An increasing number of enterprises acknowledge the significance of embracing big data, and this acknowledgment is far from being surprising. Big data serves as the driving force behind contemporary businesses and analytical applications. By harnessing the potential of big data, organizations can acquire insightful and actionable information, which, in turn, assists them in formulating more effective business strategies and decisions.

Big data is often characterized by three fundamental attributes: volume, variety, and velocity, often referred to as the "three Vs" of big data. Over time, this list of "Vs" has expanded, encompassing value and veracity as additional dimensions. Nevertheless, our focus in this discussion will be on veracity.

So, what exactly is veracity within the realm of big data? Why does it hold such significance? And where does its origin lie? Continue reading to uncover the answers to these questions and more.

Understanding Data Veracity

Before delving deeper into the concept of veracity in the context of big data, let's first explore what "veracity" actually means. The term "veracity" has a historical lineage dating back to the early 17th century, originating from the Latin word "verax," which translates to "truthful" or "true."

In the realm of big data, veracity pertains to the truthfulness of the data, essentially measuring how precise and accurate the information is. It serves as a descriptor for data quality.

Veracity in big data is typically gauged on a scale that spans from high to low. Higher veracity indicates data of superior quality, making it more suitable for in-depth analysis. Conversely, lower veracity signifies a higher proportion of unreliable or inconsequential data.

When data boasts a high veracity rating, it implies a substantial amount of valuable records that can significantly contribute to meaningful insights and results. On the flip side, data with low veracity contains a substantial portion of non-valuable and insignificant information.

Why Is Data Veracity Important?

The significance of veracity in big data arises from the fact that organizations require more than just vast volumes of data. What organizations truly need is data that is both dependable and valuable.

Insights derived from big data only hold genuine meaning if they stem from data that is reliable and valuable. Without these qualities, the insights not only lack significance but also fail to be actionable.

To illustrate this point, let's consider an example. Suppose an organization has made decisions related to its communication strategies and targeted marketing efforts. Unfortunately, the organization is relying on data with low veracity, which is both unreliable and lacking in value.

Because the organization is using data that is both unreliable and not valuable, it ends up with misguided communications and targeting the wrong customer segments. As a result, sales plummet, leading to a substantial loss in revenue. In such a scenario, the success of communication and targeted marketing efforts hinges on the availability of reliable and valuable big data. This underscores the vital importance of veracity in big data. Without veracity, making informed and effective decisions becomes an uphill battle.

Sources of Data Veracity in Big Data

Let's explore the various sources that influence the veracity of data in the context of big data. These sources encompass:

1. Statistical Biases

Data may suffer from inaccuracies, resulting in low veracity due to statistical biases. These biases manifest as errors where certain data elements carry more weight than others. When an organization calculates values with such biases, the outcome is data that lacks reliability.

2. Noise

Within a given dataset, you might encounter data that holds no meaningful value, often referred to as noise. The presence of excessive noise necessitates an extensive data cleaning process to eliminate irrelevant or inconsequential data.

3. Uncertainty

Uncertainty stands as another significant source of veracity in big data. In the context of big data, uncertainty pertains to ambiguity or doubt within the data. Even after meticulous efforts to ensure data quality, the possibility of discrepancies persists. These discrepancies can manifest in the form of duplicate data, outdated or stale information, or incorrect values, all of which contribute to uncertainty.

4. Anomalies or Outliers

Veracity can be impacted when data deviates from the expected norms. This deviation, which can occur despite the use of highly accurate tools, may result in the occasional appearance of anomalies or outliers.

5. Software or Application Bugs

While software and applications play a crucial role in processing big data, they can also become sources of veracity issues. Bugs within software or applications can lead to miscalculations or unintended data transformations, thereby affecting data veracity.

6. Data Lineage

The above-mentioned sources of veracity in big data necessitate data preprocessing and cleaning. Through these processes, inaccurate and non-valuable data can be effectively removed, leaving behind reliable, valuable data capable of providing meaningful insights.

How to Ensure Data Veracity

To maintain low data veracity, organizations must implement various strategies:

1. Data Knowledge:

Organizations need comprehensive data knowledge, including awareness of the data's content, source, flow, usage, manipulation, associated processes, project assignments, and more. Establishing the right data management practices and employing suitable data movement platforms can help in this endeavor.

2. Validating Data Sources:

Given the massive volume and diverse sources of big data, it is essential to validate the sources of data. Ideally, organizations should verify data and its sources before integrating it into their central databases.

3. Input Alignment:

Ensuring low data veracity can also be achieved through input alignment. For instance, if an organization collects customer information through a website form, input alignment can correct inaccuracies or misplacements in the data, ensuring it is correctly associated with the relevant fields.

4. Data Governance:

Data governance involves establishing standards, metrics, roles, and processes to enhance data quality, security, and overall data management within an organization. It not only improves data integrity but also ensures data accuracy.

Use Cases of Veracity in Big Data

Data veracity plays a crucial role in various industries, as illustrated in the following use cases:

1. Retail:

In the retail industry, vast and diverse data is continually collected, encompassing information on payment methods, purchased products, and customer behavior. Ensuring data veracity is essential for making accurate data-driven decisions. To gain meaningful insights, data must be of high quality, accuracy, and organization, as poor data quality can significantly reduce veracity.

2. Healthcare:

Healthcare providers utilize data from patient records, equipment, surveys, medications, and insurance companies to identify new opportunities and enhance healthcare services. Similar to retail, data veracity is vital in healthcare. Reliable and valuable data with high veracity is crucial for improving efficiency, reducing costs, and implementing best practices.

The Other "V"s of Big Data


1. Volume:

Volume, one of the original "V"s of big data, relates to the sheer quantity of data within the big data landscape. Not too long ago, data analysis dealt with relatively modest amounts of information. However, with advancements in technology, we are now confronted with data on a massive scale, measured in petabytes. It's conceivable that the data volumes will continue to grow, potentially reaching the unprecedented scale of zettabytes in the near future. This explosive growth is what truly defines the "big" in big data.

2. Variety:

Another fundamental "V" of big data is variety. Variety underscores the diverse formats that data can assume in the big data realm. In this context, data can be categorized as unstructured or structured. Unstructured data encompasses elements like text (including messages, emails, tweets, PDFs), audio, images, and video content. On the other hand, structured data comprises information such as names, addresses, dates, geolocations, and credit card numbers. The coexistence of such varied data formats presents unique challenges and opportunities for analysis within the big data domain.

3. Velocity:

The third original "V" is velocity, which pertains to the rapidity at which data is generated within the big data ecosystem. In big data, data isn't merely voluminous and diverse; it is also characterized by its swift generation. Consequently, conventional tools often prove inadequate in efficiently handling such high-velocity data. To cope with this challenge, new and advanced tools and methodologies are essential to process data streams effectively.

4. Value:

Unlike volume, variety, and velocity, the inclusion of "value" as a later addition to the "V"s of big data emphasizes the significance of data worth. Not all data carries equal importance. Some data hold greater value and are worthy of storage, cleaning, and processing, while others may have lesser relevance. Big data, as a valuable asset, is harnessed by virtually all organizations to enhance decision-making. To maximize its utility, organizations must ensure both the sources and veracity of the data. The concept of veracity in big data is particularly crucial, as it addresses the truthfulness and reliability of the data. The higher the data ranks on the veracity scale, the more dependable and valuable it becomes, whereas lower veracity data diminishes its reliability and worth. Ideally, organizations should strive for data with high veracity to facilitate sound decision-making.