WHAT IS BIG DATA ?

We hear the term “Big Data” a lot. But what does Big Data actually mean to us and how will it impact us in the near future??

The days of storing easy-to-collect, neatly structured data in relational databases are well behind us. Nowadays, humans are generating larger quantities of knowledge at much faster speeds than ever before, and therefore sort of this data is way more complex than it had been a couple of decades ago.

According to IBM, 90 percent of all the info throughout history was generated within the past two years. Our digital universe of knowledge will grow from 44 zettabytes today to around reach 163 zettabytes, or 163 trillion gigabytes by 2025. This rapid explosion of data is formally called “big data.” Such an easy name for something so all-inclusive and large. But what exactly is big data? Let’s take a glance.

What is big data exactly?

According to Gartner, the definition of massive Data –?

“Big data” is high-volume, velocity, and variety information assets that demand cost-effective, innovative sorts of information science for enhanced insight and deciding.”

Big data help marketers target their customer more strategically, help environmentalists understand sustainability within the future, help healthcare professionals predict epidemics, and far more.

The scope of big data is almost endless. Researchers in “smart cities” are using real-time data to seem at electricity consumption, pollution, traffic, and far more.?

Emerging technologies like AI and machine learning are harnessing big data for future automation and helping humans unveil new solutions.

The big data market is accelerating at seriously very high speeds. In the year 2014, big data was just a $20 billion market and experts say by 2026 it will become $92.2 billion market.

So, what made it bigger?

The devices we use like smartphones, laptops, tablets, smart televisions, gaming consoles, smartwatches, your Amazon Echo, and even our vehicles. But within the very near future, you'll expect the emergence of smart home appliances like toasters, refrigerators, smart locks, et al. to contribute to the present mix (for some homeowners, they already have).

The hardware itself simply allows for more efficient ways to share data, but the important volume of massive data comes from the ways we interact with these devices. for instance, a wearable device, sort of a smartwatch, may gather all kinds of knowledge on you. This device can track pulse, sleep quality, blood glucose levels, and even fertility cycles.

So why Big Data is needed anyway?

After the collection of data from the smartwatch. These data are often shared with healthcare providers to personalized patient care. Insurance companies also can use this data to customize your rates. That’s tons of knowledge from only one device.

So the main challenge is the way to analyze and process these data so as to derive these data set to strengthen business strategy, customer feedback, knowing market trends, efficiency and performance, demand for a product or competitor activities. Big Data solutions help companies add up out of random information, become proactive and begin setting the pace rather than continuously putting out fires and following competition.

?Characteristics of Big data (3 V’s of big data)

Big data is never easy to know, especially with such vast amounts and sorts of data today. to assist add up of massive data, experts have broken it down into three easier to-understand segments. These segments are mentioned because of the 3 V’s of massive data: volume, velocity, and variety.

1.?Volume

The first V of massive data is probably the foremost prominent one, and it refers to the “big” volume of knowledge available now and within the future.?

Big data is about volume. Volumes of knowledge which will reach unprecedented heights actually. It’s estimated that 2.5 quintillion bytes of knowledge is made every day. To place this number into perspective, if 2.5 quintillion coins were laid flat, it might cover the world five times. 40 zettabytes of knowledge created by 2020 – which highlights a rise of 300 times from 2005. As a result, it's not uncommon for giant companies to possess Terabytes – and even Petabytes – of knowledge in storage devices and on servers. This data helps to shape the longer term of a corporation and its actions, all while tracking progress.

2.?Velocity

The second V of massive data refers to the speed at which the universe of massive data is expanding.

Big data isn’t just “Huge,” it is also growing extremely fast. Let’s put this velocity in perspective by continuing our astonishing Facebook facts. consistent with insight from the Social Skinny, there are 510,000 comments posted, 293,000 statuses updated, and 136,000 photos uploaded to Facebook every minute!

Fun fact, the large data universe is expanding very similar to our physical universe of stars, planets, galaxies.

3.?Variety

The last V of massive data refers to the variability, or many various types, of knowledge that’s being generated today.

Data is big, data is fast, but data is additionally extremely diverse. Traditionally data were stored in sort of plain text and neatly structured during an electronic database.?

Big data has drastically changed the info landscape. There’s still an area for plain text data, but data formats like digital audio, video, images, geospatial, and lots of others have inherit play.

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Veracity and Value

Two additional V’s, referred to as veracity and value, these two might not be a part of the first 3 V’s, but they need become increasingly important as big data expands.

Veracity refers to the standard of knowledge. Because data comes from various resources, it’s not easy to link, match, cleanse and transform data across systems. Businesses have to find relationships, hierarchies, and multiple data linkages. Otherwise, their data can quickly go out of control.

Value is that the most straightforward V of big data. It tells, “How can we use all of this data to extract something useful for our users and thus the business?” Big data won’t bring much value if it is being analyzed without purpose.

What are the types of big data?

We know that with the utilization of more devices, platforms, and storage options, this is often not only getting to increase the quantity of knowledge, but also the sorts of data that's out there.

One sort of data is what we call structured, and another is named unstructured. But there’s also a 3rd sort of data called semi-structured. Let’s examine the differences of every data type.

Structured data

Structured data, for the foremost part, is very organized during an electronic database. If you needed to access a bit of data within the database, you'll easily do so with a fast search.

One of the foremost common samples of structured data are some things you’d see during a spreadsheet. If you’re on the phone with a student loan representative and that they ask you for your personal identification, likelihood is that they’re working with structured data.

Unstructured data

It would be nice if all data might be neatly structured, but human-generated data like photos on social media, voicemails, text messages, and more are highly unstructured.

More than 75 percent of all data is unstructured. But what does unstructured refer to? It means data that isn’t easily identifiable by machine language, and it doesn’t conform to a typical database or spreadsheet.

You may be surprised, but most unstructured data is really text-heavy. for instance, text messages are unstructured because as far as machines are concerned, humans don’t talk or type during a logical way. this is often why machine learning and tongue processing are wont to dissect human languages, slangs, jargons, and more.

There’s also machine-generated unstructured data, digital surveillance: CCTV.

Semi-structured data

The Mixture of structured and unstructured also referred to as semi-structured data. Things like XML files or emails are samples of semi-structured data because while they are doing contain tags like dates, times, and sender/receiver information, the language utilized in them isn’t structured.

Applications of Big data

Most of the companies use Big data in their decision-making processes. Every year more data-driven companies across a are emerging constantly. Here’s what some industries decide to do with all this data.

Internet of Things (IoT)

Data extracted from IoT devices provide a mapping of device interconnectivity. Such mappings are employed by various companies and governments to extend efficiency. IoT is additionally increasingly adopted as a way of gathering sensory data, and this sensory data is employed in medical and manufacturing contexts.

Financial Services

More banks are moving away from being product-centric and are focusing on being customer-centric. Banks can use Big data to help segment customer preferences through an omnichannel marketing approach. Perhaps the foremost obvious use of massive data in financial services is fraud detection and prevention.

Healthcare

We mentioned how smartwatch data are often used for personalized patient care and customized healthcare insurance rates. Predictive analysis can have phenomenal applications within the healthcare industry – allowing earlier detections of diseases and more accurate associations to certain risk factors.

Education

Big data is being used on some college campuses to reduce dropout rates by identifying risk factors in students who are falling behind in their classes.

Its application is so huge that the article could only be considered as a summary of the large Data applications and their advantages.

Wrapping up big data

Big data has put customer-centricity into the business. Big data helps businesses make faster, more calculated decisions. Through the use of big data analytics, we’re able to predict where future problems may occur and apply data-driven reasoning to resolve these problems. This just wasn’t a reality a couple of decades ago.

But the road ahead for giant data remains an extended one. Advancements in trending and rising technologies like AI and machine learning will only make big data more valuable. We sleep in a time where big data is basically gaining momentum – which may be both exciting and overwhelming.