Big Data Platform – the name creates an imagery of big fat servers with GBs of memory, TBs of hard disk space, and expensive computing resources. It is being imagined as a very high bandwidth network with multiple data sources, Terabytes and Petabytes of data, data load mechanism, data churning algorithms, batch jobs, data scientists and so on. At times, it sounds scary – especially when you have been tasked at evaluating and preparing a plan for your organization’s Big Data Analytics strategy.
Our industry has created an impression that setting up a Big Data Analytics platform needs a Hadoop ecosystem or something with all its paraphernalia. It means throwing lots of data at it, setting up the analytics engines, deploying Data Scientists to decode the patterns and wait for a few days of processing. This kind of description would make anyone think twice before embracing Big Data Analytics. This imagery marks Big Data Analytics as a tool meant for the larger companies, armed with big investments and highly skilled personnel with the big-figure salaries.
But is this the real picture behind Big Data Analytics?
We strongly believe that Big Data Analytics is for companies of all sizes, it need not start with big fat servers, large investments and specialized technologies. Borrowing from the open source platform approach, Big Data platform can also start small and grow big. Just like how the usage of Linux servers started with smaller workloads, gained confidence and then sprouted into the critical businesses applications. A parallel approach can be applied here.
How we see it is, every organization would have multiple analytics footprint requirements, functional needs, LOB or technology specific needs. The needs vary from organization to organization. For example, in an organization, you might deploy an analytics server for sales analytics which would pool in data from the sources needed for its analytics. Subsequently, in the same organization, there might be a need for Log Analytics for which you may have an Elastic kind of platform installed.  Furthermore, within the same organization there might also be another analytics server for inventory and supplier analytics.
These islands – if you can call them so, would be the most natural way to get started with your journey of Big Data Analytics. Organic growth in data, data sources and demands from businesses would lead to the larger foot print of analytics platform. You might connect with them all or you might simply concentrate on the physical aspects of certain essential platforms. We, at Ashnik, have termed this as ‘DNA’ – Distributed Network of Analysis. An important attribute that an enterprise derives out of its ‘DNA’ is the ability to get insights into newer technologies, start off with much lower risks and evaluate its needs v/s current industry technologies.
The DNA approach gives your organization great flexibility – it allows you to get results without having to get stuck with one technology. Because like we all know, new technologies and newer approaches are outgrowing one another, constantly. Often, these new concepts can invalidate the recently introduced one. Imagine if your organization has made a big bet on one of such technologies, chances are you might get stuck with a dead investment. Our industry faces this challenge enormously, since technologies are changing currents so rapidly now. Besides, engaging technology-trained manpower adds on the burden of this evolution of technologies.
The DNA approach thus allows you to try few options of technologies as they are emerging and make an informed choice when they mature. This satisfies both the objectives – you get to use the latest technologies and reap the benefits for your business at the same time, you are lowering your risks, costs and time substantially by starting in a distributed manner. That for you is the power of DNA. Pun absolutely intended, your enterprise need not be ‘coded for life’ with just one technology.
The DNA approach can be decoded in the following manner:

• Enables faster go-to-market plan
• Quick to implement, easy to integrate
• Start small, grow big architecture
• Ground up initiative than top-down
• Based on open standards for enterprise wide integration
• Easy to be used by business users

In the coming days, we will talk in-depth on ‘how to’ of the DNA approach. Stay tuned, it’s all in our DNA!