What Is Veracity in Big Data?

Key takeaways

Veracity in big data describes the accuracy, relevance, and trustworthiness of your data.

• Veracity is a key factor in big data, as it directly impacts the reliability of your insights.

• To determine the veracity of data, review the reliability of the source and employ tests, such as regression testing.

• You can use high-quality big data to drive decision-making in industries such as health care, as well as in a wide range of business applications, like marketing and advertising. 

Discover how veracity fits into the big data picture, what industries rely on this facet of data, and ways to ensure the veracity of your data. If you’re interested in developing in-demand data skills, consider enrolling in the Google Data Analytics Professional Certificate, where you will have the opportunity to learn key analytical skills, such as data cleaning, analysis, and visualization, and gain experience using tools like structured query language (SQL) and Python.

What is big data?

Big data refers to large, complex data sets that traditional data processing software can’t easily manage. The high volume of complex data generated by humans and machines daily requires its technology, tools, and methods to appropriately clean, manipulate, and analyze.

Big data comes from many sources, including smart devices, social media, transactions, images, audio, etc. This diversity results in data arriving in multiple formats, including structured and unstructured, and often demands real-time or near real-time analytics (such as with sensors or self-driving cars). As storage becomes more affordable, organizations are better equipped to store and analyze big data, using its insights to make more informed predictions, enhance decision-making, and drive analytics.

The 5 Vs of big data

As big data becomes more mainstream, understanding its features and key characteristics can help you learn how to work with it more effectively. You can typically define big data using the five Vs, which include the following:

• Volume: Refers to the scale of data continually generated

• Variety: Refers to the multiple data types of sources included in the data set

• Velocity: Refers to the speed at which data is generated

• Veracity: Refers to the relevance, trustworthiness, and accuracy of the data

• Value: Refers to the potential worth of insights generated from the data

Veracity in big data definition

Veracity in big data refers to the information's accuracy, trustworthiness, and relevance. High veracity in your data can help you form better insights, make more effective decisions, and improve the overall quality of your organizational operations.

A closer look at veracity in big data

Big data can be messy because of the volume of information coming from various sources. While it’s easy to think of big data sets as containing “all” of the information, the data you have in your big data set can be incomplete, inconsistent, and even contain missing values or errors. As you use the power of the information you collect in these large data sets, you must maintain data veracity. With more accurate and high-quality data, you can create informed insights that drive beneficial decision-making.

Veracity in big data example

Imagine you run a company that sells smart fridges. You have a big data set composed of social media posts referencing the fridges you sell. You notice an uptick in people posting about their refrigerators not working and the food going bad. You get worried and think you must release a statement apologizing for the mishaps and look into resolving the issue. 

However, your company’s data analyst notices the negative social media posts are all from an area experiencing a power outage due to inclement weather. The issue is not with the fridges at all. By validating the information and ensuring your data set contains accurate and reliable information, you avoid costly mistakes and focus your efforts in the right direction.

In some cases, veracity also extends to the relevance of data. For example, if you were looking at a data set from a large number of your smart refrigerators, you might be curious about the average internal temperature over the course of a month. You might look at temperature readings, the number of times the door opened, and other factors to get a more complete picture. However, if the smart fridges also tracked metrics like purchase price or number of items in the refrigerator, this might be less relevant to your analysis (sometimes referred to as “noise”). Knowing how to discern and manage relevant data is an important part of working with big data sets to derive appropriate conclusions.

What is an example of veracity in data?

Examples of veracity in data include factors such as the accuracy, reliability, and cleanliness of the data. Veracity ensures the insights derived from big data are accurate, helping you draw reliable conclusions from your data

Should you prioritize veracity?

In most scenarios, the answer is yes. You should prioritize data veracity. It’s critical in ensuring your data is accurate, complete, consistent, and reliable, which is essential to gaining data-driven insights and making meaningful decisions. 

If you make decisions based on erroneous information, this can affect the actions you take based on your data. Taking steps to ensure data veracity reduces risks and optimizes your outcomes, providing the confidence you need to leverage big data effectively in your organization.

However, while ideal, it’s not always practical to prioritize data veracity above all other considerations. Benefits and potential drawbacks you may want to consider include the following:

Advantages of prioritizing data veracity

• Boosts accurate decision-making

• Optimizes operations

• Discovers important insights

• Ensures data relevancy for cleaner, more informed analytics

• Increases precision and trustworthiness of data

Challenges of prioritizing data veracity

• Costs associated with quality assurance can cause financial strain

• Can be challenging to ensure the truthfulness of data

• Complex to compile data from multiple sources

• Requires professionals with a specific set of skills

• Requires appropriate organizational resources

• Can be difficult to form consistent data definitions

Who relies on data veracity?

While having accurate and reliable data is important in every field, a few fields prioritize data veracity in particular due to the nature of their operations and the potential impact of inaccurate data. These industries rely on clean, accurate data to ensure safety, drive decisions, and maintain efficiency.

Health care

Accurate patient information, diagnostic records, and treatment histories are essential for effectively using big data in health care to treat patients and tailor medical care. You need accurate, complete information to ensure medical professionals recommend appropriate treatment methods and make accurate diagnoses. 

Maintaining data veracity ensures hospitals and public health agencies have the most up-to-date information to correctly identify at-risk patients, monitor disease spread, predict epidemics, improve treatment methods, and more.

Business

Many businesses rely on big data to support informed decision-making. Big data allows you to track a large number of customer behaviors and create a more accurate picture of users’ digital footprints. By using these details, your business can create more personalized advertising campaigns and allocate resources more effectively when launching products. 

You can also use big data to reduce human errors and automate certain aspects of your business. This will help you detect irregularities more quickly and reduce risks associated with fraud or deviations from standard procedures. In addition to helping you anticipate future needs and reduce operational costs, this can improve overall business functionality.

Sensors and automation

In some scenarios, having accurate data in real-time is essential for safety, meaning the veracity of the data is a top priority. Autonomous vehicles are an example of this; you’ll likely see them increasingly on the roads. 

Autonomous vehicles use sensors to detect road conditions and information in real-time, making split-second decisions to avoid accidents and navigate safely. With the large amount of data available for detection through sensors, systems need to be able to quickly parse which information is the most relevant and trust that the data is accurate enough to act on.

How do you determine the veracity of data in big data?

You can determine data veracity in several ways, and different data professionals may rely on varied metrics or techniques. To determine data veracity, take the following steps.

• Check the source of the data set: Ensure the data is from a reliable source with documented collection procedures.

• Review the metadata: Look at information publishing dates, details observed, the timeline of observation, and more.

• Analyze descriptive statistics: Assessing descriptive measures can help you better understand your data and the underlying variable patterns.

• Employ data testing: Data integrity, accuracy, completeness, consistency, validations, and regression testing can help you check your data and ensure you meet all requirements.

Read more: What Is Big Data Architecture?

Explore our free big data resources

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