore or ban these tools or start to utilize them. Some have already taken the latter route. It also changes, how organizations operate, and especially how they can become more dynamic networks based on the situations, needs and resources.

Many companies have similar needs to find more dynamic models to operate, adjust processes based on needs and use resources rapidly where needed. This is easily in conflict with the organization charts, fixed procedures and IT systems that support processes, information sharing and communications. These needs are not only inside organizations but also with customers, partners, suppliers and other parties. It is more challenging to create and maintain dynamic networks within traditional organizations and their contact points. Networks can sometimes be different, some more hierarchical, some based on other trust artifacts. 

All this creates new needs with ICT technology to support these networks. In practice, they use informal ways of working, like video phone calls, group emails, and WhatsApp groups. But those unofficial methods don’t really include ways to manage networks, security or the systematic use of different tools. They are used to handle specific needs, not to manage networks. Most business tools have been designed to work in traditional organizations, with hierarchies, formal structures and stability.

Networks are a traditional model for people to cooperate. Digital technology offers more tools to work globally and create all kinds of networks for general or specific needs. But we don’t yet have the tools to operate these digital networks the same way people have learned to manage networks in physical life. They are based on trust that you earn and lose, and they are adjusted to daily needs. We will see new solutions emerging in this area and how the military, businesses and individuals can better create and manage digital networks.

The article first appeared on Disruptive.Asia. 

Picture courtesy Avexer - local trust networks in crisis management.

Wouldn’t it be nice if a web store could propose the exact products you want? Or your online newspaper had news and TV series you are actually interested in? Or a user interface adjusted automatically to your requirements? These have been ideas I have heard many times during my 18 years in the data and analytics business. The problem is that those terms are mainly used by people who don’t know what they are talking about when talking with other people who won’t admit they don’t know what they are talking about.

To simplify, personalization is typically based on one, or a combination of three things:

1. what preferences you indicate (e.g. tick boxes);
2. what you typically buy and how you typically use a service; and
3. what other similar people buy and how they use the service.

But none of these are as simple to realise as one would think.

When you ask preferences from people, most tick all boxes or no boxes, they either don’t concentrate or know what they really want. And if they indicate their preferences today, there is no guarantee they will match tomorrow’s preferences. 

Models can learn from yours and other similar user’s preferences. The system then starts to offer specific offers to you, that you may or may not use to buy those specific things. This in turn reinforces the system believing that you are interested only in those things. It narrows the options and offerings to you and in turn misses many things. The same happens in services like Facebook, and how it selects which people and posts to show in your daily feed.

Another angle is that the system doesn’t even try to serve or help you better. It just tries to maximize sales or keep you engaged in the service. It offers you products and content that you are likely to buy or click. It focuses on maximum, short-term monetization. 

These issues are not new. People who work with personalization, machine learning and analytics have talked about them for over ten years. But it doesn’t stop many people dreaming about personalization, putting it in their business plans and presenting it as a key use case for ML and AI.

It is not impossible that personalization could be more useful and one day we will have really valuable personalization that actually helps users. But it needs much more than what many solutions and business plans offer today. A fundamental starting point is to really understand, what people want to do and achieve in each use case. It is much harder than optimizing some clicks or processes.

Let’s take some simple examples:

• People still like to browse printed papers and online publications, and not simply focus on articles selected for them – a significant part of the browsing experience is to find things you were not specifically looking for or expected.
• Customers also like to walk around in physical stores and browse many products on the Internet, just to see different choices, get new ideas and pass time. Then there are other situations when they just want to make an immediate and specific purchase.
• Your earlier purchases or actions don’t necessarily mean you want to do the same things again.
• Movies and TV series that are recommended to you based on earlier watching behavior may not really be what you want to see, especially when based on a simple categorization of the content that doesn’t really understand your preferences. People can experience similar content in many different ways, which is completely beyond a simple ‘tags’ choice tree. 

These are simple examples, but they illustrate how personalizing an experience is not a simple algorithm achieved by optimizing a few variables. The system should know your preferences now, your state of mind, the real reasons why you have done something earlier, and it should be there to help you, not just to sell you products and services.

Personalization and AI are terms that have been diluted with stupid use and marketing of the terms. Both of them will be very important in the future. But many existing solutions and especially business plans are crap. They are crap produced by people who don’t really understand people’s needs and technology, but love to give the impression that they understand both things.

There is no simple solution to change the situation for the better, but there are certain things that would help, for example:

1. AI and personalization services that work for people that help to make the experience better based on personal data, not just to sell you more or hook you to a service.
2. Models and analytics should be based on richer use of data, not only analyzing actions in one service, but putting them properly in context and anchoring them to your characteristics.
3. Use proper terms for things, e.g. sometimes optimization of the buying process is a more honest and correct term than a fancy AI-based personalization,
4. When people market AI personalization, try to dig deeper into what their system can really do with difficult and concrete questions. Don’t accept statements like, “it is amazing, how great algorithms developed by very smart guys can automatically find you the best options,”
5. And, at least, try to analyze your own behavior in different situations, and see, what kinds of personalization could really help you in daily situations.

​Of course, there are smarter and smarter systems all the time. People are getting worried that AI knows everything about them and can utilize all that data. A system can have too much of your sensitive data, but often systems are more stupid than people expect. Real development happens with solutions that offer specific solutions for specific needs, not with those big plans that claim to solve all needs with big data and general personalization algorithms. And if it is your data in a system you can manage and that works for you, then at least you are represented and know the incentives the ‘intelligence’ works toward.

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At Prifina we are grateful to be able to work in collaboration with leading software developers in different companies and in open collaboration. We’re also fortunate to have the chance to work with amazing new technologies like GraphQL, that unlocks much more flexibility and permeability than their predecessors like the Restful API setup. 

Every once in a while we notice there that something new doesn’t have full support available for something we want to do, where we have to solve what looks like a common problem for ourselves. As we’re building our own solution to what seems like a common problem, our solution is to release it to the public domain. 

This post is about a GraphQL Data Model Editor we've built together with Startup Commons and Grow VC Group and are now releasing, so we can develop it together in collaboration with others that see value in using it.

What we’ve built: The GraphQL Data Model Editor

​Here you can see the clickable prototype. Feel free to add comments, questions or suggestions.

A complete application to create, document (for the business user and the technical user), edit, manage, publish, populate, test, verify and deploy data models in SLS to AWS, and use this model to spin up AWS infrastructure. 

Building and documenting the data model

Publishing and connecting the data model to AWS

In our internal setup, this published SLS data model can be deployed directly into AWS and used to spin up the needed backend setup. It can also be used to make amendments into the existing infrastructure, which naturally requires clear versioning, deployment protocols and access controls. 

Further development

We see the need for further development in at least the following areas:

1. Versioning (think Google docs)
2. User and access management 
3. Setup to spin up fake data en masse to populate and test data model (using e.. Faker https://github.com/marak/Faker.js/)

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How many times have you heard that the current pandemic crisis is also an opportunity? People are repeating the old John F. Kennedy quote about the Chinese word that meant both danger and opportunity. I have now seen dozens of business plans for mobile apps to get COVID-19 under control, a new tool to work remotely, enabling e-healthcare and many other similar ones. They are probably important things, but if you want to build a longer-term sustainable business during a crisis, you must go beyond the ‘obvious’.

It is important to have a concrete need, when you want to build a new product or business but, in reality, it is not so simple. When you read that people want to buy a lot of toilet paper and cans of tuna during a crisis, it can be a good business to go to sell them immediately – if you happen to have them. But it doesn’t mean that you should start building a long-term toilet paper and tuna business from scratch. Furthermore, it may also be a brand and credibility risk to jump on a bandwagon and chase a short-term trend linked to COVID-19, unless you actually have a valuable contribution that isn’t a repurposed afterthought. But an innovation could be, how to make the logistics for these and other products better, or how to guarantee the availability of basic items in all situations or help people to manage their needs better so that they don’t panic.

When the Internet became mainstream in the late 1990’s, many people wanted to start a dotcom business. When 3G came, many parties started developing mobile Internet services. When Apple opened the App Store, people started to make mobile apps. And when blockchain became well-known with bitcoin, people wanted to be bitcoin investors and build blockchain services.

But how many were actually successful? Very few it seems, but at the same time, those were important turning points for many new products and businesses. It is also highlighted that the winners are not necessarily those who go digging for gold in a new area, but who facilitate something new for these areas and people. Think of what Levi Strauss, Wells Fargo and Domingo Ghirardelli did during the California gold rush.

This is a very difficult time for many businesses and entrepreneurs. We will see in the future how high a price we will have paid for the effect of lockdowns in the economy and businesses, as well as indirectly in the lives of people, their health and long-term wellbeing. This is also the time to plan new things and start to implement them, but you must go beyond the daily demand to build something sustainable.

This lockdown period has changed the behavior of people. People have learned to work remotely, order online, use e-health services, study online, handle online meetings, sign and confirm things digitally, agree on a mortgage and home buying digitally, host social after-work sessions and many other things. And there are a lot of much less obvious changes in the market and behavior of people. When you now want to build a longer-term successful business, focus on the projected changes in the market and people, not today’s short-term demands.

It is important to remember that it‘s not just about building new services you believe will have demand in the future. But it might be that you can facilitate a platform, enable development or offer some fundamental components for those services. For example, consumer business is always hard to predict, let alone who is able to make a version that is a big success. But if you can build a component that all those new consumer services like to use, it might be a better bet. It is said, you should first focus to be #1 in your own potential market, no matter how small that market may be, if you have any hope of becoming #1 globally.

Doing things digitally online is now the first layer of most new services. What could be the next step? Data is becoming more important and now governments also want to track your location and health data. This probably raises more fundamental questions about data management and ownership models, when the crisis is over. Or if office work is now done more remotely and with online tools, could we at the same time automate them more? Or if we teach people online, can we also have AI solutions to handle more personal teaching, when a teacher cannot handle all one-to-one questions. Or if people start to make more virtual communities, how to handle trust and ‘hidden-hierarchies’ that exist in all important communities and clubs. Just to highlight some potential trends.

This is a difficult time for many people. Planning and building something new is important not only to create for a future lifestyle or business but also for mental stimulation. It is fundamental to focus on hope and the future. In many businesses and professions, it is also mandatory to focus on new things, if the old models have collapsed. But please, go beyond the obvious. If you see dozens or thousands of other people trying to promote a new idea or business, forget it. Take your time, and try to see and analyze, what is fundamentally behind those obvious phenomena and what value you can create for the long term.

The article first appeared on Disruptive.Asia.

Cash flow management one of the most important management tools for any startup or SME. Especially, it is important in special situations, like the current COVID-19 crisis. There is no miracles, how you get enough money, but at least you must know all the time, how much money you have and how much you will have in the next few weeks or months. it gives to control on the business and time to find solutions.

Grow VC Group together with Startup Commons (its portfolio company) organized a webinar to learn basics about the cash flow management. Jouko Ahvenainen, a serial-entrepreneur, who has seen several external or internal crisis in growth companies talked about tools and practices to predict and manage the company cash flow. The session has many practical tips and models to manage cash flow and also handle cash flow problems. It also reminds, the management team must work with the cash flow, it is not something finance team only does.

​You can watch the whole webinar on this YouTube Link. You can also download the presentation below.

You can also find more about Startup Commons' material that is now free for entrepreneurs here.

A friend recommended that I read an article, how McKinsey Destroyed the Middle Class. It basically summarizes how management consultants and elite business school MBAs had taken a bigger role in many companies, making middle management less important and placing ordinary people to execute simple tasks. The article also emphasizes the political aspect, and why liberals are skeptical about former McKinsey consultant and ex-Presidential candidate, Pete Buttigieg. 

But I think the story is more complex and we can already see transitions in other directions. Management consultants have often created puritanical processes and focus in many companies. But those companies have also become very vulnerable to any changes occurring inside or outside the company. More and more startups challenge them by changing the rules of business, but changes in software and the more important role of developers is also changing this top-down thinking.

We can see how consumerization has spread to companies. Employees use their own tools and software to do work more effectively than the top-down processes and legacy IT systems would do. Some innovative companies, like Supercell, have created models of more independent teams where this is possible. And in Silicon Valley, companies have already become frustrated with the traditional top down model in B2B sales where you can easily spend months or years and millions of dollars to get a corporate deal.

Open source and cloud companies offer software components and services for free or for a very low price and employees can start to use them almost independently. They can sometimes submit those small costs with their normal business expenses and no formal decisions are needed. Software developers, in particular, have felt it very difficult working with legacy IT systems and slow-moving processes when better and more effective solutions are coming to market all the time.

There is a link between elite management consulting principles and people who just start to use tools independently, or developers that make better solutions independently from open source components. The latter group are challenging the power and top down thinking of top management and inflexible models to create things. They feel, they want to make things better and use their own brains to make them better. Some could say this is part of a counterattack of the middle class.

Of course, things are not always so simple. Some visionary people in top management are happy to see this. They can even encourage lower level people to use their own brains and make things more effective and better. But there are also people in the management teams that don’t like this kind of development. They can see it challenges their position, or even worse, they cannot use projects in the traditional way to promote their own career paths inside the organization by taking credit.

I was able to participate in an interesting project of a military organization where they looked for new and more dynamic models to operate, especially in the gray area between a war and peace; Ukraine’s crisis was an example of this. The project included models where local members of the military can organize things bottom up, when something unexpected happens rapidly, and to use their daily tools like mobile phones and messaging services to organize things, yet still keep them in control. It was a very good example, how the use of everyday devices and software have empowered many more people to do things independently. The biggest challenge for the project was that the traditional big-dollar military suppliers and career officers didn’t feel they get much value from it themselves.

I see more examples of this in my daily businesses, for example,

• Prifina is developing solutions where people can manage and use their personal data to challenge the dominance of big data giants. Prifina alone has more than 11,000 developers interested in the project and also make open source components from volunteer work to enable this. There are also other communities and activities in the same field.
• Robocorp enables the use of open source software and cloud services to make RPA solutions. They have created a new job category called Software Robot Developers, basically any talented software developer can start to implement and offer software robots to handle all kinds of daily tasks. This is very different from top down models where consultants identify processes and then hire IT consultants to implement RPA solutions with expensive software licenses.
• Many distributed models, like distributed ledger or distributed AI, are looking for models where independent people and smaller companies can work as an equal partner inside a network, not where a big company makes the rules that impact subcontractors, too.

​We are seeing the dominance of the management and process consultants getting weaker. Of course, things don’t happen overnight, but we have seen the turning point. The political impact can be more complex to predict. And then there are also more complex cases like Uber in the Atlantic article, where there is literally no corporate hierarchy through which drivers can rise up to join management. One can argue it is really an example for top down control, but at the same time it offers more independent work for many people to drive and make money as they wish. In business we see many nuances of these models, politicians today prefer to see things as black or white.

The article first appeared on Distruptive.Asia.

Once upon a time, in a small and dark country, there was a small bank. Well, actually, it was not such a small bank for this country. It was also an important bank for many people, especially in the countryside, because this bank was also near its customers and the local staff really knew those customers personally and had forged meaningful relationships. 

The leader of the bank had a vision. He saw that banking business would change and that it would be better to act before that change happened. He wanted to drive the change, but he was leading the change from the castle in the heart of the capital, and well away from his customers.

They decided to innovate at the bank. They thought that very basic saving accounts and offering loans with heavy paperwork were not the businesses of the future. They thought it would be a great idea to offer people new services they would really want to use and integrate finance options to those services. People, they thought, weren’t normally looking to them for a finance service so why not to offer them together. They could combine their old strength of knowing customers personally and utilizing modern technology and new services.

They hired many new people. People who had technology competence to build new modern services, people who had created new businesses from scratch, and people who had seen elsewhere that it is better to act before disruption destroys a business.

These people were excited about new opportunities to build a totally new business. They got freedom to develop new ideas and implement them. The bank was to have a new and more important role in its customers’ lives.

However, there was also a group of traditional banking folk inside the bank and in the finance sector. They didn’t really like that the bank was starting to do something that was not ‘traditional’. They felt the bank had broken the rules and it became difficult for them to tolerate this rebellion any longer.

Other people inside the bank also felt they were not in their comfort zone, with all kinds of new things happening. They complained, that it was confusing. From this an ‘empire strikes back’ plan was hatched.

When it came time to get a new leader for the bank, it became an opportunity for the old banking gang to get control back. They started their work to ensure the next leader would take a step, no, many steps, backwards.

Their plan worked. They managed to get a traditional finance wizard to lead the bank. He had the track record of doing things the traditional way. He had also read more traditional business management best sellers than most airport bookshops stock. He was an excellent leader to implement the ‘empire strikes back’ plan.

It didn’t take long time for the plan to start working. The bank decided to stop new activities, and focus on traditional banking. People who came to the bank to do new things, decided to leave. And those who decided to stay, started to repeat the mantra “it is great to have a clear focus, it was so confusing earlier,” although earlier they had been excited about new things. But they were also smart people and they wanted to protect their positions under the new leader.

The new leader wanted to make it clear, that this was another new era in the bank. He wanted to take the bank back to the local social clubs of the bankers in the country. They didn’t want to use modern digital technology, instead keeping their old IT model and developing it further.

By adopting a puritan management and process consultant attitude the bank not only killed off the innovate trial and learn model, but it also managed to get rid of the traditional strengths of the company – the local presence and personnel that really knew their customers personally. They wanted to take the HSBC, Chase, or you name it model and adopt it to this small bank without realizing even the big banks were struggling with fintech and other disruptions that were emerging.

It was a successful ‘revenge’ by the old banking gang and process consultants. The new innovations and businesses were killed off within a couple of years, and not only that, but the personal touch went too. No one can say they were ineffective because peace did come back to the bank and the local finance community. People in HQ were happy in their comfort zones and continued their old activities as if nothing had happened.

They are now living happily ever after, or at least they will be, until the day the disruption wave really comes and changes everything. In some markets, it might have arrived already!

The article first appeared on Disruptive.Asia.

Photo: Sleeping Beauty Castle, Wikipedia.

Being a data scientist has been a hot job for several years. Concurrently, we have more and more questions about ethics and who the industry really develops value for. If you are a data scientist and want to create state of the art things, you probably must work for one of the giant data companies and accept their models to utilize data. Could this change soon?

We still remember the days, when you had to work directly or indirectly with a mobile carrier or Nokia, if you wanted to make an application for a phone. It also had a very small likelihood that your app ever got to consumers and basically you did what some business folk had decided people wanted. Then came Apple’s App Store and overnight everyone in garages or bedrooms around the globe were able to make and publish mobile apps. It became a consumer market.

We also know very well what it’s like to work for a bank and create finance services. You are a part of a huge machine and only work on some small components. No wonder, when blockchain opened the market to finance services, it activated millions of people to develop things. Of course, it hit some hype too, which often happens, and we are still in the early days of how distributed ledgers will change many services.

How could the same happen to data scientists and AI developers? Or is it so that Google, Facebook, Amazon, the NSA and some others dominate the data market so overwhelmingly that no one can challenge them? Or at least so that individual developers or small companies cannot ever compete with them?

Personal control and ownership of data are becoming very important. Privacy regulations, like GDPR in the EU and CCPA in California, are only one part of that. More and more companies are emerging to develop solutions for personal data control and also many big companies are starting to see the benefits from the new model. More companies could better compete against the data giants and they could also decrease their own risks and liabilities, if consumers could keep their own data.

There are also technology needs to make more distributed data and AI solutions. For example, many personal assistant-type services require availability, latency and security where it would be better to have local data that is utilized in analytics and AI. We would move from very centralized massive big data cloud services to distributed data in local devices and consumer’s own repositories.

All this will also change the data application market and how to generate business with them. It opens a market to new actors and also independent developers to offer their applications direct to consumers. When consumers have their own data, they are able to utilize many new services and applications in their daily lives. It is similar to over 10 years ago in the mobile application market.

Of course, this needs many components in the ecosystem until it really works properly. We need a framework to develop these applications, an active developer community and enough parties to orchestrate the ecosystem and services. We already see a lot of development in this area, so there are probably not so many missing components anymore.

Whereas data scientist work has been to develop algorithms, make more or less advanced data mining or develop some ad or sales targeting services, this new development could change job descriptions significantly. Those things are all still needed, but there will be more opportunities to innovate totally new services on data, make new business models for consumer data apps and start to offer AI apps directly to consumers too.

We are approaching a disruption point in data services. It won’t only be about privacy, consumer control or distributed data and AI, but it will also introduce a significant change in how data services and applications are developed. It opens business opportunities to new companies and developers. This is development that has also happened in other software areas, from centralized business management driven systems to more independent services, an open market and individual developers. It gives more freedom to consumers, how they want to use data. And for developers, what kind of data applications they really want to develop and bring to market.

You can explore more at Github: “Liberty. Equality. Data.”

Sign up to Prifina’s Developer API.
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The article first appeared on Disruptive.Asia.

Fintech progress seems to be as unclear in Asia as most other regions. China is probably the leading fintech market, but as a whole, fintech development is still more like an evolution for launching new services with some more tech than really disrupting anything. Lending is the most important service category, but it is not always what it looks like.

Autumn has again been the time for several fintech events in Asia. For example, I moderated a couple of Horasis think-tank discussions with top level fintech experts and influencers from China and the rest of Asia. The common conclusion was that it is not easy to summarize the status of fintech. There are several interesting new services and technologies, but nothing has really disrupted traditional finance services – yet.

Typical fintech discussions include comments about Chinese money transfer services, several mobile and online lending services, online identification, KYC and AML solutions and importance of data in all new services. We have seen some big success stories in payment and money transfer services globally, like Stripe, TransferWise and Square, but not too many other unicorns have emerged yet.

Several online and mobile lending services are making significant money. One could ask, is it really fintech, alternative finance or loan shark business. For example, Indonesia has closed several P2P lending services, that haven’t been real P2P lending, but more similar to high interest rate lending to poor people. They include some tech, like loan applications with a mobile app, but it is hard to call them disruptive businesses.

We can say new services are often:

1. New applications to handle the customer interface,
2. Expanding the customer base to formerly unbankable people,
3. Collecting and utilizing more data on these people.

All these things are relevant and can be good for those people who were previously outside traditional banking services, exposed to loan shark business that were using these people. But it is not really about forcing the existing Asian banks to change in any significant way. One discussion I participated in included jokes about how terrible some online banking and mobile services are, when banks have just built some new front-ends onto legacy IT systems that make the services even slower than before.

Blockchain and distributed ledger versions have been the candidates to seriously challenge the old finance systems. Now we have suffered the hangover of the ICO hype and it has cast a shadow on all blockchain solutions. It doesn’t mean that distributed ledger solutions couldn’t be the real challenger, but it will take some time and more solutions that can prove their real value, not just selling fancy tokens.

One panelist crystalized the fintech problem in a nice way: “we don’t see a real disruption until we move from fintech to techfin.” This can really illustrate the problem of slow progress. Most fintech solutions haven’t really been able to use technology to totally change existing services, processes and infrastructure. It has been more about using stepping-stones to introduce some more tech and develop services. Many fintech people also come from the finance industry and try to convince others that strong finance competence is necessary to build new services. But they don’t have an understanding and competence of how technology disruption really works.

Some components that are typically important for faster technology disruption:

I have sometimes summarized those points by saying, when we have the ‘Uber of finance services,’ meaning a company that has resources to make an excellent service, push it to the market and also challenge incumbent players and the old regulation.

It is easy to see that finance services will encounter a significant disruption. The difficult question is, as always, how and when it will happen. Let’s be frank, we are still in the very early phase of fintech and most fintech companies have a hard time to find their purpose and business model. The main reason is that they still simply try to be upgrades to existing finance services and not create new, disruptive and totally digital finance services. Maybe we will never see the big breakthrough to techfin while fintech continues to change finance services.

The article first appeared on Disruptive.Asia.

There are many ways, companies analyze and monitor consumers. They know what you buy, how much money you spend, where you move, what products you search for, what your preferences are and many other things. If we compare this to the military world, it is like one party spying and using sophisticated intelligence to monitor another party in order to identify them, determine what to do next, what is the right timing to do something and what are the strengths and weaknesses of the other party.

But in the military world it is normal that both parties try to collect information, gather intelligence and also have counter-intelligence, otherwise the situation significantly favors the party that dominates information. In the consumer market it is very much that only one other party uses sophisticated intelligence and benefits. How could we change that?

Based on customer data, businesses typically try to understand, for example, the following aspects:

• Who are the best targets for a marketing campaign?
• What is the best next offer and action for each customer?
• How to keep the existing customers and prevent churn?
• What is the price sensitivity for different products for different customer segments?
• Who are the most relevant customers for a company or product to get the best value from the business?

At the same time consumers have many questions too, how they should buy products and services and where, for example:

• Where do I get a product for the best price?
• Which product is the best value for my needs and preferences?
• When should I leave an existing service provider and change to another one?
• What is the best time to buy a product or service?
• Does a loyalty program make sense to me?

Consumers think of these questions quite often and try to find answers to them, but it is typically based on very limited data, analytics and processing capacity. They might have seen a few offers, don’t have all details, then quickly try to make some conclusions in their heads. At the same time, the other side, businesses, use smart algorithms, a lot of collected and purchased data and a lot of cloud computing power to get a consumer ‘on the hook’.

It sounds like an unfair situation. Isn’t it also against a main principle of free markets, that each party should have all relevant information, otherwise the market cannot function properly? At least, if we think of the military example, a military organization would quickly spring to action if the other party dominated the intelligence frontier.

Fortunately, we have now arrived at a situation where consumers could really start their own counter-intelligence action. Only 5 to 10 years ago this would have been very difficult. Technology, regulation and business changes have enabled this, for example:

1. Consumers can now get enough storage and computing capacity for a reasonable price to analyze data.
2. Data and finance regulation (e.g. GDPR, CCPA, PSD2) have entitled consumers to collect their own data from many services.
3. More business and information available online, e.g., product prices with product details. 
4. Machine-to-machine type buying and transactions are becoming possible.
5. Many businesses also see opportunities in more balanced data and analytics models, when now the data giants can easily start to dominate the market.

But we are not there yet. There are some missing components and this is still too complex for consumers. They need better services and user experiences to really achieve it, because it currently requires too much proprietary data collection and technology building.

They need better tools to collect the data from multiple sources and tools consumers can use to manage their own data and find relevant information from businesses. They also need data models that can interface with businesses and better algorithms, especially to fight for consumer’s interests. And we are very close to seeing these tools. 

The last 15 years have made data the epicenter of business, but it hasn’t been balanced development. It has meant the dominance of businesses over consumers, but also on the business side it has been dominance of some companies that have got much more data than others. When enough parties that don’t like that dominance get together things start to change and technology and services are developed to challenge the dominance. We are now at that point – it is the time of consumer counter-intelligence.

The articles first appeared on Disruptive.Asia.