Companies have been collecting data for years. Useful data can offer competitive advantages and be the basis for many services and better customer experience. There have also been many companies that have wanted to become data aggregators, collecting and selling data. But the big data success stories are not in selling data. Sometimes data is almost a toxic asset. What can we learn from the ways that data has been best utilized and monetized? We now have the same question with personal data, and many parties want to repeat the same old mistakes.
Fifteen years ago, in one of my earlier startups, we developed a marketing slogan: Data – the black gold of the 21st century. It was and is still a relevant comparison, but to make money from data is very different from the oil business. There you have separate business lines to drill and refine oil and then sell refined products. We can see something similar in the data business, but making big money in the value chain is very different in the oil and data business.
Google, Facebook and Amazon are the superpowers of the data market. They primarily collect and then build services that utilize data. They might buy some third-party data, but it is not their primary way to get data, and they don’t actually sell data. The reputation of companies that focus on trading data is nowadays quite shaky. As a person who runs data operations for a Silicon Valley giant once said to me, they are more and more skeptical about buying data when they don’t know its sources, how accurate it is, how those companies that are selling it got hold of it and how they conduct their businesses.
Don’t get me wrong, some companies make significant revenue by selling data, and some companies spend hundreds of millions buying data. But it hasn’t been an area to build unicorns and companies that shape the world as was expected maybe 10 or 15 years ago. Then there were a lot of expectations for data exchanges and other creative data trading business models.
Today data is traded more like a commodity than a unique source of value add. Companies buy outside data to enrich their data and help their solutions to utilize data better. The real value is achieved when companies build solutions to use data in marketing, sales and operations. One could even claim, the winner doesn’t have the most data, but the best tools to utilize the data. Of course, the Internet giants have heaps of data. Still, banks, telecom carriers and retailers have lots too (and the opportunity to collect more), but they have generally been slow to utilize it. Those successful companies also offer the data’s value to their users, like Google search, maps and other services, and Amazon’s better customer experience.
We are now seeing early days of personal data, i.e. how people can utilize their own data. Some initiatives and companies want to build solutions based on ideological views; people have moral rights to own and control their data. Those haven’t done too well; only a small set of people are interested in these ideological projects.
Then there are those companies that want to help people collect their data and sell it. This has many practical challenges, including how to get a data market to work with enough demand and supply. Pricing is also a complex challenge, as are the associated terms and conditions, whether you sell your data for one purpose and how to track its use. It is not easy to get this personal data market working correctly. The user value promise is often disappointing, like being paid a few dollars monthly to watch ads.
The most obvious option that has worked with the big data businesses for over ten years is forgotten. Why not offer people better tools to collect and utilize their data. When some companies want to help people to control and use their data by selling it, it is similar to recommending Google, Amazon and Facebook to sell all data they collect. Those companies have achieved their current position and power by having top tools to utilize the data they get. It is the same with individuals. If you want to empower them with their data, you need to offer the best tools to utilize that data personally.
Utilizing personal data will include many concepts, and we don’t know them all yet. We need an open market to innovate and develop those tools. But it can have, for example, tools to plan better personal finance, find the best prices, manage better health and wellbeing, and get help in all kinds of daily needs and activities. The longer-term vision is to build personal AI that offers a dashboard to guide all daily activities.
As with data businesses, personal data could also be enriched with external data sources. For example, public data like price comparison, traffic, public health and map data combined with personal data making it more powerful. Data model training for Machine Learning and AI improves when it can use data from many users.
In many ways, the best way to utilize personal data is similar to what the leading data companies have done for years. But it seems that with a new business opportunity, many parties first go to very complex models, like justifying data with ideological thoughts or wanting to build a blockchain-based data exchange with digital rights management systems. Often the simplest and best solution is to copy one that has worked earlier elsewhere.
The article first appeared on Disruptive.Asia.
Artificial Intelligence (AI) is popping up everywhere, at least in discussions. Intelligent systems are being used in many places, and they are becoming smarter. But the real bottleneck is not the intelligence or ‘brains’ of the systems; it’s that AI also needs ‘hands’ to do things.
AI has become a very popular keyword over the last five years. Most company management groups and boards want to see some AI development in their organizations. Unfortunately, the reality, and actual use cases and expectations are not always in line. The biggest problem is not having smart enough machine learning (ML) or AI models to analyze data, handle tasks and make decisions.
Let’s take a simplified AI task. A system collects data, analyzes the data, makes needed conclusions and decisions and sends the results for operative use. If a whole system is built to work around AI, like a self-driving car, the capability to analyze the data and make decisions can be the bottleneck. But most systems are different.
We can take another example utilizing AI – automating insurance claim processing. We have the same phases, but data and interactions with other systems are much more complex:
In this example, we can see that the data analytics and decision-making is a small part of the overall process flow. There are many other parts, especially getting data from several sources, formatting the data, entering decision data to other systems and triggering actions in different systems. And what makes this even more complex is that typically the data is in many different formats and a part of the information is missing or is inaccurate (just think the claim form the policyholder fills and add attachments). Even the case of a data value being “null” needs to be handled, “null” is not “zero” and depending on the data set, it can have meaning or not. There are many handlers needed.
One of my companies implemented this kind of system several years ago. Although it was quite a digitally advanced insurance company and environment (Scandinavia), there was still a lot of work to be done. A typical rule of thumb in the data business is that 60% to 80% of the work is to pre-process the data. This is reality when you try to implement AI in any enterprise with many existing systems, and some of them can be quite old-fashioned. Just think SAP, Netsuite and links to banking systems.
We can even think of a more modern solution to get data from several wearable devices (Apple Watch, Fitbit, Withings, Garmin, Oura, etc.) to one place and bring it into a format that you could build ML/AI solutions on top. Even to collect all that data is not as simple as you would think, even when people talk about open APIs. APIs are still not so common, and while an API will be structured, the quality of data included can vary from one source to another.
A term I have started to like is ‘AI hands’. It means solutions, how to get data collected from many old and new systems, format them in one place and then get the processing results to operative use in other systems. Companies often forget or ignore the development of ‘hands’ when it is fancier to talk about the latest innovations for the ‘brains’. As always, great thinking is rarely enough; you must collect and organize information first and then get things done based on your thoughts.
In reality, these ‘hands’ are like software robots (RPA) that can work with different systems and devices. These include additional software components (e.g. OCR, NLP, data cleaning, APIs) to get the data and trigger actions (e.g. sending emails, start payment, start delivery). Other useful tools are webhooks that can trigger background tasks, for example, in the serverless environment and such as verifying data and running NLP. This means the capability to work with a vast number of different systems and formats.
Open source is often the best way to support many kinds of needs from small and rare systems to major systems. There are many data formats and even unformatted data that no company can implement in their proprietary system. Here, open source is the only option. These ‘hands’ and ‘brains’ should be based on commonly used and widely available programming languages (e.g. Python) that help get ‘brains’ and ‘hands’ work together utilizing open source components.
To get more AI and ML use, we need more and better ‘hands’ for AI. Management groups must also invest in these capabilities if they want to implement and utilize AI. And it is the same with consumer services, someone must offer the solutions where the data is available in a usable format, and there are tools to get results in real use. In last year’s Gartner Hype Cycle, many AI solutions were on the hype peak. AI ‘hands’ are needed to improve productivity.
The article first appeared on Disruptive Asia.
Automation and digitization should increase the productivity of work. But productivity growth has been flat or declining in most developed countries during the past 20 years. This has been visible in countries where most jobs, and especially new jobs, are not in manufacturing, but in services and information work. So, it would be fair to assume that technology and digitization don’t help improve productivity. Henry Ford, Jeff Bezos and Larry Page didn’t win big because they optimized old operations; it’s because they created totally new operating models. Opportunity lies in developing new ways to do things, not optimizing old ones.
World-famous economists, like Daron Acemoglu, Greg Mankiw and advisors of many governments, try to understand reasons for slower productivity growth. I won’t attempt to understand all the macro-economic factors, but to focus on small practical questions like what could be the bottlenecks with digitization and automation of information work.
I wrote earlier about how we need real digitalization, not consulting projects. The problem of many automation and digitization projects is that they just try to optimize the existing processes and implement them in legacy IT systems. Both those processes and systems were developed before the current opportunities of digital services were readily available. The optimal model would be to build new processes with the latest technology focusing on the company’s real value to its customers. If you automate old processes that are unnecessary to offer customers value, it doesn’t improve productivity. That’s why genuinely digital companies like Amazon, Facebook, Google, Netflix, Alibaba and many startups win business from old companies.
It takes quite a lot of courage from management and investors to disrupt old models instead of just trying to ‘optimize’ them. The reality is that to fine-tune old models with old IT could give you a small percentage improvement in productivity, but if you want to achieve much more, maybe 100 or 1,000 per cent gain, you must create new models to operate with the latest technology.
I also wrote earlier about the trending low-code and citizen-development, and how it can rarely help implement robust well-planned solutions. This is another example, why automation of processes doesn’t always bring significant value when citizen-development is trending in automation. Suppose a company must create new models to operate so that customers can communicate digitally with it, and they digitize all internal and supplier interactions. In that case, it doesn’t work if each employee (i.e. citizen-developer) starts to automate their routines from the pre-digital era.
It’s a sad fact that real automation also makes some work unnecessary. If you just let employees automate something they don’t like, it doesn’t make a company significantly more effective. Of course, by getting rid of boring routines, each individual and department can become more effective. But in reality, significant changes need much more fundamental changes. A record shop doesn’t become a new Spotify simply because employees automate some of their routine work. And a bricks-and-mortar retailer doesn’t become a new Amazon when employees automates their routines. Those companies need a new way to operate with new processes and new roles for their employees. Uncovering existing processes and automating them might bring some savings, but if you create new ways to operate based on new tools, you can create a whole new business.
AI, digitization and automation (including RPA, robotic process automation) are at the heart of these changes. They are hype terms nowadays, and it is easy to make fun of them. Their reputations suffer if those technologies are not appropriately utilized; they become window-dressing, like lipstick on a pig. Suppose you put a little bit of AI and a little bit of automation on top of your old processes and systems. In that case, it is not making them more digital or intelligent, and it’s just adding one more layer of complexity and arguably, technical problems. Some companies would like to use machines to observe people and use AI to create automation to perform the same tasks. It sounds like an exciting tech vision, but it’s a strange idea that the optimal model for machines would be to copy how people have done something traditionally.
Henry Ford didn’t build a car for everyone by asking old-style workshop car builders to automate some of their routines. Jeff Bezos didn’t digitalize retail by asking guys who receive telephone orders and fill paper order forms to use VoIP calls and scan order papers. Google founders didn’t revolutionize the online ad business by making an online copy of the yellow pages. They created new models from scratch, how they could offer the best value to their customers with the latest technology. But many companies still try to develop their operations by adding new tricks to old models.
Automation, AI, and digitization will change most businesses, and they will significantly change the way information works. Improving existing processes is a multibillion-dollar opportunity, but creating new, more effective models to operate in hundreds of billions or trillions. Improvements bring short term wins; new operating and business models create companies that prevail in the future.
All these require courage from management and investors. They must be brave enough to discard old models to operate and old systems. It is nice to promise each employee that nothing will change or promise two per cent stable growth to investors. Still, as we have seen in retail, this model leads to huge collapses, significantly when competitors change the business and market rules. Those leaders who want to create big successes should start to build their operations based on software robots, AI and digital processes, not just hope the old models can be done a little bit better. And they should start today.
The article was first published on Disruptive Asia.
A personal trainer gives you instructions on what to do at the gym. In most cases, she or he asks only basic things from you, like your target, to either lose weight or grow muscles, and maybe how often you have visited the gym before. A growing group of wellbeing consultants tell you, how to sleep, eat and work better. They might ask you to keep a sleep and food diary. These days, people have more and more wearable devices to measure daily activities, heartbeat, sleep, blood glucose and many other things. But there is still a very weak link between data, wellbeing and training services. However, this will change.
I have read about sleep consultants whose primary task is to teach people to repeat some words when they try to sleep. They say it helps you to relax and sleep better. However, people nowadays have several devices that measure their sleep, heart rate when they go to sleep, sleep intervals, even body temperature and how tough their day has been. Wouldn’t it be better if those sleep consultants could utilize your data, and not only teach mantras?
During the COVID lockdown, many fitness centers were closed. They started to offer online services, including virtual personal trainer sessions, online exercise classes and videos on how to train at home. But this is mainly one-way communication. The fitness center doesn’t take your data to create a more personalized plan for you. Why not? Technically it would be quite feasible, but they would have to develop new services for this model. Many customers would be ready to pay more for personal services than standard classes.
The world is full of services to lose weight. People pay for online services to get instructions for daily eating and exercising. Some services help track your calories when you record your daily food entries. Most services are still elementary and don’t use data available from wearable devices. Nowadays, you can even track blood glucose in real-time. It would be quite useful with exercise, heart rate and sleep data for personal weight control services.
The wearable market is increasing. The smartwatch market, in particular, is growing steadily, approximately 20% annually based on market research and is expected to reach almost $100 billion by 2027 from $150 billion this year. Smartwatches take market share from some other early devices that only measured steps and heart rate data, basic things. At the same time, new categories are growing, like smart rings (e.g. Oura) and blood glucose, metabolic health apps (e.g. Levels and Veri). Withingswas part of Nokia for some years, but Nokia sold it back to its founders and wrote it off, just when the market started to grow. It is a company that has a more extensive range of products from watches to digital blood pressure and under-mattress sleep tracking equipment.
So, people are starting to gather a lot of personal data. But many people are still confused, how to utilize this data. Apple Health is a service that helps combine data from several devices if you have an iPhone. But it is probably the most confusing and worst UX product Apple has. As with business data, people need tools to utilize the data, and raw data is hard to understand.
There are also other health data sources. DNA tests offer information on personal genetic profiles. Digital health care records are starting to become available in some countries. This data could also be combined with wearable data.
This sounds like a perfect match. Wellbeing services should start to become more personal and based on real data, not only some standard instructions, because people are, in fact, individuals and different. Wearable devices provide more and more data points that are hard to interpret. Both those parties could improve their businesses if they learned to utilize the other party’s services better.
How can this happen in practice? There are, at least, three ways to do this:
Any professional business consultant usually analyzes a company’s numbers and processes before starting to give instructions. It would be bizarre to have a consultant that would try to get a company to better health, without looking at its existing data. But in wellbeing consulting it is still very typical. This will change in the next few years, and we’ll see wellbeing services based on actual personal data. And this market will grow fast; people are ready to pay for better overall health and wellness.
The article first appeared on Disruptive Asia.
When I started my career in the 1990s, I worked as a software developer for a company that produced slot machines and casino systems. One day, a group of consultants popped up to our department. They came to tell us that our software development was not very efficient and that with new visual tools, the same work could be achieved much more effectively. They promised to redesign software for our latest gaming platform in six months with a couple of developers. We had previously taken two years with almost 20 people to do the same thing. Our management bought their story. So, they started to rewrite the software, and from then on, we all had to adapt to drag-and-drop visual state-machine development tools.
The same is happening again. Low-code and citizen-development are trending again, and companies are actively selling their expensive tools allowing anyone to design software or automate tasks. Why have costly developers when you can teach your employees to manage their daily needs with simple drag-and-drop tools? The whole software industry will be changed again!
Office work automation (e.g. RPA tools) is one fashionable area citizen-developers have taken on. So, too, with data applications. Why have expensive data scientists when you can just offer low-code tools to anyone to get information and insight from raw data? I have even heard of those same low-code tools enabling individuals to make apps using their personal health data. Sounds nice?
Three months later, those consultants came back to us. They told us it didn’t make sense to redevelop the whole gaming platform software, but they could create a smaller piece to prove their case. So, it was agreed they would only develop new software with their model and tool in small components, starting with a device that recognized coins when the players entered them.
But is it so simple? Why are the world’s leading software companies in Silicon Valley paying $250,000 annually for good developers, if they can just take random guys from the streets (or at least offices) and get them to make software with low-code tools? Or why complain about a shortage of data scientists, if you can get any office assistant to find relevance from data with low-code tools.
Two more months (total time now five months) and the consultants came back to us. This time, they told it didn’t make sense so they would rewrite the code we had already done. They could write a manual on designing better quality software, and they could also sell their design tool to us so that we could use it to improve our software planning.
Some people build their own home, and others use ready-made design drawings. But would you like to go to a skyscraper or a bridge designed by a ‘citizen civil engineer’? Or would you like to take citizen-pilot flight with an automated aircraft? Why is it necessary to have more expensive professional pilots?
I don’t mean we should have official accreditation to be a software developer, but it’s a fact that the most complex systems in the world nowadays are built with software. It is not simple to build complex critical systems. It is much more complicated than designing a skyscraper or a bridge. For construction, you have precise formulas to make calculations, but many structures of software solutions are so complex that you cannot have formulas or simple models to prove that they work. I have personally seen people with no experience or education, trying to understand how to develop software, especially robust software. It doesn’t work correctly; a study shows eleven of twelve citizen-developer projects fail.
There are tasks people can program easily. Some people make Excel macros for their own purposes. People make some simple tools to help them in daily tasks; they know how to use them, with no need to handle wrong data entries or particular situations. At the same time, it is not ideal to leave more complex software development to citizen-developers with these simplified tools.
It is also good to be clear with definitions. Sometimes low-code marketing uses examples, like design tools, that need no code at all. Low-code is a software development approach that requires little or simplified coding to build applications and processes. So, a drag-and-drop graphics design tool for end-users is not a low-code development tool until you want to convince your audience that it as a great example of low-code.
I was just listening to an organization that has invested in citizen-development tools and used hundreds of hours to teach thousands of their employees how to use these tools. But they can still only do basic things. The management admitted, they would not let them make any mission-critical or important solutions and processes or implement more complex software.
Finally, after six months in my early career case, the consultants could implement no software with their visual tool. They came to us with a manual for better coding and organized a half-day workshop. To be honest, after all these years, I don’t remember too much from that session, but one of their claims was that visual tools are better than software code, because people are naturally visual. Our developers disagreed with them because they didn’t feel these visual tools worked for serious programming needs. After the workshop, we never heard from those consultants, and we continued to make machines with professional programming languages.
Those consultants were paid for those six months and their design tool, then they found the next customer (victim). The same is going on again; companies are buying software licenses and training to get all their people to make software. Don’t get me wrong; I believe software development tools and methods are developing, and many tools can help. But it is crucial to understand the difference between personal tools to automate something or make Excel macros and making reliable software that can run many essential systems and processes. The reality is the world needs more professional software developers and more reliable software. We must not mix professional software development and its tools. With some simplified tools, every office worker can make some macros or automate their own simple tasks; they are totally different domains.
The article first appeared on Disruptive Asia.
This is normally the time to make predictions for the coming year. Typically, the focus is on tech and business trends and evaluate which ones could get next year’s timing right. This time it’s different. In 2020 the pandemic was a disruptor of normal trends. It stopped some businesses, changed some and accelerated others. So, what we can expect to see when vaccines hopefully turn the tide of the pandemic?
If we briefly summarize 2020, it accelerated digital businesses by a few years, stopped travel and hospitality businesses, moved many activities from bricks and mortar to online and taught people to use many new tech tools. In 2021 the questions are, which of these trends will continue, which will turn back time to pre-pandemic and which businesses have changed forever.
One or two years won’t change human beings fundamentally. People can learn to use new services and products, but basic needs don’t change. Let’s take, for example, how people have adapted to food delivery services, but they still want to meet other human beings. People also look for easy solutions but usually hesitate to do things they don’t understand or haven’t tested. But home delivery and Zoom meetings, because they had to be adopted, became everyday options, that we quickly learnt to use effectively.
So, what’s the outlook for 2021? We must think about the things people have learned in 2020 and also what they missed in 2020. Then we must also consider which technologies and services took a leap in 2020. We can also evaluate, which trends started before the pandemic, and those that the pandemic has accelerated. Based on this, we can assess a little more accurately what we can expect to see.
Digital services are helping people in many situations. Virtual meetings help us save time and money. Digital signatures make it easier to handle agreements and use legal services. Home delivery makes grocery shopping more straightforward and faster. Sometimes it is more effective to work from home. These have been obvious changes in 2020, but they are still good examples of trends that will continue after the pandemic.
Airlines, hotels, restaurants and many other hospitality services took quite a beating in 2020. Many people have changed their views on travel and eating out, and are questioning if they need to take so many flights in future. This part is probably much more complicated. People still want to see new places, see other people, and break from daily routines and environment. But at the same time, many businesses are probably having second thoughts on the value of business travel and physical meetings.
People now see the value of physical meetings and hospitality services in a new light, having lived without them for so long. People have also noticed they can work just as effectively from home or remote places. Nevertheless, data indicates that flight bookings for late 2021 are strong and that new business models, like monthly subscription for flights, are emerging.
Retail businesses have suffered most from lockdowns and restrictions. Many retailers, even well-known, long-established department stores and chains, are closing down. But it would be a mistake to think the pandemic has been the only reason for this. Bricks and mortar retail has been in trouble for years, and surprisingly, why it has taken such a long time for some customers to adopt online shopping and use home delivery services.
The COVID situation has not only impacted consumer businesses. B2B business has also changed. We haven’t had trade shows, conferences and meetups to find new products, services and contacts. This has pushed the adoption of ‘self-service’ online sales channels, but at the same time, traditional ’face-to-face’ sales are vital for most B2B businesses. There is no doubt that B2B companies have also suffered, and there will certainly be bankruptcies after the pandemic when companies are forced to take a reality check.
Based on the above, here are some of my predictions for 2021:
The article first appeared on Disruptive Asia.
TikTok is a big success story but also a big political issue. A lesser-known part is how TikTok is disrupting the social network model in its virality. It reminds me of the old debate, which is more important, personal interests or social networks.
Is it possible that the traditional social network concept has reached its limits? Is the TikTok model changing the whole social platform landscape?
Over 15 years ago, a small team and I started what was probably the first social network data analytics company in the world (Xtract). This was well before the success of Facebook, LinkedIn or Twitter. We started to work with different kinds of companies that had some social connection data, including telco and online services. We made tools to analyze the data with the intent of targeting marketing activities.
Our software analyzed billions, even trillions of data points, and we did research, too, on how influence in social networks works. Why would people be influenced by other people to buy something, churn or become active users? The outcome was that it was not only the influencer or social network that mattered. It depended also on the context, for example, which product was in question. It is quite natural to understand how one person can influence you on which car to buy, and another person which books you read, and sometimes your own opinion might matter more than that of your social network.
There are many ways to analyze consumer behavior to understand preferences and how best to profile them. Profiling can be based on all kinds of available data, but we can divide it into four main categories:
Now we come back to TikTok’s model. It has snowballed, with over 500 million users globally. But TikTok is not really a social network service, even though virality is at its core. People are sharing videos, not primarily to their social network, but instead based on categories and hashtags. Users have excellent tools to make their videos, and they can utilize existing ideas and materials, e.g. duets with other videos, and then share them. They can also see how different categories and hashtags get views and also target their videos based on this and in that way to utilize ‘trends’.
This model also gives much more opportunities to new users to attract lots of viewers. In the traditional social network, it takes a time to get contacts and followers. And in the conventional video services (like YouTube) the algorithms favor those who have published for a long time and amassed a large number of views. It is sometimes said the Chinese business model with less respect to IPRs and copyrights allows everyone, every day to take the latest ideas and products and try to make them better for tomorrow. TikTok, in a way, follows that principle, everyone can see the trending content and utilize it to build his or her own success.
This is not only relevant for TikTok and videos. In a recent discussion with the chief scientists of our earlier data analytics company, we came back to the old theories on how personal interests and social networks drive behavior and could we see TikTok phenomena in some other services too.
We concluded that actually, we see limits in social networks in having discussions about interesting topics. For example, on Facebook, your discussions have been limited mainly to people who are your contacts. If you have a special interest area, after a few years with the same friends, it is not so fruitful to discuss there anymore. Hashtags don’t work on Facebook. It is the same issue in many social networking services, including LinkedIn. On Twitter, you can better follow specific topics. Still, it has so many messages that also there you must typically focus on the most popular messages from those who have a lot of followers.
Then we come to another problem of social networks. They have a lot of fake profiles, and people’s networks have been diluted when they have accepted too many friends. So, social network services have a dual problem: they limit your discussions and available content, and they don’t actually represent your real network. For example, if asked by each of your LinkedIn contacts if you would make an introduction to a close contact for each of them? I couldn’t do it because my network is so extensive, and I don’t know all my contacts well enough. When we can only have one network in a service, it includes too many connections for multiple purposes, like building real trust, but too few contacts for special interest area topics.
Could this mean that TikTok is not the only video platform that is a problem for many politicians, but the first sign of a new type of internet service to come? Could we start seeing more services that can combine people’s different interests better, help to get attention to interesting content without a huge follower base and enable us to create social networks around different interest areas and purposes? We would also need services where you can build trust networks for various purposes. Who are people you can recommend, who you trust to get business introductions, who you want to network with for your work, and what is your real personal trust network?
Maybe we will soon step into a post-social-network time that tries to better combine natural behavior with personal interests and different networks for different purposes. This can mean, we see two types of networks: 1) those that enable you to focus on your interests whether music, literature, science, special hobby or whatever; 2) real trust networks for different purposes, for business, personal life, hobbies and personal interests. The current social networks are now too much of everything and too little of anything.
The article first appeared on Disruptive Asia.
The dictionary defines trust as “to believe that someone is good and honest and will not harm you, or that something is safe and reliable.” Trust can be a difficult thing for people to grasp, but in the digital environment, it can be even more complex. We need trust in most daily situations, but with digital, virtual and cyber services such important parts of our lives, we need to better think, what digital trust really is.
The Covid-19 situation has accelerated the use of many virtual and digital services. In early March I was told that I must travel physically to sign an estate inventory for a meeting with other heirs. In April I was told I must not come physically and I must sign documents online. For me, this is a good example, how rapidly things can change, when otherwise it could take 10 years to approve this kind of change for laws and rules.
Even basic things, how to sign documents online is quite a mess today. DocuSign has a good position globally to sign documents, but it is not ‘official’ in all countries or situations. It has great usability, but it includes compromises between usability and security. In some countries authorities, banks or other service providers offer more secure signing solutions, e.g. based on e-ID cards or mobile identity tokens, but they are more difficult to use.
Maybe the strangest document signing was one official service in the USA, where signing was to type my name between slash symbols (seriously, this was the instruction: “The appropriate person must electronically sign the form by personally typing in any combination of alphanumeric characters preceded and followed by the forward-slash symbol (/); e.g., /mike miller/, /efr/, or /374/). This electronic signature should not be typed in by someone else on behalf of the proper signatory.”). Another extreme is my Hong Kong-based bank that compares documents I send to a sample of my signature and every second time I fail to write my signature in the same way.
Signing is just one very simple example of trust, but we have more complex things. Is the person I meet really who they claim to be? Are they going to keep their promise? If I talk confidentially, are they going to keep this information to themselves? If they buy something from me, are they going to pay, or do they have money to pay? These and many other questions in business and personal life crop up.
In physical life, we have solutions to handle several trust questions. People have ID cards to prove their identity. There are systems like credit scores, payslips and financial statements to prove the capability and history to pay. Human beings have also learned all kinds of signs (how people behave, facial expressions, personal history, and many other things) to make estimates, who and what they trust or don’t trust. Often the trust is also transferable. If I trust someone and he recommends that I trust someone he trusts, I will probably trust them.
In the online and digital world, we have more components and variables to evaluate and it makes it more complex to evaluate trust. Maybe we don’t see the other person at all, only his telephone number or email address. If we see someone online, how do you know the person is really who they claim to be. When we physically meet, people build trust with each other over time, but how can this work in the digital environment. If I share some documents and information online with a person, how can I ever know if and how the other person uses and shares them?
We also have solutions to handle these things virtually. For example, we need security devices and apps to get to our bank accounts; companies have access controls to their services and networks to use their virtual tools. For many of these services you still need to do something physically, e.g. visit somewhere or send some documents by mail. But doing something physically first is really a usability challenge for many online services, and COVID-19 has now put us in many situations where it is not even possible.
This is exactly the reason we have lower security in services where usability is better and it is not too difficult to start to use them. DocuSign is enough for many signatures; Zoom is secure enough to handle meetings; WhatsApp is the easy solution for daily chatting and email is the easiest way to send many documents. But we have seen enough cases that these solutions have also their risks, sometimes significant. We know they are enough for most needs, but many needs also go beyond the trust level they can offer.
This has demonstrated, in a very practical way, that we need new solutions to handle digital trust in daily situations. Those solutions need to have good usability and offer the right level of trust for each need. The cybersecurity discussion is easily very polarized. We have cybersecurity freaks that claim no system is secure enough and that no system with ordinary level usability can be secure. Then we have those ignorant people who are ready to use any system that is just an easy solution. We have many kinds of solutions for digital identity and security, but as a whole this area is still quite messy.
One reason is that the thought process to develop them is often very technical and focuses on one specific aspect of security. Maybe we should think more about what trust really means in different situations, and how people have handled it for thousands of years. A simple example is transferable trust or how your personal trust network could help you in digital services. Maybe in that way, we can find concepts and technologies to create real digital trust between people and devices.
The article first appeared on Disruptive Asia.
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:
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:
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:
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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