What is Big Data, its Size and Challenges?

Data can become a threat if one exploits them with the malicious view to analyze and create profiling. However, proper analysis would scale you up quickly. Your profit margin will swell at a rocket speed. Amazon, IBM, Google and many more corporate biggies are consistently mining data for grabbing more business opportunities. That’s why big data is a sensation of this age.

Let’s catch the big data concept first.

What do you mean by big data?

Big data is a collection of digital data over the internet. These are a few means that contribute to this collection:

Big data involve tons of data. It’s much bigger than what you expect. You can’t count it on your fingers. Its valuable lies under hidden patterns. Those patterns end at customer profiling through data mining. That’s why market research, data mining services and data processing services companies find it irresistible. It’s like a staple food for their business.

In the words of Douglas Merrill, “Big data isn’t about bits, it’s about talent.”

If you crunch it intensively and derive useful patterns, you won’t require much elbow grease. You would have a clear picture of what your audience searches, what it likes, what it buys and even, how much it can spend. What you need is just a pair of analytical eyes that can see beyond figures in it. It’s a pure talent to tap the hidden patterns and form game-changing strategies.

How big is big data? What are its characteristics?

If you compute the exact size of the big data, it may force you to exclaim with a surprise. Michael E Doriscoll on Quora defined it in an easy-to-understand manner. Catch an excerpt of it:

John Marshey in 1990s coined the term ‘big data’. Now, it’s the hottest business terminology. Alone Twitter is a producer of over 90 million tweets a day. Walmart collects over 1 million customer transactions every hour. Its database stores the extracted data from the warehouse of 2.5 petabytes.

Even, Wikipedia called it a cost-free byproduct that takes rise of digital communication. But, you can’t ignore it. The underlying patterns won’t allow you to underestimate it. They reside in a crude structure inside. The analysts utilize them perfectly. The consequent decisions prove no less than an atom bomb. It changed the game of politics in the USA. The victory of Donald Trump made a history. Its credit goes to big data only.

Characteristics of Big Data:

What are big data technologies?

The big data world has a wide scope to improve efficiency of business operations so that opportunities could stem up. It can be possible through big data mining, which cannot be possible sans the strong support of associated technologies. Here is a quick overview of associated technologies:

Difference Between Big Data and Big Data Mining:

A software can’t do the wholesome data mining. You have to have effective computing tools to do so. However, the small industries manage it through Excel and R. As the industry size grows, the bigger data management tools are deployed, like indexed files, monolithic DB, Hadoop and distributed DBs.

Its size appreciates. Thanks to the advent of machine learning! Now, every Internet of Thing (IoT) produces something to analyze. If you consider Alexa- an Amazon’s product, it would surprise you with the suggestions that reflect your desire & instinct. It’s no magic, but a pure work of artificial intelligence.

What makes big data so important for various industries?

A Nobel Prize Laureate ‘Ronald Coase’ said, “Torture the data, and it will confess to anything.”

Why such an incredible economist called to exploit it?

Actually, it can tell you the profile of whosoever, for example a customer’s profiling that carries these components:

This is indeed a boon of the big data analytics. The geeky data analysts delve in the data analysis. They industriously spot and derive valuable patterns out the gathered details.

The big data have many more faces to show up. You can create a company’s profile or derive the psychology of the whole community. It’s difficult to measure up the fathom of its features and capabilities.

Likewise, you have to defeat these challenges if you really want to make money out of statistical figures.

Big Data Challenges:

1. Capturing Data: Capturing stands for spotting and extracting. The market researchers like to capture data. The reason I’ve already elaborated above. Obviously, what it values is more important. And, most of the companies don’t have expertise over capturing data. Therefore, they look at the InfluxData like sources to access open or close source data. It costs a lot if you get it from the Github-an open source tool kit of 2014, has 120,000 sites.

2. Data Storage: If you’ve less than 10 GB data, a hard disk would be enough to store. Now, reverse the situation. If it exceeds more than 1 TB, would you go for the cloud or Hadoop? Drill in your head that both have a defined storage structure. You have to pay a hefty amount as its service charge. They’re secure undoubtedly. But, you can’t match up the instinct of a hacker. Around 27,000 MongoDB databases faced off an assault of ransomware in December, 2017, according to an article on silicon.co.uk.

3. Data Source & Analysis: The meat-space or what we call it a digital space has an unending requirement of analysis. The artificial intelligence and machine learning are consistently changing the way we look at data. You can’t be satisfied with the data sets that are not based on real-time.

Reason: The decision based on such data has approximately 95% chance to generate revenue. They amplify and screen the patterns what actually your target audience wants. On that basis, you make up an effective marketing strategy. You require expensive analysis tools. Therefore, big data analysis is a big challenge.

4. Search: The fluid environment of trading world has hundreds of barriers to misguide the search. With limited sources and outdated technology, you can’t search the path-breaking ways. The sources, like data processing and analysis tools, are a premium service. And, if you don’t have hands on of research, you can’t reach the appropriate data chunks.

5. Sharing &Transferring: Have you read about data migration? It’s a process to re-locate the data. Cloud computing is a gift for the data-keepers. SaaS (Software as a Service), PaaS (Platform as a Service) and IaaS (Infrastructure as a Service) are here. But again, the security threats are also here. They tend to crawl in. Affording a dedicated server requires a deep pocket. But if you can’t, you have to satisfy with the shared network. Consequently, uncertain downtime, slow traffic, unwanted ads and online activities occur that you can’t stand with.

6. Visualization: Visualization re-shapes the data. It can be a chart, graph, table or any image that simplifies what you want to convey. The onlookers tend to ignore long, boring and clumsy presentation. This is why data visualization came into the picture. But, this art needs a master or an expert. A novice can disfigure the message and the outcome could be dangerous.