Some people are very worried about their data. They contend that data stored in the cloud is open to advert targeting, compromises legal ownership, open to theft and abuse (multi-tenancy arguably making this easier), legally accessible by many Governments and not exactly open (i.e. in silos) in terms of passing data between organisations. All of this is oppressive, reduces flexibility and is ultimately little different to proprietary software.

These feelings are attributed to practical experience, distrust of large organisation motivation, appreciation that data centres are inevitably farmed out to third-world countries and doubts over centralised security. On this latter point, certainly hackers could break your firewall accessing your personal data but the effort/risk simply isn’t worth it just for you. The same effort/risk for millions of people’s data is a different matter entirely (not exactly like locking your door when you’re not at home).

Cloud vendors including social networks aren’t (understandably) forthcoming about their security protocols. Key targets for security concerns are Google/Facebook since they have been most successful at getting our data to date.

People evidently believe they want control of their own data back. What options do they have? There are parallels here with the start of banking:

  1. Leave it where it is but demand more visibility/control. Cloud vendors can expose detailed controls to the user e.g. privacy/security/access/ownership etc. Some options free, maybe some on a payment scale e.g. reducing the level of advertising/data mining. This is complicated for consumers especially since they need to do this consistently across several services. They like things nice-and-easy.
  2. Take it back. Putting your data back into your own network is impracticable. You need to remove duplicates, comply with legalities, elect who can access it, keep it current, ensure its connected to the newest services, access it remotely through your firewall, tag it, back it up, archive it, analyse it, share it and maybe sell it. You need USB keys to move data around and you’re at risk of direct physical loss/theft. You need to handle all this using common standards (so you know it can be accessed in future). Directly controlling your own data is a lot of work for all but the most paranoid/justifiably wearisome.
  3. Leave it where it is but apply competitive pressure. Data portability allows you to pull your data out of one site and put it into another at will. This too is convoluted. It is also somewhat of a nuclear option in that consumers won’t actually do it unless cloud abuses are so flagrant (and widely reported) that they feel compelled to and competing sites exist that can import it using a common format and their friends do it too i.e. almost never. The threat of it is arguably enough to keep cloud vendors mostly honest. Despite making in-roads over the last year, Facebook comes in for most criticism here (since at time of writing, it doesn’t allow you to download your social graph or emails). Google with Chrome OS though will surely trump this (due to its sheer cloud nature) when released.
  4. Leave it where it is but apply third-party pressure. It is still unclear how much pressure third-parties such as the Cloud Security Alliance, ENISA, general certification or indeed entire Governments can realistically muster against distributed clouds operating under multiple jurisdictions.
  5. Give it to a specialist. A mostly utopian ideal is the concept of the personal data locker. This focuses on holding your identifying information, financial credentials and personal information e.g. allergies/airline seating preferences centrally online with a trusted dedicated organisation. With your permission, companies/services you subscribe to e.g. social networks pull data from your locker – each using it for their own value-add functions. It could also act as an agent – storing your purchase criteria – providing deals to you and perhaps even trading your data on a open market for a return.

Continuing our banking analogy, personal data becomes less about control and more about oversight, trust, commoditization, service differentiation, commission and regulation.

The obvious missing element with cloud/data is an open market for trading – much like banking/cash relies upon today. Google are fine getting into Enron-esque energy trading to moderate their data centre energy requirements. Why not building the foundation for a data trading market? They are ideally placed to analyse, quantify, monetize and then sell data. Will someone else steal the lead much like Facebook did with our social graph? Building a data trading market might actually allow them to build on Facebook’s social graph and make real money trading. It would create a commission-based eco-system where much needed data integration/consolidation/MDM could be funded. In this world, Facebook are relegated to merely banknote printer.

Which consumer model above will prevail? Much like retail banking at least they all will. There’s no silver bullet. Some people prefer direct control/hoarding/easy access, others will trust specialists as they are too busy (and pay for the privilege), others will lobby Government (maybe they closely identify w/ a particular ideology).

Cloud has been around for ages (pre-1990 it was called – Terminal). It is only in the last decade though that there has been both a wealth of data and the widespread desire/capability to do something with it. An open data trading market is needed to consolidate and then drive forward personal data management.

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Following on from last post, clearly one of the key benefits of digital cash is that it does not incur transaction fees. This is the main method of monetization for services currently providing cashless transactions. The digital cash concept is not exclusively people-focussed/altruistic; there are ways to make an on-going business out of it. Here are the obvious ones off the top-of-my-head. I’m sure there are lots more (?):

  1. Affiliate fees. The validator site can also function as a free service, able to connect to your bank(s), retrieve your bank account details/transactions and provide value-add services with the data e.g. Mint. In particular, would allow you to see where your money is being spent and give you hints on how to save money. Money would be made through affiliate fees/recommendations.
  2. Marketing. Digital cash contains with it, details of what was bought, when and for how much. Analytics analyse consumer transaction patterns, build spending usage pie charts and suggesting relevant ways to save or make more money via competitive offers. Marketing managers would purchase the analytics to analyse usage patterns, create marketing campaigns and target specific demographics and customer types e.g. through Google AdSense. All of this would be tied to the (anonymous) cash rather than the individual.
  3. Purchase sharing. A modish site (Blippy) enables the controlled sharing of purchases to see what others are buying online and in real life. Blippy lets you share purchases by syncing already existing e-commerce accounts e.g. iTunes, Netflix, Woot, eBay. It is thought to monetize through turning links on Amazon purchases into referrals/featured vendors. Analysing purchase data is a gold-mine for analytics/consumer behaviour insight. Card companies cannot share this information but having consumers proactively share with others overcomes this obstacle (and when aggregated sold on). Facebook’s Buy With Friends feature works in a similar way for virtual goods only. Purchase sharing in general is a great way to drive group buying behaviour e.g. Groupon.
  4. Exchange service. Digital cash could be freely exchangeable into physical cash and vice versa. Existing currency exchange outlets would have the infrastructure for this and this would afford them a new revenue opportunity. Also a non-monetary currency exchange could be established. There is a trend toward metacurrency: treating movement of non-monetary flows of attention, participation and trust e.g. frequent flier miles, college degrees/grades/credits, five-star ratings, certifications, bus passes, votes, your eBay rating, scores, coupons much like physical cash. Transactional commission may be made on converting between these. Obviously you cannot buy votes or college degrees but it is certainly possible to buy coupons or frequent flier miles.
  5. Banking. Digital cash being essentially being stored in a personal data locker – free secure cloud storage identified to you. As with a regular bank, it would store your digital cash for when you need it (download to your wallet) and (like banks) make money by market speculation.
  6. Loyalty/payment card service. Existing proprietary loyalty/payment cards such as those operated by Tesco and Starbucks respectively could be outsourced to a new consolidated, cheaper service. Anonymity could be preserved with this new solution and subscriptions could be charged to the store.

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Following-on from last post, there were some questions around how you might actually get digital cash, there are three basic methods of obtaining digital cash that I can immediately think of:

  1. It could be given. Directly acquired by interaction with others (tap and pay). This is the simplest method. It incurs zero transactional charge, is quick and can be performed offline.
    1. Conceivably, digital cash could be printed out using a home printer (essentially becoming physical cash). The value is in the number; the bits containing its identity (value/history/security key). The number could be rendered as an abstract pattern as a security measure. Mobile app vision solutions e.g. Google Goggles on Android and Word Lens on iPhone are highly sophisticated and could read details of printed digital cash, enabling transactions/validation to be conducted, allowing printed digital cash could be exchanged without a mobile. Fundamentally, it’s no different to one-use vouchers validated by merchants. If the consumer copies a voucher and tries to double-spend, it would be caught at POS.
  2. It could be earned. This can be physically earned (in a way identical to above) e.g. a paymaster gives you $100 for a day’s work (tap and pay). Other consumers/online solutions can also give you digital cash (downloadable to your mobile) in exchange for some service/incentive; for example, with Twitter, you earn a credit when someone acknowledges your tweet e.g. one cent for a view, three cents for a click, five cents for retweets and eight cents for a favourite. In this way, it behaves in a similar way to virtual cash only becoming digital cash once downloaded.
    1. Conceivably, digital cash could be earned by enabling the mobile app to use idyll CPU cycles to run a distributed version of the validator. In this way, a separate validator web site would be unnecessary (w/ associated costs). It would function in a similar manner to Bitcoin, an open source virtual cash solution for desktops. However, due to the complexity of decentralising the encryption algorithms involved, the need to maintain a “spent” database, the limited processing power of mobiles and the need for physical separation to prevent hackers back-engineering the validation process, this may not be entirely practicable. The situation is worth monitoring however since it makes restricting digital cash solutions e.g. by Government sanction very difficult.
  3. It could be bought. When bought online, PayPal or similar could be used. It would be analogous to going to an ATM. PayPal last year announced PayPal for Digital Goods. Payment transactions cost 5% plus $0.05 per purchase under $12, lower than most previous micropayment transaction standards. Alternatively it could be physically bought by visiting a currency exchange with higher exchange rates.

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Physical cash drives 60% of all transactions. Everyone knows it has a few issues though: production/distribution costs, counterfeiting/terrorism use, loss/theft, coinage doesn’t pay interest, you need a purse/wallet and a digital divide (cannot use a physical $1 note to pay for a digital newspaper) amongst others.

Why can’t we directly replace physical cash with digital cash?

By digital cash here, I mean specifically anonymous digital cash: a $10 note simply being a string of digits – a number. This number would most usefully be stored on a mobile for payment purposes (or anything else digital). What stops people inventing their own numbers – making their own money? – Cryptography. Cryptography acts like the intricate banknote design/watermark – preventing counterfeits.

How does it work? At a high level, validators and consumers have public-key encryption keys. Public-key encryption keys come in pairs. A private key known only to the owner and a public key, made available to everyone. Whatever the private key encrypts, the public key can decrypt and vice verse.

Digital cash itself accumulates the complete path the digital cash made through the economy and therefore “grows each time it is spent. The history of each transaction (minus the identities involved) is appended and travel with it as it moves from person to person, merchant to vender. When deposited (or validated), the validator checks its database to see if the piece of digital cash was double spent. If it was copied or spent more than once, it will appear twice in the “spent” database. The validator uses the transactional history to identify dates and potentially locations associated with the double-spend and reacts appropriately e.g. contacts authorities/blacklists etc. It has more tracking potential than physical cash (despite still being anonymous).

The validator can always reconstruct the path the digital cash took through the economy (except who owned it). The validator will know what was bought, where it was bought, when it was bought and how much was paid. A side benefit of anonymity i.e. not including identities in transaction history means that the war being fought over global Internet identity is conveniently side-stepped (Facebook, Google, Twitter, OpenID [even Angry Birds] etc.).

If your mobile (or any other device containing your digital cash) is stolen or lost, you could remotely deactivate the cash (and additionally claim it back). This process would not (by necessity) be fully anonymous but it could be just tied to your mobile number, making it semi-anonymous in much the same way that Standard Bank in SA have done with mimoney.

In respect of transactional proof, the basic rule is: everyone can prove that they took part in a transaction but no-one can prove that someone else took part in a transaction.

Why is digital cash a good idea for both consumers and merchants? Simply, cost, convenience and privacy in that order.

  1. Cost
    1. Credit/debit card based solutions are expensive. Merchants prefer cash since it is the cheapest to process. A British Retail Consortium (BRC) survey claims that credit cards account for 11% of transactions (but 49% of merchants’ costs in accepting them). The biggest single cost of card payment collection is the bank merchant service charges. These cost the UK merchant 2p per transaction for cash, compared with nearly 8p for debit cards, 35p for credit cards and 53p for cheques. These costs are inevitably passed onto the consumer in the form of higher prices. Credit/debit cards companies simply cannot reduce this charge and are obliged to focus on target based rewards programs in order to compete. Credit/debit card solutions may not be available to all. Finally many consumers have bad credit ratings and find it difficult to obtain credit/debit cards. This also affects PayPal since it takes these cards into its wallet. In the US, this figure is reportedly 25%. Finally, merchants are never quite sure how much a credit card transaction will cost. MasterCard and Visa charge hundreds of different rates (interchange fees) for every type of card that runs through their network. MasterCard for example has 243 different fees.
    2. PayPal based solutions are commonly portrayed as the liberator of cashless payment however they also incur substantial fees: +2.9% + 0.30$ per transaction, +1% for transactions from abroad and +3% for transactions in a different currency. This could reach +6.9% + $0.30. This is 2-4 times as much as banks charge. Again, these costs are inevitably passed onto the consumer in the form of higher prices.
    3. Carrier billing based solutions are new and very considerably in terms of cost. However they also make money/transaction. It depends on volume e.g. takes around 5-10%. On top of this, there are carrier charges. They take anywhere from 25-45% of the transaction amount. Carrier billing solutions typically pay merchants once a month which affects their liquidity. Carriers are currently reticent of supporting transactions of physical goods due to issues of returns/payment disputes and so typically have $100 transaction limits for virtual goods only. Finally, carrier billing solutions are not widespread. They are unsuitable as a complete replacement for physical money, particularly in the developing world due to mobile coverage.
  2. Convenience
    1. Card/PayPal/Carrier solutions are all centralized, you lose a bet and want to give your friend $10; one of you has to pay the cashless overhead? Who? You and three friends share a dinner at a restaurant and you pay the bill in full. Your three friends each then need to be able to transfer a quarter of the total amount to you – creating four transactions in total (and four times more transactional revenue for payment processors). You all also need to be online.
    2. Card/PayPal/Carrier solutions just don’t work offline. With digital cash, if you and a friend are both offline, as your friend is in your social graph, you can accept it money he gives you at face value (and then perhaps validate it later when you are online). A bit like a cheque. The concept of trustspace could also be used to grade and evaluate trust outside a user’s social graph.
      1. With trustspace, consumers would be rated by how many times their balance has reached zero (since here you have contributed exactly as much to society as society has contributed to you). To avoid rating manipulation (a sort of new credit rating), trustspace could be parameterized by personal turnover, a damping factor and a connectivity index derived from the number of other people you have dealt with. Your rating would diminish over time and so you would need to continue earning/purchasing with digital cash to stay active.
  3. Privacy
    1. Card/PayPal/Carrier solutions log all transactions made by the consumer. If you lose a bet and give your friend $10, that transaction is recorded. If you go into a bar and rack-up a large tab, that transaction is recorded. If you pay for prescription medicine, a DVD in a bar, “herbal” remedies, a massage or Playboy magazine; all –all those transactions are recorded and used, at the very least, to target marketing to you. There is a growing demand for data privacy and consumers want the option to remain invisible to a payment made on their behalf. Privacy is not so much a blanket consumer need to be unseen in terms of online/digital activity but a desire for easy control. The recent WikiLeaks scandal compelled some services to stop handling Wikileaks’ business including payment services (inc. PayPal). Anonymous digital cash helps fund enterprises that, for whatever reason, others object to.

Additionally, there are wider economic benefits to digital cash:

  1. Being a zero transaction cost solution enables consumers to sell online content and would be an alternative to advertising revenue meaning a reduction in distracting/invasive banner ads (micropayment). The fact that it is free means that it will stimulate a free market economy on the web where the best people, organisations and content will rise to the top because they can be directly paid. This, in turn, would make it easier to find things of most importance (currently we are spending 53% of our time searching for the right information).
    Consumers should also see an improved web experience through a freer market economy.
  2. A 10% shift in consumer spending, from chains/Internet to locally owned retail (currently being driven out of business due to Internet purchases), would create nearly 1,300 new jobs and over $190M in increased economic output for San Francisco alone. More jobs and more economic output in a specific geography where you own a house also means houses increase in value.

Twenty years ago, a digital cash solution was developed called ecash. It was sold by a company called DigiCash. A now defunct US bank and a handful of small European banks went live with it in the mid nineties but DigiCash went bankrupt later that decade and assets were acquired by InfoSpace in 2002. There was a similar story with CyberCash (over a similar timeframe). They went bankrupt in 2001 and assets were acquired by PayPal in 2005. No commercial organisations are now known to be operating these or similar systems.

Both organisations are considered to have failed due to security, implementation and administrative problems. They also made the validators – banks (when they really just needed to be web-services) which added unnecessary overheads/slowed down time-to-market. Countries historically used to back bank notes with gold but this is barely done any more. Canada for example has no gold backing for its currency.

Media interest in digital cash coincided with the dot-com bubble of 1995-2000. The vast majority of books on the subject date from that time. Online finance in general has stalled since then e.g. W3C abandoned attempts to incorporate micropayment into HTML. InfoSpace itself was a notorious dot-com casualty (in March 2000 stock reached $1,305/share but by April 2001 had crashed to $22/share).

Other than the fact that over two-thirds of the world still have no Internet access, there are several convergent trends right now that could build a landscape for a digital cash solution:

  1. Smart-phones replacing traditional dial and text mobile phones.
  2. Cheap/pervasive contactless NFC technology with an open API. Estimated 40-50M phones on market in 2011. Widespread NFC adoption of payments (149+ projects worldwide). High-profile assertions that mobile is the safest way to pay.
  3. A thriving start-up culture (possibly due to the recession?).
  4. Widespread and growing mobile/app culture. Mobile app market to be worth $25BN by 2015App downloads to increase 605% by 2014. There is also more evidence that consumers are more likely to buy using a mobile app than regular web applications. Location services are on the rise.
  5. General distrust of existing financial services (esp. banks) with consumers being open to alternatives. With both cash and clients in limited supply, barter networks e.g.
    Dibspace , ITEX, BarterCard and IMS are becoming popular. P2P lending in particular e.g. Zopa, FriendsClear, LendingClub is becoming accepted.
  6. Rising social graphing acceptance (bolstering trust issues).
  7. Widespread acceptance of storing personal details in the cloud via trusted sites.
  8. Digital signature/public key based cryptography acceptance.
  9. 25% of consumers have poor credit scores.
  10. Distinct lack of retail banking innovation.

What about other forms of cashless payment?

Visa’s payWave system was introduced last year as a digital wallet for the iPhone although this requires a separate case for the mobile (basically containing the same chip as new Visa cards). AT&T, Verizon and T-Mobile also announced as Isis, a joint venture to equip mobiles with NFC chips; linking them to credit/debit cards. Barclaycard has signed on to issue credit accounts through this system. Google have said that they will work with industrial partners for their digital wallet solution and with them pulling players together (highlighting better loss rates); previous collaboration issues should be resolved. Google clearly say though they want to replace your credit card not your cash.

Square allows anyone to accept physical credit card payments through a mobile or computer by plugging in a free sugar-cube-sized device so no expensive card reader is required. Of course this solution suffers from credit/debit card dependency.

Obopay lets consumers and businesses purchase, pay and transfer money through a mobile phone using a mobile application, text message, mobile Web or Obopay.com. It works on any mobile phone and any US carrier; again though it is tied to a card, in this case MasterCard debit.

Start-up Bling Nation went live in the US last year. They partner with both PayPal and banks, who then offer consumers a Bling Nation and “Bank” branded chip that can be stuck onto any mobile device. The chip allows any user to make a payment directly out of their checking account similar to a debit payment.

Rovio is taking a carrier billing approach with their payment system Bad Piggy Bank, intended for in-app payment of virtual or online goods. Zong have a similar approach. They also run a points system if you link your credit/debit card to your mobile and have reportedly processed transactions from 15M unique consumers.

All of these solutions are various themes on Card/PayPal/Carrier solutions and so also carry their failings. It could be argued that consumers and merchants are having installed on their behalf technological solutions that continue established order in preference to directly addressing their needs. In much the same way that PayPal principally extended the credit card model into the online world, current social/cashless payment solutions are seemingly doing so now.

Digital cash, in theory at least, straddles online and physical worlds better than any other solution. It empowers consumers and merchants through clear cost, convenience and privacy benefits over existing/planned solutions (physical cash or cashless payment solutions) and affords wider economic benefits to both.

The market has moved away in the last couple years from online/online solutions (payer/payee respectively) e.g. PayPal toward online/offline e.g. Groupon, Uber. On the topic of Groupon, receiving/generating a coupon then integrating this with digital cash (applying usage constraints/”increasing” your digital cash as appropriate) automatically within your digital wallet would be very powerful. It is quite possible the market will shift again to an offline/offline model in the near future. Is the best way to realise this – digital cash?

Obviously, big issues of system and national security, cryptography restrictions, economic stability and consumer acceptance would need to be overcome. As Minsky said “creating money is easy; the hard part is getting it accepted”. However, alternative payment systems are already being adopted; being 20% of all online transactions. We are also seeing a surge in self-organized /managed citizen activism especially around finance and digital cash perhaps hooks into this trend too.

UPDATE: There are several ways to obtain digital cash.
UPDATE: Possible ways to monetize digital cash.

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Thinking about geo-location (GPS) and its mobile under-utilization, I considered what applications it actually is well suited to. Checking-in and earning badges to become major of somewhere just has to be a short-term fad even if they do begin to enable deals (?).Obviously there is finding local stores/services but that is pretty well covered by Google Maps and others. Crowd-sourcing e.g. seeing where contacts have been might work if your contacts left comments about stores/services (which they don’t). Seeing your contacts are close-by so you can call them and meet-up doesn’t appear popular (you kind of already know where your close friends are). The forecast for the geo-location market leader – foursquare is hazy.

So what mobile app could really make use of it? Unlikely as it may seem at first, I came up with dating; although as we’ll see it may be unattainable for social reasons (much like even though we already have well implemented video chat, people reject it).

Why what’s the problem with online dating?

As online dating becomes more main-stream/more commoditized – even reportedly now used by celebrities, it also suffers from lack of innovation/differentiation. Online dating came to prominence on sheer convenience; being able to trawl through hundreds of profiles w/out leaving the comfort of your home. But scanning through ever more prospects due to poor matching/sparse profiles and an increased social need to be mobile/online have eroded this convenience.

Social and mobile technologies should be able to add value but they seem only to be starting to. Looking at online dating as an application area (I had to research it) and comparing it with the speed-dating space (slightly more experience) there is perhaps a solution waiting to be found.

Some sites solve several issues but, in general, online daters need to:

  1. Maintain a separate/proprietary profile.
    1. Tedious to enter/maintain personal information already online. Few dating sites are really focused on matching anyway; too complex/expensive and not immediately appreciated by the dater. It’s about minimizing acquisition cost/extending CLV. They don’t necessarily want to get people off the site.
  2. Periodically check to see if there are people to contact/someone has contacted them (pull).
    1. Average visit/site time is 22 minutes. Finding time for dating is a real problem, necessitating some to even look for ghost-writing solutions.
  3. Deal w/ communication awkwardness.
    1. Communicating by sending messages to strangers and then being rejected more times than not is unnatural, demoralizing long term and doesn’t fit w/ the personality of anyone but extroverts.
  4. Determine the best way to leave a date if they don’t click w/ the other person.
  5. Pay on a subscription basis.
    1. Market leaders for online dating – Match and eHarmony monetize on subscription. Plenty of Fish is the only free site in top-10. Sites charge $35/month on average.
      1. Free sites have their own issues though. As there is no barrier to entry, profile quality is poor as people put less work in. Many actually prefer paid sites for this reason.
  6. Deal w/ incomplete information.
    1. Feedback that could be useful to prospective daters is lost.
      1. People typically don’t reply if they feel there wasn’t a connection. Consequently, the rejected person is unsure whether to approach the person again.
      2. Future prospective daters of the rejected person cannot take previous criteria (from others) into account before meeting.
  7. Handle remaining social stigma among peers (despite relaxing recently).
  8. Infer second degree social graphing.
    1. Having 175 friends (pretty typical for those dating) means people can have 60,000 friends of friends (Friend of a Friend – FOAF). It is likely that many of these people are dating. Identifying these people is useful since their immediate friend can be contacted as they will provide a source of additional information (about their friend). Determining these connections becomes part of the date.
  9. Cancel their account after a few successful dates.
    1. After a few dates if the daters vaguely consider themselves “in a relationship”, then it is expected that they cancel their accounts and live happily ever after.
  10. Work w/ poor, non-existent or additionally paid for mobile versions of sites.

How could these problems be solved?

A free mobile app, connected to an existing/widespread profile that, once you register (one time/through Facebook) pushes meetings to you i.e. the solution opportunistically knows that you are available (from looking at respective schedules) and are physically w/in a five minute journey (from GPS) of someone that matches you/you match (based on filter criteria you have previously entered) and so arranges it automatically (pushes it). You get the other persons picture, a profile summary and a named coffee shop (in-between you both) together with a time. You can accept/reject there and then. If you reject, the meeting is simply off (could be because the solution itself decided there simply wasn’t enough time for it so fewer social rejection issues). If you accept, you meet for a time-boxed duration at a coffee shop w/ the app managing the duration and other details (like a speed date assistant).

It prioritizes similar people (of the correct orientation). Everyone needs a facial photo on their profile so that they can be recognised. Basically, you don’t need to log into the site again (unless you want to) as the mobile app will push everything to you.

No-one meets up anywhere other than a coffee-shop halfway between the two people and no-one knows where the other person is other than at the coffee shop. Coffee shops are necessary since they are an easily found public space. As such the solution would work better in cities. The solution is more general than dating i.e. intended to be used by everyone. For example, you could express an interest in whether to buy the new iPhone and then meet someone who has one. Online dating would certainly be the beach-head application though.

Post meeting, you “thumb-up” or “thumb-down” the other person. If you both mutually thumb-up each other, the solution informs you and gives you the ability to exchange 1:1 contact details. This information can be condensed into a simple personal reputation score which can be used as selection criteria e.g. you can elect to only meet people w/ a high reputation.

Since the solution is not specifically dating related, reputation scores can be made available (via API) to other sites. Existing reputation solutions are principally based upon Twitter followers/retweet activity e.g. Klout, Peerindex or Tweetreach. This allows those that don’t use Twitter to compete online. Since it is more personal than tweeting, it is also potentially a more accurate reputational assessment for other sites to use e.g. when offering deals. Wider reputation usage helps drive traffic and because it is solution specific – makes replication by competitors difficult.

The solution described above addresses the online dating problems in the following ways:

  1. Maintain a separate/proprietary profile.
    1. Solution uses Facebook Connect to enable one-click login. Truncated versions of people’s Facebook profiles also become their dating profiles. It is not a Facebook app and no-one can see whether you use it or not. People are already using Facebook to flirt and this solution extends that but removes the bad etiquette associated w/ trawling for dates.
  2. Periodically check to see if there are people to contact/someone has contacted them.
    1. Solution saves time. Trawling through online dating sites to find (pull [rather than push]) someone interesting to you is time-consuming. Push model enables solution to work around your schedule. Increased efficiency e.g. people can email each other for weeks but only really know whether they connect after a few minutes conversation. Makes the whole experience low expectation (good for first dates).
  3. Deal w/ communication awkwardness.
    1. Relying on the mutual knowledge aspect of mutual thumb-ups e.g. I know that you don’t know that I thumb-upped you unless you thumb-up me – would remove much communication awkwardness.
  4. Determine the best way to leave if they don’t click w/ the other person.
    1. Meetings are for five minutes only. After five-minutes, an alarm would sound through both phones. Timing starts once both people’s phones are in immediate proximity to each other (to handle one/both people being late to the meeting).
  5. Pay on a subscription basis.
    1. Solution is w/out cost (clearly a winner). Barrier to entry/profile quality issues are partially addressed by the reputation score.
  6. Deal w/ incomplete information.
    1. Through the reputation score.
  7. Handle remaining social stigma among peers (despite relaxing recently).
    1. Solution is not specifically a dating solution. Even people that don’t want to date may be interested as it enables them to opportunistically and simply meet new and interesting people or discuss specific topics w/out planning in advance.
      1. People’s lives are increasingly scheduled and many are drawn to the idea of occasional “curve balls”.
      2. Can offer beforehand to buy someone a coffee if they are able to discuss a particular issue from a position of experience e.g. rearing a puppy, fixing a computer, getting into a particular industry or continuing online questions from Q&A sites e.g. Quora. This could also work for sales e.g. you get a free coffee if you sit through a five minute pitch.
        1. Similar to Facebook Like button, a “Meet” button could be federated to other sites to indicate a willingness to discuss a particular topic.
    2. Since it is based around your schedule i.e. whether you are physically close enough to meet there is less dating investment enabling it to be treated casually.
    3. The time-box nature makes it more akin to speed dating which inherently carries less social stigma than online dating.
  8. Infer second degree social graphing.
    1. Through Facebook social graph. This has a freely available API.
  9. Cancel their account after a few successful dates.
    1. The solution moves on from scheduling that initial coffee shop date to bars for successive dates to theatre/film scheduling for later dates. Even years after two people have got together through the solution, they could still use the solution as a proximity meeting facilitator.
  10. Work w/ poor, non-existent or additionally paid for mobile versions of sites.
    1. Solution uses location awareness on exception basis (push)/much more simplified than current mobile dating sites (basically simplified versions of web sites). Conversation topics based on mutual interests could also be suggested seconds before you meet. It is the only access point to the service and free.

The solution would certainly be open to abuse. People are travelling five minutes for a five minute meeting. They can set filters to ensure the solution just pushes meets that they want and if it’s apparent that the person is disingenuous – they leave after one minute (total six minutes wasted) then leave poor feedback or report/block the person – similar to email spam. It should actually be easier to control than email spam since you can leave poor feedback/report the person. It’s true you have potentially wasted 5-10 minutes of your life though (w/ email spam it will be seconds to read the email). Then again consider risk/reward: W/ email you risk minutes/day w/ spam against the reward of instant written communication anywhere in the world. W/ mobile dating (or meeting), you risk 5-10 minutes against the reward of meeting someone cool/learning something new. People are already risking 22 minutes every time they visit an on-line dating site to meet someone cool (and unable to multi-task), so it could be posited as a time saving.

Following on from our high-level design, some detail on how to potentially realise it. Starting with – how might it make money? It would always be free to use but development cost should be able to be offset in short-term:

  1. Short-term. Affiliate marketing would be impacted due to the “proximity/push” model i.e. people are not regularly hitting a specific site w/ which to see ads.
    1. Small, targeted ads would need to be embedded and cycled w/in the mobile app and shown when date messages are pushed. The option for a paid version of the mobile app could remove those ads e.g. the Angry Birds model.
    2. There is a trend for free online dating sites to be launched to great press (focussing on some minor innovative angle) and then closing down several months later e.g. Thread (FOAF), CrazyBlindDate (randomness). Likealittle (flirty commenting) has only recently been launched and received 1M uniques in first month (reportedly 20M page views). Conservatively taking uniques and using the Amazon Affiliate program, this would have generated $10,000 affiliate revenue (assume 1% click-through rate on average price $10). On page views this would have been $200,000.
    3. Online donations e.g. through Flattr can generate a surprising amount of income e.g. Flattr reportedly kept WikiLeaks afloat for a period.
  2. Medium-term. Assuming the solution successfully transcends short-term “viral effect”, the amount of active registered users dictates revenue.
    1. Plenty Of Fish for example, reportedly makes $30/registration and £10M annually through affiliate advertising. This is on 250,000 uniques each day.
    2. Once the concept is proven, it could pivot to become a back-end service (accessible via API) for other dating sites i.e. they provide their own user experience and value adds e.g. specialist matching algorithms, niche markets. The solution would handle Facebook integration, tracking, messaging, rating/feedback and meeting handling. The solution becomes a natural monopoly (due to network effects) that future dating sites need as daters expect it. It could conservatively be licensed to the top-50 dating sites for $500/month ($300,000/year).
    3. Equivalent services/outlets available around the interim point e.g. coffee shops/bars can compete for business w/ the solution paying the solution for “referrals”.
  3. Longer-term. A working solution would be attractive to acquisition.
    1. Facebook may see it as a way to extend its brand – moving out of the digital world into the physical world/gaining deeper social data.
    2. Coffee shops e.g. Starbucks may simply see the solution as a means to drive sales (interim coffee shop meeting points). Starbucks are keen to develop their loyalty programme e.g. they recently partnered w/ foursquare to unlock a “Barista badge“.
    3. Location-aware apps e.g. foursquare may view it as a way to extend the short-term conceit of checking-in/obtaining badges etc.
    4. Other dating solutions may find it attractive to compete/diversify (much like Match tried to compete at the free end w/ Plenty of Fish by releasing Down To Earth.
    5. Personal assistants e.g. Siri may see it as complementary.

What competition is there?

There is a much competition. Some solutions solve some of the problems above but none solve all of them and none have the proximity/push cornerstone approach supported by a speed dating assistant:

  1. Classic online dating sites e.g. Match, eHarmony and Plenty of fish.
    1. Market is fragmented/specialized e.g. 50+, Jewish, European (Meetic) etc. at the lower/start-up end.
  2. Local speed dating outfits that also have an online presence e.g. SpeedDater.
  3. Dating Facebook applications e.g. Zoosk and Flirtable.
  4. Dating sites that use Facebook social context e.g. AreYouInterested.comDateBuzz, SpeedDate.com and Chemistry.com.
    1. DateBuzz has gained press for its innovative use of letting daters rate other dater’s profiles. Profiles provide a fraction of the detail gained from even a five minute meeting though and are open to gaming/deception. Also, there is a case to be made for saying that daters don’t actually know what they want until they experience it.
  5. Friend locators e.g. Facebook Places, Google Latitude and face2face.
    1. Facebook Places is geared toward friends/checking-in much like Gowalla or foursquare. Latitude is more organic in that it simply tracks you and allows you to see other friends. Both Facebook and Google are unlikely to provide direct online dating support themselves in order to protect their brand but it is conceivable.
  6. Mobile dating apps that use location awareness e.g. Grindr, Skout, Flirtomatic and MeetMoi.
    1. Grindr is very niche. MeetMoi is more general. Neither have speed date assistant capabilities e.g. interim coffee-shop scheduling, strict date timing and reputation scoring. Both also require proprietary profiles.
    2. Skout and Flirtomatic use Facebook profiles and are probably closest to the solution however they hamstrings themselves by focussing on chatting w/ others in your location rather than proactively scheduling a meet with them while it is possible (they may only be there for a few minutes). You can chat anywhere. Geo-location only becomes useful to online dating once the opportunistic meeting element is introduced.
    3. This approach is widespread in China although this is done on older technology e.g. many Chinese wireless service providers offer dating services based upon signal triangulation to drive text message usage.
  7. Sites that use scientific matching foundations e.g. ScientificMatch and GenePartner.
    1. These two actually use DNA comparison. While this may be more accurate than proprietary matching algorithms, it is still reliant on matching rather than chemistry and chance. Cost too is prohibitive for now.

To sum-up, a mobile dating solution (essentially: Social + Mobile + Speed dating) bought to market now could earn distinct competitive advantage if the social adoption question could be resolved:

  1. Market. Online dating in general is a $1BN global market (some say $4BN), has yearly 10% growth and is known to weather economic storms. Market for free online dating solutions is fragmented. Plenty of Fish is the only one that has a real market (followed by OKCupid) yet the paid market is market is shrinking. This solution is wider than online dating; effectively creating a new market – proximity meeting enablement.
  2. Innovation. Online dating solutions have yet to capitalize on mobile and (to a lesser extent) social. W/ smart-phones set to be ubiquitous, this is a clear opportunity for innovation. The infrastructure/audience for geo-location in particular is there but wasted on games e.g. earning badges/becoming mayor etc. Mobile usage also enables new approaches to online dating. In particular, the opportunistic proximity/push feature is a clear differentiator. Ultimately though, the innovation here is – to handle the ten problems w/ current online dating slightly better than everyone else. It is this that gives the solution a real chance of progressing past being a short-term trend to become a brand.
  3. Risk/Reward. Roughly six weeks to build onshore on Android (assuming design completed) and cost around $35,000 to get a first release out from good developer. Refine app (assume 80 hours costing additional $8,000). Releasing a beta then would cost roughly $51,600 ($35,000 + $8,000 + 20% contingency). Plenty of Fish is reportedly mostly a one-man band. Development cost could be offset by combination of affiliate advertising/ad-free payments and donation w/in 3-6 months. A separate upper limit estimate for total cost (complex Android app) is $80,166. If the solution took just 10% of Plenty of fish business, this is £1M/year revenue on low overheads. Finally, there are clear approaches to extend solution functionality and acquisition from several varied sources would be possible.

Ultimately, this solution could potentially create an important new communication channel while addressing the online dating problem. By far, the biggest question is simply whether people (particularly women) would adopt the concept of having meetings (dates) proactively scheduled for them on-the-fly as they go about their lives. Conversely, there is a sort of chance/synchronicity element that they might consider romantic while men might be drawn more to the time saving/efficiency angle. As no-one has really done anything like this before, no-one can say. What can be said though is that it isn’t a huge investment to find out and like many of the dates that would be pushed to your mobile; it is probably worth checking-out.

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Around 2000, I worked on a project with a co-worker – he was maybe fifty and had worked in the IT industry – initially at IBM then contracting and consulting for nearly thirty years. At the time; he was heading up a web-development and creative team for a small consultancy firm which he saw as a new opportunity for him. He had a good reputation, people liked him and he had a vast amount of experience from being involved in literally hundreds of projects – developing, managing, business developing etc.

One day he slips into conversation – as an aside really; that he has never worked on a project that was delivered. Every project he worked on had been cancelled, merged into another programme, didn’t meet its financial goals (as with infrastructure consolidation) or his involvement ceased before it ever went live (if any of them ever did – he didn’t know). He said it in a resigned manner, almost as a badge of honour that he had got through all that with no job satisfaction in terms of delivery payoff whatsoever.

I have seen this scenario repeated many times in the decade since; although perhaps none quite so extreme. I have myself worked on hundreds of projects in many roles and can count the number of successful systems deliveries on one hand. People naturally want to associate themselves with successes rather than failures so it is perhaps understandable that this is not a common consultant topic.

A whitepaper released late last year attempts to put a figure on the worldwide cost of IT project failures. This turns out to be $6.2 Trillion and it doesn’t look like sensationalism. The US in particular is apparently losing almost as much money per year to IT failure as it did to the financial meltdown (with no end in sight). The paper makes an attempt to factor in what it calls indirect costs; basically a lost opportunity cost from the time wasted on failed/abandoned projects. It does not however take into consideration the wider indirect costs of people training for careers that are not actually delivering, IT staff disillusionment (turnover), operational failures for delivered IT systems (one in five businesses lose £10,000/hour through systems downtime) and associated security failures.

The paper has received some condemnation (due to its base assumption of a 65% IT project failure rate) but there is dearth of analysis in this area and quoting this figure is about as good as it gets right now. 50-80% figures have been quoted in one form or another for decades. CIO thinks rates are actually rising due to the recession. People have the choice of either working with a figure (challenging assumptions/statistics etc) or burying their head in the sand.

We will never know the exact figure for IT project failure. Similarly, we will never know whether efficiency/functional benefits of those systems that have worked have paid for the failures i.e. What has IT really given us? We are simply reliant upon these systems going forward. What can we do to reduce future project failure rates?

Although there are superficial similarities with scientific/experiment and IT/project communities respectively, the “accept defeat” approach of the former is routed in constant learning whereas IT is surely about delivering benefit now. Learning is only the priority for the largest, most stable, lowest turnover organisations; that is to say – next to none of them. The scientific regimen of independent assessment however is invaluable for IT projects. Tool-based PM consultants such as Bestoutcome are probably as good as the big management consultants for this purpose though. Techniques from the engineering community (when introduced to IT) have not had a huge effect.

The paper ends with a call to arms to simplify – IT/business communications, projects goals etc.

I agree in principle but this is oversimplification – when your realm of influence is the organisation you are in. I have seen many projects that although conceptually simple and with genuine IT/business agreement start to fail the moment integration with other organisations – vendors/hosting providers/recruiters/sub-contractors/data sources are required. Despite solutions being simple and manageable inside your organisation – just a few touch-points with others (basically anything worth doing) cause them to be complex and therefore unpredictable/at risk no matter what your collective capabilities. There is even a case for blinkered-simplification/procedures actually contributing to project failure: Complexity at least brings an element of flexibility, allowing you to react if the project starts going bad.

Better project managers, SOA, ramped-up co-worker involvement, Facebook-like “hackathons”, daily IT/business meetings, PRINCE certifications, extranets, more rigorous cost control and mathematical complexity models within your organisation will have minimal effect on the success of your multi-touch-point projects – for they are already in the realm of chaos. Even improved PM collaboration through tools such as Asana will have a minimal effect on success rates. The role of the “good project manager” is perhaps the most scrutinized, personality-driven, divisive and misunderstood of all IT positions. Radical open enterprise models such as BetterMeans that effectively remove Project Managers in favour of automation and decentralized decision making will similarly be ineffective. Although in the case of the open enterprise model, I do agree that this will ultimately prevail (crowd-sourcing, creativity enabling and ultimately efficiency/cost) but not for decades (due to the need to directly attack the failure rate first).

Although there are certainly sizeable success increases to be made if you are experienced in the particular technology, the IT project failure rate will only consistently and materially fall once there are flexible cloud services that organisations can get 80% of all their needs by just subscribing to them.

Integration ceases to be the bottleneck. The other 20% being “secret sauce” value-add-ons developed in-house; probably mainly algorithms that, by definition, do not require integration with other organisations or heavy project governance. Other components of the 20% would be device specific exploitation code; essentially building the so-called App-Internet model (rather than full-Cloud). Organisations already recognise the economic justification for cloud computing so it is perhaps inevitable that project failure rates will eventually fall by default.

Cloud transition will take organisations years yet however. Both InfoWorld and Gartner have arrived at 2013 for broadly when the majority of organisations will run cloud. This may be optimistic. In the interim (three more years+ of high project failure rates?), delivery is simply better served by being built upon cloud solutions now; building partnerships with cloud providers if they can and leveraging their buying power if they cannot. Also, interim/limited functionality cloud solutions must be considered in preference to bespoke development/on premises deployment. In a real sense the project (that they would have otherwise completed themselves) becomes a creativity-driven; architectural investment and commercial partnership instead. Both Project Management and Enterprise Architect roles will need to shift accordingly.

It would be interesting to see any future IT project failure analysis split between organisations that have implemented virtualisation, those that have implemented private cloud and those that have implemented public cloud. The project failure and cloud connection is not well documented.

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Google is obviously pretty successful due in large part to its search service. Despite them not having the best social networking track record, it’s an activity that maps well to the social world. We search for things – our keys, a present, a holiday, our next partner, a restaurant to take a friend to – our next role – ourselves. Google doesn’t give us exactly what we want but that’s OK because we don’t know exactly what we want either. It’s useful to be given a few social curve-balls.

Despite us all mainly using Google or Bing (Yahoo, Ask etc. all gone now), new search engines e.g. Blekko still regularly surface with varying differentiations and significant funding.

What if, rather than vague searches (inevitably) generating prioritized yet potentially vague results (M:M) [in terms of question:answer]; we could ask a specific question that provided a generally accepted, precise result (or if that question had previously recently been addressed, a link to same) that we (and others) could refer to (to later justify our logic). What if we wanted – an actual answer (1:1)?

Consider – “Which is the largest State in the US?” The question is vague (by land or population?) also Google is mainly matching keywords rather than actually understanding the question (and of course we get many results so the full exchange is M:M). Actually, Google have been delivering factual answers to some queries for five years. It only works for some though (it doesn’t work in this case). Regardless, Google’s first result correctly tells us Alaska and additionally gives us comparison with other States and square mileage too. We aren’t guaranteed getting the same result when we query in future (so accountability goes out the window) but the answer is OK for most purposes. If we make the question “Which is the largest Republican State in the US?” we still get an answer but takes us a few minutes to manually cross-reference the links. If we ask “Which is the largest, happiest Republican State in the US?” we simply don’t get a usable answer.

Someone living in Alaska is worried about perceived rising Russian immigration and they want to know if it has risen in their adult lifetime. There are no obvious figures on the Web. They could tweet/email the Governor and (if enough others have asked broadly the same question) then he might post a response on his blog (if he has one) once his research team have arrived at some defensible statistics for him to refer to (assuming they can find some).

This simple/current scenario has challenges and inconsistencies on both question and answer sides. Here are a few:

1) People have to feel so strongly about the issue that they have to find time to interact – search for any similar questions/answers, write their email, monitor the Governor’s twitter-stream/blog for an answer. They feel as though they don’t want to put themselves forward unless others feel the same. Conversely, enough people have to use similar phrasing of the question in order to kick-start the answer process.
2) The answer (if it is given) is not explicitly tied to the question. It could be made on his blog, a successive TV appearance, in a press release or personal speech.
3) There is no disambiguation or prioritization of questions – meaning that there is just so much noise that the Governor or his team simply don’t have the time to answer.
4) The answer could be presented in different ways – perhaps to achieve political ends or simply because they are underused by Government e.g. taking Russian immigration over a twenty year time-frame may show no year-on-year increase but taken over a two-year time-frame it may show a very different story.
5) There is little opportunity to to-and-fro with the question and answer thread – blending anecdotal evidence, redefining terms.
6) It’s a naive voter with a question – asking a team of well researched people. Odds are there will be a soft soap answer as independent experts are not involved in the process to moderate.
7) Statistics could come from Federal or State government, commercial, charity or other bodies. Also there is unlikely to be any checks and balances e.g. does the US figure for people incoming match with the Russian figure for people outgoing?
8) Any presented figures will be difficult to understand (unless an intuitive and consistent graphical approach is used).
9) Lawyers may need to be consulted to define the term “immigrant”.
10) There is no accepted “end” to either the question or answer.

In business, we need – to commit resources to a business plan, to indemnify ourselves against litigious customers, to make our point in meetings and to provide defensible input to decision making. Consumers largely search for things (M:M) while companies have to query things (1:1), although as with social networking, there’s some overlap (M:1) in the middle. Companies lose out as they maintain a blinkered-view missing critical information not captured through their own processes. Consumers lose out since they spend so much time searching for things (and the Internet is so rich) that, like travelling, the journey becomes the destination.

Google Search Appliance has done very well in the enterprise. There is a need for a one-ring-to-rule-them-all “get things” function. Also, our work and home lives are merging and when the answer you present is (close enough to be) unanimously accepted and is able to be referenced in any future analysis of your decision – then it will always stand. Once you don’t have to think about the logistics of getting answers, you can concentrate on actually doing things with them (for both consumers and the enterprise). To do this, quite simply – we need a single answer, a 1:1 model that works for everyone – like search – a Q&A service.

We have “I’m Feeling Lucky” of course (M:1) but this is an indulgent lucky dip that actually loses Google revenue. The other way around (1:M) doesn’t work since it entails definitively stating the question e.g. using something like QBE or NLQ (both of which are hard to do right) but still getting a range of results. What other services are currently available? Facebook released Facebook Questions earlier this year. This is slated to be integrated into Community Pages (already includes Wikipedia content). It is developing but is still very social/free-text. Wikipedia is an encyclopaedia rather than a Q&A service. Yahoo Answers has more of a wiki/free-text approach. It probably is the widest used but is also known for being random and open to abuse. PeerPong is expert-focussed. Off-topic a little, for product reviews, there are Hunch, DooYoo, EpinionsMahalo (their Answers service is a bit wider focussed than just products though but still very social). There are mobile Q&A services such as Mosio and ChaCha. Quora (founded by ex-Facebook CTO Adam D’Angelo) appears to stack up against Facebook Questions and again is very social. Social Q&A in general is big. StackExchange is an open-source platform for developing your own social Q&A service. In the enterprise, Opzi is attempting to be a corporate Quora. MSFT shut down their offering last year. Qhub runs a niche hosted Q&A service. Some social ones such as Hunch have repositioned themselves as recommendation services.  Many just cater to niches e.g. StackOverflow/development. OSQA is a free open-source oneSeveral dot-com bubble casualties have refocused on discussion based Q&A e.g. Ask has refocused on a mobile Q&A service similarly Startups.com.There are other start-ups.

All of these services are in some way niche, most require an account (which will put off many) and most provide multiple answers (1:M), although collectively they will probably end reliance on reference libraries (and on the product side – magazines like “Which?”). They helpfully hand you a piece of the jigsaw rather than solving the jigsaw for you. As has previously been said “None of these sites are Google-killers. In fact they make Google stronger because the questions and answers often will be indexed – extending Google’s reach into the tail”. Quora in particular has been recently heralded as a killer Q&A service but asking it “What are the most effective ways to engage news audiences?” receives a bunch of people’s opinions; whereas we could have a single list in order of effectiveness that has been produced through actual operational data and curated by either a Journalist or Statistician. If there were any dissention around the term “effectiveness” then that could be hammered out through social interaction. There wouldn’t be a need for a – discussion. As with all Quora questions, you need to read all responses to get a full answer but even then its just a subset of people that responded. Another example – RockmetIt better than Flock?

If the industry is framing Web 2.0 is the Social web and Web 3.0 as the Semantic web – it seems churlish not to leverage the powers of both in the Q&A service. The benefits of great data integration using common terms (Semantic) and crowd-sourcing (Social) in a Q&A service are obvious. By the same token, some answers will always require a (semi)/professional element to them (Expert) e.g. Someone asking a question about Alaska’s closeness to Russia (surprisingly – 2.5 miles) might get a quick answer but it will take an expert to plan how to get from one to the other through that particular route. Expert knowledge also comes into play around the presentation side – knowing which facts to present to most effectively answer questions e.g. the largest State question above – given to another search engine also gives Alaska but the square mileage is significantly less. A Geographer knows this is due to the second result not including water. There are other components but, in the main, both questions and answers have – social, semantic and expert elements.
Back to the Alaska question (“Which is the largest, happiest Republican State in the US?”). This requires both a semantic element (to determine the largest State) and a social element (who is happy?). If we make this “Which is the largest, happiest Republican State in the US liable to switch to Democratic?” then there is an expert element too.
Onto the proposed Q&A service – it needs to check a few boxes right off the bat:

1) Social. It would have to be based upon a popular, real-time social system. Despite perceived issues investing in their ecosystem, Twitter is arguably better than Facebook (or any other) for this purpose but it also needs to amplify and expand the reach of questions: present persistent questions that have undergone a process of disambiguation (both automated using semantic algorithms and manual through a public process of “backing” existing questions). Questions also need to be prioritized based on number of backers. Something like Kommons, Replyz  or maybe Formspring or Open Media could work here. Amplify is maybe too discussion focussed. The focus here is on directly (directly to the tweeter) quantifying the importance of a question and the validity of an answer. Social media monitoring like Crimson Hexagon could offer supporting services but they typically rely upon sentiment analysis whereas we really need direct engagement through voting or other mechanisms.
2) Semantic. We all need to be sure at the very least that we are talking about the same data. In short it will need an index. Google have Rich Snippet functionality but this is not a common approach adopted by most site ownersGoogle also acquired Metaweb earlier for this year for this purpose but something more open like Sindice would really work providing organisations have an incentive to cooperate (integration/ontologies/micro-formats etc.). Providing and articulating the incentive is the toughest piece of this by far. Then – we want an interface on the results (produced by the index) that allows us to de-select terms that we are not interested in so that the system knows for next time but also to publish the results of our tinkering and link it to the threads in the question or answer. Something like Sig.ma would do this.
3) Expert. We need data sets that are both selected and curated by subject matter experts. Curated answers can often be the most useful. Google acquired Aardvark earlier this year for this purpose but something like Wolfram Alpha or Qwiki would work. Involving Wolfram Alpha would add other benefits too – computational power (through Mathematica) to aggregate on-the-fly, leading NLQ support to translate questions, updatable widgets to support the presentation side and the semi-celebrity name of Stephen Wolfram attracting academic curators. All Wolfram Alpha content would need to be indexed and we would probably need to extend the publication side to be able to deselect returned facts as required (doesn’t appear to currently be available).

We are talking about potentially lots of social and semantic data needing to be agreed and presented. We’ll need simple data visualization with a basic level of interactivity so, with the example above; the user can slide the time scale to represent his adult lifetime. Something like Many Eyes, Hohli, StatPlanet or some improved Google Charts should do here. Also, we will want to store all our questions and answers forever so that we can reference them. Partnership with a very large EMC-like network storage partner will be required.

The resulting composite solution would be the most advanced Q&A service in the world. Q&A is so important and multi-faceted that it needs multiple solutions to work together. Its influence, if accepted, would be profound.

The service would drive out real answers (reference and topical) through a combination of social, expert and semantic approaches and actually referentially improve itself – as bringing social attention to the data would force organisations to improve data. Usability-wise – it wouldn’t hamper ad-hoc users by having a scary QBE type interface or by blinding users with spreadsheets of data. Conversely, it wouldn’t be so simple that it misses the point of what people want. It would be a natural extension to tweet activity (retweet, favourite, reply etc.). Opportunities for monetization would be rife: not least advertising with the solution’s wider collaborative user base, increased opportunities for interactivity and greater relevancy. New forms of credibility and relevancy would also become available. Journalism would shift to sourcing stories from datasets.

The traditional search space e.g. “Alaska Jobs” is already being eroded by location-based services. Google is certainly well placed to build a killer Q&A service as is Facebook with its obvious social advantages and semantic/graph plans. Google is confusing though – on the one hand, they have the right investments and infrastructure but on the other they still appear search/data/click driven – even imperiously so (“…designers…need to learn how to adapt their intuition”). They also have issues with innovation.

The big search players generally recognise current search solutions do not meet the needs of the user but they don’t have obvious solutions. There is an opportunity for innovation and for weaning people of this style of information seeking. There are smaller players that, if they cooperated now, could produce a more potent, compelling, flexible and lasting solution than any. Their biggest problem would be being creative about incentives for organisational involvement on the semantic side. Let’s hurry though – its hard going searching for things. We’ve got work to do now. We have questions. 

>Statistics are hugely underused. Even when they are used, they are improperly used. Much of the bolstering of weak arguments, miscommunication and ad-hoc ideology creation performed by individuals and organisations from the industrial revolution onwards are down to poor statistical use.

Entire industries are devoted to generating statistics; duplicating monstrous amounts of effort and when they are referred to e.g. in sales pitches, organisational reports, building/product tolerances, war crimes tribunals, political and environmental manifestos, news articles and business plans – they are so heavily caveated as to make them insensible. This is by contrast to the relatively deterministic, transparent and auditable approach organisations use to produce BI to support their own business decisions. Coupling this BI with governmental statistics is often necessary for the best decision making support, so commercial BI is itself hamstrung by statistics.

There are two key reasons for this:

1) Statistics are hard to find. If you are looking for, for example, the number of people that work in London currently (a simple enough request that many service organisations would need to be aware of), you will find this close to impossible. The UK Office for National Statistics (ONS) does not have this on their main site. Nor does the Greater London Authority or the newly released UK government linked data site. After you have wasted maybe twenty minutes of your time, you will be reduced to searching for “how many people work in London” then trawling through answers others have given when that same question has been asked. You will receive answers but many will be by small organisations or individuals that do not quote their sources. In the worst case, you may not even find these – instead using an unofficial figure for the whole of the UK which you have had to factor down to make sense just for London. If you search hard enough you will find what you are looking for at an ONS micro-site (completely different URL to ONS) but this data is over five years old.

2) Statistics have a poor image. The blame for this, in part, may be attributed to the famous Disraeli quote – “Lies, damn lies and statistics” in which he set generations of professionals into thinking they were akin to a practicable yet modish Victorian politician by disregarding statistics and cocking-a-snoop at the establishment in favour of their own experience. Showing you are practicable with maverick tendencies while (in overtly disregarding information that may cast doubt on your decision-making) shoring yourself up from failure – are powerful incentives. By contrast, other famous statistical quotes have been forgotten (“Statistical thinking will one day be as necessary for efficient citizenship as the ability to read and write” [H.G Wells]). Short-sighted governmental data integration, hugely delayed/over-budget government data-centric projects such as the UK National Health Service’s Records System, confusion over keys statistics e.g. number of asylum seekers and high-profile data losses haven’t helped matters since.

There is also a bit of a myth that statistical interpretation is an art and that the general public can be confused – even sent entirely the wrong message by engaging in statistical understanding. Only statisticians can work with this data. Certainly there is this side to statistical analysis (basically anything involving probability, subsets, distributions and meta-statistics) but for the most part, both the general public and organisations are crying out for basic (the answer to one question without qualifiers e.g. where/if etc.) statistical information that is quite simply – on a web-site (we can all just about manage now thanks), produced by or sponsored by the Government (we need to have a basic trust level) with a creation date (we need to know if its old). If statistics are estimates, we need to know that and any proportions need to indicate the sample size. We need this since we are now sophisticated enough to know “8/10 owners said their cats preferred it” has less impact than if we are talking about a dataset of ten cats rather than 10K cats (we don’t really need this though since we’re capable of working out proportions ourselves).

We don’t want graphs since the scale can be manipulated. We don’t want averages since it is similarly open to abuse (mean, median or mode?).  If we make a mistake and relate subsets incorrectly then the people that we are communicating to may identify this and that in itself becomes part of the informational mix (perhaps we were ill-prepared and they should treat everything else we say with care). We actually don’t need sophisticated Natural Language Processing (NLP), BI or Semantic Web techniques to do this. It would be nice if it were linked data but concentrate on sourcing it first. We really are not that bothered about accuracy either (since its unlikely we are budgeting or running up accounts on governmental statistics).

Mostly we are making decisions on this information and we are happy rounding to the nearest ten percent. Are we against further immigration? Is there enough footfall traffic to open a flower shop? Do renters prefer furnished or unfurnished properties in London? Which party has the record for the least taxation? What are the major industries for a given area? We just need all the governmental data to be gathered and kept current (on at least a yearly basis) on one site with a moderately well thought-out Query By Example (QBE)-based interface. That’s it.

Reading and writing have been fundamental human rights in developed countries for decades. Broadband Internet access is fast becoming one too. Surely we need to see access to consistent, underwritten government statistics in this vein too. Where other political parties dispute the figures, they should be able to launch an inquiry into them. Too many inquiries will themselves become a statistic – open to interpretation. It is absolutely in the interest of organisations and current affair-aware individuals.  

>Google Wave is/was an interesting product. It is nothing less than an attempt to oust email as our primary communications medium and therein lays its story. It is basically Instant Messaging with two additional functions – the ability to automate a response so the user doesn’t really see a difference between a human and a program (or Robot). It also supports mini-applications (or Gadgets) in a similar way to iGoogle and Facebook, blurring the lines between conversations and documents. Each individual exchange (or Wave) is logged and can be added to at anytime. It is better than email because it is real-time and richer exchanges can be made. It is worse than email because using this functionality is confusing for all but the tech savvy (having multiple Robots, Gadgets and humans all involved in the same Wave is rife with issues of ownership, progression and timing). Also the success of email is due to its ubiquity – everyone can use it. Hardly anyone knows what a – Wave is or what to do with it.

It was un-ceremoniously released to the general public a couple months ago (although some developers have been using it by invitation since its tech-celebrated debut last Summer) and pronounced dead by Google this week.

In Google’s own words – “Wave has not seen the user adoption we would have liked”. After less than fifty working days? For such a new and cart-upsetting product? With hardly any permeation of Wave concepts to the general public, next to no marketing and no specific commercial targeting? Of course! It was inevitable that this would be the outcome if an organisation were to review a key product launch after so short a time frame with so little support. This has to fall in the space of Google testing out new concepts/obtaining user feedback, with little fanfare (and so little possibility of failure) with a view to releasing the (inevitable) email replacement again at some point in the future.

Rich real-time communication supported by an easy/interactive interface to a computer (rather than a simple search dialogue) are most definitely the way forward. At the very least, collaborative programming environments benefit from this type of interface, as does trading and social networking proper. Google Wave uses the Extensible Messaging and Presence Protocol (XMPP) protocol which enables the most efficient peer-to-peer communication currently available. These needs are not going away and neither will the constituent parts of Google Wave (in some form).

Wave Robots are perhaps the most interesting component right now. In operation, they are a bit like a Turing Test or a Twitter-bot in that they facilitate a conversational and collaborative approach to establishing (and getting) what you want. Moving past the simple search/response model we have now, it is inevitable that there will need to be some interaction, some toing-and-froing and narrowing-down for something to understand (disambiguously) what it is that you actually want. It happens in real-life and so probably needs to be modelled in virtual-life.

Wave Robots are coded relatively straight-forwardly in Java using a Wave API. This element of Wave remains significantly undersold. As an example, I developed one to allow collaborative SPARQL queries to be made against any open linked data. A few working SPARQL queries have been uploaded to give you an idea.

Queries can be collaboratively changed in real-time and results can be sent out to a named Google Docs account as a spreadsheet. Once in Google Docs, there are several charting options available to make the data more accessible. Its like a hugely more powerful Google search (for those of a technical persuasion). You can add other Robots to the Wave to allow syntax highlighting of the code e.g. Kasyntaxy (although at the moment, this doesn’t seem to specifically support SPARQL). As its all Robot based, its server-side and so you can use it on your mobile device – whatever that may be. Just add querytheweb@appspot.com to your Wave contacts to use. Type “cycle” to cycle between one of two endpoints. An endpoint is basically a SPARQL query engine. The two used are both generic meaning that they are not tied to a particular data set. This means your queries will need to use the FROM clause to identify which data you are querying (by URI). Type “help” to get a list of other options.

This Wave Robot took just a day to develop in Java and was deployed to Google App Engine. It is basic but even as it stands, it is still probably the best way to access and present open linked data currently available.

However long Google maintains/runs/supports Wave, its constituent parts will, at some point, be mainstream. The demise of Google Wave is not the demise of the email replacement concept. Get ahead of the game and develop some Wave Robots now. Get used to the concepts and the working environment Google has provided until the end of this year. Your work will be able to re-surface in some new product next year.

UPDATE: Interesting Scoble commentary on Google Wave ending here.

>Hello Starbucks. You have made enormous success of the past fifteen years and have become an integral part of 21st century global cultural fabric. You have a store on the Great Wall of China, introduce new words to us like Yirgacheffe and are a bit like Viagra and The Simpsons (you aren’t technically the best but you’re easier to find and we have so much fun with you – we don’t care). We’d come to you eight-days-a-week if we could. You are to be applauded.

You have reached somewhat of an impasse though. You aren’t growing much; many of your stores are busy only at lunchtime and your brand doesn’t make us think – “that’s progressive!” anymore. Free Wifi/Foursquare deals, exclusive album sales and instant coffee will not get you those 40,000 stores you wanted a few years back. Your mantra of “A Starbucks on every corner” remains a good one. We know it’s tough out there but you just need to stick with your plan; maybe be a little bolder. Here’s what you do:

1) Recognise that you have to diversify. Your huge rent bill will surely eventually cripple you. You either need to dramatically cut costs (how? [Given your locations]), increase demand for coffee (how? [Given everyone drinks it anyway) or expand into new markets.
a. In 2008, the FM market was approximately $846BN, with approximately half ($426BN) apportioned to internal services meaning that the outsourced FM market in 2008 was worth around $420BN. It is surprisingly difficult to obtain free global branded coffee shop market sizes but for the UK at least, this is a $2.5BN market (2009). Let’s assume the UK is 5% of the global market (pretty standard), leaving a total market of $50BN. This means the FM market is roughly ten-times the size of the branded coffee-shop market.
2) Build more stores. Very roughly and taking the UK as a case in point; you have 750 stores and there are 30M people employed in the UK. With a few (contentious) assumptions, if you increased the number of stores tenfold (7,500), each store would need to accommodate just 200 people (1.5M/7,500). Obviously, it would need to take place over some years. A burgeoning senior citizen population, increased contract working and home-working will reduce the market, making the figure more manageable longer term.
a. Assume half actually work in an office (the rest in retail stores, hospitals, lumberjacks, machinists in plants, plumbers, nurses etc.). This takes the potential market down to 15M.
b. Assume half work for big name organisations that will want to maintain their own premises (taking it down to 7M – basically the SME market).
c. Assume half of those actually work in an office at any given time (the rest visiting clients, training, sick/vacation, travelling, WFH etc.) taking it down to 3M).
d. Assume you lose half the remaining people to other coffee-shops as the market is quite fragmented at the lower-end (taking it down to 1.5M).
3) Use your Starbucks card. At the moment this is used as a store card (arguably faster than paying otherwise). Put an RFID chip in and use it to track peoples employing organisations, access times and automatically bill the organisations according. You can hugely undercut existing FM services if you open-up this new revenue stream. You also expand your coffee market.
4) Build meeting rooms. Organisations need secure ad-hoc meeting rooms (HR, competitive, strategic discussions etc.). Let’s assume all new stores have one. These would need to be empty by default i.e. not having coffee drinkers in them and controlled by an online booking system. Let’s also put sophisticated video conferencing facilities in each one. Of course meetings are going to overrun and the people outside waiting for the next slot are going to have to either play nice/assertively claim their room but this happens in offices already. You might want to partner with others for larger, scheduled meetings.
5) Deploy IT Infrastructure. Cyber-cafes may be on the wane as cheap mobile devices rise but you would need to pop Internet terminals in your stores to mop up those without laptops at any given point. The shift to cloud-based computing means organisations won’t need development/file/application servers because they won’t have IT departments. Each store is also going to need a couple of wireless printer/scanner/copiers.
6) Go stealth. To avoid monstrously over-selling your brand, you are going to need to expand your stealth experiments on a wider scale. Focus individual stores on the areas they are in (creative/business/education etc.). Maybe change the decor to fit-in with local murals on the walls. It may be healthy to engender some competition between them. There would clearly need to be more variety in (interior and exterior) store design.
7) Forget the Baristas. Everyone knows this isn’t a skilled job. Stop pretending it is. It’s not like they spend years learning the correct Frappuchino for the chocolate Starbucks coin customers eat. They’re a bit like your Starbucks cards – over-engineered. Do give them training but make this in basic IT services in addition to working the coffee machine. They should need to know how to reboot the router, connect to it and any of the various wireless devices you have in your store from most portable devices, reset passwords, create accounts and escalate issues – that sort of thing. Ultimately, they’ll thank you for it. Future employers will place much more emphasis on IT service skills.
8) Culture shift slightly.
a. “Third-place”. This internal marketing needs to go. Yes – there’s a place for a safe haven, a “third place”, that place outside of work and home where you know that you will be greeted with a smile and some respect. This is more than a coffee shop though. It is now a hackneyed term anyway. It was used at the Playstation 2 launch and is employed by countless gyms over the world. Is it really harder to create another market than get a good chunk of one (or both) of the existing ones?
b. Seat-saving. This needs to go. Someone cannot come in, sit down on one of your sofas and then “save” seats around them; dissuading potential users as their “friends are coming”. This prevents people from using stores for more than a quick coffee i.e. to work. Hot-desks are essential. It has to be first-come-first-served. Subtle advertising cues should be able to make this culturally frowned upon so it ceases to be an inhibitor.
c. Table Service. Your service isn’t great at lunchtimes. Queues can be large. People on laptops are dissuaded from leaving their laptop but they still want a coffee. Your new Trenta sizes may address this issue (slightly) but your smaller competitors offer table service for the same price.
d. Enhance security. You cannot have hoodies/Hells Angels/gypsies/beggars etc. associating with Senior Executives (can you?). You are likely going to need a security guard in most stores to gently dissuade them. Can’t they all do double-duty as Baristas too though? Security Barista? IT Barista? Table-service Barista? They can be more Pokémon than Borg.
e. Get out of food. You are not known for your food. Stay with chilled things that go well with hot coffee e.g. muffins, cakes, chocolates, biscotti etc. The hot breakfast sandwiches, wraps and salads all need to go. They take too long, are odorous, other brands do them better, people don’t want them in their office and will also want a break from you (their workplace) to go get them anyway. Get a food partner if you must and link it to your Starbucks card. We can work it out.

You have the cultural and economic reach to become our workplace. This isn’t something you can do quickly. It’s a goal over the next fifteen years. You can choose to move up from being an escape to being a destination. That journey will mean you need to take a leap and recognise that you’re big enough right now and that you’ll have missed service elements along the way (but that others will fill-in and contribute to the new eco-system). It may also mean you concentrate on the back-office, lose a bit of your élan/put your brand on the back-burner and cancel that order for corporation T-shirts.

A little like those faceless East-India type holding companies that keep going for hundreds of years. That’s OK though. You have certainly let your face grow long of late but to paraphrase The Beatles further; you are the coffee man. They are the coffee men. You are the water-cooler.

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