What is Big Data? Let's answer this question!

Big Data is a phrase that gets bandied about quite a bit in the media, the board room – and everywhere in between. It’s been used, overused, and used incorrectly so many times that it’s become difficult to know what it really means. Is it a tool? Is it a technology? Is it just a buzzword used by data scientists to scare us? Is it really going to change the world? Or ruin it? First, let's just say that Big Data is getting bigger every day "fast" so fast that 90% of the world's digital data was created in the last two years.

What Is Big Data?

Big Data? In its purest form, Big Data is used to describe the massive volume of both structured and unstructured data that is so large it is difficult to process using traditional techniques. So Big Data is just what it sounds like – a whole lot of data.

The concept of Big Data is a relatively new one and it represents both the increasing amount and the varied types of data that are now being collected. Proponents of Big Data often refer to this as the “datification” of the world. As more and more of the world’s information moves online and becomes digitized, it means that analysts can start to use it as data. Things like social media, online books, music, videos, and the increased amount of sensors have all added to the astounding increase in the amount of data that has become available for analysis.

Everything you do online is now stored and tracked as data. Reading a book on your Kindle generates data about what you’re reading, when you read it, how fast you read it, and so on. Similarly, listening to music generates data about what you’re listening to, when how often, and in what order. Your smartphone is constantly uploading data about where you are, how fast you’re moving, and what apps you’re using.

What’s also important to keep in mind is that Big Data isn’t just about the amount of data we’re generating, it’s also about all the different types of data (text, video, search logs, sensor logs, customer transactions, etc.). When thinking about Big Data, consider the "seven V's:"

• Volume: Big Data is, well … big! With the dramatic growth of the internet, mobile devices, social media, and Internet of Things (IoT) technology, the amount of data generated by all these sources has grown accordingly.
• Velocity: In addition to getting bigger, the generation of data and organizations’ ability to process it is accelerating.
• Variety: In earlier times, most data types could be neatly captured in rows on a structured table. In the Big Data world, data often comes in unstructured formats like social media posts, server log data, lat-long geo-coordinates, photos, audio, video, and free text.
• Variability: The meaning of words in unstructured data can change based on context.
• Veracity: With many different data types and data sources, data quality issues invariably pop up in Big Data sets. Veracity deals with exploring a data set for data quality and systematically cleansing that data to be useful for analysis.
• Visualization: Once data has been analyzed, it needs to be presented in visualization for end users to understand and act upon.
• Value: Data must be combined with rigorous processing and analysis to be useful. 

Big Data Terms

Inevitably, much of the confusion around Big Data comes from the variety of new (for many) terms that have sprung up around it. Here is a quick run-down of the most popular ones:

• Algorithm – mathematical formula run by software to analyze data
• Amazon Web Services (AWS) – a collection of cloud computing services that help businesses carry out large-scale computing operations without needing the storage or processing power in-house
• Cloud (computing) – running software on remote servers rather than locally
• Data Scientist – an expert in extracting insights and analysis from data
• Hadoop – a collection of programs that allow for the storage, retrieval, and analysis of very large data sets
• Internet of Things (IoT) – refers to objects (like sensors) that collect, analyze and transmit their own data (often without human input)
• Predictive Analytics – using analytics to predict trends or future events
• Structured v Unstructured data – structured data is anything that can be organized in a table so that it relates to other data in the same table. Unstructured data is everything that can’t.
• Web scraping – the process of automating the collection and structuring of data from websites (usually through writing code)

Importance of Big Data:

Big Data is important not in terms of volume but in terms of what you do with the data and how you utilize it to make analysis in order to benefit your business and organization.

Big Data helps analyze:

• Time
• Cost
• Product Development
• Decision Making, etc

Big data when teamed up with Analytics help you determine the root causes of failure in businesses analyse sales trends based on analysing the customer buying history. Also, help determine fraudulent behavior and reduce risks that might affect the organization.

Uses of Big Data

Big Data technologies are very beneficial to businesses in order to boost efficiency and develop new data-driven services. There are a number of uses for big data. For example, in analysing a set of data containing weather reports to predict the next week's weather.

Here are some Uses of Big Data and where it is used

•  Health Care
•  Detect Frauds
•  Social Media Analysis
•  Weather
•  Public sector.

Contribution of Big Data in Health Care

The contribution of Big Data in the Healthcare domain has grown largely. With medical advances, there was a need to store large amounts of data on patients. Big data is used extensively to store the patient's health history.

This data can be used to analyse the patient's health condition and to prevent health failures in the future. In these two interesting Big data visualization examples we can first hand see the power of Big data:

• Google famously showed that they could predict flu outbreaks based on when and where people were searching for flu-related terms:

•  When you catch a sore throat do you also end up getting an ear infection? GE has the answer or at least an attempt to answer some of these questions. Health Infoscope is a compilation of 72 million electronic records and shows the connection of one disease with another. It also shows the strength of the connection and the likelihood of catching one disease due to the other.

Detect Fraud

Fraud detection and prevention is one of the many uses of Big Data today. Credit card companies face a lot of frauds and big data technologies are used to detect and prevent them.

Earlier credit card companies would keep track of all the transactions and if any suspicious transaction is found they would call the buyer and confirm if that transaction was made. But now the buying patterns are observed and fraud-affected areas are analysed using Big Data analytics. This is very useful in preventing and detecting fraud.

 Social Media Analysis

The best use case of big data is the data that keeps flowing on social media networks like Facebook, Twitter, etc. The data is collected and observed in the form of comments, images, social statuses, etc.

Companies use big data techniques to understand the customer's requirements and check what they say on social media. This helps companies to analyse and come up with strategies that will be beneficial for the company's growth.

Weather

Big Data technologies are used to predict the weather forecast. A large amount of data is fed on the climate and an average is taken to predict the weather This can be useful to predict natural calamities such as floods, etc.

Public Sector

Big Data is used in a lot of government as well as public sectors. Big data provides a lot of facilities such as power investigation, economic promotion, etc.

Big Data is used in many other cases such as the Education sector, Insurance Services, and Transportation. Security Intelligence, etc. Big data has become an important part of analysis and is needed in order to understand the growth of businesses and build strategies to help them grow further.

Why Has It Become So Popular

Big Data’s recent popularity has been due in large part to new advances in technology and infrastructure that allow for the processing, storing, and analysis of so much data. Computing power has increased considerably in the past five years while at the same time dropping in price – making it more accessible to small and midsize companies. In the same vein, the infrastructure and tools for large-scale data analysis have gotten more powerful, less expensive, and easier to use. According to

As technology has gotten more powerful and less expensive, numerous companies have emerged to take advantage of it by creating products and services that help businesses to take advantage of all Big Data has to offer. According to Inc, in 2012 the Big Data industry was worth $3.2 billion and growing quickly. They went on to say that “Total [Big Data] industry revenue is expected to reach nearly $17 billion by 2015, growing about seven times faster than the overall IT market”.

Businesses have also started taking notice of the Big Data trend. In a recent survey, “Eighty-seven percent of enterprises believe big data analytics will redefine the competitive landscape of their industries within the next three years.”

Why Should Businesses Care?

Data has always been used by businesses to gain insights through analysis. The emergence of Big Data means that they can now do this on an even greater scale, taking into account more and more factors. By analyzing greater volumes from a more varied set of data, businesses can derive new insights with a greater degree of accuracy. This directly contributes to improved performance and decision-making within an organization.

Big Data is fast becoming a crucial way for companies to outperform their peers. Good data analysis can highlight new growth opportunities, identify and even predict market trends, be used for competitor analysis, generate new leads, and much more. Learning to use this data effectively will give businesses greater transparency into their operations, better predictions, faster sales, and bigger profits.

The Future

What the future of Big Data really holds, no one can predict. The rapid development of new technologies, especially in the machine learning space, will undoubtedly usurp any predictions we try to make. What is certain, is that Big Data is here to stay. The amount of data we are producing is only going to increase and by analyzing it, we can learn and eventually be able to predict some pretty cool things. Very soon, Big Data will touch and transform every industry and every piece of your daily life.

Conclusion

Whether or not you believe the hype about whether Big Data will change the world, the fact remains that learning how to use the recent influx of data effectively can help you make better, more informed decisions. The thing to take away from Big Data isn’t its largeness, it’s the variety. You don’t necessarily need to analyze a lot of data to get accurate insights, you just need to ensure you are analyzing the right data. To really take advantage of this data revolution, you need to start thinking about new and varied data sources that can give you a more well-rounded picture of your customers, market, and competitors. With today’s Big Data technologies, everything can be used as data – giving you unparalleled access to market factors.

If you are interested in topics like this go check out my Medium profile where I publish articles about deep learning, machine learning, and iOS development.

Thanks for reading!