Big Data is these days, one of the hottest trending technologies that have found its way in virtually all sectors. From healthcare and eCommerce to retail and eGovernance, everyone is applying Big Data at one point or another.
The problem is, designing Big data applications isn’t anything like designing other mobile applications. In fact, you would hardly find a Big data application developer that offers an end-to-end solution. The most you should expect are individual tools and components that fit into your existing system architecture. Yes, you can spend a million dollars and build the complete infrastructure but honestly, most firms either do not have that kind of cash or inclination to invest in a technology with little or no immediate returns.
Another problem is that the ROI of Big data applications isn’t as tangible as other technologies. For instance, when you deploy mobile apps for your business, you instantly experience higher footfalls but with Big Data apps, all you get is insights on your existing customers and it falls upon you to extract the true value out of it. Keeping these two problems in mind let’s try and chart out how you proceed with designing your Big Data application:
Define your priorities
All datasets are valuable but some are definitely more valuable than others. Depending on what you intend to achieve, you will first need to decide which datasets you are going to process because processing them all is simply infeasible. And as much mentioned, development services too rarely offer end-to-end solutions and so you can hire those developers for custom-built components.
Shifting goal posts is fine
This may sound a bit contradictory to what we just pointed out- defining priorities, but Big Data by its nature, is uncertain and the flow from project conception to deployment may not be linear. That is, there is no guarantee that the datasets you prioritize and mine will yield requisite insights. In that case, you should be able to swiftly move on to new datasets and iterate the process. It’s kind of looking for oil- most your attempts would yield little value but once in a while you are ought to hit the jackpot.
Big Data is a long-term investment
As described earlier, the insights obtained from Big Data carry little worth in themselves if not exploited by subsequent measures. If you add to that the iterative nature of Big Data, it becomes imperative that while hiring a developer for Big Data application, you must be aiming for the long-term returns or you most certainly will be disappointed.
As an extension to the previous point, if you are going to be repetitive and not get returns for a considerable period, it makes sense to start slow and cheap. This way, you’d save your resources from burning out on a single attempt and sustain your Big Data project for a longer time.
Create an open field
Big Data is all about experimentation and observing what works, what does not and what impact do they have. Obviously, you can’t start experimenting with your live application but if you create a sandbox and let loose all the regular rules, you developers would eventually stumble upon something takes your closer to your goal. Be it a change in database operation that improves the performance or a shift in UI that enhances user experience.
Overall, designing Big Data application is a task that requires quite a patience and expertise- something few enterprises have. So it always preferred to simply hire mobile app developers to work on their project- consistently testing their designs and running regression analysis while the enterprise swiftly handles its operations.