I just completed a 3 city tour in Asia (Beijing, Taipei and Seoul) where I was presenting and discussing the topic of ‘the after math of Big Data’. My Presentation was called ‘Information2020:Beyond Big Data’. What intrigued me most, was the reaction of people and to hear the questions that surrounded Big Data. Especially when I said Big Data is just Data. I really got the sense that the hype has really hit many big Asian cities and it continues to be at its peak and there is a lot a frenzy within organisations trying to work out where to start.
A journalist asked me how big is the “Big Data” market today. My response was that there still is no such thing as a “Big Data” market, as it was a concept and that there are a huge amount of technologies that are already accounted for in other areas that could have the potential of making up a solution – for example there would be hardware components to cater for storage and high movement of data, multiple software components from Information Management, to integration components to more advance analytics techniques. There are also services components. I pointed out that many organisations could be utilising many of these components today and not calling this “Big Data”. The real question is what value are organisations obtaining from looking at their data sources differently?
I had a lot of questions on where to start. In fact one person asked, “I know I have to do ‘Big Data’ so what type of Hadoop engine should I buy?”. My response to that last question was Hadoop may in fact not be the answer they’re looking for. Rather the question you need to start with is what is the business problem you’re trying to address, what outcome are you try to achieve? Now the outcome maybe an exploratory type of outcome, but to start with the technology is the wrong place to start. The old analogy of I have a hammer, so what nail do you want me to hit comes to mind. The problem is not always a “nail” rather it could be a “screw” and so a hammer is not the only solution and therefore not the only tool in the toolbox you’re going to need.
I pointed out that Gartner has some great research that can help organisations think about the business outcomes they need to consider as a starting point. For Gartner clients, the document “Toolkit: Big Data Business Opportunities From Over 100 Use Cases” ID: G00252112, is one of the best places to start. It helps the organisation think differently with respect to looking at their data differently. If you start with the business objective, then all the other components start to line up – the data source needed, the people/skills and capabilities needed, the process impacted, the gaps in the architecture and infrastructure that needs to be closed, the use case and it’s impact to the business and how success is going to be measured.
The business objectives can be categorised 4 main buckets below:
- • Operational excellence: Process efficiency, cost reduction
- • Customer intimacy: Enhanced customer experience, improved customer service, more customer intelligence
- • New business: Newmodels, new products and services, new markets, information monetization
- • Risk management: This also includes fraud detection and compliance management
So the starting point for any “Big Data” initiative should be here – what business objective are you trying to address, then you can get in to the other elements. You don’t have to start big either, our advice is that these initiatives need to start small, as a Proof of concept, and that an organisation develops the skills and capabilities as this initiative matures. A ‘Big Data’ initiative doesn’t need a ‘Big Bang’ approach.
So start ‘small’ but think ‘big’.
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