With the technology that has already reached the pinnacle of its highest use implementation, you would be quite aware of its major functionalities, processes, uses, and overall importance. In August of 2015, it slipped off Gartner’s 2015 Hype Cycle for Emerging Technologies and created a huge buzz in the tech-driven world.
If you haven’t been all that tech-savvy and missed on crucial information on what is Big Data, this write-up will furnish you with details on all that you need to know at the outset to understand the technology better.
History of Big Data
When John Graunt was researching the bubonic plague ravaging Europe in 1663, he had to cope with enormous volumes of information. This was the first instance of big data. The first individual to ever employ statistical data analysis was Graunt. The study of statistics later broadened to encompass gathering and analysing data in the early 1800s. In 1880, the world first became aware of the issue with abundant data.
According to the US Census Bureau's estimate, handling and processing the data gathered during that year's census operation would take eight years. Herman Hollerith, a Bureau employee, created the Hollerith Tabulating Machine in 1881, lessening the calculation required. Data developed at an unforeseen rate during the 20th century. Big data is now at the centre of evolution. At that time, magnetic information storage devices, message pattern scanning devices, and computers were also developed. To store millions of fingerprint sets and tax returns, the US government constructed the first data centre in 1965.
What is Big Data?
As Gartner defines it – “Big Data are high volume, high velocity, or high-variety information assets that require new forms of processing to enable enhanced decision making, insight discovery, and process optimization.”Let's dig deeper and understand this in simpler terms.
The term ‘big data’ is self-explanatory − a collection of huge data sets that normal computing techniques cannot process. The term not only refers to the data, but also to the various frameworks, tools, and techniques involved. Technological advancement and the advent of new channels of communication (like social networking) and new, stronger devices have presented a challenge to industry players in the sense that they have to find other ways to handle the data.
From the beginning of time until 2003, the entire world only had five billion gigabytes of data. The same amount of data was generated over only two days in 2011. By 2013, this volume was generated every ten minutes. It is, therefore, not surprising that a generation of 90% of all the data in the world has been in the past few years.
All this data is useful when processed, but it had been in gross neglect before the concept of big data came along.
Pro-Tip: To learn more about Big Data and get your foot in the Data Science industry door, consider professional certification training in Big Data or allied technologies, such as Impala, Cassandra, Spark, and Scala.
Now, as you have learned what is Big Data, let's get to know the source of Big Data.
The Three V’s of Big Data
We'll start with the one that is the most evident. Big data is all about quantity. data volumes that, in reality, may reach hitherto unimaginable heights. There will be 40 zettabytes of data generated by 2020, representing a 300-fold increase from 2005, according to estimates that 2.5 quintillion bytes of data are created every day. As a result, Terabytes and even Petabytes of data in storage and servers are now commonplace for big businesses. While tracking success, this data aids in shaping a company's future and activities.
The expansion of data and the significance it has taken on have changed the way we think about data. We used to underestimate the value of data in the business world, but because of changes in how we obtain it, we now often rely on it. Velocity simply gauges how quickly data is entering the system. While some data will be provided to us in batches, others will arrive in fits and starts. Additionally, since not all systems will process incoming data at the same rate, it's critical to avoid making assumptions before obtaining all the information.
Data used to be given in a single format from a single source. Previously given in database files like excel, csv, and access files, it is now being delivered through tech like wearable devices and social media in non-traditional formats, including video, text, pdf, and graphics. Although this data is helpful to us, it demands more labour and analytical abilities to interpret it, manage it, and make it function.
Why Big Data
With the development and increase of apps and social media and people and businesses moving online, there’s been a huge increase in data. If we look at only social media platforms, they interest and attract over a million users daily, scaling up data more than ever before. The next question is how exactly is this huge amount of data handled and how is it processed and stored. This is where Big Data comes into play.
And Big Data analytics has revolutionized the field of IT, enhancing and adding added advantage to organizations. It involves the use of analytics, new age tech like machine learning, mining, statistics and more. Big data can help organizations and teams to perform multiple operations on a single platform, store Tbs of data, pre-process it, analyze all the data, irrespective of the size and type, and visualize it too.
How Does Big Data Work?
Analytics of big data involves spotting trends, patterns, and correlations within vast amounts of unprocessed data in order to guide data-driven decisions. These procedures employ well-known statistical analysis methods, such as clustering and regression, to larger datasets with the aid of more recent instruments.
Every company has a distinct approach to data collection. Thanks to modern technology, businesses are now able to collect unstructured and structured data from a variety of sources, including cloud storage, mobile apps, in-store IoT sensors, and more.
2. Organise the Data
For analytical queries to yield correct answers, data must be appropriately organised once gathered and stored, especially if the data is big and unstructured.
3. Clean Data
All data, regardless of size, must be scrubbed to increase data quality and produce more robust findings. Duplicate or unnecessary data must be removed or accounted for, and all data must be structured appropriately. Dirty data may conceal and deceive, leading to inaccurate findings.
4. Analysis of Data
It takes time to transform huge amounts of data into a usable form. Advanced analytics techniques may transform huge data into significant insights once available. Among these large data analysis techniques are:
- By finding anomalies and forming data clusters, data mining sifts through enormous datasets to find patterns and linkages.
- Using historical data from a business, predictive analytics analyses future projections to discover potential hazards and opportunities.
- Deep learning layers algorithms to uncover patterns in even the most complicated abstract data, emulating human learning patterns.
The Sources of Big Data
Black Box DataThis is the data generated by airplanes, including jets and helicopters. Black box data includes flight crew voices, microphone recordings, and aircraft performance information.
Social Media DataThis is data developed by such social media sites as Twitter, Facebook, Instagram, Pinterest, and Google+.
Stock Exchange DataThis is data from stock exchanges about the share selling and buying decisions made by customers.
Power Grid DataThis is data from power grids. It holds information on particular nodes, such as usage information.
Transport DataThis includes possible capacity, vehicle model, availability, and distance covered by a vehicle.
Search Engine DataThis is one of the most significant sources of big data. Search engines have vast databases where they get their data.
Additionally, Bernard Marr, a Big Data and Analytics expert, has come up with his brilliant list of 20 Big Data sources that are freely available to everybody on the web. Some of them are briefed about here.
- Data.gov – where all of the US Government’s data is freely accessible, and information ranging from climate to crime is available.
- Analogous to this is the UK Government’s portal, Data.gov.uk, where metadata on all of UK books and publications since 1950 can be gathered.
- There is also the US Census Bureau – which covers valuable information like population, geography, and other data. Identical to this is the European Union Open Data Portal, comprising the census data from European Union institutions.
- And something closer to our interests – the Facebook Graph, provides the application program interface information (Graph API), after gathering info from all the data that is shared publicly by its users.
- In the healthcare sector, there is the Healthdata.gov and NHS Health and Social Care Information Centre, from the US and the UK, respectively.
Google Trends, Google Finance, Amazon Web Services public datasets, are all similar examples. From these examples, it is clear that big data is not about volumes alone. It also includes a wide variety and high velocity of data. In 2001, Doug Laney - an industry analyst-articulated the 3 Vs of big data as velocity, volume, and variety.
The speed at which data is streamed, nowadays, is unprecedented, making it difficult to deal with it in a timely fashion. Smart metering, sensors, and RFID tags make it necessary to deal with data torrents in almost real-time. Most organizations are finding it difficult to react to data quickly.
Not many years ago, having too much data was simply a storage issue. However, with increased storage capacities and reduced storage costs, industry players like Remote DBA Support are now focusing on how relevant data can create value.
There is a greater variety of data today than there was a few years ago. Data is broadly classified as structured data (relational data), semi-structured data (data in the form of XML sheets), and unstructured data (media logs and data in the form of PDF, Word, and Text files). Many companies have to grapple with governing, managing, and merging the different data varieties.
Veracity (the quality of the data), variability (the inconsistency which data sometimes displays), and complexity (when dealing with large volumes of data from different sources) are other essential characteristics of data.
After understanding what is Big Data, and its source, we must learn the benefits of Big Data to become a Big Data Engineer.
Types Of Big Data (With Examples)
Organised data is easy to evaluate and sort since it has predetermined organisational characteristics and is provided in a structured or tabular schema. Each field is independent and accessible individually or with information from other fields since it is specified. Since of this, structured data is very significant because it enables the rapid data collection from numerous database locations.
Unstructured data refers to information that lacks predetermined conceptual meanings and is difficult for conventional databases or data models to comprehend or analyse. Most large data is made up of unstructured data, including facts, dates, and numbers. Video and audio files, mobile activities, satellite photos, and other types of big data
A combination of unstructured and structured data is semi-structured data. As a result, it incorporates some characteristics of structured data, but also lacks a clear organisation and does not adhere to relational databases' or data models' official formats. For instance, semi-structured data is frequently seen in JSON and XML.
Characteristics Of Big Data
- Big Data is defined as data that is extremely large. It refers to a large set of datasets that are growing rapidly over time and have an enormous scope.
- Examples of big data analytics include social media platforms, aircraft engines, and financial markets.
- Structured, unstructured, or semi-structured big data are the three possible types.
- A few properties of big data are volume, variety, velocity, and variability.
- A few benefits of big data include better decision-making, improved customer service, and improved operational efficiency.
Big Data Use Cases
360-Degree View Of The Product
Big data is often used by businesses to create dashboard applications that offer a 360-degree perspective of the consumer. These dashboards gather information from many internal and external sources, evaluate it, and then provide it to customer care, sales, and/or marketing staff in a way that supports their work.
Enhanced Client Acquisition And Retention
Big data allows businesses to better understand client interests, usage patterns for products and services, and why customers cease using or buying from them. Businesses may more precisely determine what customers are looking for and track their behavioural trends by using big data apps. They may then use those patterns to enhance their offerings, increase conversion rates,
Better Cybersecurity And Fraud Prevention
For businesses, combating fraud is a never-ending struggle. Organisations use big data analytics to spot trends of fraud or abuse, spot oddities in system behaviour, and stop criminal actors. Big data systems may sift through enormous transactions and log data on servers, databases, apps, files, and devices to identify, stop, detect, and mitigate possible fraud.
Forecasting And Pricing Optimisation Improvements
While it may not be able to predict the future with absolute precision, big data allows corporations to see patterns and trends before others do. Early detection of shortfalls in product manufacturing, for instance, enables businesses to make necessary adjustments, preventing costly errors down the supply chain. Early demand information can enhance sales forecasting or assist in establishing the ideal pricing before a product enters the market. Big data has, in fact, aided businesses in making wiser decisions by providing them with knowledge of the likelihood of certain outcomes.
Big Data Best Practices
- Before incorporating big data analytics into your projects, the first and most important step that has to be completed is analysing and comprehending the organisational goals and business requirements.
- The second greatest big data approach is finding out what kind of data is entering the business and what data is produced inside.
- Understanding and analysing what is missing is the third practice. After gathering the necessary data for a project, determine any extra information that could be needed and where it might be obtained.
- It's time for the firm to comprehend which big data technologies, such as predictive analytics, stream analytics, fraud detection, data preparation, sentiment analysis, and so on, are most appropriate after studying and gathering data from various sources.
Examples Of Big Data
A huge amount of transportation data is used by GPS smartphone applications, which help us get from point A to B in the shortest amount of time. Government organisations and satellite photos are two suppliers of GPS data.
Targeted customer categories have traditionally been the focus of advertising and marketing campaigns. In the past, marketers have used focus groups, survey results, TV and radio preferences, and other methods to attempt and predict how consumers would react to advertisements. These techniques were, at best, informed guesses.
Financial and Banking Services
Detection of Fraud
Banks track customers' spending habits and other activities to spot unusual behaviour and anomalies that might indicate fraudulent transactions.
Management of Risk
Banks can monitor and report on company operations, Metrics, and employee behaviours thanks to big data analytics.
Government organisations gather enormous amounts of data, but many of them, particularly at the local level, don't use cutting-edge data mining and analytics tools to get the most out of it.
Examples of organisations that use data analysis to identify fraudulent disability claims and tax evasion include the Social Security Administration and the IRS. The FBI and SEC use big data tools to track markets and look for unethical corporate activities. The Federal Housing Authority has been utilising big data analytics to forecast mortgage default and repayment rates for years.
Advantages of Big Data
- Today’s consumer is very demanding. He talks to pass customers on social media and looks at different options before buying. A customer wants to be treated as an individual and to be thanked after buying a product. With big data, you will get actionable data that you can use to engage with your customers one-on-one in real-time. One way big data allows you to do this is that you will be able to check a complaining customer’s profile in real-time and get info on the product/s he/she is complaining about. You will then be able to perform reputation management.
- Big data allows you to re-develop the products/services you are selling. Information on what others think about your products -such as through unstructured social networking site text- helps you in product development.
- Big data allows you to test different variations of CAD (computer-aided design) images to determine how minor changes affect your process or product. This makes big data invaluable in the manufacturing process.
- Predictive analysis will keep you ahead of your competitors. Big data can facilitate this by, as an example, scanning and analyzing social media feeds and newspaper reports. Big data also helps you do health-tests on your customers, suppliers, and other stakeholders to help you reduce risks such as default.
- Big data is helpful in keeping data safe. Big data tools help you map the data landscape of your company, which helps in the analysis of internal threats. As an example, you will know if your sensitive information has protection or not. A more specific example is that you will be able to flag the emailing or storage of 16 digit numbers (which could, potentially, be credit card numbers).
- Big data allows you to diversify your revenue streams. Analyzing big data can give you trend-data that could help you come up with a completely new revenue stream.
- Your website needs to be dynamic if it is to compete favorably in the crowded online space. Analysis of big data helps you personalize the look/content and feel of your site to suit every visitor based on, for example, nationality and sex. An example of this is Amazon’s IBCF (item-based collaborative filtering) that drives its “People you may know” and “Frequently bought together” features.
- If you are running a factory, big data is important because you will not have to replace pieces of technology based on the number of months or years they have been in use. This is costly and impractical since different parts wear at different rates. Big data allows you to spot failing devices and will predict when you should replace them.
- Big data is important in the healthcare industry, which is one of the last few industries still stuck with a generalized, conventional approach. As an example, if you have cancer, you will go through one therapy, and if it does not work, your doctor will recommend another therapy. Big data allows a cancer patient to get medication that is developed based on his/her genes.
Challenges of Big Data
- One of the issues with Big data is the exponential growth of raw data. The data centres and databases store huge amounts of data, which is still rapidly growing. With the exponential growth of data, organizations often find it difficult to rightly store this data.
- The next challenge is choosing the right Big Data tool. There are various Big Data tools, however choosing the wrong one can result in wasted effort, time and money too.
- Next challenge of Big Data is securing it. Often organizations are too busy understanding and analyzing the data, that they leave the data security for a later stage, and unprotect data ultimately becomes the breeding ground for the hackers.
So have we decoded the enigma of big data for you?
I believe this article has helped you understand what is Big Data, and if you are still curious to know more, here’s another write-up – Is Big Data Overhyped? That dives deeper into the importance of the technology and the hype factors that surround the domain.
Here’s an introduction to the Big Data and Hadoop training and Data Engineering Certification Program offered by Simplilearn, which will help you to master the concepts of the Hadoop framework and also prepares you for the Cloudera CCA175 Hadoop Certification Exam.