Embracing analytics is much tougher when it comes to confronting what real-time challenges mean in this field. Your data has to be very appropriate at each moment. A single minute can cost you millions of dollars if misplaced. That’s how tough is analyzing big data, researching firms and putting it up all together into a single vertical. There are times when the minutes and seconds count in to be very crucial and no delays can be accepted. Like in flight landing, a single delay can harm lives. Big data analytics has been taking the picture up from science experiments, sensor detection, radar communications to social media activities and what not. Every single action is determined by what was done previously or how was it done earlier.
With this forming the major layout, challenges are also gearing up. You need to have elite communications and predictions that could actually help. The intelligent transport systems, financial market trading or military operations demand real-time decisions to hit the performance factors. Now, these real-time insights could vary from organization to organization. Some might accept delays; some might look for slight delays to set the other variable on time and some might not even handle a nanosecond delay. This vague real-time definition is the biggest challenge to overcome.
Big data differs from other forms of data as it is categorized with 3 V’s: Velocity, Variety and Volume. Data is usually collected from various sources and is then processed further according to the demands. Decision-making, organizing and accessing are some major works to be then related. Each application has got some architecture to follow. Such in a way that it is able to handle data spikes, shortage and is able to scale up with the growing data whenever necessary. We need an architecture that does not fade away with the usage like if it’s of no use after 1 year then probably having it right now isn’t a good option. So, scaling up with the architecture is again a challenge to tackle.
Having the work done should not be the only goal when we are into analytics. Why because, if your system breaks at some time or is unable to process some data at any point of time your internal processes should have some backup. If the only goal you had was your external outlook, probably then maintenance would be an issue. If the system fails, there should be some good internal processes that could have the capability to back up the entire failure. If some random internal processes are there, then it would be very difficult to handle the issues at runtime.
The last big challenge is to make this entire shift. Employees working on the old traditional work practices have to be somehow convinced to take up this way. There are a variety of escalations that can be stepped onto and huge tasks can be very efficiently affected by this. Gradually the entire paradigm would be stepping into this so why not now? If there are areas where the employees aren’t comfortable enough to look for, then trainings can be organized. Managers could probably scale up some traditional issues and make the entire team realize how analytics can help them ace. So, there has to be some change to be taken care of and it’s better to experience right now then to be late and struggle at further stages.
Coming to the gist of what we have is: Real-time analytics demands much more of our efforts and hard work. There are still challenges we need to look after. The faster we grow at this, the better we would be later. So making a change is the need of the hour and scaling up with the market trends is what is required the most.