Smart contracts using blockchains could help IoT devices to share data appropriately and securely.

The success of the Internet of Things (IoT) depends on sharing data between different devices but it needs to be done appropriately. But there are some brand new technologies that might support appropriate data sharing and they can do it on a massive scale. 

Schengen Agreement

Sharing (data) is good for us. The more we share then the more perspectives we have on any given consumer or any given problem. In retail this means getting a better understanding of what customers need even before they do. For the IoT this means combining the sensors and abilities of many devices to get a fuller picture of what’s needed and having more options on how to do it.

Firms that manufacture IoT products only get direct data from the customers that use their products. One device only ‘experiences’ a part of a customer’s life. To get a fuller understanding of a single customer’s life different devices need to share data, preferably devices that help the same customers in different ways.

The classic example is a shopping app on your phone that uses data from your refrigerator to suggest recipes based on what you actually have in stock. Or a firm that uses data from all the devices that it has sold in the past to give advice to specific customers, like Waze uses crowd sourced data on traffic jams to warn its users.

But really useful data can come from external sources.  

Just like good feedback and useful suggestions, external information is more likely to give you a new perspective.  So devices that share data with many other devices will tend to get more useful information and make better decisions.  Decisions that are either more personalised, because of complementary information about the same user, or decisions that are chosen from more options, because of information on more user situations.

However, the problem is that firms don’t like to share with other firms for commercial reasons. More importantly, firms can’t just share customer data with any firm that they want to because of data protection legislation. How would you feel if your data was shared with anyone and everyone?

Of course you need some physical way of sharing the data, like Temboo’s stack or a platform like EVRYTHNG. But you also need some rules too.

The problem is that sharing data makes for better user services but sharing data must be done appropriately.

I’ve blogged about this before here, here and here. But now I’ve figured out how to do it using new technologies like smart contracts and blockchains.

Here’s the solution: if you want to share your data, meaning sell it or even just release the value in it for free, then first you need to stop talking about ‘data’. Be more specific. Being very specific is the key to reducing data sharing sensitivities.

If you ask me whether you can share all my data with everyone, everywhere and let them use it as they choose and pass it on to whoever they want to, forever. Then I’m going to get a bit tense.

But if you tell me exactly how you want to use it, why you need to use it, who else might get a look at it and what they are entitled to do with – either the data or the products of the data – and how long this is for. Then I will be much more open to your suggestions.

I still won’t give you my medical data so that your algorithm can charge me a higher insurance premium. And I hate it when it feels like my data is being used to sense when I will pay more for dodgy science fiction books on my Kindle or more expensive airline tickets.

But generally speaking a highly specific definition use of will remove a lot of my reservations. And real anonymisation will remove all of them. Q: who owns this anonymous data? A: dunno.

So how can we apply this to the IoT one devices at a time?

It is relatively easy to run a loyalty card programme because it is centralised and largely automated. But the IoT needs to be decentralised. If every device asks you or its manufacturer for permissions and instructions every time it needs data – or when is asked to supply data – then the IoT will not get very sophisticated.

So how do we ensure highly specific data sharing? Well, we need to design smartness into the IoT. So let’s use smart contracts.

Smart contracts are a mixture of contract law and software. The rules that govern a relationship between two parties are embedded in the software that facilitates the same relationship. To put it another way, the contractual terms are embedded in the thing that they govern. So they control it to help ensure that agreements are kept.

Software is made of rules and instructions for use but contracts also need proofs of things like identity, ownership, endorsements, permissions and rights to do things. And they need to be cryptographically secured, decentralised and machine readable so that every device can automatically ‘negotiate’ with other devices as required.

Fortunately all this can be written into blockchains, the distributed and cryptographically encoded databases that underlie digital currencies. In principle, the data sharing rules for each device can be broadly pre-specified; for consuming third-party data as well for supplying data.

Smart contracts could automate a multitude of interactions between IoT devices and pre-specify what constitutes appropriate behaviour in different circumstances.  Automation is important because the more devices the better and the more granular the interaction then the smoother the service. Pre-specification is important because you do not know which devices your devices will meet.

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IoT paranoia: can your devices trust the other IoT devices that help them?

For the Internet of Things (IoT) to function well then lots of IoT devices need to work together properly. But how can these other devices be trusted? Blockchain technologies might be the answer.

Lego block and chain a

The idea of any device with a chip and a web connection working with any other device has lots of potential. At home your freezer could partner with your cooker to swap ideas for meals and precise cooking instructions that are based on what is actually in your freezer and how your cooker best heats up food.

At work your office devices could work together to help you and your colleagues.  Your car could be operated by your phone and vice versa. The Internet Of Everything means any device could potentially work very closely with any other device.

The trust problem

But what happens if one of these devices has been hacked? Maybe your phone has a virus. Or if you need your car to communicate with someone else’s car – to organise routes or a place to meet up – then maybe the other car will infect yours? So how can we make the IoT secure?

The problem of ‘trust’ is bigger than just avoiding infection. How can you trust a device that you do not own or control? Maybe a toll booth will charge you incorrectly, maybe a person will use a phone app to pretend to be someone else. Maybe an IoT device will just give bad information. The cause might not even be hacking, maybe just a software bug, human error or biases caused by differences in peoples’ taste or perspective.  How can all these be avoided?

The coordination problem

Also, whatever your IoT project is about, the issue is more than just about whether you can trust some device. There is also a coordination problem. The potential of an IoT ecosystem lies in the combined sensors and capabilities of many devices. For example, many cars sharing congestion information is much more valuable than the information from just two cars. So the problem is to get many devices to work appropriately with many other devices. Trust and appropriate behaviour need to be guaranteed for all the devices that work on a particular problem or service.

It all boils down to two problems. How can you trust your own devices not to be hacked and how can you trust devices that you do not control to do what they are supposed to do – in a joined-up way?

How can we be sure of the identities, past behaviours and current permissions of other people and devices? How can we coordinate many devices so that they work in a joined-up way? And how can our devices do this checking automatically?

Fortunately a similar problem has already come up with digital currencies like Bitcoin. Digital currencies also need to be trusted and they need many people to join-up in agreeing that a particular buyer owns the digital cash to be able to pay for something. Buyers want to choose from lots of sellers so all the sellers have to agree that they trust the currency – even though the amount of cash each buyer and seller owns changes each time someone buys something.

Cryptocurrencies, like Bitcoin, solve this problem by using a distributed ledger like blockchain . A distributed ledger is a database of transactions that is shared and checked across many computers. And transactions can be money transfers or they can be IoT devices sharing data with each other.

Solution: You are what you do and what you do can be recorded

Blockchain technology is much more than the foundation of Bitcoin. Transaction data can include peoples’ identities, devices’ identities or any other useful data such as how they behave. Recording transactions makes it possible to know the real identity of every device and what it has done in the past.

New transactions are cryptographically recorded into blocks and each block in the chain of blocks is cryptographically linked to the previous block to stop tampering. The data in each block is encoded and part of that code is based on the contents of the previous block. To successfully tamper with this record would mean hacking all the computers in the network simultaneously, whilst at the same time guessing how to decrypt each block and the links between them.

Blockchain technology preserves trust in three ways: multiple copies of a single blockchain are shared and continuously checked; the data in each block is encrypted and to decode one block you need to decode the preceding blocks.

So, the problem of IoT devices trusting each other could be solved by using a blockchain technology to encrypt recordings of past behaviours and current permissions. And the problem of coordinating many devices could be solved by sharing a single blockchain database across a network of devices.

Of course there are many distributed ledger technologies and many types of blockchains. My point is that technologies like blockchain solve the trust problem by continuously checking multiple copies of a database that is securely synced.  And automatically synchronising multiple copies of the same database is also a strong basis for coordinating multiple devices.

Trust and coordination are two things that IoT ecosystems will badly need. So blockchain technology looks like it will be a foundation of the IoT ecosystem and much more.

Privacy: Is there a missing third party in our emerging Big Data society? (new white paper)

Personal data can be used for great harm as well as for great good. The more that data is shared between organisations then the more value it can create.

But personal privacy is becoming more and more of an issue, although the way firms handle, share and reuse data is much too complicated for most individuals to be fully aware of or able to deal with if data is mishandled.

For the last year I’ve been running roundtables, interviewing experts and going to workshops to try to look for some answers to these problems. This white paper explains some findings so far.

Personal Big Data: Is there a missing third party in our emerging Big Data society?

 

Executive summary

New Big Data technologies are rapidly changing marketing, healthcare, government, financial services, retailers and whole supply chains.

We are rushing towards a ‘Big Data society’ that is using data analytics to more efficiently target resources and to deliver incredibly personalised user experiences. But the precise use of resources and the personalised delivery of services require access to deeply personal consumer data.

Personal data can be used for great harm as well as for great good. The more that data is shared between organisations then the more value it can create – and the more difficult it is to control who uses it and what they use it for.

The change in how organisations use our personal data is happening whether we like it or not and we risk destroying trust if consumers are harmed or even surprised, by how their personal data is used. We need consumers to trust how their data is used or they will be slower to engage by sharing their data. This will delay the benefits of a Big Data society and leave the UK to be potentially overtaken by other countries with a different view of the importance of consumer trust.

But current systems of legislation and regulation are based on older technologies and ways of working that did not include cheap access to mass data sharing capabilities and personalised data analysis in real-time.

Our investigation incorporates the views of experts from regulators, government, commercial data firms and consumer privacy organisations. It concludes that there are several missing roles in our emerging Big Data society – a missing ‘Third Party’.

This ‘Third Party’ would support individual consumers to deal with networks of large and small firms; help firms to share and use data in new ways in return for doing so appropriately; aid regulators to bridge the gap between the market and individual consumers, staff and firms; and give privacy and consumer organisations a platform to help more consumers and to engage with more firms.

We propose a solution, a design for a ‘Third Party’ that engages the attention and resources of the different stakeholders to watch and help each other. Firms would have a strong interest in behaving appropriately; and in turn they would encourage their staff to behave appropriately and become more successful in the process.

Here is the full white paper: Personal Big Data white paper 3.0.

Common threads running through three recent Big Data roundtables

Holy-grail-round-table-bnf-ms-120-f524v-14th-detail

Source: Wikimedia commons

In the last few weeks I’ve been to three roundtables that were full of experts on Big Data and Big Data analytics or business users of the insights from such analytics. There were also a few Members of Parliament and senior industry regulators.

The first roundtable was at Econsultancy, the second was at the think tank Reform and the third was a Personal Big Data roundtable. These round tables addressed three very different aspects of Big Data but there are some common threads that stretched through all of them.

The first common thread was the richness and variety of the topics that we discussed. Big Data is a new and emerging set of technologies and right now we are at Big Data 1.0 not Big Data 2.0. The discussion at these roundtables, just like Big Data articles on the web, was as unstructured as Big Data is itself. When a structure forms we will call it Big Data 2.0.

Some people were focused on the hardware, some liked to talk about the data it handles. There was a huge amount of discussion about ‘data’ and less about what data or which data. Indeed there was a general thirst for examples, case studies and illustrations of uses of Big Data.

There were also lots and lots of metaphors like ‘Data is the new oil’ or my own biased favourite ‘Big Data is like the minute-to-minute personal diary of everyone and everything’. When it is unclear what ‘something’ is, a something that is emerging as we develop how we use a these technologies, then metaphors are very useful. They help use to generate potential forms that we can check and test for usefulness.

Even the experts have different points of view and many questions about what form, or forms, Big Data will take. However, there are a few rough characteristics starting to take shape and this is what I hope to describe here.

The second thread that ran through these roundtables was that there was more talk of the hardware and the data themselves rather than of the actual services that Big Data analytics could create.

It is relatively easy to deconstruct a service after it has proved highly popular. But thinking up that highly popular service in the first place is very hard. Right now we have some new hardware, access to vast amounts of raw material data and a complicated range of analytical tools but it is unclear how to combine all these into specific configurations that produce the ‘killer apps’.

One way around this might be to start out with some commonly valued objectives and work backwards to try and connect them to the outputs that we know that our new analytical techniques can produce.

For example, both government and industry are perennially keen to [1] increase services or sales, and [2] make savings. And we know that a key role of these emerging analytical techniques is to help us accurately understand the needs of people – on a more personal and individual basis.

So we should be looking for analytical techniques that suggest the unmet needs of citizens and customers – because knowing unmet needs helps us to increase services or sales. And more precisely tailoring the services that we already provide could reduce wasted resources and make savings.

These analytical techniques are based on analysing the individual interest graphs and contexts of peoples’ lives, e.g. here, and they are the foundation of Big Data services.

The third thread was about balancing the societal and individual privacy aspects of Big Data. Economic growth from new Big Data firms and services depends on consumer trust. But these services depend on organisations sharing consumers’ data between themselves.

Few organisations share enough of a person’s life to understand their needs very deeply. But sharing data for good or for profit generates questions like How do I control my data? How do I share in the value that it is used to create? and How do I fix it when my data is hacked or stolen?

The Personal Big Data roundtable in March brought together some of the leading experts in data analytics, retail, healthcare, financial services and some key industry regulators. These questions were at the top of our agenda but they were also touched on in the other two roundtables.

The point is this: consumer trust depends on regulation, which depends on legislation, which in turn depends on policy. But current regulation, legislation and policy are inadequate for handling the opportunities and dangers that Big Data presents society – they are not so much out of date, it is more that they have been made technologically irrelevant.

From my research I am starting to see how the regulation and legislation could be developed in order to support the societal benefits that we hope to gain. To do this we need to help legislators and regulators to start this change process – stories and case studies will help but there are no case studies for some of the more complicated inter-relationships and business models that are yet to emerge.

The forth thread concerned the people, citizens and customers, that we are describing increasingly accurately with these new technologies. People do not only vary in terms of their needs for different services, which is why we analyse their data. They also vary in their attitudes to privacy – people exist on a spectrum of sensitivity with some not caring about data privacy and some being highly sensitive.

Also, people rarely read through user agreements – they do not have the time nor the training, e.g. when they download an app that will give the app firm access to the content of their mobile phone and their location on a 24 hour a day real-time basis.

But most interestingly of all, when you spend a lot of time surrounded by experts, it is worth noting that most people lack an awareness of just how the technologies that I talk about here are changing their personal and work lives right now. There is a huge need for education and awareness if people are to get the most out of these new services and use them safely.

There are two main implications from the discussions that I have had the pleasure of being part of in the last few weeks.

The first is that there seems to be some vacant niches in the Big Data ecosystem, to use another metaphor. There are some unfilled roles, like a broker that would manage a person’s data and deal with firms on their behalf; a defender of a person against harm; a fixer of such harm; an educator that teaches people what they need to know about our unfolding Big Data society; a new form of regulator to uphold public interest; or even a third party ‘dating agency’ for firms and their data.

These roles might not exist within the same organisation. Indeed some of these roles may be taken up by regulators or they may fall to multiple competing Third Parties rather than a single organisation.

The second implication is that there is a huge and complex gap between the raw material Big Data, on the one hand, and the consumer needs that it could be used to satisfy on the other. We know that we have lots of data and we know that we can buy-in, swap or access more data. We know that we have some sexy, fast, new hardware and unbelievably clever analytical software.  We even know that we want to hit the same old organisational targets of doing more with less.

But we do not know which particular data to use; which particular software to install and learn to use; which specific way of using the software, which analytical services to produce out of all those that we could; which consumer needs to target, even which consumers to target. The huge and complex gap is made up of all the dependencies in the last sentence and we are only now starting to come up with Analytical Strategies that can bridge it.

Mobile Big Data: how to link very large scale analytics with very small scale personal needs.

Finger of God

[Source: Wikimedia Commons]

Retail is being drastically changed by new digital technologies like Big Data analytics and the services that mobile smart phones can deliver. Big Data has been discussed a lot but there is little analysis on what it can specifically do for firms and their customers.

Also, apps that run on Mobile devices, like smart phones and tablets are revolutionizing multi-channel retailing and customer relationship management. But mobile apps are usually just catalogues and directories when they could be personal shopping ‘sat navs’like your best friend owned the store.

Most pressingly of all, there is very little useful thinking, or practical advice, available on the overlap between Big Data analytics and personalised services that are delivered by your phone, i.e. the links between the outputs of very large scale analytics and the very small scale personal needs of individuals.

Firms are still working out what they can do with these technologies. Customers are still deciding how they want to use them.

We need a roadmap

New digital technologies are producing a bewildering number of options: new shopping and customer relationship technologies, new Big Data information resources, new analytical possibilities and new business strategies.

Firms need an underlying roadmap for taking advantage of these new tools and resources as they unfold and develop. We need to connect back to the fundamental business objectives of commercial success and amazing customer service in the face of a chaotic digital landscape.

This is the first of three linked posts that show the significance of these technology-driven changes; explain the underlying processes at work; point out the business challenges on the horizon; and map out the strategic options that are now possible for retailers, their customers and the brands that they partner with.

Part One covers how new Big Data and mobile technologies are changing marketing, retail and the rest of business from a business strategy perspective rather than from a technological perspective.

Part Two maps out and explains the confusing new options and approaches that these new digital technologies are now making possible. From the retailer’s perspective, from the customer’s perspective and from a business strategy perspective.

Part Three explains some ideas on how to deal with the emerging possibilities described in the first two parts – from a strategic and analytical perspective, then from an implementation perspective and then with a consideration of how these fascinating changes will continue to unfold.

Part One: A mobile app is like the finger of God

Mobile apps are location-here, segment-of-one, stage-of-now, and downloaded by YOU.

Every app can potentially give you incredibly personalised recommendations, suggestions and advice based on knowing where you are, who you are, being with you every minute of the day and being trusted by you (you downloaded it).

These dimensions are revolutionising how firms communicate with you, learn about you and produce services for you. Everyone you know and most people that you don’t know also has a phone so you could multiply the previous sentence’s possibilities by most of the human population.

But neither firms nor consumers have fully worked out how to use these dimensions. Using mobile phones to figure out a person’s location has grabbed a lot of headlines and initiated a few start-ups and service features like geofencing.

Services that run on smart phones naturally segment customer populations down to a single individual because we rarely share our phones. So phones are platforms for personalisation, i.e. precise learning about individual needs as well as giving customised advice and information.

Also, we carry our phones around everywhere and a lot of people never switch them off – so services can potentially be real-time and anytime, whenever customers need them. Most importantly of all, customers choose to download the app and then give the app the permissions it needs to work.

So services that are delivered by phone apps [or mobile sites] have the capability for complete personalisation in terms of ‘where’, ‘who’ and ‘when’ plus they are a potential bridge for two-way exchange of information.

What goes around comes around

Complete ‘who’, ‘when’ and ‘where’ personalisation is great but an app [and the huge supply chain behind it] needs to know which service options, product variants, SKU number, model, user experience configuration or other permutation of what it could potentially provide is best for each particular ‘who’, ‘when’ and ‘where’ customer at the moment that they tap the screen to say they want it.

This need to decide which service would best fit the immediate needs of a specific consumer will be even more pressing when apps suggests useful things without an actual request from the consumer. Like Google Now is starting to do.

The absolute best thing about mobile phone apps and services is not their in-build sense of ‘who’, ‘when’ and ‘where’ – it’s the information that the consumer gives to the app owner or service provider [and the huge supply chain behind it] plus the permissions to use it.

Mobile apps are not just a bridge to God-like services – they are a two-way bridge to God-like services.

‘God-like’ means not quite omniscient, i.e. you have to give the app some clues as to what you need from it.  Mobile apps know who you are [you registered], when you ask for something or contextually might need something [they are always on] and where you are [they are in your pocket]. So if the app does not abuse the data permissions that you give it [an unresolved issue] and keeps being indispensible then it can potentially act as the ultimate loyalty card.

Introducing the ultimate loyalty card – the Mobile + Big Data version

Big Data is the minute-to-minute personal diary of everyone and everything. Mobile apps have the potential to be the pen that writes your Big Data diary.

All our on-line transactions, communications and surfings are recorded and increasingly stored, analysed and used. But there are still vast gaps in our personal Big Data diaries. For example, a huge retailer like Tesco with a tremendously sophisticated loyalty programme, like Clubcard, only directly knows about the part of your life that is your shopping-life. It can buy-in shared data from its partners to get a better insight into your specific needs. But it can never know your full Big Data diary.

But a mobile app [or collection of apps and mobile services] – that you trust enough to give the right permissions to about collecting your personal data and that you interact with as you go through your life events – could potentially know your whole Big Data diary. The Big Data diary of your on-going minute-to-minute life.

Just think of how helpful [or dangerous] this could be. Just think of the services, new services and benefits to society that this could be the platform for. Nobody could accuse those people behind Siri and Google Now of aiming too low.

Linking smart phones and Big Data means everything in the cloud delivered to you, right here and now. The intimacy, immediacy and relevance of smart phone apps is combining with the vast scale and power of Big Data and the cloud.

This generates many confusing and unfolding possibilities. A massive aggregation of information and other resources is combining with a highly specific understanding of customised requirements. Very large scale Business Intelligence is colliding with very small scale human needs.

The scale and variety of Mobile Big Data is both an enabler and a barrier. For firms and government it changes the problem from ‘we would like to do’ to ‘which should we do?’ plus ‘are we sure that’s the best thing we could do?’ There are too many new possibilities in too many new areas.

Part Two maps out and explains the confusing new options and approaches that Mobile + Big Data is now making possible. It does this from the retailer’s perspective, from the customer’s perspective and from a business strategy perspective.

The flipside of the personal data coin: useful suggestions on one side and ownership, control and responsibility on the other

Think about how Tesco Clubcard and many other firms are finding out more and more of our needs, interests and other personal data – in fact every time we use a phone or a web browser to make a transaction or to communicate with friends we generate data that firms collect and can then analyse. These are the firms that we buy things from or that we ask for information to help us buy things. Or they are the firms whose services help us in other ways.

Whatever you do online generates data – when you search, when you click through web sites, when you register for information or when you ask questions you are generating data for those firms that are able to see what you are doing. Your broadband supplier, your web browser supplier and the owners of the web sites that you visit all know what you do because it is their service that helps you do it.

Firms can also get very detailed location information and timing information – especially when you use your phone for browsing or on the web. And that’s great when they use it for suggesting really useful things that you would never have thought of – like Amazon’s ‘People who bought these books also bought these’.

But what happens when firms lose your data or when they get hacked? (And many will) So how can you be protected? What happens when you say ‘its my data, I want to control how it is used!’ And what about sharing the value that this data generates with the person it is about, i.e. me?

So, on one side of the coin there’s the increasingly detailed personal information (Interests, Location, Time) that firms can use to help people a lot and create immense value. But on the other side of the coin there are unresolved issues of control, share of value and fixing damage when its lost or stolen. This general lack of clarity about control, value and fixing damage also means that the role of the regulators is also unclear because these issues are what the regulators’ roles are based on.

I think I have a solution to most of the problem of fixing damage, and the control problem, but I’d be interested to hear of any ideas that help with the share of value problem. I know how to create it, read my blog posts, but sharing value is different.

For example, what if I buy tomatoes from Sainsbury’s and then Sainsbury’s sells data on my buying behaviour to its tomato supplier so as to help it produce ‘better’ tomatoes. Then what if the tomato supplier makes more profit from ‘better’ tomatoes, e.g. because they are more popular. Sainsbury’s might have a clever deal to get part of that value. Like a reduced price on he supplied tomatoes or a percentage of the tomato supplier’s increase in profit.

But how can I get part of that increased profit? Should I get part?

I’m looking for different ways to look at this issue and for some underlying logic to work out how to share the value that is created. Any suggestions?

They are looking at you: Google’s telescope versus Facebook’s telescope.

I love the way that Google+ is an even bigger reason to login and give Google a token to link together all my other interactions with Google products. Each of which tells Google a little bit about what I’m interested in.

Google products are not there to get customers to use the web, they’re there to watch customers use it. I work with loyalty card and customer data firms and Google’s array of products give a much better cumulative view of each customer’s interests than any single big retailer’s loyalty scheme, even those of Tesco Clubcard or Boots Advantage Card in the UK.

Think  about those lines of small radio telescopes that you see in the desert. Astronomers combine the data from each of the perspectives of the individual dishes into one big view – and the wider the telescopes are spaced out then the broader the perspectives have access to and the more insight they can gain.

In digital marketing and ecommerce insights are about ‘who’ is interested in ‘what’ products and services, ‘when’ and ‘where’ – even if they do not know it themselves.

Loyalty programmes do a great job of helping to figure this out but their insights are limited by the actual transactions and the relationship that generate the data. For example, an insurance company knows a lot about a customer’s ‘insurance life’ but that’s just like looking through a key hole at the rest of the customer’s life.

Supermarket chains get much broader perspectives than insurance companies because they sell customers things that help them in more diverse parts of their lives. But even that is a small part of their whole lives. Truly indispensible, personal and timely suggestions need to be in the context of large parts of who each customer is and what they do (and what they want to do). Especially if they do not know themselves.

Sure, you could buy-in data but bought-in data is generally more indirect and aggregated than the data that comes from your own relationship with that customer. The more removed from the particular relationship that you want to influence then the less relevant and understandable it is – bespoke always fits better than off the shelf.

Helen Taylor’s post on Econsultancy got me thinking about how Google has developed a very broad array of perspectives on each customer’s life and how it is using Google+ to glue them together and to dig deeper. The +1 button is the simplest way to tell Google what is interesting. But all of Google+’s features help to generate deeper insights and each one gives a subtly different perspective on customers’ interests:

Streams – tells Google different things that the member might be interested in. On timeline to enable insights about trends at the person and group levels.

Circles – tells Google which members might be interested in these different things. Members can segment by some preset categories (Friends,Family,Acquaintances, Following) and define more categories themselves. Analysis of these user-defined categories will give valuable insights into how members thing about their different interests in terms of interest-to-interest associations and higher level groupings of interests (like analysing the category structures of folksonomies and socially generated tag clouds).

Brands can segment by global versus local because its useful for them. So brand partners can also signal to Google what type of members interest them.

Hangouts – helps Google to get the sort of deep insights that only come from closely monitoring small groups of people talking openly. As Helen said, these are panel sessions. The Hangouts On-Air feature enables panel session content to be broadcast, stored and edited. The members and brand partners who choose to view this content are telling Google about their own interests.

The other features of Google+ (and other Google products) are designed to cumulatively generate live and updating ‘process Interest Graphs’, i.e. very wide arrays of perspectives on each member’s life.

Each perspective is a key hole on a person’s life and together they give much more diverse, and deeper, insights for Google’s brand partners than a loyalty programme can – maybe more than Hunch or Gravity can as well. So Google can partner with more brands and do so in more actionable ways.

Facebook, LinkedIn and Twitter have very different ‘arrays of telescopes’ to Google and these give them very different arrays of perspectives to look at their members’ interests from. First, each of these social networks focuses its arrays on different general aspects of their member’s lives. Although they do overlap – and Google+ seems to overlap the most:

Facebook – entertainment and social life, short-term issues, life curation

LinkedIn – work life, short-term issues and long-term projects, network curation

Twitter  – all your life, immediate issues, bare bones content

Facebook and LinkedIn have much tighter feedback loops between members – in terms of more levels of connection (ways to directly exchange content) and some features that enable actual two-way conversations.

Twitter is a bare bones way to connect with people who you think might have interesting things to say. Mostly its about broadcasting with some ability for loose two-way communications.

Second, each of these social networks uses different features to get the data that gives them their arrays of perspectives. Google use product-based features outside of their social network, as well as inside like the others.

The bottom line is that they all try to be really useful in their chosen aspect of their member’s lives because they know that being really useful help requires clarification and clarification leads to much deeper customer knowledge than bare transaction data.

Update: Facebook is looking at combining information across its other services here.