This data is used by organizations to drive decisions, improve processes and policies, and create customer-centric products, services, and experiences. Big Data is defined as “big” not just because of its volume, but also due to the variety and complexity of its nature. Typically, it exceeds the capacity of traditional databases to capture, manage, and process it. And, Big Data can come from anywhere or anything on earth that we’re able to monitor digitally. Weather satellites, Internet of Things (IoT) devices, traffic cameras, social media trends – these are just a few of the data sources being mined and analyzed to make businesses more resilient and competitive.
This kind of data is the simplest to organize and search. It can include things like financial data, machine logs, and demographic details. An Excel spreadsheet, with its layout of pre-defined columns and rows, is a good way to envision structured data. Its components are easily categorized, allowing database designers and administrators to define simple algorithms for search and analysis. Even when structured data exists in enormous volume, it doesn’t necessarily qualify as Big Data because structured data on its own is relatively simple to manage and therefore doesn’t meet the defining criteria of Big Data. Traditionally, databases have used a programming language called Structured Query Language (SQL) in order to manage structured data. SQL was developed by IBM in the 1970s to allow developers to build and manage relational (spreadsheet style) databases that were beginning to take off at that time.
This category of data can include things like social media posts, audio files, images, and open-ended customer comments. This kind of data cannot be easily captured in standard row-column relational databases. Traditionally, companies that wanted to search, manage, or analyze large amounts of unstructured data had to use laborious manual processes. There was never any question as to the potential value of analyzing and understanding such data, but the cost of doing so was often too exorbitant to make it worthwhile. Considering the time it took, results were often obsolete before they were even delivered. Instead of spreadsheets or relational databases, unstructured data is usually stored in data lakes, data warehouses, and NoSQL databases.
As it sounds, semi-structured data is a hybrid of structured and unstructured data. E-mails are a good example as they include unstructured data in the body of the message, as well as more organizational properties such as sender, recipient, subject, and date. Devices that use geo-tagging, time stamps, or semantic tags can also deliver structured data alongside unstructured content. An unidentified smartphone image, for instance, can still tell you that it is a selfie, and the time and place where it was taken. A modern database running AI technology can not only instantly identify different types of data, it can also generate algorithms in real time to effectively manage and analyze the disparate datasets involved.
The range of data-generating things is growing at a phenomenal rate – from drone satellites to toasters. But for the purposes of categorization, data sources are generally broken down into three types:
As is sounds, social data is generated by social media comments, posts, images, and, increasingly, video. And with the growing global ubiquity of 4G and 5G cellular networks, it is estimated that the number of people in the world who regularly watch video content on their smartphones will rise to 2.72 billion by 2023. Although trends in social media and its usage tend to change quickly and unpredictably, what does not change is its steady growth as a generator of digital data.
IoT devices and machines are fitted with sensors and have the ability to send and receive digital data. IoT sensors help companies collect and process machine data from devices, vehicles, and equipment across the business. Globally, the number of data-generating things is rapidly growing – from weather and traffic sensors to security surveillance. The IDC estimates that by 2025 there will be over 40 billion IoT devices on earth, generating almost half the world’s total digital data.
This is some of the world’s fastest moving and growing data. For example, a large international retailer is known to process over one million customer transactions every hour. And when you add in all the world’s purchasing and banking transactions, you get a picture of the staggering volume of data being generated. Furthermore, transactional data is increasingly comprised of semi-structured data, including things like images and comments, making it all the more complex to manage and process.
Modern Big Data management solutions allow companies to turn raw data into relevant insights – with unprecedented speed and accuracy.
Big Data analytics allows product developers to analyze unstructured data, such as customer reviews and cultural trends, and respond quickly
In an international survey, McKinsey found that the analysis of Big Data from IoT-enabled machines reduced equipment maintenance costs by up to 40%.
In a 2020 survey of global business leaders, Gartner determined that “growing companies are more actively collecting customer experience data than nongrowth companies.” Big Data analysis allows businesses to improve and personalize their customers’ experience with their brand. "
The COVID-19 pandemic was a sharp awakening for many business leaders as they realized just how vulnerable their operations were to disruption. Big Data insights can help companies anticipate risk and prepare for the unexpected."
When businesses apply advanced Big Data analytics across all processes within their organization, they are able to not only spot inefficiencies, but to implement fast and effective solutions."
The insights gleaned from Big Data can help companies save money, please customers, make better products, and innovate business operations."
Big Data management is dependent upon systems with the power to process and meaningfully analyze vast amounts of disparate and complex information. In this regard, Big Data and AI have a somewhat reciprocal relationship. Big Data would not have a lot of practical use without AI to organize and analyze it. And AI depends upon the breadth of the datasets contained within Big Data to deliver analytics that are sufficiently robust to be actionable. As Forrester Research analyst Brandon Purcell puts it, “Data is the lifeblood of AI. An AI system needs to learn from data in order to be able to fulfill its function.”
As with architecture in building construction, Big Data architecture provides a blueprint for the foundational structure of how businesses will manage and analyze their data. Big Data architecture maps the processes necessary to manage Big Data on its journey across four basic “layers,” from data sources, to data storage, then on to Big Data analysis, and finally through the consumption layer in which the analyzed results are presented as business intelligence.
This process allows for meaningful data visualization through the use of data modeling and algorithms specific to Big Data characteristics. In an in-depth study and survey from the MIT Sloan School of Management, over 2,000 business leaders were asked about their company’s experience regarding Big Data analysis. Unsurprisingly, those who were engaged and supportive of developing their Big Data management strategies achieved the most measurably beneficial business results.
Picture 10 dimes in a single large box mixed in with 100 nickels. Then picture 10 smaller boxes, side by side, each with 10 nickels and only one dime. In which scenario will it be easier to spot the dimes? Hadoop basically works on this principle. It is an open-source framework for managing distributed Big Data processing across a network of many connected computers. So instead of using one large computer to store and process all the data, Hadoop clusters multiple computers into an almost infinitely scalable network and analyzes the data in parallel. This process typically uses a programming model called MapReduce, which coordinates Big Data processing by marshalling the distributed computers.
Traditional SQL spreadsheet-style databases are used for storing structured data. Unstructured and semi-structured Big Data requires unique storage and processing paradigms, as it does not lend itself to being indexed and categorized. Data lakes, data warehouses, and NoSQL databases are all data repositories that manage non-traditional datasets. A data lake is a vast pool of raw data which has yet to be processed. A data warehouse is a repository for data that has already been processed for a specific purpose. NoSQL databases provide a flexible schema that can be modified to suit the nature of the data to be processed. Each of these systems has its strengths and weaknesses and many businesses use a combination of these different data repositories to best suit their needs.
Traditional disk-based databases were developed with SQL and relational database technologies in mind. While they may be able to handle large volumes of structured data, they simply aren’t designed to best store and process unstructured data. With in-memory databases, processing and analysis take place entirely in RAM, as opposed to having to retrieve the data from a disk-based system. In-memory databases are also built on distributed architectures. This means they can achieve far greater speeds by utilizing parallel processing, as opposed to single node, disk-based database models.