analytics qualified

analytics qualified (Photo credit: Sean MacEntee)

Analytics platform is supposed to simplify enterprise access for big data analytics but it may not necessarily serve the purpose especially if an integrated MapReduce analytics engine is missing from the scene. Furthermore, the platform has to acknowledge the fact that there should be no requirement for your analysts to learn new languages or get familiar with unknown interfaces.

While dealing with data deluge you will try your best to take it in your stride and may decide to choose an analytics platform for this purpose. However, it should be known to you that not every platform will play a major role in letting you use the data deluge to your advantage. Therefore, before going ahead with the platform you should always find out whether or not it has been specifically optimized for big data analytics. Simply put, the platform is supposed to enable you to leverage data science and if it doesn’t, then you have made the wrong choice.

Firstly, you’d only give some serious thought to big data analytics when you have a team of quants or data scientists. Interestingly, it is very much possible for you to do the needful without the assistance of any such team. This is because if the platform is good then it will eliminate the need for a team that comprises data scientists. The main reason behind elimination is that the former is likely to have pre-defined MapReduce modules. Just to let you know, you also ought to find out if these modules can help you with pattern matching, marketing attribution, and other such cases which could be of great importance.

Secondly, while you may not require hiring a new team anymore but your analysts will probably have to learn new programming languages. The irony is that they may already know SQL. In such a situation, you have to ensure that the platform uses an SQL-MapReduce framework because if any such framework is there then without even learning new languages your analysts will most certainly be successful in harnessing the power of data science. Thirdly, it is also for you to find out whether or not the platform will succeed in driving your business through big data analytics especially if you do not have any prior experience with MapReduce.

Interestingly, determining if the platform has been designed for ease of enterprise adoption is not that difficult because you simply have to look for two key factors, i.e. end-to-end parallelism and analytic processing. Just to let you know, if these two factors are there in the picture then you are more certainly looking at a platform that has the potential to make the most of the MPP architecture. Last but not least, while opting for the analytics platform, it should be your decision that what type of deployment options you’d like to exploit.

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