Big data: all you need to know

Big data: all you need to know

Summary: Big data's the big buzz word of 2012. So what's behind the hype?

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Steps to big data

Before you go down the path of big data, it's important to be prepared and approach an implementation in an organised manner, following these steps.

  1. What do you wish you knew? This is where you decide what you want to find out from big data that you can't get from your current systems. If the answer is nothing, then perhaps big data isn't the right thing for you

  2. What are your data assets? Can you cross reference this data to produce insights? Is it possible to build new data products on top of your assets? If not, what do you need to implement to be able to do so?

  3. Once you know this, it's time to prioritise. Select the potentially most valuable opportunity for using big-data techniques and technology, and prepare a business case for a proof of concept, keeping in mind the skill sets you'll need to do it. You will need to talk to the owners of the data assets to get the full picture

  4. Start the proof of concept, and make sure that there's a clear end point, so that you can evaluate what the proof of concept has achieved. This might be the time to give the owner of the data assets to take responsibility for the project

  5. Once your proof of concept has been completed, evaluate whether it worked. Are you getting real insights delivered? Is the work that went in to the concept bearing fruit? Could it be extended to other parts of the organisation? Is there other data that could be included? This will help you to discover whether to expand your implementation or revamp it.

So what are you waiting for? It's time to think big.

Topics: Big Data, TechLines, Australia

Suzanne Tindal

About Suzanne Tindal

Suzanne Tindal cut her teeth at ZDNet.com.au as the site's telecommunications reporter, a role that saw her break some of the biggest stories associated with the National Broadband Network process. She then turned her attention to all matters in government and corporate ICT circles. Now she's taking on the whole gamut as news editor for the site.

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9 comments
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  • Logic and statistics

    Are what you need for big data.

    So-called big data tools like Hadoop lack the support for logic provided by modern data management methods like the relational model and are therefore wholly unsuitable for such work.

    The big data tools are a re-run of antiquated methods that have already been shown to be flawed in theory and unmanageable in practice.

    In short big data is nothing but new marketing selling obsolete methods.
    jorwell
  • Logic and statistics

    Are what you need for big data.

    So-called big data tools like Hadoop lack the support for logic provided by modern data management methods like the relational model and are therefore wholly unsuitable for such work.

    The big data tools are a re-run of antiquated methods that have already been shown to be flawed in theory and unmanageable in practice.

    In short big data is nothing but new marketing selling obsolete methods.
    jorwell
  • Logic and statistics

    Are what you need for big data.

    So-called big data tools like Hadoop lack the support for logic provided by modern data management methods like the relational model and are therefore wholly unsuitable for such work.

    The big data tools are a re-run of antiquated methods that have already been shown to be flawed in theory and unmanageable in practice.

    In short big data is nothing but new marketing selling obsolete methods.
    jorwell
  • You don't understand what a relational DBMS is

    Dear Suzanne, a relational DBMS doesn't "store" data. Disk drives are for storing data. A relational DBMS is a logical representation of data.

    This is why it doesn't make sense to talk about RDBMSs not being scalable. It is a little bit like saying that long division isn't scalable because your only implementation is paper and pencil.
    jorwell
  • Sorry for the multiple postings

    The new comment system is obviously using a schema-less DBMS that has no support for constraints like primary keys and therefore duplicate entries are not rejected.
    This worked in the old comment system.

    "And that was the start of one hell of a mess, big data, big bad data".
    jorwell
  • Extensive....

    I really, really, really, really, really, really, really, could have used a 'view-as-one-page' option on this one. I mean, would have it really been so difficult to at least offer a .pdf download?
    Regulus
  • Excellent article

    Hi,

    I am sorry for leaving a late comment. This article is excellent. Thank you very much for the documentary researches you have made. Searching for some documentation to explain big data to my manager, your article is the best summary I found so far on this topic. Thanks again.
    RelaxWalk
  • BIG Data

    Seem like the classic solution looking for a problem
    buellda
  • Excellent article

    I agree with relaxwalk. This is really an excellent article.
    Despite the yes/not tribes, and my personal opinion on the subject, it's really well documented and well explained, from the beginning to the end. A long one, but really useful.
    It goes directly to my 'notebook' on articles on that topic at first position.
    Thanks.
    L.Martinez