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Ajay- Why did you choose Rapid Miner and R? What were the other software alternatives you considered and discarded?
Analyst- We considered most of the other major players in statistics/data mining or enterprise BI. However, we found that the value proposition for an open source solution was too compelling to justify the premium pricing that the commercial solutions would have required. The widespread adoption of R and the variety of packages and algorithms available for it, made it an easy choice. We liked RapidMiner as a way to design structured, repeatable processes, and the ability to optimize learner parameters in a systematic way. It also handled large data sets better than R on 32-bit Windows did. The GUI, particularly when 5.0 was released, made it more usable than R for analysts who weren’t experienced programmers.
Ajay- What analytics do you do think Rapid Miner and R are best suited for?
Analyst- We use RM+R mainly for sports analysis so far, rather than for more traditional business applications. It has been quite suitable for that, and I can easily see how it would be used for other types of applications.
Ajay- Any experiences as an enterprise customer? How was the installation process? How good is the enterprise level support?
Analyst- Rapid-I has been one of the most responsive tech companies I’ve dealt with, either in my current role or with previous employers. They are small enough to be able to respond quickly to requests, and in more than one case, have fixed a problem, or added a small feature we needed within a matter of days. In other cases, we have contracted with them to add larger pieces of specific functionality we needed at reasonable consulting rates. Those features are added to the mainline product, and become fully supported through regular channels. The longer consulting projects have typically had a turnaround of just a few weeks.
Ajay- What challenges if any did you face in executing a pure open source analytics bundle ?
Analyst- As Rapid-I is a smaller company based in Europe, the availability of training and consulting in the USA isn’t as extensive as for the major enterprise software players, and the time zone differences sometimes slow down the communications cycle. There were times where we were the first customer to attempt a specific integration point in our technical environment, and with no prior experiences to fall back on, we had to work with Rapid-I to figure out how to do it. Compared to the what traditional software vendors provide, both R and RM tend to have sparse, terse, occasionally incomplete documentation. The situation is getting better, but still lags behind what the traditional enterprise software vendors provide.
Ajay- What are the things you can do in R ,and what are the things you prefer to do in Rapid Miner (comparison for technical synergies)
Analyst- Our experience has been that RM is superior to R at writing and maintaining structured processes, better at handling larger amounts of data, and more flexible at fine-tuning model parameters automatically. The biggest limitation we’ve had with RM compared to R is that R has a larger library of user-contributed packages for additional data mining algorithms. Sometimes we opted to use R because RM hadn’t yet implemented a specific algorithm. The introduction the R extension has allowed us to combine the strengths of both tools in a very logical and productive way.
In particular, extending RapidMiner with R helped address RM’s weakness in the breadth of algorithms, because it brings the entire R ecosystem into RM (similar to how Rapid-I implemented much of the Weka library early on in RM’s development). Further, because the R user community releases packages that implement new techniques faster than the enterprise vendors can, this helps turn a potential weakness into a potential strength. However, R packages tend to be of varying quality, and are more prone to go stale due to lack of support/bug fixes. This depends heavily on the package’s maintainer and its prevalence of use in the R community. So when RapidMiner has a learner with a native implementation, it’s usually better to use it than the R equivalent.
Here is an interview with Hjálmar Gíslason, CEO of Datamarket.com . DataMarket is an active marketplace for structured data and statistics. Through powerful search and visual data exploration, DataMarket connects data seekers with data providers.
HG- DataMarket is my fourth tech start-up since at age 20 in 1996. The previous ones have been in gaming, mobile and web search. I come from a technical background but have been moving more and more to the business side over the years. I can still prototype, but I hope there isn’t a single line of my code in production!
Funny you should ask about the 10 things that have surprised me the most on this journey, as I gave a presentation – literally yesterday – titled: “9 things nobody told me about the start-up business”
* Do NOT generalize – especially not to begin with
* Prioritize – and ﬁnd a work-ﬂow that works for you
* Meet people – face to face
* You are a sales person – whether you like it or not
* Technology is not a product – it’s the entire experience
* Sell the current version – no matter how amazing the next one is
* Learn from mistakes – preferably others’
* Pick the right people – good people is not enough
* Tell a good story – but don’t make them up
I obviously elaborate on each of these points in the talk, but the points illustrate roughly some of the things I believe I’ve learned … so far
Both Amazon and Google have entered the public datasets space. Infochimps has 14,000+ public datasets. The US has http://www.data.gov/
So clearly the space is both competitive and yet the demand for public data repositories is clearly under served still.
How does DataMarket intend to address this market in a unique way to differentiate itself from others.
HG- DataMarket is about delivering business data to decision makers. We help data seekers find the data they need for planning and informed decision making, and data publishers reaching this audience. DataMarket.com is the meeting point, where data seekers can come to find the best available data, and data publishers can make their data available whether for free or for a fee. We’ve populated the site with a wealth of data from public sources such as the UN, Eurostat, World Bank, IMF and others, but there is also premium data that is only available to those that subscribe to and pay for the access. For example we resell the entire data offering from the EIU (Economist Intelligence Unit) (link: http://datamarket.com/data/list/?q=provider:eiu)
DataMarket.com allows all this data to be searched, visualized, compared and downloaded in a single place in a standard, unified manner.
We see many of these efforts not as competition, but as valuable potential sources of data for our offering, while others may be competing with parts of our proposition, such as easy access to the public data sets.
Ajay- What are your views on data confidentiality and access to data owned by Governments funded by tax payer money.
HG- My views are very simple: Any data that is gathered or created for taxpayers’ money should be open and free of charge unless higher priorities such as privacy or national security indicate otherwise.
Reflecting that, any data that is originally open and free of charge is still open and free of charge on DataMarket.com, just easier to find and work with.
HG- The scene is quite vibrant, given the small community. Good teams with promising concepts have been able to get the funding they need to get started and test their footing internationally. When the rapid growth phase is reached outside funding may still be needed.
There are positive and negative things about any location. Among the good things about Iceland from the stand point of a technology start-up are highly skilled tech people and a relatively simple corporate environment. Among the bad things are a tiny local market, lack of skills in international sales and marketing and capital controls that were put in place after the crash of the Icelandic economy in 2008.
I’ve jokingly said that if a company is hot in the eyes of VCs it would get funding even if it was located in the jungles of Congo, while if they’re only lukewarm towards you, they will be looking for any excuse not to invest. Location can certainly be one of them, and in that case being close to the investor communities – physically – can be very important.
We’re opening up our sales and marketing offices in Boston as we speak. Not to be close to investors though, but to be close to our market and current customers.
Ajay- Describe your hobbies when you are not founding amazing tech startups.
HG- Most of my time is spent working – which happens to by my number one hobby.
It is still important to step away from it all every now and then to see things in perspective and come back with a clear mind.
I *love* traveling to exotic places. Me and my wife have done quite a lot of traveling in Africa and S-America: safari, scuba diving, skiing, enjoying nature. When at home I try to do some sports activities 3-4 times a week at least, and – recently – play with my now 8 month old son as much as I can.
Hjálmar Gíslason, Founder and CEO: Hjalmar is a successful entrepreneur, founder of three startups in the gaming, mobile and web sectors since 1996. Prior to launching DataMarket, Hjalmar worked on new media and business development for companies in the Skipti Group (owners of Iceland Telecom) after their acquisition of his search startup – Spurl. Hjalmar offers a mix of business, strategy and technical expertise. DataMarket is based largely on his vision of the need for a global exchange for structured data.
To know more, have a quick look at http://datamarket.com/
Real Steel succeeds where Cowboys and Aliens failed. It manages to marry the odd couple-
robot movies with boxing movies, and the charming presence of Hugh Jackman, a new kid on the block,
and much better CGI than Transformers ensure this is a sweet family, buddy movie for a weekend.
It may not rate very high on originality , as all sports movies do with the struggling underdog theme, the training, the Rocky deja Vu
—-but it does pack a very nice visual punch. See it to believe it, Robot boyz. Real Steel is a real steal!
Here is an interview with Scott Gidley, CTO and co-founder of leading data quality ccompany DataFlux . DataFlux is a part of SAS Institute and in 2011 acquired Baseline Consulting besides launching the latest version of their Master Data Management product. (more…)