It’s not uncommon to hear that big data technology has brought revolutionary changes to the IT industry. But, what exactly defines information as “big data”?
While big data is primarily defined by its large volume, there are a few other aspects that qualify information to be defined as “big data.” When we say “big data” we are referring to large, voluminous data sets that are usually very high in complexity, mostly unstructured and coming from disparate, data sources. Another defining characteristic of this data is that traditional analytical tools fail to be able to manage the scale, complexity and unstructured nature of this information. Accordingly, more specific tools and techniques are needed to process, store and analyze it – thus making it “big data.”
Before the term “big data” became widespread, the term used with reference to large data sets was VLDBs — “very large databases” — and the information in these was handled by DBMS, or “database management services.”
Big data technology, then, is any software or utility that can analyze and process huge amounts of data that may be complex and may even be real-time, and which cannot be managed through traditional data processing tools. These large data sets can, however, address business challenges that cannot be addressed otherwise. In other words, businesses can derive actionable insights from such data analysis and even make predictions for the future based on these data insights.
The Three V’s of Big Data: Another interesting way of defining big data is by the three ‘V’s: volume, velocity and variety:
Data that is in very large volumes. For some organizations, it could be in the range of many terabytes, while for others it could go into petabytes and exabytes. Typically, this data is also unstructured.
This refers to the high rate at which big data is generated or collected, and could also include the rate of some level of basic processing or any action taken on the data. In IoT-driven smart products, the velocity of data collected from sensors on appliances may be so high that the data moves directly to memory and real-time or near-real-time actions may need to be taken on this data.
Traditional data was in very fixed types of formats and therefore could fit into traditional relational database structures. Big data, however, encompasses different types of data, such as unstructured or semi-structured, which require different tools and technologies to process and analyze it.
Big data can fall into either one of three main categories: structured, unstructured or semi-structured. Let’s look at examples of each of these:
This type of data can be used in the original form for further processing and does not need to be converted into a digital format or does not require changes in format in order to be used for analysis. A simple example may be employee salary records in a company’s HRMS system.
This refers to a more random data type that does not have any specific preset formatting and therefore needs some preprocessing in order to be used for analysis. Converting this type of data into structured data may require resources, as well as time. Examples include email messages, multimedia files, or even search engine result outputs. As you may have guessed, this type of data lacks a specific structure according to which it would be possible to into a traditional row-columns database structure or into specific data fields.
As the phrase itself suggests, this type of data has some level of inherent hierarchy or categorization, but still lacks definitive features of structured data and therefore still cannot reside in relational databases. However, with some pre-processing, this data can be made ready for analysis and database storage. Examples are RFID data, CSV or XML files.
Big Data Technologies and Tools:
Several procedures or steps are involved in pre-processing and analyzing Big Data in order to extract usable and valuable insights from the data. The analysis then helps reveal hidden patterns in the data, emerging trends or even customer preferences, which can provide valuable support for making data-driven decisions. In addition, cloud computing and AI/ML help reduce manual work and automate certain procedures.
The first step is data acquisition, which means identifying and then collecting big data. The next step is data storage. Since the traditional DBMS systems are unable to handle the volume, velocity, and variety of big data, newer storage methods have emerged, such as a process called magnetic, agile, deep (MAD). In this, ‘magnetic’ means attracting all data sources irrespective of the quality of the source; ‘agile’ refers to the need to adapt quickly to the velocity of big data; and ‘deep’ refers to the complex nature of the analysis required to deal with big data. The most common storage solutions for big data that have evolved today are based on distributed storage and MPP (Massive Parallel Processing). For example, Hadoop provides a software framework based on distributed storage and processing.
Use Cases for Big Data Technology:
There are several ways in which different industries and sectors outside of the tech industry are leveraging big data technology to their advantage. Analytics of volumes of unstructured data that was not previously possible is now generating powerful insights, predictions, customer preferences, market behaviors, and new service opportunities.
Product companies like Proctor & Gamble are collecting and processing voluminous data from diverse sources, such as social media, focus groups in different geographies and test marketing initiatives. With big data technology, they are building predictive models using data attributes of past and new products and relating these to commercial success. Past data also helps companies like Netflix in the media industry to plan new launches and roll-out the type of content that has been most popularly consumed in the past.
In various heavy industries, such as oil and gas and engineering and construction, which involve the use of machinery, big data technology has made predictive maintenance of mechanical and remote devices possible. By using data analytics to indicate the likelihood of potential problems or mechanical failures before they actually happen, these organizations are able to save on maintenance costs and avoid production downtimes.
In the sports industry, big data is being leveraged to understand patterns of viewership of major sporting events, such as FIFA and Wimbledon, in addition to analyzing the performance of teams and individual players with statistical analysis of game data.
The finance sector is also a major user of big data analytics, with exchange commissions using it to prevent illegal trading on the stock markets and also to reduce fraudulent financial transactions in the banking industry.
In the healthcare sector, big data is harnessed to identify and trace the global spread of virus infections. It is also used by health ministries to analyze data from country-wide census surveys on the state of healthcare in their countries.
Big data analytics will assume greater significance as we enter an age of IoT and connected devices. Operational efficiency is an area in which we may possibly see the biggest impact of big data technology. The other area of significant impact is driving innovation using predictive analytics and data-driven insights into market trends and preferences.
Business that want to utilize data analytics will need to strengthen their capabilities in collecting, storing and analyzing data by having a clear idea of what data to gather, which sources to gather it from, how and where to store it, and what tools and technologies to adopt in order to process it for actionable business insights.
Furthermore, with the big data and analytics industry growing at lightning speed, it is proving to be one of the biggest job creators today. Most organizations are now ramping up their teams with expert in-house data analysts and data scientists who will be able to harness the power of big data in a systematic and scientific manner to help business growth.
Big data is also quickly becoming integral to tackling global problems, such as climate change, by analyzing pollution data across geographies and studying complex factors and aspects of the data. Smartphones and many other smart devices today have sensors that are gathering exabytes of data that may be useful in public healthcare and administrative issues, as well as safety and security.
The possibilities with big data truly are limitless!