The Ultimate Guide to Understanding
Big Data

Introduction to Big Data

Smart phones, computers, smart watches, connected cars—we live in a world where almost everything pumps out information about hardware performance and owner use on a continuous basis. From the amount of steps you take in a day, to the distance you travel to the nearest gas station, to the number of minutes you spend on your favorite app, the amount of data you consciously or unconsciously produce is truly colossal, and it’s only getting bigger. In fact, we’ve created more data collectively in the last five years than in the entire history of humankind before then.

But what are the implications of producing and storing data faster and to such exaggerated ends? Who would want that data? And why do we use it?

Welcome to the world of big data. Let’s take a look at how this concept has affected us until now and how it will continue to shape our new “normal” forever.

Big Data Definition

What is the Definition of Big Data?

Big Data noun

Big data is a relatively new and evolving term, so there isn’t a precise definition that’s universally accepted. Generally speaking, big data normally refers to collections of data that have become too large and too complex to be processed and analyzed through traditional methods and software. This data is being produced by certain obvious sources, such as your phone or computer, but also from anything like regular convenience store and restaurant purchases to the performance of your car.

Big Data DefinitionIn businesses big and small, big data is a major talking point, and rightly so. Data on users, leads, clients and humanity in general can radically change the landscape of a company’s business undertakings when leveraged properly. But there’s been so much hype around this term that the definition has become even more confused.

In practical terms, big data includes all digital inputs like web behavior and social network activity. Some argue that it includes traditional data used by marketers, too, but only to the extent that this data is made digital. Whatever the scope, it’s important to break big data down to better understand this vast mix of information. In the tech world, we big data down into:

  • Structured Data – this includes information with a hard-value input. It’s the easiest to interpret, and often the easiest to collect. And with its easy interpretation, it’s also a major source of the changing landscape of marketing and digital services.
  • Unstructured Data – this comes from information that is collected but not organized or easily interpretable. For example, Tweets are text-heavy examples of unstructured data that each constitute a unique string of thought from a user (as compared with hard-value input like the hour that user logged in).
  • Multi-Structured Data – this refers to data gathered from form or transactional information, such as a user’s interactions with a platform. Weblog data, for example, which includes a combination of text and images of where a user clicked across a web session, is an influential source of multi-structured data.

At C-suite meetings all over the world, businesses talk about big data. It’s all the rage, and everyone wants to know how to use it. But is everyone really on the same page? And in smaller organizations, is there a way a company can leverage third-party services to collect the same competitive level of big data ? Let’s take a look below.

Big Data Terminology

  • Data Mining

  • Dark Data

  • Cloud Computing

  • Predictive Analysis

  • ETL

  • Machine Learning

  • Structured Data

  • Unstructured Data

  • Data Scientist

  • Data Analyst

  • Dirty Data

  • Neural Network

Big Data News

Latest Developments in Big Data News

The field of big data is continually growing with core technology advancements, software and hardware improvements, and new products. Staying up to date with the latest big data news is a vital component of staying on top of this rapidly growing industry. IoTTechnologies.com cover the latest in internet of things news, cloud computing news, and big data news.

Big Data Explained

A Simple Explanation of Big Data

In the real, tangible world, we’re used to organizing things by certain observable properties. Whether it’s author or title names in the library, labels in spice cabinets, or colors in loads of laundry, we’re accustomed to seeing, feeling, tasting, hearing, or even smelling things in order to qualify and order them. Big data is information about anything and everything, so it isn’t going to look, sound, or feel like any one specific thing.

In order to describe these digital collections of data, we can’t rely on just our senses to classify. Instead, we often define big data in terms of the following five characteristics:

1. Volume

Big Data VolumeAlmost every action we take today generates data. iHealth records our sleeping patterns, Amazon our purchases and Google our searches. Our Fitbits track our steps, and Progressive will likely give us a discount for sharing our acceleration and deceleration rates.

Data volume has been growing at an exponential rate, so much so that 90% of all data had been created in the last two years according to 2017 research by DOMO. The larger the volume data, the more potential for rich and comprehensive insights, and the greater challenges companies face in storing, processing and extracting said insights.

2. Variety

Big Data VarietyText, audio, machine to machine conversations, structured to unstructured. Data variety refers to all the different “types” of data in the collection. Formats such as jpg, pdf, mp3, rar, docx. Weather data, home noise volume, viewing history, contact preferences. It’s all being recorded, and in order to access insights contained within we need tools capable of analyzing a myriad of file types simultaneously.

Knowing whether your collection contains images, audio, text, code, or any other type of data can help you approach your analysis more efficiently and effectively. A lexicographic-sorting algorithm may be great in a data set of names, but probably won’t do much good in a set of pictures or audio clips.

3. Velocity

Big Data VelocityLong gone are the days of scheduled updates, or intentional uploads. Data velocity refers to the speed at which the information is created and processed. Lifetimes of video data is now livestreamed, tags are scanned, and sensor data influencing driving decisions needs to be processed at near to real time speed.

4. Veracity

Big Data VeracityData veracity refers to the noise or abnormalities that a collection of data carries. In simpler terms, this is the quality of the data. Knowing the data veracity is important in order to calculate a margin of error when analyzing data, or simply to know whether or not to use it—just because the data are out there doesn’t mean they’re accurate.

5. Value

Big Data ValueData value is pretty straightforward: it refers to what the data is actually worth. But this might not necessarily be in a monetary sense. For example, a data set containing the names of the pets in your neighborhood is going to have a much lower amount of data value to a phone company than a set containing the number of text messages sent every month.

Categories of Big Data

Big Data Categories Explained

As mentioned before, data is an incredibly vague term that refers to all sorts of information that differs in content, file type, size, and a plethora of other ways. Instead of categorizing big data by the countless forms that it can take, big data is often categorized by how structured it is. More specifically, it can be considered structured, semi-structured, or unstructured.

Category A: Structured Data

Big Data Structured DataStructured data is quick and simple to use and analyze, as it is already sorted in an accessible way. This data often goes into fixed fields, is organized and follows a strict model such as row-column formatted charts nor does not require additional manipulation in order to be extensively searched, analyzed, or used.

An example of structured data would be the zip codes, cities or birth places of users on a social platform. Anything that can be easily or automatically sorted (and that shares the same “tag” of data type) is being aggressively leveraged by developers and marketers today in the race to the front page of big data.

Category B: Unstructured Data

Unstructured data is data that is not already organized in an accessible way and is estimated to make up more than 80% all data. This data might have a structure internally, but that structure isn’t something that can easily be used to directly analyze the information. As the vast majority of big data is unstructured it is often thought as the most important and to hold a plethora of unearthed ‘digital currency’ or competitive insights within it. Creative and new methods are required to efficiently analyze this information.

One of the best examples of unstructured data is a list of Tweets from users. The basic tag of this data type would be the same, but the input is unique across users, languages, subtexts and meanings, and this data is much harder to leverage to any end.

Category C: Semi-Structured Data

Big Data Semi-Structured DataSemi-structured data is data that isn’t organized as neatly as structured data, but does possess some organizational properties that allow the information to be organized and analyzed quicker and more efficiently than completely unstructured data. Only a very small amount of big data is considered semi-structured.

An example of this “gray area” type of data is where a text or spreadsheet document is later reassessed for its metadata. The document itself would be considered unstructured data with unique input and no easy means of sorting or deciphering the data. But adding metadata tags for the general topic of the content, location where it was originally created and other information renders the document semi-structured.

How Does Big Data Work?

What is Big Data and How Does it Work?

What big data actually does is provide information in large quantities to those interested in using it. A cell phone company might see that their phone usage data sets show more text messages than phone calls, and in response they might focus more on the texting features of their next product than the voice call properties. Big data is much less about what it does and more about what can be done with it.

Examples of what some major companies are doing with big data can help to shed more light on how this data really works. The question of how the use of this data affects the end user, too, is one that requires an understanding where big data is currently being applied:

  • Whether we’re interacting with social networks, shopping for products or using messaging apps on our phones, we create an enormous store of data every day. Companies are measuring the use of their services and planning development around this data.
  • Companies are also combining big data with artificial intelligence and machine learning technologies to provide users with even more personalized experiences. An example would be Google’s uncanny knack for knowing what you wanted to search for, or a travel website automatically populating your flight information in from a search performed earlier in the week.
  • Data is also being produced by hardware systems like healthcare equipment at the hospital or the computer in a car. This information helps companies create preemptive warning flags when something is at risk of going wrong.

Data is information and a company should always strive to have as much information as possible. More data means more analytics and better optimization, marketing design, product evaluation and more. There is a certain point, however, where more data isn’t necessarily a net-good. When this data is personal information on users, for example, the privacy of the source should be respected.

Key Components of Big Data

What are the Core Components of Big Data?

Now that you have a thorough understanding of the definition of the big data, we will examine the most common technological components of big data.

Devices

Big Data DevicesEvery signal that leaves your phone, computer, TV, smart fridge, connected car, or any other device immediately becomes part of big data in one way or another. As we produce and purchase more and more devices, and as those devices become more integral parts of our everyday lives, the amount of information that we create in just moments becomes absolutely enormous, and it’s only going to grow larger. In fact, mankind has already created over 2.7 zettabytes of data—that’s 2,700,000,000,000 gigabytes!

Goods, Services, and Utilities

Big Data ShoppingLike devices, we might not be conscious of just how much information we create even by simply paying our bills. When you pay your electricity, gas, or water bill, the utility company records how much of each utility you use and how you use it. This information can absolutely be used for these companies to optimize their processes, as well as to adjust prices and models. Similarly, when you buy a burger at your favorite burger shop, what you purchased, how much of it you purchased, and how much you paid are all pieces of information that can absolutely be analyzed and used in the future for that and any company that may find the information useful. Any purchase you make contributes to big data, and that is in large part why the amount of data we are seeing is growing so quickly.

People

Big Data PeoplePerhaps the biggest component of Big Data is people. Data is an abstraction of each of our whims and personalities that we’ve created—they aren’t actually real, they aren’t tangible. For that same reason, people are the most important part of big data. We’re both the ones that create these massive amounts of data, and we’re also the ones that care enough to analyze those data sets. Less philosophically, once big data is created, data analysts and data scientists are the people that make the whole big data world go ‘round. Without analysts to organize and analyze the data, and without people to then act on that data and extracted insights, big data would simply be an world of information with no impact.

Big Data Companies

Discover Innovative Big Data Startups and Companies

IoT Technologies (Internet of Things, Big Data, Cloud Computing) Companies and Startups

It takes bold visionaries and risk-takers to build future technologies into realities. In the field of big data, there are countless companies and startups across the globe working on this technology. Our mega list of internet of things, cloud computing, and big data companies, covers the top companies and startups who are innovating in this space.

Big Data Applications

Government 

Big Data Applications in GovernmentBig data isn’t simply limited to utility companies or other private entities that use analytics to adjust their approaches, governments can absolutely make similar adjustments with insights from big data. Looking at data collected from the entire country over time, a government can use this information to make changes to general things like voting procedures or specific things like public park maintenance in a way that will benefit the general interests of the people (while also being able to cut back on costs).

Healthcare 

Big Data Healthcare ApplicationsBig data plays an enormous role in the medical field. Information about illness and disease in big quantities can help us rapidly identify and control epidemics through properly analyzed data and appropriate response procedures. Data shows what treatments require more intense attention and which treatments don’t, making it easier to budget healthcare costs. Significant testing in the UK has shown cost reductions and general improvements in efficiency all thanks to analyzing its big data and putting those insights to practice in the NHS. From remote medical alerts to connected fire extinguishers, hospitals are starting to find ways to be more informed and more secure through their data.

Internet of Things 

Big Data Applications in Internet of Things (IoT)The Internet of Things and Big Data go hand-in-hand with one another. As the Internet of Things expands, big data grows more rapidly. This is how and why your phone might remind you to do a little more exercise today, or that you need to get more milk for the week. The larger and more informed the Internet of Things becomes, the more information different companies and entities have to work with, and the more possibilities in everyday applications become possible.

Big Data Tools

When dealing with Big Data, the best approach is to custom-tailor your method of analyzing and organizing data to your specific needs. However, building this system from the ground up isn’t easy, and for that reason you’ll find plenty of tools to help you crunch big data efficiently.

Big Data HadoopHadoop is a popular open-source software that scales down stores large amounts of data efficiently for multiple needs and interests. It works over a network of systems to be able to execute massive amounts of computations and handle massive amounts of data without having to worry about specific hardware failure.

 

Big Data Tool ClouderaCloudera is the most popular commercial version of Hadoop on the market. They provide the same service in essence, but Cloudera offers a more user-friendly setup and experience.

 

Big Data Tool MongoDBMongoDB is great for managing data that changes often, as well as data that is unstructured or semi-structured. It is often used for data storage in applications, content management, and more.

Big Data Conclusion

Big Data is far from being an exclusive and awe-inspiring tool wielded only by Amazon and Google, these are just a few ways that big data has become more relevant and the tools that make assessing this data possible. All of us are already benefiting from big data in our day-to-day lives. The emergence of self-driving cars, SIRI, Facebook’s uncanny ability to correctly tag you and your friends – as chauffeurs, concierges, even manual data entry, all of a sudden, machines can doing this for us big data. Big data analytics and derived algorithms make for an exciting world, and the applications are endless. While no one can say what the future holds, it seems certain that we will see the emergence evermore big data applications in our lives.

IoT Technology Guides

Different Types of IoT Technologies

Although big data is considered to be a foundational technology, other exciting technologies have been derived from it. Further explore these technologies by continuing with one of our other “Ultimate Guide to Understanding” web resources on Internet of Things or Cloud Computing.