Image via CrunchBase

With Facebook’s IPO just around the corner, the timing of Google’s latest press blitz should probably be regarded with a healthy dose of suspicion, but the unveiling of the Knowledge Graph is an important step in Google’s journey — and a reaffirmation of values diluted by recent dalliances in social networking. Writing for The Atlantic, Alexis Madrigal perhaps describes it best;

“To me, this update is the epitome of what Google does best. The graph makes the process of Googling something faster, easier, and better. The corporate imperative to keep people searching on Google in the face of renewed competition matches up very nicely with consumers’ desires for the best, fastest search experience. That hasn’t always been the case with the company’s social search integration, so this update feels so refreshing. It’s like a friend in the midst of a midlife crisis returning the Porsche and embracing a trusty new four-door.”

(my emphasis)

Exactly. As I’ve written before, I strongly believe that semantic smarts should be hidden very, very deep, and that semantic technologies are at their best when they quietly and unobtrusively make some existing process better. That’s why I like TripIt so much. It gets travel plans into my calendar faster and more accurately than I could type them, and throws in a whole heap of added value as a byproduct of the data ingest process.

Google’s Knowledge Graph is similar; it works with existing search behaviour, and unobtrusively adds a little extra value.

It’s going to be fascinating to see which direction Google takes this capability, and my latest column for SemanticWeb.com explores that in a little more detail.

Those (like me) based outside the US need to remember that none of this works on sites other than google.com right now.

And if you’ve not seen Google’s introductory video, take a look.

Related articles

• With Knowledge Graph, Google Can Finally Tell The Difference Between Apple Inc. And Apples(fastcompany.com)

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• Google Goes Back to What It Does Well: Finding Things(readwriteweb.com)

County Arcade, Leeds

The global CloudCamp movement continues to grow, with events over the next few weeks in Denmark, Germany, Nigeria, Ghana, Kenya, and across the United States. And now, I’m very pleased to announce that the English city of Leeds is joining the party.

CloudCamp events have been taking place in the UK for years, and the London gatherings have picked up real momentum. Outside London, we’ve seen a few events in Warrington, Newcastle, and Edinburgh. We believe that the time is now right for something more regular; a place in which the cloud-building, cloud-using, cloud-interested and cloud-exploring can come together for talk, beer, pizza and more… without having to jump on a train to the deep south.

CloudCamps are interesting events, with a real emphasis on informality. I’ve attended several around the world, and am always impressed by the energy in the room, and by the welcome extended to newcomers. As the main CloudCamp site describes,

“CloudCamp is an unconference where early adopters of Cloud Computing technologies exchange ideas. With the rapid change occurring in the industry, we need a place we can meet to share our experiences, challenges and solutions. At CloudCamp, you are encouraged you to share your thoughts in several open discussions, as we strive for the advancement of Cloud Computing. End users, IT professionals and vendors are all encouraged to participate.”

Jeremy Jarvis at Brightbox and Karyn Fleeting and Joel Turner at Tinderbox Media have been driving this event forward, and they’ve invited me on board to help out. I also get to be MC on the night.

We’ve got speakers and sponsors committed, with more of both to come. If you think you should be one of those doing the speaking or the sponsoring, do let us know.

So, if you like talking Cloud, if your boss has ordered you to learn Cloud, or if you’re just keen to understand a little more about what this Cloud thing can do for you, stick the evening of 14 June in your diary, sign up (for free) on Eventbrite, and come along to the Hilton DoubleTree in Leeds for an evening of fun, learning, beer, and more.

See you there!

Image of the County Arcade in Leeds by Flickr user Francisco Perez

Image: NASA

Amazon’s globe-encircling cloud infrastructure is compelling to many. From Virginia to California, from Ireland to Singapore, and from Japan to Brazil; wherever you find yourself there’s a local instance of the same familiar set of services. And, in all likelihood, Australia will soon be added to the list. For those primarily interested in just serving both Europe and the US, the list of options grows to include Rackspace, GoGrid, CloudSigma and a few others. And yet, despite the buying power and increasing ubiquity of these larger players, there seems to be plenty of space left for smaller entrants. For prospective customers only concerned with a single country or region, for example, the choices are almost too many to count, and choosing between them becomes a complex and multi-faceted affair.

Take Brightbox, for example. As my family know all too well, those pesky timezones mean that many of my evenings are punctuated with calls to or from the States, where so much of the innovation in this sector continues to take root and grow. Either that, or I’m creeping out of a sleeping house to catch early trains for the 150 mile journey south to London. It was therefore refreshing to talk to someone in this industry whose offices are only 50 miles away in the UK city of Leeds.

Established back in 2005 as a Ruby shop capable of hosting apps on dedicated hardware, Brightbox has evolved to place increasing emphasis upon the provision of infrastructure. In 2010, the company began to seriously explore the possibility of offering a generic cloud infrastructure environment. This was in the days before OpenStack, but Eucalyptus existed and was attracting interest. But according to Brightbox co-founder Jeremy Jarvis, 2010′s Eucalyptus lacked some key resilience features (load balancing, multi-data centre capabilities, etc) that the team believed were critical… so they built their own system from the ground up. And, at the end of September last year, Brightbox Cloud entered general availability.

The Brightbox cloud operates out of two UK data centres, with planning underway for a third. Both data centres are now owned and operated by Telecity, which acquired the two independent data centre providers with whom Brightbox had launched. Brightbox owns the racks and (Dell) hardware, and also ensures provision of redundant network access into the data centres. Customers are predominantly drawn from across Europe, but Jarvis says he’s seeing some customers coming from as far away as Australia, New Zealand, and Brazil. The company sees its potential growth through eventual expansion to data centres on the European mainland, but Jarvis says he’s “much less interested in setting up yet another” North American operation. The company is profitable, employs ten staff, and is seeing steady growth in usage.

Brightbox does not (yet) offer a web management console, but Jarvis describes this as a conscious decision and also something of an asset. The company has instead focused their attention upon crafting a rich, capable and intuitive API (and associated command-line interface). According to Jarvis, the developers that the company tends to target have responded favourably to the API, describing it as “nice” and “more consistent” than the various APIs offered by Amazon’s growing suite of services.

A focus upon developers (and the growing Dev/Ops movement) was also a strategic decision, and Jarvis cites examples in which ‘mere developers’ have proved instrumental in securing significant contracts with Brightbox from their employers. Corporate purchasing processes may, finally, be evolving. Despite the success of Platform as a Service (PaaS) offerings such as Heroku and EngineYard, Jarvis also suggests that Brightbox is seeing growing evidence that developers are seeking more fine-grained control over infrastructure than PaaS typically offers. A platform abstracts the underlying complexity of infrastructure, making it easier for application builders to focus upon creating the specific services they wish to provide. But abstractions typically require compromises, with configuration decisions being made for everyone on the platform on the basis of ‘normal’ requirements. For developers with non-normal requirements (and they may actually be the majority of users), IaaS is more likely to offer the fine-grained control that they need.

But the cloud infrastructure world has come a long way since Brightbox began planning their product two years ago. OpenStack has arrived, and (despite a growing body of nay-sayers) is credible. Rackspace, HP, and others are on the cusp of delivering real clouds to real customers on the back of its codeline. Eucalyptus appears to have turned a corner, and pulled back from a brink that I (and others) saw them teetering on the edge of. Canonical’s marriage of Ubuntu to OpenStack is now just one of several ways to get the same cloud code, capabilities and apis onto machines running inside your own data centre. Amazon just keeps on doing what Amazon does, incrementally adding features, cutting prices, and becoming ever-harder to not choose.

In that world, surely a little cloud provider operating their own bespoke solution from the wrong end of the Leeds-London railway line, in a country on the wrong side of both the English Channel and the Atlantic Ocean has no hope? Surely they should just pack up shop, and either adopt OpenStack/Eucalyptus fast… or find a new line of work?

Jeremy Jarvis disagrees, vehemently. And he’s not alone. Look at Edinburgh’s Flexiant, Glasgow’s SymetriQ, and a whole host of other companies that have built their own solutions from nothing. Others, of course, have recognised the value in taking OpenStack, Eucalyptus, Microsoft’s Windows Azure, and similarly established technologies, and making them their own. Look at Eduserv’s Swindon data centre, or the hosted desktops from East Yorkshire’s own GoCloud.

So how can Brightbox (or Flexiant, or SymetriQ, or any of the other non-conformers) compete? How, indeed, can they survive? Jarvis suggests that “Buy British” continues to carry weight here. Even without raising the scary (and often, actually, wholly irrelevant) spectre of PATRIOT Act-powered snooping, a significant proportion of UK (or European) companies like the idea of buying services from UK (or European) suppliers. They like that the documentation is spelled correctly. They like that telephone support is (more or less) in their timezone. They like that the development team shows up at local events, and that it’s a person buying the drinks in the bar, rather than the disembodied marketing budget of some far-off corporation.

Purchasing decisions for something like cloud infrastructure are complicated. Often, they’re probably quite illogical. Price isn’t always the deciding factor (and even if it were, smaller providers like Brightbox aren’t ridiculously expensive in comparison to their larger competitors). Having been the friendly face at a local developer event might swing that big contract. Or having a “nice” API. Or implementing niche features in firewalls, or networking, or port forwarding might each grab the attention — and loyalty — of specific sectors of the long tail. Tesco might sell ‘everything’ to ‘everyone,’ but we still have room in our lives for the SPAR corner shop, and for the upscale deli with the nice cakes.

The same’s true in the cloud, although I can’t help feeling that we’re going to see quite a rapid decline in entirely new cloud infrastructures as the next generation of niche cloud boutiques take OpenStack or Eucalyptus and mould them to their requirements. They probably won’t be making those decisions because (like we Clouderati) they agonise endlessly about interoperability or portability or the de facto standard of the Amazon Web Services stack. For most of their SME customers, those things simply don’t matter. Instead, they’ll be adopting OpenStack or Eucalyptus because the grunt work (and the marketing) has been done. It simply costs less to take something off the shelf than to develop it yourself from scratch. But for companies like Brightbox, where that investment has already been made? Well, for them there may still be plenty of prospective customers out there.

Related articles

• Eucalyptus grabs $30M from IVP to push the open-source private cloud forward (venturebeat.com)
• Announcing Brightbox Cloud – the UK’s first true IaaS platform! (brightbox.co.uk)
• Platform as a service ready to rev up, says Gartner (zdnet.com)
• Hosting providers aim to challenge cloud leaders (zdnet.com)
• Joyent Brings Its Public Cloud To Europe (techweekeurope.co.uk)
• Rackspace launches OpenStack-powered next-gen public cloud (venturebeat.com)
• Ubuntu Cloud Portal: Brightbox 12.04 daily images now available, discounts for testers and Ubuntu members (cloud.ubuntu.com)
• Ubuntu Linux heads to the clouds (zdnet.com)

Computer rendering of the Chicago Spire. This is not the current design as of July 12, 2008. (Photo credit: Wikipedia)

We have become accustomed to the simple yet all-powerful search box. ‘Advanced’ search options and arcane query syntaxes have largely been replaced by the learned behaviour of throwing some words at Google*, ignoring the sponsored links, and (usually) finding what we want somewhere in the first 5-10 proper results. A Google search is certainly impressive (especially to those who really remember how poor some of the earlier search engines were), but it remains far from perfect. Do Google’s limitations create a big enough opportunity for others to grab credible market share?

In recent weeks, I’ve received a flurry of information on partial alternatives to Google’s market-dominating search engine. Most appear useful in their own niche, but I doubt even their creators would be surprised to learn that none tempt me to change my Google-powered default search behaviour.

Far more damaging for their prospects, any hope they had of attracting my occasional use is dashed by the very way that they seem to work. They may excel in certain verticals, or in particular types of search, but most make the unfortunate mistake of expecting me to mould my behaviour to them. The pain of remembering how to concoct effective queries for each of these tools far outweighs the gain of their ‘better’ search result, creating a vicious spiral from which they must surely struggle to escape.

Take London-based Sehrch, for example. Behind a name that’s impossible to pronounce or communicate to others (say “Search for that on sehrch,” and 99.99% of those you tell will end up here rather than here) lies an interesting attempt to bring structure to web search, with a little help from data sources like Freebase and DBpedia.

Google could probably find you buildings with 150 floors (just the Chicago Spire, according to both my first page of Google hits and the trusty Wolfram Alpha), but might struggle to find those that were higher. Sehrch finds 14, and they appear in a neat list that’s free of the other stuff that cluttered my Google results. I’m confused that the Chicago Spire (sehrch agrees that it has exactly 150 floors) appears in a search that was quite clearly looking for buildings with more than 150 floors, but the other 13 appear to be valid. I’m not a tall building aficionado, so don’t know how many buildings Sehrch failed to find, but it certainly did better than Google (where the results are a mess, and would require careful reading) or even Wolfram Alpha (which reckons there are two ‘notable’ buildings with more than 150 floors). All of the searches returned a mix of actual buildings, planned buildings, and cancelled buildings.

But — and it’s a big but — both Google and Wolfram Alpha were pretty straightforward to search with normal search behaviours. Sehrch was not.

Wolfram Alpha took a perfectly realistic plain-text search for “buildings with more than 150 floors” and interpreted it to arrive at a query that the system could understand and operate upon. Sehrch, on the other hand, expected me to build a query from the 130,342 object properties and 249,777 object types that it understands. Frankly, if this search hadn’t been one of the examples, I doubt that I’d have formulated (type:building) (floors>150) correctly.

Extracting meaning and structure from data, and making it available to deliver better search results is a valid and useful thing to be doing. If you want to know about female teenage pop stars from Sweden, Sehrch can give you six. Both Google and Wolfram Alpha might be able to get there too, but I gave up trying to work out how. Sehrch may be returning ‘better’ results, but it’s too different to use.

Wolfram Alpha understands the power of meaning and structure too, but is getting better at hiding the power behind pretty user-friendly queries.

Even Google, the home of brute force computation across the unstructured mess of the Web, recognises the power of meaning and structure, and is doing something about it.

Enter some maths into a Google search box, and you don’t get a list of web pages containing calculators. You get the answer.

Enter a flight code into a Google search box, and you don’t get a list of airline or airport web pages. You get the time the flight is expected to land.

Enter a stock market code into a Google search box, and you don’t get a list of stock exchanges or companies. You get the share price, and a graph showing how it’s changing.

Type ‘showtimes’ into a Google search box, and you get a list of films showing at cinemas near you.

Google is getting better at structure. The company bought Freebase. The company is one of those behind schema.org. It’s investing in WikiData. Google knows that structure and meaning matter, and it’s applying itself to baking both into the search experience with which users are already familiar. Google is getting better, but it’s improving by doing more to anticipate the user’s needs, not by forcing the user to adopt arcane query syntax.

I use Google every day. For some searches, it’s really not (yet) the best place to answer my query. In those situations, I’ll turn to some other tool. Am I going to turn to one like Wolfram Alpha which works in a very different way, but hides that behind a box that typically takes the queries I’m used to typing? Or am I going to turn to one like Sehrch, which works in a very different way and expects me to work in a different way, too?

Sadly for Sehrch, until it finds a way to hide search syntax from the casual user, all its clever search capabilities are going to go unused. And it’s not alone. As I mentioned at the start, I’ve received pitches from a load of similar companies recently. All are interesting. All expect me to change too much without offering enough benefit in return. All therefore, ultimately, fall short.

Structure is good. Meaning is powerful. But I want the computer to infer, discover, reason and suggest. The last thing I want is to go back to typing arcane search syntax. And I very much doubt that I’m alone.

Note: Yes, I know that other big-name search engines like Bing exist and are broadly comparable to Google in scope and capability. But, honestly, they’ve never demonstrated a compelling reason for me to switch away from Google either. Feel free to substitute the name of your favourite mainstream search engine everywhere I wrote ‘Google’.

Related articles

• Siri makes up 25% of all Wolfram Alpha searches (venturebeat.com)

Image via Wikipedia

Most of us recognise that the Earth is warming and that — despite our planet’s temperatures having dramatically risen and fallen before — we humans must accept some measure of responsibility for the current changes.

Already consuming at least 1.1-1.5% of global power, and only forecast to grow ever-more rapacious, the data centres that power our information economy are surely one area in which we can, should, and must find ways to reduce consumption. And, although by no means perfect, data centre builders, operators and suppliers are paying attention. Individual servers are becoming more efficient. Buildings are being cooled by the wind and by toilets. Waste heat is warming homes. Newer facilities are being sited to save the planet (and money) by drawing power from hydro-electric schemes rather than coal, oil, gas, or the once-again-unpopular-and-scary Nuclear.

And then there’s solar. On the face of it, the ultimate renewable. The sun shines. It does so every day. It will continue to do so every day for millions of years to come. Every watt diverted to a data centre has absolutely no effect whatsoever upon the sunlight that the rest of us receive today, or that we shall receive tomorrow. There’s certainly a location trade-off to be made, as the sunshine rarely reaches the ground for much of the day in locations where the air is cool and damp enough for all that fresh air cooling to work, but there should certainly be a case to be made for both. If you’re in Iceland or Ireland or Oregon, sunlight is not something to rely upon for meeting your ravenous appetite for energy. But if you’re in sunnier climes, surely it’s a no-brainer?

Apple, for one, is constructing a 171 acre (69 hectare) solar farm next to its North Carolina data centre. That’s big. It’s apparently the biggest commercial deployment in the United States and — with some help from WolframAlpha — it’s also equivalent to 1.6 Vatican Cities or 13 Great Pyramids.

More tellingly, it’s also a lot of fields and trees.  If all those mirrors reduce the impact of Apple’s data centre on the environment, then maybe — just maybe — the loss of forest and farmland and open space is a cost worth paying. But Amazon’s James Hamilton does some maths, which he shares in a recent blog post, and he reckons that all the mirrors in that 69 hectares contribute around 3.2 MW to the data centre’s power requirements; requirements that Hamilton calculates to be around 78 MW. That’s 4%, which really is not that much at all. It’s worth noting that the figures for the solar array’s power output and the data centre’s consumption are only Hamilton’s estimates, but he tends to know what’s he’s talking about. In the absence of solid numbers from Apple, you can do a lot worse than believe the maths of someone like Hamilton.

So… why the enthusiasm for solar? Are data centre operators taking reductions where they can get them, and calculating that a 4% contribution to the fuel bill is worth 69 hectares? They might be right. Maybe. Or are they being more callous and calculating than that? Are the ‘savings’ on energy bills and greenhouse emissions essentially negligible, and are trees and fields being turned over to big mirrors simply so that companies can look good? They get a tick in the “green” box. They get a mention from Greenpeace. Customers, consumers and law makers think they care. Whilst actually, they don’t care at all.

Related articles

• The Switch SuperNAP data centre – one of the most impressive I’ve been in (greenmonk.net)
• Apple slaps mega-solar panel field on new ENORMO data centre (go.theregister.com)
• In cooler climates, outdoor air systems can save energy dollars for data centres, retailers, warehouses (cleanbreak.ca)
• Colt Telecom and Verne Global’s dual renewably sourced data centre in Iceland (greenmonk.net)
• Facebook Data Centre Uses As Much Power As The Entire County It’s In (gizmodo.com.au)
• Greenpeace, Facebook in truce over energy (news.smh.com.au)
• Solar power gets public vote (evoenergy.co.uk)
• The revenge of Thomas Edison (business.financialpost.com)
• California’s Military Bases Could Generate 30x More Electricity From Solar Power Than They Consume (treehugger.com)
• Apple’s iCloud Has Solar Lining (wired.com)

This is an extremely worrying attitude, and I can only hope that those who hold it realise the error of their ways before they make a catastrophic mistake that adversely affects the rest of us.

Data scientists are an increasingly capable bunch, and the tools at their disposal sometimes appear almost magical in their capability to derive insight. Competitions such as those run by Kaggle (more on them in a moment) clearly show that an aptitude for numbers and analysis can deliver some remarkable results, even when that analysis is being undertaken by individuals who lack specific domain expertise.

But to suggest that simply “letting the numbers speak for themselves” is an effective way to make real decisions is, quite simply, bonkers. Data is merely one input to an effective decision making process. Prior knowledge, policy considerations, and an awareness of experimental bias, sampling error, and quaint notions such as ground truth continue to play a fundamental part.

Data scientists undeniably bring a wealth of skills to the table, but so do domain specialists. The domain specialists would be unwise to presume that they can continue to keep pace with exploding data volumes without judicious application of data science. But for data scientists to presume, even for a moment, that they and their algorithms can replace domain expertise is laughable.

Moving forward, we need both domain skills and data skills. Sometimes those skills may be present within a single individual, especially as practitioners within more data-intensive domains equip themselves with the skills required to continue functioning as data volumes blossom. At other times, they will be brought together in the makeup of a team that comprises domain experts and data scientists. It remains unclear whether it would normally be quicker or easier to teach a domain specialist data skills, or vice versa.

One is not ‘better’ than the other, and instead we need to concentrate on finding ways to make it as easy as possible for both groups to work together. How, for example, do we set about ensuring that conflicting use of superficially ‘obvious’ technical terms does not derail the process from the outset? How do we package and convey deep-seated presumptions from one group to the other, and how do we create the common space within which number-cruncher and specialist can work together?

Mike Driscoll, who chaired a debate on the issue at Strata, offers more, including one of the many ways in which domain knowledge remains a vital aspect of the process;

“One of the conclusions reached was that, when a problem is well-structured (or to Drew Conway’s point, when a good question is posed), it is much easier for machine learning to succeed. Kaggle’s strength as a contest platform is that domain experts have already framed the problem: they choose the features of the data to use (feature engineering or “feature creation”, as Monica Rogati calls it) as well as the criteria for success. This is the first, hardest step in any data science project. After this, machine learners can step in and develop the best algorithms for classifying and predicting new data (or, less usefully, explaining old data).”

In responding to Brockmeier’s post, Strata co-chair Alistair Croll also makes an important point;

“Of course, understanding which data to apply to a problem, and when to listen to the numbers, is a nuanced thing.

One thing about data is that it often has non-obvious, and disruptive, nuggets within it that threaten the status quo. And many ‘domain experts’ thrive on their political skills rather than their actual results. So part of the debate is really about housecleaning to replace anecdote with evidence—an uncomfortable cultural shift.”

Too true.

Data Science — and the data scientist — are here to stay, and they bring tremendous value with them. But they’re an adjunct to domain knowledge, not a replacement for it.

Image is of a woodcut by Jörg Breu displayed as Emblema LXXV (emblem 75) in the Book of Emblems (Latin: Emblematum liber) by Andrea Alciato (1531). Image taken from Wikimedia Commons, where it is stated to be in the public domain.

Related articles

• What Makes a Good Data Scientist? (datascience101.wordpress.com)
• Data scientists came out of the closet at Strata (petewarden.typepad.com)
• The data scientist v2.0 (informationarbitrage.com)
• Need A Data Scientist? Probably (datascience101.wordpress.com)
• IBM VP Anjul Bhambri on the Era of the Data Scientist (readwriteweb.com)
• Strata 2012: Big Data is Bigger than Ever! (thenoisychannel.com)

Image via Wikipedia

The Open Knowledge Foundation (OKFN) promotes the creation, dissemination and use of ‘open knowledge.’ As part of this activity OKFN developed a data repository called CKAN, and has seen this become increasingly important to a range of data dissemination activities such as data.gov.uk and publicdata.eu.

In this podcast I talk with OKFN Director Rufus Pollock and CKAN Product Owner Irina Bolychevsky, to learn more about CKAN, its use in the context of open data, and the wider implications for dissemination of any data (whether open or closed).

Following up on a blog post that I wrote at the start of 2012, this is the tenth in an ongoing series of podcasts with key stakeholders in the emerging category of Data Markets.

Related articles

• Open Knowledge Releases Open Data Handbook 1.0 (readwriteweb.com)
• Data Market Chat: Leigh Dodds discusses Kasabi (cloudofdata.com)
• Data for the public good (radar.oreilly.com)
• Data Market Chat: Stephen O’Grady of RedMonk examines the bigger picture (cloudofdata.com)
• Data Market Chat: Nick Edouard discusses BuzzData (cloudofdata.com)

But as Kasabi CTO Leigh Dodds is keen to stress, you don’t need to be a Semantic Web developer or enthusiast to derive value from what Kasabi has to offer.

Like many others in the nascent data market space, Kasabi’s commercial models are still emerging, and Leigh shares some of the thinking that lies behind their current proposition.

There is some background noise on this recording, which will hopefully not affect your enjoyment of the conversation.

Kasabi is an initiative of Talis. I am a former employee of and current shareholder in Talis.

Following up on a blog post that I wrote at the start of 2012, this is the ninth in an ongoing series of podcasts with key stakeholders in the emerging category of Data Markets.

Related articles

• Data Market Chat: the podcasts are a-coming…(cloudofdata.com)
• Data Market Chat: Tyler Bell discusses Factual (cloudofdata.com)
• Data Market Chat: Hjálmar Gíslason discusses DataMarket.com (cloudofdata.com)
• Data Market Chat: Chris Hathaway discusses AggData (cloudofdata.com)
• Data Market Chat: Flip Kromer discusses Infochimps (cloudofdata.com)
• Data Market Chat: Nick Edouard discusses BuzzData (cloudofdata.com)
• Data Market Chat: Stephen O’Grady of RedMonk examines the bigger picture (cloudofdata.com)
• Data Market Chat: Shion Deysarkar discusses Datafiniti (cloudofdata.com)
• Data Market Chat: Piyush Lumba discusses Microsoft’s Windows Azure Marketplace (cloudofdata.com)

Piyush Lumba, Director of Product Management for Azure Data Services at Microsoft, talks about what the Marketplace can do today and shares some of his perspectives on ways that the nascent data market space could evolve.

Following up on a blog post that I wrote at the start of 2012, this is the eighth in an ongoing series of podcasts with key stakeholders in the emerging category of Data Markets.

Related articles

• Data Market Chat: the podcasts are a-coming…(cloudofdata.com)
• Data Market Chat: Tyler Bell discusses Factual (cloudofdata.com)
• Data Market Chat: Hjálmar Gíslason discusses DataMarket.com (cloudofdata.com)
• Data Market Chat: Chris Hathaway discusses AggData (cloudofdata.com)
• Data Market Chat: Flip Kromer discusses Infochimps (cloudofdata.com)
• Data Market Chat: Nick Edouard discusses BuzzData (cloudofdata.com)
• Data Market Chat: Stephen O’Grady of RedMonk examines the bigger picture (cloudofdata.com)
• Data Market Chat: Shion Deysarkar discusses Datafiniti (cloudofdata.com)
• Big data in the cloud (radar.oreilly.com)
• ‘Roswell’: Another key component of Microsoft’s cloud strategy (zdnet.com)

CEO Shion Deysarkar talks about the development of the web crawling capability within his other company, 80legs, and then goes on to explore the ways in which this impressive capability is being put to work in locating authoritative and comprehensive data for which customers are prepared to pay.

Corroborating different sets of data from across the web, Deysarkar argues, results in a resource more accurate, more timely, and more complete than is available elsewhere.

Datafiniti is currently in beta, and initially focussed upon providing data to address the needs of specific industry verticals.

Following up on a blog post that I wrote at the start of 2012, this is the seventh in an ongoing series of podcasts with key stakeholders in the emerging category of Data Markets.

Related articles

• One API for the Web, Oh the Possibilities (programmableweb.com)
• Datafiniti: Is a Successful Data Search Engine Possible? (arnoldit.com)
• Data Market Chat: the podcasts are a-coming…(cloudofdata.com)
• Data Market Chat: Tyler Bell discusses Factual (cloudofdata.com)
• Data Market Chat: Hjálmar Gíslason discusses DataMarket.com (cloudofdata.com)
• Data Market Chat: Chris Hathaway discusses AggData (cloudofdata.com)
• Data Market Chat: Flip Kromer discusses Infochimps (cloudofdata.com)
• Data Market Chat: Nick Edouard discusses BuzzData (cloudofdata.com)
• Data Market Chat: Stephen O’Grady of RedMonk examines the bigger picture (cloudofdata.com)