Big Data.

What Is Big Data?

Big data refers to massive, complex data sets (either structured, semi-structured or unstructured) that are rapidly generated and transmitted from a wide variety of sources. 

These attributes make up the three Vs of big data:  

1. Volume: The huge amounts of data being stored.
2. Velocity: The lightning speed at which data streams must be processed and analyzed.
3. Variety: The different sources and forms from which data is collected, such as numbers, text, video, images, audio and text.

These days, data is constantly generated anytime we open an app, search Google or simply travel place to place with our mobile devices. The result? Massive collections of valuable information that companies and organizations manage, store, visualize and analyze.

Traditional data tools aren’t equipped to handle this kind of complexity and volume, which has led to a slew of specialized big data software platforms and architecture solutions designed to manage the load.

Big data is essentially the wrangling of the three Vs to gain insights and make predictions, so it’s useful to take a closer look at each attribute.

Volume 

Big data is enormous. While traditional data is measured in familiar sizes like megabytes, gigabytes and terabytes, big data is stored in petabytes and zettabytes. 

To grasp the enormity of the difference in scale, consider this comparison from the Berkeley School of Information: One gigabyte is the equivalent of a seven minute video in HD, while a single zettabyte is equal to 250 billion DVDs.

This is just the tip of the iceberg. According to Statista, the production of data is more than doubling in a five-year span, with an expected 180 zettabytes to be produced globally by 2025. 

Big data provides the architecture handling this kind of data. Without the appropriate solutions for storing and processing, it would be impossible to mine for insights.

Velocity

From the speed at which it’s created to the amount of time needed to analyze it, everything about big data is fast. Some have described it as trying to drink from a fire hose. 

Companies and organizations must have the capabilities to harness this data and generate insights from it in real-time, otherwise it’s not very useful. Real-time processing allows decision makers to act quickly, giving them a leg up on the competition. 

While some forms of data can be batch processed and remain relevant over time, much of big data is streaming into organizations at a clip and requires immediate action for the best outcomes. Sensor data from health devices is one example. The ability to instantly process health data can provide users and physicians with potentially life-saving information.

Variety

Roughly 80 to 90 percent of all big data is unstructured, meaning it’s unorganized and difficult for conventional data tools to analyze. Everything from emails and videos to scientific and meteorological data can constitute a big data stream, each with their own unique attributes.

Benefits of Big Data

Though the large-scale nature of big data can be overwhelming, this amount of data provides a heap of information for professionals to utilize to their advantage. Big data sets can be mined to deduce patterns about their original sources, creating insights for improving business efficiency or predicting future business outcomes.

Some notable areas where big data provides benefits include:

• Cost optimization
• Customer retention 
• Decision making
• Process automation 

How Is Big Data Used?

The diversity of big data makes it inherently complex, resulting in the need for systems capable of processing its various structural and semantic differences. 

Big data requires specialized NoSQL databases that can store the data in a way that doesn’t require strict adherence to a particular model. This provides the flexibility needed to cohesively analyze seemingly disparate sources of information to gain a holistic view of what is happening, how to act and when to act.

When aggregating, processing and analyzing big data, it is often classified as either operational or analytical data and stored accordingly.

Operational systems serve large batches of data across multiple servers and include such input as inventory, customer data and purchases — the day-to-day information within an organization.

Best Big Data CompaniesView Top Big Data Companies Hiring Now

Analytical systems are more sophisticated than their operational counterparts, capable of handling complex data analysis and providing businesses with decision-making insights. These systems will often be integrated into existing processes and infrastructure to maximize the collection and use of data.

Regardless of how it is classified, data is everywhere. Our phones, credit cards, software applications, vehicles, records, websites and the majority of “things” in our world are capable of transmitting vast amounts of data, and this information is incredibly valuable.

Big data analytics is used in nearly every industry to identify patterns and trends, answer questions, gain insights into customers and tackle complex problems. Companies and organizations use the information for a multitude of reasons like growing their businesses, understanding customer decisions, enhancing research, making forecasts and targeting key audiences for advertising.

Here are a few examples of industries where the big data revolution is already underway:

Big Data in Finance

Finance and insurance industries utilize big data and predictive analytics for fraud detection, risk assessments, credit rankings, brokerage services and blockchain technology, among other uses.

Financial institutions are also using big data to enhance their cybersecurity efforts and personalize financial decisions for customers.

Big Data in Healthcare

Hospitals, researchers and pharmaceutical companies adopt big data solutions to improve and advance healthcare.

With access to vast amounts of patient and population data, healthcare is enhancing treatments, performing more effective research on diseases like cancer and Alzheimer’s, developing new drugs, and gaining critical insights on patterns within population health.

Big Data in Media & Entertainment

If you’ve ever used Netflix, Hulu or any other streaming services that provide recommendations, you’ve witnessed big data at work. 

Media companies analyze our reading, viewing and listening habits to build individualized experiences. Netflix even uses data on graphics, titles and colors to make decisions about customer preferences.

Big Data in Agriculture 

From engineering seeds to predicting crop yields with amazing accuracy, big data and automation is rapidly enhancing the farming industry.

With the influx of data in the last two decades, information is more abundant than food in many countries, leading researchers and scientists to use big data to tackle hunger and malnutrition. With groups like the Global Open Data for Agriculture & Nutrition (GODAN) promoting open and unrestricted access to global nutrition and agricultural data, some progress is being made in the fight to end world hunger.

Along with the areas above, big data analytics spans across almost every industry to change how businesses are operating on a modern scale. You can also find big data in action in the fields of advertising and marketing, business, e-commerce and retail, education, Internet of Things technology and sports.

Big Data Tools 

Understanding big data means undergoing some heavy-lifting analysis, which is where big data tools come in. Big data tools are able to oversee big data sets and identify patterns on a distributed and real-time scale, saving large amounts of time, money and energy. 

Here’s a handful of popular big data tools used across industries today.

Apache Hadoop 

A widely used open-source big data framework, Apache Hadoop’s software library allows for the distributed processing of large data sets across research and production operations. Apache Hadoop is scalable for use in up to thousands of computing servers and offers support for Advanced RISC Machine (ARM) architectures and Java 11 runtime.

Apache Spark 

Apache Spark is an open-source analytics engine used for processing large-scale data sets on single-node machines or clusters. The software provides scalable and unified processing, able to execute data engineering, data science and machine learning operations in Java, Python, R, Scala or SQL.

Apache Storm

Able to process over a million tuples per second per node, Apache Storm’s open-source computation system specializes in processing distributed, unstructured data in real time. Apache Storm is able to integrate with pre-existing queuing and database technologies, and can also be used with any programming language.

MongoDB Atlas

With a flexible and scalable schema, the MongoDB Atlas suite provides a multi-cloud database able to store, query and analyze large amounts of distributed data. The software offers data distribution across AWS, Azure and Google Cloud, as well as fully-managed data encryption, advanced analytics and data lakes.

Apache Cassandra

Apache Cassandra is an open-source database designed to handle distributed data across multiple data centers and hybrid cloud environments. Fault-tolerant and scalable, Apache Cassandra provides partitioning, replication and consistency tuning capabilities for large-scale structured or unstructured data sets.

History of Big Data

Data collection can be traced back to the use of stick tallies by ancient civilizations when tracking food, but the history of big data really begins much later. Here is a brief timeline of some of the notable moments that have led us to where we are today.

1881

• One of the first instances of data overload was experienced during the 1880 census. The Hollerith Tabulating Machine is invented and the work of processing census data is cut from ten years of labor to under a year.

1928

• German-Austrian engineer Fritz Pfleumer develops magnetic data storage on tape, which led the way for how digital data would be stored in the coming century.

1948

• Shannon’s Information Theory is developed, laying the foundation for the information infrastructure widely used today.

1970

• Edgar F. Codd, a mathematician at IBM, presents a “relational database” displaying how information in large databases can be accessed without knowing its structure or location. This was formerly reserved for specialists or those with extensive computer knowledge.

1976

• Commercial use of Material Requirements Planning (MRP) systems are developed to organize and schedule information, becoming more common for catalyzing business operations.

1989

• The World Wide Web was created by Tim Berners-Lee.

2001

• Doug Laney presented a paper describing the “3 Vs of Data,” which becomes the fundamental characteristics of big data. That same year the term “software-as-a-service” was shared for the first time.  

2005

• Hadoop, the open-source software framework for large dataset storage is created.

2007

• The term “big data” is introduced to the masses in the Wired article “The End of Theory: The Data Deluge Makes the Scientific Method Obsolete.”

2008

• A team of computer science researchers published the paper “Big Data Computing: Creating Revolutionary Breakthroughs in Commerce, Science and Society,” describing how big data is fundamentally changing the way companies and organizations do business.

2010

• Google CEO Eric Schmidt reveals that every two days people are creating as much information as people created from the beginning of civilization until 2003.

2012

• IBM study says 2.5 quintillion bytes of data are created daily and that 90 percent of the world’s data has been created in the last two years. 

2014

• More and more companies begin moving their Enterprise Resource Planning Systems (ERP) to the cloud.
• The Internet of Things (IoT) became widely used with an estimated 3.7 billion connected devices or things in use, transmitting large amounts of data every day.

2016

• The Obama administration releases the “Federal Big Data Research and Strategic Development Plan,” designed to drive research and development of big data applications that will directly benefit society and the economy.

2019

• Over 95 percent of businesses face some form of need to manage unstructured data.

2020

• 59 percent of organizations state that they plan to move forward with the use of advanced and predictive analytics.

2022

• World projected to produce over 180 zettabytes of data by 2025.