Follow the Data

A data driven blog

Data services

There’s been a little hiatus here as I have been traveling. I recently learned that Microsoft has launched Codename “Dallas”, a service for purchasing and managing datasets and web services. It seems they are trying to provide consistent APIs to work with different data from the public and private sectors in a clean way. There’s an introduction here.

This type of online data repository seems to be an idea whose time has arrived – I have previously talked about resources like Infochimps, Datamob and Amazon’s Public Data Sets, and there is also, which I seem to have forgotten to mention. A recent commenter on this blog pointed me to the comprehensive knowledge archive network, which is a “registry of open data and content packages”. Then there are the governmental and municipal data repositories, such as

Another interesting service, which may have a slightly different focus, is Factual, described by founder Gil Elbaz as a “platform where anyone can share and mash open data“. Factual basically wants to list facts, and puts the emphasis on data accuracy, so you can express opinions on and discuss the validity of any piece of data. Factual also claims to have “deeper data technology” which allows users to explore the data in a more sophisticated way compared to other services like the Amazon Open Data Sets, for instance.

Companies specializing in helping users make sense out of massive data sets are, of course, popping up as well. I have previously written about Good Data, and now the launch of a new seemingly similar company,  Data Applied, has been announced.  Like Good Data, Data Applied offers affordable licenses for cloud-based and social data analysis, with a free trial package (though Good Data’s free version seems to offer more – a 10 MB data warehouse and 1-5 users vs Data Applied’s file size of <100 kb for a single user; someone correct me if I am wrong). The visualization capabilities of Data Applied do seem very nice. It’s still unclear to me how different the offerings of these two companies are but time will tell.


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4 thoughts on “Data services

  1. Thank you very much for your coverage of Data Applied!

    Good Data is a good looking product with solid analytics. We also share a common vision that analytics should not require a Phd. Our focus however is slightly different. Here are what I think are some key differences:

    – While we include conventional reporting capabilities, we focus more on hardcore data crunching algorithms (ex: data clustering, association rule mining, decision trees, time series forecasting, correlation analysis, etc.). This means we’re open to analyzing industrial, survey, marketing, sales, social, scientific, or even law enforcement data. With our API, we are the kind of “base” one could use to build rich analytics into applications focused on sales, marketing, or ERP.

    – Often, compliance rules prevent data from leaving the intranet. For this reason, we offer a dual on-premise / in the cloud solution. Some products such as Microsoft Dynamics CRM follow the same dual approach, which Microsoft calls “the power of choice” (i.e. intranet or cloud or hosted, you choose). We want our users to be in control.

    Another obvious difference is that Good Data is well funded by VCs, while we are 100% self-funded. Our free limits are currently a bit low, but we’re working on increasing them. Users who are interested in better trial licenses should feel free to contact us at!

    All this is just the beginning. Over time, we should see even more types data-related services emerge: data cleansing, data verification, data enrichment, etc. These are definitely exciting times for those of us who love crunching data – the field is wide open!

    Dominic Pouzin
    Data Applied

  2. It will be interesting to watch and see how the analytic data services businss evolves. An important question to ask before signing up (ironically) is, “How do I get my data out should I want to leave?” If you can’t get a satisfactory answer to that question, you may be “locked-In” to a specific provider. To avoid lock-in, you need to ask your provider two questions:
    1) Can I get a copy of my data at any time without charge or for a nominal fee, and
    2) Can the output be in the same data warehouse structure it was stored in and can I use the structure in any way I see fit (open source style data warehouse architecture).

    If you are interested in open data warehouse model (ODWM) concept, visit us at and take a look at the aproach as well as an instance of ODWM built for Project Control (PCDW).

  3. Thanks for the comments Dominic. I would agree with the value proposition. Most pople who are doing analysis don’t want to also have to deal with data organization. Based on your comments, My guesss is that that is Data-Applied’s core business proposition.

    I see two business models developing for the cloud: I’ll call them the, “Good” service model and the “Great” service model. The Good service model traps its customers and provides ‘pretty good’ features (the features only have to be only good enough to prevent the customer scrapng their account). The Great service model attracts customers because it is the best provder of service. Trapping the customer isn’t necessary because they are best at what they do.

    Fortunately its easy to tell the difference between Good and Great. Just look at the contract. The Good will have strong or punitive barriers to exit and the Great won’t.

    Two questions are frequently raised: “what happens to providers with Great service but less than steller products?” and “what about providers that use the Good service aproach but have an excellent product?”. The result of the first case is obvious. This provider won’t survive. In the second case the provider with an excellent product won’t remain excellent for the simple reason that its management will eventually realize that their customers are present because of lock-in not because of excellence. Excellence costs money and lock-in doesn’t. Gravity will eventually win.

  4. Mikael Huss on said:

    Thanks, both of you, for your comments. Very illuminating. The point about how to get your data out is often overlooked.

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