Introduction

The world around is changing rapidly, we live a data-driven age now. Data is everywhere, from your social media comments, posts, and likes to your order and purchase data on the e-commerce websites that you visit daily. Your search data is used by the search engines to enhance your search results. For large organizations, this data is in the form of customer data, sales figures, financial data, and much more.

You can imagine how much data is produced every second! Huge amounts of data are referred to as Big Data. 

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Let us start with the basics concepts of Big Data and further proceed to list out and discuss the characteristics of big data.

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What is Big Data?

Big Data refers to the huge collections of data that are structured and unstructured. This data may be sourced from servers, customer profile information, order and purchase data, financial transactions, ledgers, search history, and employee records. In large companies, this data collection is continuously growing with time.

But the amount of data a company has is not important, but what it is doing with that data. Companies aim to analyze these huge collections of data properly to gain insights. The analysis helps them in understanding patterns in the data that eventually lead to better business decisions.

All this helps in reducing time, efforts, and costs. But this humongous amount of data cannot be stored, processed, and studied using traditional methods of data analysis. Hence companies hire data analysts and data scientists who write programs and develop modern tools. Learn more about big data skills one needs to develop.

Characteristics of Big data with examples will help you understand the various characteristics properly. Many Big Data characteristics have been discussed below precisely:

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Types of Big Data

Big Data is present in three basic forms. They are – 

1. Structured data

As the name suggests, this kind of data is structured and is well-defined. It has a consistent order that can be easily understood by a computer or a human. This data can be stored, analyzed, and processed using a fixed format. Usually, this kind of data has its own data model.

You will find this kind of data in databases, where it is neatly stored in columns and rows. Two sources of structured data are:

• Machine-generated data – This data is produced by machines such as sensors, network servers, weblogs, GPS, etc. 
• Human-generated data – This type of data is entered by the user in their system, such as personal details, passwords, documents, etc. A search made by the user, items browsed online, and games played are all human-generated information.

For example, a database consisting of all the details of employees of a company is a type of structured data set.

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2. Unstructured data

Any set of data that is not structured or well-defined is called unstructured data. This kind of data is unorganized and difficult to handle, understand and analyze. It does not follow a consistent format and may vary at different points of time. Most of the data you encounter comes under this category.

For example, unstructured data are your comments, tweets, shares, posts, and likes on social media. The videos you watch on YouTube and text messages you send via WhatsApp all pile up as a huge heap of unstructured data.

3. Semi-structured data

This kind of data is somewhat structured but not completely. This may seem to be unstructured at first and does not obey any formal structures of data models such as RDBMS. For example, NoSQL documents have keywords that are used to process the document.

CSV files are also considered semi-structured data.

After learning the basics and the characteristics of Big data with examples, now let us understand the features of Big Data.

Read: Why to Become a Big Data Developer?

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Characteristics of Big Data

There are several characteristics of Big Data with example. The primary characteristics of Big Data are –

1. Volume

Volume refers to the huge amounts of data that is collected and generated every second in large organizations. This data is generated from different sources such as IoT devices, social media, videos, financial transactions, and customer logs.

Storing and processing this huge amount of data was a problem earlier. But now distributed systems such as Hadoop are used for organizing data collected from all these sources. The size of the data is crucial for understanding its value. Also, the volume is useful in determining whether a collection of data is Big Data or not.

Data volume can vary. For example, a text file is a few kilobytes whereas a video file is a few megabytes. In fact, Facebook from Meta itself can produce an enormous proportion of data in a single day. Billions of messages, likes, and posts each day contribute to generating such huge data.

The global mobile traffic was tallied to be around 6.2 ExaBytes( 6.2 billion GB) per month in the year 2016.

Also read: Difference Between Big Data and Hadoop

2. Variety

Another one of the most important Big Data characteristics is its variety. It refers to the different sources of data and their nature. The sources of data have changed over the years. Earlier, it was only available in spreadsheets and databases. Nowadays, data is present in photos, audio files, videos, text files, and PDFs.

The variety of data is crucial for its storage and analysis. 

A variety of data can be classified into three distinct parts:

1. Structured data
2. Semi-Structured data
3. Unstructured data

3. Velocity

This term refers to the speed at which the data is created or generated. This speed of data producing is also related to how fast this data is going to be processed. This is because only after analysis and processing, the data can meet the demands of the clients/users.

Massive amounts of data are produced from sensors, social media sites, and application logs – and all of it is continuous. If the data flow is not continuous, there is no point in investing time or effort on it.

As an example, per day, people generate more than 3.5 billion searches on Google.

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4. Value

Among the characteristics of Big Data, value is perhaps the most important. No matter how fast the data is produced or its amount, it has to be reliable and useful. Otherwise, the data is not good enough for processing or analysis. Research says that poor quality data can lead to almost a 20% loss in a company’s revenue. 

Data scientists first convert raw data into information. Then this data set is cleaned to retrieve the most useful data. Analysis and pattern identification is done on this data set. If the process is a success, the data can be considered to be valuable.

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5. Veracity

This feature of Big Data is connected to the previous one. It defines the degree of trustworthiness of the data. As most of the data you encounter is unstructured, it is important to filter out the unnecessary information and use the rest for processing.

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Veracity is one of the characteristics of big data analytics that denotes data inconsistency as well as data uncertainty.

As an example, a huge amount of data can create much confusion on the other hand, when there is a fewer amount of data, that creates inadequate information.

Other than these five traits of big data in data science, there are a few more characteristics of big data analytics that have been discussed down below:

1. Volatility 

One of the big data characteristics is Volatility. Volatility means rapid change. And Big data is in continuous change. Like data collected from a particular source change within a span of a few days or so. This characteristic of Big Data hampers data homogenization. This process is also known as the variability of data.

2. Visualization 

Visualization is one more characteristic of big data analytics. Visualization is the method of representing that big data that has been generated in the form of graphs and charts. Big data professionals have to share their big data insights with non-technical audiences on a daily basis.

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Fundamental fragments of Big Data

Let’s discuss the diverse traits of big data in data science a bit more in detail!

• Ingestion- In this step, data is gathered as well as processed. The process further extends when data is collected in batches or streams, and thereafter it is cleansed and organized to be finally prepared.

• Storage- After the collection of the required data, it is needed to be stored. Data is mainly stored in a data warehouse or data lake.

• Analysis- In this process, big data is processed to abstract valuable insights. There are four types of big data analytics: prescriptive, descriptive, predictive, and diagnostic.

• Consumption – This is the last stage of the big data process. The data insights are shared with non-technical audiences in the form of visualization or data storytelling.

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Advantages and Attributes of Big Data 

Big Data has emerged as a critical component of modern enterprises and sectors, providing several benefits and distinguishing itself from traditional data processing methods. The capacity to gather and interpret massive volumes of data has profound effects on businesses, allowing them to prosper in an increasingly data-driven environment. 

Big Data characteristics come with several advantages. Here we have elucidated some of the advantages that explain the characteristics of Big Data with real-life examples:- 

• Informed Decision-Making: Big Data allows firms to make data-driven decisions. It helps businesses analyse huge amounts of data and can get important insights into consumer behaviour, market trends, and operational efficiency. This educated decision-making can result in better outcomes and a competitive advantage in the market.
• Improved Customer Experience: Big Data and its characteristics help in understanding customer data enabling companies to better understand consumer preferences, predict requirements, and personalise services. This results in better client experiences, increased satisfaction, and higher customer retention.
• Enhanced Operational Efficiency: The different features of Big Data analytics assist firms in optimizing their operations by finding inefficiencies and bottlenecks. This results in cheaper operations, lower costs, and improved overall efficiency.
• Product Development and Innovation: The 7 characteristics of Big Data offer insights that help stimulate both of these processes. Understanding market demands and customer preferences enables firms to produce new goods or improve existing ones in order to remain competitive.
• Risk Management: Various attributes of Big Data help by analysing massive databases, firms can identify possible hazards and reduce them proactively. Whether in financial markets, cybersecurity, or supply chain management, Big Data analytics aids in the effective prediction and control of risks.
• Personalised Marketing: By evaluating consumer behaviour and preferences, Big Data characteristics allow for personalised marketing techniques. This enables firms to design targeted marketing efforts, which increases the likelihood of turning leads into consumers with the help of Big Data and its characteristics. 
• Healthcare Advancements: Attributes of Big Data are being employed to examine patient information, medical history, and treatment outcomes. This contributes to customised therapy, early illness identification, and overall advances in healthcare delivery.
• Scientific Research and Discovery: Big Data is essential in scientific research because it allows researchers to evaluate massive datasets for patterns, correlations and discoveries. This is very useful in areas such as genetics, astronomy, and climate study.
• Real-time Analytics: Big Data characteristics and technologies enable businesses to evaluate and react to data in real-time. This is especially useful in areas such as banking, where real-time analytics may be used to detect fraud and anticipate stock market trends.
• Competitive Advantage: Businesses that properly use Big Data have a competitive advantage. Those who can quickly and efficiently assess and act on data insights have a higher chance of adapting to market changes and outperforming the competition.

Application of Big Data in the Real World 

The use of Big Data in the real world has become more widespread across sectors, affecting how businesses operate, make decisions, and engage with their consumers. Here, we look at some of the most famous Big Data applications in several industries.

Healthcare 

• Predictive Analysis: Predictive analytics in healthcare uses Big Data to forecast disease outbreaks, optimise resource allocation, and enhance patient outcomes. Large datasets can be analysed to assist in uncovering trends and forecast future health hazards, allowing for proactive and preventative treatments.
• Personalised Medicine: Healthcare practitioners may adapt therapy to each patient by examining genetic and clinical data. Big Data facilitates the detection of genetic markers, allowing physicians to prescribe drugs and therapies tailored to a patient’s genetic composition.
• Electronic Health Records (EHR): The use of electronic health records has resulted in a massive volume of healthcare data. Big Data analytics is critical for processing and analyzing this information in order to improve patient care, spot patterns, and manage healthcare more efficiently.

Finance

• Financial Fraud Detection: Big Data is essential to financial business’s attempts to identify and stop fraud. Real-time transaction data analysis identifies anomalous patterns or behaviours, enabling timely intervention to limit possible losses.
• Algorithmic Trading: Big Data is employed in financial markets to evaluate market patterns, news, and social media sentiment. Algorithmic trading systems use this information to make quick and educated investment decisions while optimizing trading methods.
• Credit Scoring and Risk Management: Big Data enables banks to more properly assess creditworthiness. Lenders can make more educated loan approval choices and manage risks by examining a wide variety of data, including transaction history, social behaviour, and internet activity.

Retail 

• Customer Analytics: Retailers leverage Big Data to study customer behaviour, preferences, and purchasing history. This data is useful for establishing tailored marketing strategies, boosting inventory management, and improving the overall customer experience.
• Supply Chain Optimisation: Big Data analytics is used to improve supply chain operations by anticipating demand, enhancing logistics, and reducing delays. This ensures effective inventory management and lowers costs across the supply chain.
• Price Optimisation: Retailers use Big Data to dynamically modify prices depending on demand, rival pricing, and market trends. This allows firms to determine optimal pricing that maximises earnings while maintaining competition.

Manufacturing 

• Predictive Maintenance: Big data is used in manufacturing to make predictions about the maintenance of machinery and equipment. Organisations can mitigate downtime by proactively scheduling maintenance actions based on sensor data and previous performance.
• Quality Control: Analysing data from the manufacturing process enables producers to maintain and enhance product quality. Big Data technologies understand patterns and abnormalities, enabling the early discovery and rectification of errors throughout the production process.
• Supplier Chain Visibility: Big Data gives firms complete visibility into their supplier chains. This insight aids in optimum utilisation of inventory, improved supplier collaboration, and on-time manufacturing and delivery.

Telecommunications 

• Network Optimisation: Telecommunications businesses employ Big Data analytics to improve network performance. This involves examining data on call patterns, network traffic, and user behaviour to improve service quality and find opportunities for infrastructure enhancement.
• Customer Churn Prediction: By examining customer data, telecom companies can forecast which customers are likely to churn. This enables focused retention measures, such as tailored incentives or enhanced customer service, to help lessen turnover.
• Fraud Prevention: Big Data can help detect and prevent fraudulent activity in telecommunications, such as SIM card cloning and subscription fraud. Analysing trends and finding abnormalities aids in real-time fraud detection.

Job Opportunities with Big Data 

The Big Data employment market is varied, with possibilities for those with talents ranging from data analysis and machine learning to database administration and cloud computing. As companies continue to understand the potential of Big Data, the need for qualified people in these jobs is projected to remain high, making it an interesting and dynamic industry for anyone seeking a career in technology and analytics.

• Data Scientist: Data scientists use big data to uncover patterns and insights that are significant. They create and execute algorithms, analyse large databases, and present results to help guide decision-making.
• Data Engineer: The primary responsibility of a data engineer is to plan, build, and manage the infrastructure (such as warehouses and data pipelines) required for the effective processing and storing of massive amounts of data.
• Big Data Analysts: They interpret data to assist businesses in making educated decisions. They employ statistical approaches, data visualisation, and analytical tools to generate meaningful insights from large datasets.
• Machine Learning Engineer: By analysing large amounts of data using models and algorithms, machine learning engineers can build systems that are capable of learning and making judgments without the need for explicit programming.
• Database Administrator: Database administrators look after and administer databases, making sure they are scalable, secure, and function well. Administrators that work with Big Data often rely on distributed databases envisioned to manage large volumes of data.
• Business Intelligence (BI) Developer: BI developers construct tools and systems for collecting, interpreting, and presenting business information. They play an important role in converting raw data into usable insights for decision-makers.
• Data Architect: Data architects create the general architecture and structure of data systems, making sure that they satisfy the requirements of the company and follow industry best practices.
• Hadoop Developer: Hadoop developers work with tools such as HDFS, MapReduce, and Apache Spark. They create and execute solutions for processing and analyzing huge data collections.
• Data Privacy Analyst: With the growing significance of data privacy, analysts in this profession are responsible for ensuring that firms follow data protection legislation and apply appropriate privacy safeguards.
• IoT Data Analyst: Internet of Things (IoT) data analysts work with and analyse data created by IoT devices, deriving insights from massive volumes of sensor data collected in a variety of businesses.
• Cloud Solutions Architect: As enterprises transition to cloud platforms, cloud solutions architects develop and deploy Big Data solutions on cloud infrastructure to ensure scalability, dependability, and cost efficiency.
• Cybersecurity Analyst (Big Data): Experts in Big Data analyse enormous amounts of data to identify and address security issues. They employ advanced analytics to detect patterns suggestive of cyberattacks.

Conclusion

Big Data is the driving force behind major sectors such as business, marketing, sales, analytics, and research. It has changed the business strategies of customer-based and product-based companies worldwide. Thus, all the Big Data characteristics have to be given equal importance when it comes to analysis and decision-making. In this blog, we tried to list out and discuss the characteristics of big data, which, if grasped accurately, can fuel you to do wonders in the field of big data!

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