Actionable Solutions to Counter Data Mining Issues by Hadoop

Competition has been in the air of the business biosphere. But in the present scenario, it’s hazier than ever. Have you ever seen the messaging app as an end-to-end solutions providing landscape in ages? Today we have FB messenger to which artificial intelligence makes smarter like a human by senses. How did Mark Zuckerberg conceive that idea? It simply is the miracle of iconic decision driven from big-data.

Why big data?
Big data hides transformative decisions. Let’s understand through this example. Do you know why retailers call to outsourcing market research? To be at the top is undoubtedly the biggest goal to achieve. But without capturing deep insight, topnotch is just a dream. The actionable decision-making underlies this insight. Let’s say, retailers come to know what customers need and what their attitude is through mining their data. By blending them up in the driven decisions, viable plans are drafted. As a result, the customers get motivated to buy. On its basis, the retailers come in win-win situation. Competitive edge is all theirs while the relevant personalized offers come in to the customers’ pocket.

If talk about telecom industry, it stays busy in deriving personalized offers to collect fat revenue. Manufacturing industry mines big data to maintain optimal maintenance cycle. By discovering the updated tools, it replaces the rotten & outdated components before they fail. Increased up time & customer satisfaction tell its viability. Even, government entities mine data for shelling against cyber-attacks.

Issues that need to be conquered while data mining:  

1. Structuring unstructured data: Suppose the startup entrepreneur wants to drill the audience behavior for which it requires real time data. With the help of an outsourcing data mining company, it scrapped APIs of various eCommerce websites. But the data can be in different format. And who knows would it be adaptive to its system or not. Therefore, its structuring is a real challenge.
With hadoop- the infrastructure software, you can store and process the huge data sets in a cost-effective manner.

2. Productive results are tricky: Suppose you have unprocessed data in tera bytes which would require putting it on the server. But for processing, you need to transfer it to the machines where there is the processing software. While transmitting that huge data from server to processing machines, an agile and speedy network should be there. But what if that network is so slow? What if the network has loops for hackers? Likewise, there are several issues the shoot during transmission of data sets.
Hadoop storage can help you combat this problem easily. By channelizing data processing system or software to the nodes or servers where data repository is, it resolves this problem.

3. Scarcity of resources: Data management is a complex procedure that involves many resources, like servers, processing software, data pro and data analysts. Only professional and experience data analysts can take out viable solutions. But their retention and sustainability is rare.
Hadoop is a professional itself that processes and manages data like a veteran.

4. Quick spinning of tech-transformation: It’s quite common view that tech gets developed rapidly. Suppose you developed a website and the very next day an integrated feature is discovered to embed the artificial intelligence based-chat or messaging bot. And your web developers have no expertise over integrating such feature. Therefore, it’s a major challenge to battle it out.

However Hadoop is adaptive and quite relieving platform for data outsourcing and processing organizations. But progressive transformation is quite hard to catch on.