10 Characteristics Of Big Data And How You Can Use Them

Updated 9 July 2022

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Big data is in most industry sectors, helping companies use customer and market data to shape their business strategies. You might find this a fascinating subject if you have an intellectual curiosity and a capacity for critical thinking. To prepare for a career working with big data, you can start by understanding its characteristics. In this article, we describe ten characteristics of big data to help you understand it comprehensively.

10 characteristics of big data

Here are characteristics of big data, which allow you to understand it more effectively:

1. Volume

Volume refers to the amount of data you have. Technology enables access to many types of data, like environmental data, traffic data, financial data, medical data and statistical data. For instance, you can collect data when someone books a flight, orders a song, streams a movie or browses accommodation for a holiday. All of this contributes to a massive volume of data in the order of zettabytes. Organisations can use this to analyse most of this data and receive an insight into customer behaviour and the marketplace.

2. Velocity

Velocity describes the speed at which companies process data. With RFID (Radio Frequency Identification) and new information systems, many organisations typically work with petabytes of data. This data might remain relevant for a short amount of time. Companies often process data practically in real-time to identify threats, opportunities and other insights before their competition. With big data, you perform analytics on the volume and variety of data while it is still in motion. For example, IoT (Internet of Things) devices continuously transmit enormous volumes of data every millisecond to their manufacturers.

3. Value

Value is a way to measure the usefulness of a dataset to an organisation. It is a core characteristic as it shows you how well the data matches a company's goals and whether it can help a company better itself financially or strategically. In practical terms, this is a measure of its ability to generate income and solve business problems through insights. You can measure value through these variables:

Time: This defines whether the value of data remains constant, increases or decreases.
Legality: Since you cannot use certain data because of legal restraints, this measures how valuable data is after considering legal limitations.
Context: This is a measure of the relevance of a dataset.
Quality: Data needs to be accurate, complete and reliable.
Acquisition: This refers to the cost of getting the data.
Training: This is how well a dataset can train an AI (Artificial Intelligence) or ML (Machine Learning) system.

4. Variety

Variety refers to the different types of big data. For instance, an organisation may deal with data coming in from sensors, smart devices and social media platforms. This includes traditional relational data and more complex data like click-stream data, email, sensor data and unstructured web data. Big data engineers focus closely on variety, as it affects the efficiency of data processing. Before you process this collection of structured, unstructured and semi-structured data, you may clean this data to develop coherent datasets that you can then analyse.

5. Veracity

Veracity refers to the accuracy of data. This includes the quality of the data itself, along with the trustworthiness and reliability of source, type and processing method. Veracity is a key metric, as high veracity yields more accurate and dependable insights. To improve this factor, you need to remove biases, abnormalities, inconsistencies, duplication and volatility. Some organisations hire third parties to verify the authenticity and accuracy of their data source and to ensure that they have chosen the right processing method to improve veracity.

6. Validity

While veracity deals with the accuracy of data, validity explores its legality. This is because of new regulations, including:

GDPR: This is the General Data Protection Regulation, which sets standards for data protection and privacy.
Data provenance: This checks where a piece of data originated and the processes and techniques by which someone produced it.
Transparency: This is the right of a person to know whether a company collects, uses, consults or otherwise processes any of their personal data.

Given that governments and other regulatory bodies look at the ways companies source their data, validity is crucial. Try to collect your data source in an organized and responsible manner to ensure its accuracy.

7. Volatility

This is a measure of data's rate of change or its lifetime. It defines how long you can store relevant data. For example, customer sentiments can change dramatically on social media. If you are processing this data to shape your marketing strategy, try to keep it less than a week old to ensure the data is relevant. Data like weather patterns that change slowly can be considered less volatile as it is often more predictable.

8. Visualisation

Using visualisation, you show your insights from big data through visual tools such as charts, graphs and maps. As a big data professional, this is how you communicate to a non-technical audience and help them easily understand the trends and insights you observe. Given the massive volume of data available and its many types, visualisation plays a crucial role in helping people use, understand and interpret this data. Visualisation can have many benefits:

your decision-making gets streamlined through clear data-driven insights
your understanding of customers and customer experience improves
you can help businesses strengthen customer relationships and improve their revenue streams
you can locate areas for improvement within a company's operational procedures
you can improve operational efficiency and productivity
you can identify and mitigate risks
you can suggest improvements to marketing and product strategies

Related: Types of Graphs and Charts

9. Vulnerability

With personal information being such a huge part of the data circulating online, people have many concerns regarding its safety. Vulnerability is a measure of data security, particularly of customers' private information. For example, if a hacker breaches a banking website, this puts sensitive information, like customers' account details, credit card information and passwords, at risk. Companies set up data security teams to overcome this threat by setting up suitable guidelines to protect data.

10. Variability

The variability of big data differs from its variety. For instance, consider a restaurant menu that comprises three items. The number of unique items is the variety, but variability is when you order the same item on the menu and it tastes different every time you order it. Practically, variability can come from inconsistencies in the data, which you can use to locate through outlier detection methods. Other causes of variability are different data types and sources, and inconsistent speeds at which data comes into your database.

How are these big data characteristics handled by data scientists?

Here is how you can use the ten characteristics of big data as a data scientist, including the means by which you can handle them:

Volume and velocity

Volume and velocity are the physical constraints of a dataset. This means that you complete these through hardware and computer systems. The better the storage and data transfer capacity of your network, the heavier the velocity and volume you can process.

Vocabulary, vagueness, value and viability

Controlling the extent of a dataset's vocabulary and vagueness helps you increase its value and viability. To do this, you can use tools like AI and ML which both have logic that discriminates between classifications. For instance, it can differentiate between member and non-member functions in and out of the class body.

Big data control methods

Manipulating the big data characteristics through suitable hardware, software and tools like AI and ML helps you take control of your methods. This way, you can derive optimal value from a dataset and derive key insights. This allows you to make data-driven business decisions.

Who deals with the characteristics of big data?

Data scientists are most likely to work with the various aspects of big data. They understand it, process it and offer expert insights into data for business stakeholders who might not be experts at analysing big data. Because of this expertise, the average salary of a data scientist is ₹8,49,842 per year. The amount you earn can also depend on the type, size and industry of the company that hires you.

Salary figures reflect data listed on Indeed Salaries at time of writing. Salaries may vary depending on the hiring organisation and a candidate's experience, academic background and location.