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4 ways to optimise your existing big data investments

 

http://www.itproportal.com/2015/07/05/4-ways-to-optimise-your-existing-big-data-investments/For big data, the day after tomorrow is here. Gone are the days of CIOs and CMOs talking about big data and data analytics as something they are just starting to explore or eventually plan to look into at some point in the near future. At the same time, big data is rarely, if ever, talked about as the so-called next big thing anymore by industry analysts and experts.

Now, in no way does this mean big data is dead – just the opposite, in fact. It means big data has finally arrived and is quickly maturing. Consequently, an ever-growing number of IT leaders are now shifting from thinking about the possibility of making investments in big data platforms to thinking about how to get more out of the investments they’ve already made in Hadoop and other big data platforms and technologies.

And with good reason, for when it comes to extracting true value from big data, “build it and they will come” simply doesn’t suffice. Standing up a Hadoop cluster or investing in a new big data platform or analytics application is just one step (and it shouldn’t be the first – identifying a business question that your company would benefit from answering should always be your first step) on the path to turning data into actionable business insight. With that in mind, let’s examine four ways businesses can optimise their big data investments.

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Replicate Key Content

Replication remains one of the most critical and yet most often overlooked ways companies can optimise big data investments. Successful data analysis today requires bringing together modern unstructured data with traditional structured data. This data blending remains a challenge for many organisations, one that can be solved with replication.

Leverage the Power of Prediction

As valuable as replication is, it would be a mistake to think that true analytic value is achieved solely by bringing data together into a single platform. Doing so may enable you to achieve a clear view of what’s happened in the past, but what’s needed today more than ever is the ability to make predictions about what’s going to happen in the future. Prediction is a powerful asset in the world of big data – arguably the single best way to maximise the value of your data reservoir.

Augment Pre-Packaged Analytic Applications

Many vendors have taken to offering pre-packaged analytics applications that deliver a certain degree of analytic functionality, and many customers have invested in these offerings. These pre-packaged applications do have value – they abstract complexity and roll up many hours of analytic work and development into a simplified offering. Just be aware that most of these pre-packaged offerings are vanilla in nature, and will only meet a portion of your analytic needs. To optimise this investment, organisations should consider augmenting pre-package analytic applications with smaller, complimentary data marts that better align to the specific analytic needs of the business. This is likely where you’ll uncover the secret sauce, so to speak, that drives companies to invest in data analytics in the first place.

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Make Use of Metadata Management

The well-documented explosion of data impacting organisations large and small has created a corollary explosion in metadata – or data about your data. Smartly managing that metadata layer is critical to deriving maximum value from your big data investments. You can track and analyse metadata directly in a system such as Hadoop. Doing so will tell you things like how old a dataset is, how frequently it changes, how often you run reports against it, and whether or not it needs to be archived. In other words, understanding your metadata will tell you whether a given dataset is or isn’t valuable, so that you can create a more robust system around genuinely high demand data. Going a step further, with today’s metadata management capabilities, you can even start to predict which data is the most likely to need retiring or archiving at a given point in time. Read more…

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