The years at Cloud of Data have sometimes been a bit scary, sometimes utterly amazing, but always something I have never regretted. I have seen a lot, I have been a lot of great places, I have met some amazing people, I have learned a lot, and I have hopefully contributed something to the companies, organisations and individuals with which I have worked over the years.

But it feels like it’s time for a change. It feels like it’s time to spend more time talking to the organisations that use this stuff, and less time talking to or working with the organisations that make it. And that is the proposition that Forrester offers me.

I am sure I’ll still go many of the same places, and attend many of the same events. Indeed, I may get to go to more of the places I want to go. Although linked to Forrester’s London office and often working from home in East Yorkshire, the role is very definitely global in scope.

But it’s time to let Cloud of Data have a rest. This blog, my daily Tumblr feed, and my Forbes channel will probably become more-or-less dormant. I’ll be blogging for Forrester, I’m sure. I’ll also still be tweeting, and opinions over on Twitter remain real, personal, honest… and mine. Forrester, I am sure, expects nothing less.

And now to leave this hotel room and walk along the Strand to the start of my first day. There’s a lot to look forward to, and a lot to learn.

Quite what this 13-year Mac user will do when handed the company (Windows) laptop remains to be seen… Politely say thank you and just keep using the Macbook Pro I’m writing this on, I suspect…

I’ll be writing about similar topics to those I cover here, including cloud computing, big data, and points in between.

You can follow along here, and subscribe to the RSS feed here.

It was around the middle of 2009, I think, that Michael Wolf first contacted me about contributing content to a new subscription research service, Gigaom (then GigaOM) Pro. The core proposition, as Om Malik described it at the time, was interesting to me, and I was happy to be involved:

“GigaOM Pro is a new approach to research focused on delivering timely, high-value reports and analysis for technology innovators and insiders at a fraction of the cost of traditional market research vendors. Organized around technology verticals, GigaOM Pro provides subscribers with in-depth market research on emerging technologies, including Green IT, Infrastructure, the Connected Consumer, and Mobile, as well as daily insight into developing trends and events.”

The Gigaom team delivering this research service was initially small. It grew, of course, adding Research Directors, editorial specialists, project managers, sales staff, and the other pieces required to support existing clients and customers while attracting new ones. But almost every analyst contributing to the site (including myself, even at my most involved) remained freelance. Across a pool of several hundred analysts, Gigaom and its clients could draw upon a wide range of perspectives, backgrounds and skills. When a specific project was commissioned, Gigaom’s staff identified a suitable analyst and then worked with them to ensure successful delivery. Analysts remained independent of Gigaom and its clients, and neutrality of mindset was actively encouraged.

I, and others, wrote reports, ran webinars, delivered analysis and consultancy in person, in writing, and on the telephone. For a year, in 2011, I curated what was then called the Infrastructure section of the site. That ended in 2012, as Jo Maitland (now at Google) joined Gigaom’s expanding permanent staff and worked with her colleagues to  continue growing this subscription-based business. One of the aspects of that role I’d particularly enjoyed was providing a short perspective on the day’s important news. That lives on, more or less, as the One Short Thought I share over on Tumblr each day. Nowadays, though, I try pretty actively to avoid the ‘main’ story of the day and settle instead on the one that’s most interesting.

More recently, my involvement with what came to be called Gigaom Research shifted to focus far more on underwritten research papers. Never sponsor-loving white papers (despite the misunderstandings of more than one client over the years!), these offered a useful opportunity to associate an underwriting company’s name with carefully neutral consideration of a market segment relevant to their business. Done right, these were a good way to provide Gigaom commentary on trends and issues that might not attract sufficient eyeballs to justify the investment of non-underwritten analyst time. Underwriters could rely upon being treated fairly, even if the final result didn’t always paint their product in quite the rose-tinted colour they might initially have wished. More than once, Gigaom’s Research Director for Custom Publishing, Matthew Spady, had to intervene to remind clients that we weren’t writing about them. But they always ended up happy. And, more than once, I was involved in projects where fierce rivals were willing and able to contribute their perspectives to papers notionally paid for by ‘the enemy.’ That strikes me as a good thing for all concerned.

Gigaom as a whole should have survived. It’s still not entirely clear why it didn’t, although I and others have theories. Some of those sharing their theories are even in possession of a relevant fact or two. The Research business, which was in the process of tightening its focus and emphasising its core value proposition, struck and strikes me as valid, viable, and profitable. I hope that someone sees the value in building upon that foundation, finding ways to bring the best of the Gigaom Research model to an ever-wider audience.

Until then, it’s time for me to find some other projects to fill the gap left in my schedule by a number of Gigaom reports that will presumably never be delivered.

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“In the past year we’ve seen massive growth in Artificial Intelligence (AI) and deep learning. Our own Derrick Harris has been covering this area for years and we have decided it’s time to give this rapidly growing area a platform (and conference) of its own.”

Personally, it’s great to see this area getting real attention from Gigaom. But I’m reminded of just how long some of these issues have intersected (or, often, passed close-by) my own career…

Back in the mid-1990’s, I was researching a Ph.D in the Archaeology Department at the University of York. Nothing particularly semantic or artificially intelligent about that… except that a fellow student was building a neural network that inferred all sorts of things about Viking sheep, based on cues derived from photographs of their excavated teeth. And expert systems were in vogue, for a while, doggedly following rules to identify such esoterica as the precise Dragendorff form of Samian pottery fragments. I stuck to teaching computers how to build maps of the archaeology below our cities. Much prettier.

Fast forward around ten years to Talis where, for a while, a team was assembled that pushed the envelope on what might be possible with an open, extensible and connected semantic platform. Alongside the technical developments, significant effort was invested in raising awareness of the potential around semantic technologies. Podcasts, two blogs, a print magazine (!), invitations to present globally. All now, sadly, gone from the Interweb as the company turned in a very different (but, given the unanticipated arrival of a global financial crisis, possibly understandably safe) direction.

As Richard Macmanus noted at the time, there was a brief flurry of interest in ‘semantic’ amongst the Web 2.0 crowd…

There was also a far deeper, and more sustained, interest in putting core semantic technologies to work in solving big, hard, slow problems. Events like the long-running Semantic Technology Conference (coming up in San Jose this August) offered a rare opportunity for academics and business leaders (and lots of spooks) to mingle. Other events tended to cater to a more academic crowd, making it harder for the big ideas to really get tested in business situations.

ZDNet had a semantic web blog, which I wrote for a number of years. Subsequent site reorganisations have broken a number of the URL redirects, but all the content does still seem to be there if you craft the right searches. There was also a site called SemanticWeb.com, now part of the same organisation as the Semantic Technology conference, where I wrote a regular column for a while. There, too, not every link survived the site’s transition unscathed.

Here in Europe, the funding stream that’s now concerned with ‘big data’ and the ‘data value chain’ was far more effusively intrigued by semantics, natural language processing, pattern recognition, and related matters. I’ve reviewed a lot of proposals there, and the change in the language of funding calls and bids has been interesting to observe.

And now, as Gigaom’s post noted, “we’ve seen a massive growth in Artificial Intelligence.” Everyone seems to be talking about it, and they don’t necessarily always mean what an AI researcher would mean. Much of what we’re seeing goes back to those enthusiastic days before the crash, even if some of the language and emphasis has shifted. Bottlenose, which I wrote about this week? Brain-child of Nova Spivack, of Twine fame in 2008. Microsoft’s Machine Learning, Business Intelligence and Analytics stuff? A lot of it is underpinned by people, technology and ideas from Powerset. Google’s Knowledge Graph, most recently seen telling us what the symptoms of a common cold might be? There’s lots of Metaweb and Freebase under the hood.

The language has definitely shifted. Artificial Intelligence, which was stuffy, esoteric, academic, and a bit pie-in-the-sky even to the semantic technology crowd, is now the term du jour. Semantic Web is only mentioned in whispers, if at all, although some of it sees practical and pragmatic adoption as part of initiatives like the Google-backed schema.org.

I made a conscious decision, when I left Talis, to focus on the emerging opportunities around cloud and data. Hence the name. Cloud, increasingly, is table stakes. It’s background, foundation, platform. It’s not interesting for itself, but for what it lets you do. And what it lets you do is gather, process and act upon oodles and oodles of data. Software like Hadoop, and data-based trends like open data mandates from Government create a fertile mix of capability and content. Computing is faster, cheaper, better. Data is better, more comprehensive and more available.

Now, maybe, all that semantic stuff can have its day… powered by cloud and data. And Structure Intelligence can play its part in telling — and shaping — this next chapter.

Disclaimer: I am an active member of Gigaom Research’s Analyst Network. They pay me for various things. They do not pay me to plug their events.

Image is a close-up of the modern replica of Charles Babbage’s Difference Engine, photographed by Larry Johnson.

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How to Lie with Maps

It wasn’t so much a handbook for the nefarious, as a primer for those who wished to understand – or avoid – the traps and pitfalls so easily baked into both physical and digital maps. A slight change in colour palette, a shift of projection, an emphasis of this over that and a superficially factual and accurate map flips from portraying one truth to suggesting (or trumpeting) a very different one. Sometimes it’s deliberate. Sometimes it’s a (hopefully!) unfortunate accident.

It’s time, I think, for How to Lie with Data. There are plenty of books telling data scientists (whatever they are) and others how to visualise data, how to tell stories, and how to persuade. The great Edward Tufte‘s earlier books were doing the rounds alongside Monmonier’s guide, and did much to expunge the cartographic equivalent of ‘chartjunk‘ from the creations of those of us sometimes tempted to over-do the computer-enabled excesses of cartography.

But there’s a real gap in need of filling today; a short book to illustrate some of the easy and obvious pitfalls as we rush to make everything data-based, data-supported, and data-rich. It would be a book written to open the eyes of those approaching the data visualisations of others with a little too much credulous naivety. It would also be a book written to remind the enthusiastic visualisers of data to think a little harder. Just because you can use that particularly jarring juxtaposition of scarlet and puce in your palette, maybe the perception of your data would be more balanced if you didn’t. And we know that the data looks more convincing if you flip the axes and start counting from 1,000 instead of 0… but wouldn’t it be more professional to make it clear what you’ve done?

We have beacons of good practice like Information is Beautiful (plus book) and Flowing Data. Now, though, let’s have Information can be really ugly when put in the hands of bad people and those with more data than skill.

A bit of a mouthful? Then How to Lie with Data will do nicely.

So. Who’s going to write it? I’ll be at the front of the queue, recommending it to an awful lot of people who really should know better…

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Private clouds are real. It’s well past time to grow up and accept this.

Not every IT workload is most logically run in a cloud, now or in the future. But, for those workloads where cloud is advantageous, it seems likely that public cloud will eventually supplant both private cloud and hybrid cloud deployments. Public clouds are getting cheaper, they are addressing legacy regulatory hurdles, and they are increasingly meeting every valid concern that IT buyers and users might have about entrusting mission critical cloud workloads to them.

However, in 2014, there are still workloads or situations which are suited to a cloud-like treatment but more suited to a private or hybrid cloud deployment than to a public cloud. This may be because of the sometimes prohibitive cost of running steady workloads for long periods of time in a public cloud. It may be because of existing investment in hardware, data centres or people. It may be because of some as-yet unresolved legal, compliance or regulatory hurdle. It may simply be because of fear of change. Or there may be some other reason.

IT suppliers know this, and there’s an unhealthy flood of ‘private cloud’ solutions on the market, many (most?) of which are simply last year’s product with a new name and a cloudy logo. This behaviour is unfortunate, unhelpful, distorting, confusing, disingenuous, (occasionally) fraudulent, and (sadly) inevitable. But behind the snake oil, the FUD-fuelled pitches and the lies there are real private cloud solutions, delivering real value to real customers.

And we attack them. We attack them all. We say there’s no such thing as private cloud. We say private clouds are false clouds. We say you haven’t got a cloud if you bought the hardware. We say there’s no CapEx in cloud. And it all gets a bit silly.

Someone is always paying for the hardware in a cloud. There is always CapEx for someone. If you’re consuming elastic, on-demand, self-service, metered computing over the network from Amazon, we’re happy to agree that you’re using a (public) cloud. If you replace Amazon with Microsoft or Google or Rackspace, we’re still happy. It’s still a (public) cloud. Despite the huge capital expenditure outlay at every single one of these companies. Despite the fact that — at their scale — it’s not limitlessly elastic at all. They have physical constraints. Sometimes they hit them. You, the individual customer, rarely notices.

Now imagine you’re a research scientist at an international pharmaceutical company, or a developer at a global bank, or a product manager inside a large retailer. The organisation for which you work is large, it has scale, it has clout, and it has buying power and realises economies of scale when it spends its money. Someone, somewhere in that organisation, spends money to buy hardware and places that hardware in a pool that you and your thousands of colleagues can access. The hardware in that pool is designed and configured to be elastic, on-demand, self-service, metered, and accessible over the network. Devolved budgets, chargeback, and all the other financial shenanigans that big companies love mean that you’re billed by IT for the resources you consume. You never bought the server. You simply pay for what you use. It’s a (private) cloud.

Amazon’s cloud might be bigger (and therefore able to elastically scale further before it breaks), but the internal cloud you’re using scales a lot too. Amazon might have more buying power, and therefore see a bigger discount from suppliers than your organisation does, but Amazon has to factor in a profit margin for itself.

The bigger these clouds get (whether public or private), the more likely it is that additional resources will be there when an individual customer needs them. The bigger these clouds get (whether public or private), the greater the purchasing power and the more useful the economies of scale. Amazon’s data centres are probably bigger than those of most individual corporations, so Amazon has an advantage there. But that’s all it is – an advantage. Not a lock on the right to be called ‘cloud.’

For the end user, a private cloud can, should, will and does look exactly like a public cloud. It’s there when they need it. It grows if they ask it to. And they’re billed for what they use.

It’s a cloud.

And their IT department is a cloud provider.

For IT, of course, the picture is rather different. They are expending CapEx. They are having to buy hardware. They are having to hug tin. They are having to stay up all night, making sure everything keeps working. They need to monitor load and demand and all the rest of it, to make sure resources are always available when the customer tries to access them. They need to patch and secure and monitor and maintain and tweak and cajole and replace and fix… and occasionally kick. It’s still a cloud. AWS staff do all that stuff. They’re running a cloud. Azure staff do all that stuff. They’re running a cloud. Rackspace staff do all that stuff. They’re running a cloud. It might make sense for enterprise IT to also do that, today. It might be technically, financially, and commercially prudent, today. It’s far less likely to be so in a few years.

But for the end user? The customer? The research scientist, the developer or the product manager? They’re just using a cloud. If it does what they need it to do, at a price and with a reliability that meets their requirements, it really doesn’t matter whether the cloud they’re using is public or private.

It’s still a cloud.

Image shared on Flickr by Dawn Ellner.

Since then the company has moved in a different direction, focusing far more attention upon the tools and services required to work with data, and far less upon offering a place for customers to find data. Infochimps was acquired by CSC last year, and dropped into the larger company’s big data business unit to accelerate it towards far greater market visibility and penetration.

In this podcast, I talk with Infochimps CEO Jim Kaskade. We quickly cover some of the history, before looking in more detail at the ways in which Infochimps/ CSC can differentiate itself in an increasingly crowded market. A degree of technology agnosticism clearly helps here, and Jim talks about the way in which his team will help customers deploy any Hadoop distribution rather than being tied (as Cloudera or Hortonworks would be) to the home-grown offering. We also discuss recent investment in the space, with Jim noting that — despite Cloudera’s recent $900 million round — CSC still has deeper pockets and a longer reach.

We touch upon Hadoop’s role within the growing big data ecosystem, asking if it’s as important as its backers tend to claim, and discuss the attributes of a successful move from the near-ubiquitous big data pilot project towards the far less common production deployment of a big data solution.

A small example from British Airways this morning shows some of the opportunity in the travel sector, whilst also highlighting the rather large gap between (my) expectation and (this trip’s) reality.

On Saturday, I booked one of my not-infrequent trips to the United States. I tend to build a picture of a possible trip gradually over a week or two, visiting several sites, comparing different options, looking at any ways in which status or points can sensibly be put to work, and generally working out how best to complete the trip. By the time it comes to the act of booking, I know what I want to do and will typically complete everything using a few sites in a pretty short space of time. Over about an hour on Saturday, the British Airways site sorted my flight,  Starwood Preferred Guest sorted my stay in America, Accor dealt with the pre-flight night at Heathrow, and East Coast (a UK rail company) dealt with getting from home to London and back. All the confirmations, of course, routed straight into TripIt.

Today, this arrived in my inbox;

Joined up thinking at work. Upsell, partnership, mining of data for insight, and all the rest. Superficially, at least, it’s a pretty good example of putting existing data and relationships to work.

And yet, this particular message fails on a number of levels. Some were within BA’s control, others are quirks of my behaviour that the airline might (possibly?) not be expected to fully understand.

First, let’s take a look at the mis-steps that BA could (and should) have been able to avoid…

• “When you’re in Los Angeles,” the message opens. Erm. My booking is for a return trip from the UK to San Francisco. On the outward leg, it happens to work out more than 60% cheaper to fly from London to Los Angeles and then catch a shuttle for the short hop from LAX to SFO. My booking with British Airways is for the whole journey by air. They know that I connect to an American Airlines flight to SFO, a couple of hours after reaching Los Angeles. They know that my journey home is from San Francisco to London, direct (and, in another odd pricing quirk, in a Club bed for about £50 more than the Premium Economy seat I was originally looking at). So which particular bit of their workflow decided that I needed hotel accommodation in Los Angeles? Do they know something about connections from LAX to SFO that they’re not telling me? Is this a subtle hint that my stay in Tinseltown may be longer than the quick trip to the Lounge I’m expecting?
• “You’ll need to become a HHonors Member. But that’s easy. Join and book now.” Erm, again. I am a HHonors member. I have been for years. My HHonors account is already linked to my BA account, and I already earn British Airways Avios points for Hilton stays. Shouldn’t the British Airways marketing machine know that? I might be unhappy for it to know how often I stay with Hilton (much less than I used to, for various reasons), how much I spend with Hilton, or other details of the internal workings of my relationship with Hilton… but I’m perfectly happy (and, presumably, not alone in this?) for them to know that there is an existing relationship between the me known to BA Executive Club and the me known to Hilton HHonors. Indeed, large parts of that relationship are very visible in the movement of points in and out of my Executive Club account.

And now, the personality quirks which made this message less powerful than it might otherwise have been…

• The biggest of these is timing. As I mentioned, I booked my flight on Saturday. This message arrived on Monday. It’s possible, of course, that BA has masses of data to suggest that people book hotels two days after they book flights, but I don’t work that way. I doubt I ever have. My hotel was booked within about 30 minutes of the flight, maybe even less. This message should have been in my inbox on Saturday, alongside or very soon after the booking confirmation email for my flight. Two days is too long. I’ve moved on to the next trip.
• The call to action seems off. The message is triggered by a known booking of a trip to a specific (albeit wrong) place at a particular time. Where are the suggested Los Angeles Hiltons for my consideration? Where’s the teaser of the benefits I could expect (“enjoy a complementary room upgrade and breakfast by the pool at the Hilton blah blah”)? Where, even, is the link back to the BA hotel booking service and it’s roster of SoCal Hiltons?
• For me, at least, a message like this sent after I booked my flights came too late. I already knew where I wanted to book, because I’d done my research. Some of that research is still visible on the British Airways site, as some of my searching there over the past couple of weeks included considering combined flight and hotel packages for this trip. That, presumably, is visible to BA’s back-end systems as they go about their analysis of the way that I and other users of the site behave. This message might have worked better if it had come during or after some of that research. Failing that, it could have been worded as something to consider for my next trip?

Smarter use of data offers travel companies the opportunity to better understand their customers. It also offers them the opportunity to sell and upsell in a far more targeted and responsive fashion. For the traveller, there are plenty of benefits to be gained as our travel providers target us with offers and options which might actually meet our needs or improve our journey.

British Airways is doing an awful lot to better understand its customers, especially its premium and frequent fliers. There’s usually some small example of that data at work on every trip with the airline… which makes me that much more likely to keep choosing them. But emails like today’s clearly show that the systems behind the scenes aren’t yet as joined up or as smart as we sometimes like to believe.

Image of British Airways A380 and the Red Arrows from Wikimedia Commons, attributed to user ‘Airwolfhound.’

In this latest podcast, I talk with Quentin Clark to get a view on how the various pieces are starting to fit together. Quentin is Corporate VP with responsibility for the Data Platform Group, and our conversation quickly shows that there’s a lot going on there.

In just over half an hour we barely scratch the surface, but some of the opportunities — and some of the challenges — certainly become apparent. Maybe we can revisit some of the specific areas of opportunity in future conversations…

Image, ‘York Station approaches’, from All About Railways by F.S. Hartnell. Now in the public domain, and shared on Wikimedia Commons.

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When you walk into a shop and buy something, a plethora of tangible and intangible signals reassure you that all is proceeding as planned. You have the goods you purchased. You probably have a printed (or emailed) receipt. You may have change in your pocket. The cashier actually interacted with you, whether with a cheery smile, calm efficiency, or brusque disregard. You leave, clear that something happened.

Most online services, of course, recognise that it’s important to maintain these cues; even when buyer and seller are continents apart. Amazon confirms a transaction on-screen, and then follows up with an email to acknowledge your purchase. It then sends further emails when goods despatch, and offers a variety of methods to track the process from ordering to delivery. In this, it is far from alone.

And yet, now and then, I come across online businesses which fail to grasp the importance of feedback, information, and acknowledgement. And, every time, it jars. Why, in 2014, are otherwise good businesses failing to recognise that they’re dealing with human beings, and that those human beings are programmed to expect interaction, reassurance, acknowledgement?

The latest online business to — apparently — ignore this important aspect of the human makeup is bill.com. A few recent projects have been for clients that have started to use the well-funded online payments and accounting service. For them, the service may well be great. It may do everything its marketing suggests, to streamline the process of receiving, processing, and fulfilling invoices. It may offer them great tools, and a great user experience. For me, submitting an invoice, the process is dreadful.

The initial premise is good. Simply email an invoice (in my case, generated automatically by FreeAgent) to a *@bill.com email address. No client-specific Excel spreadsheets to complete. No nasty Salesforce kludge, which looks like its UI was designed by a distracted toddler and their bad-tempered teddy bear. Just an email address to receive an invoice that my own systems are all geared up to create and track. This, I thought, is going to be great. The future of billing has arrived.

And then the wheels fall off. Nothing. Absolutely nothing happens. No emailed acknowledgement of receipt. No ability to track where an invoice is in the approval process. No email to say that payment is coming. Tens of thousands of dollars invoiced, and you know nothing for weeks; until the money eventually shows up — unannounced — in your bank account.

The first time this happened, I assumed that my client had perhaps failed to select an option to have email acknowledgement sent. They were, I knew, new to bill.com. They might not have it all set up correctly.

Apparently not. Apparently it’s a good suggestion. For future updates. Really? For a company that’s been trading since 2006? Surely the electronic equivalent of the smile of acknowledgement, the grunt of recognition, the closing of the loop is rather more fundamental than that?

Are we really meant to drop out of this shiny,  futuristic and time-saving automated process to contact the client?

Hi. I just submitted my invoice for that project. Can you please confirm receipt?

How does that help anyone? It’s more work for me, it’s more work for them. And all the stupid computer had to do was send an email to let me know mine got through. A handshake, if you will. In both the networking and the real-world senses of the term.

As we move more and more online, we still need to acknowledge, reassure, report. Bill.com doesn’t, and it should. A human being at bill.com manages to do it on Twitter, obviously. Shouldn’t the same response to human beings’ need to feel engaged with be baked deep into their product?

Image by Flickr user vkanaya