Veracity in Big Data – More and more organizations recognize the importance of adopting big data. This comes as no surprise. After all, it is what fuels businesses and analytical applications today. Using big data, organizations can gain meaningful and actionable insights that help them to make better business strategies and decisions.
Big data is often characterized by volume, variety, and velocity. These three are the three “V”s of big data. Over time, the “V”s are extended, with value and veracity coming into the scene. We won’t be talking about them all, however. Instead, we will focus on veracity.
What is veracity in big data? Why is it important? Also, where does it come from? Read on. We have the answers below, and more.
What Is Data Veracity?
Before we delve further into veracity in big data, let’s talk about what veracity means first. The word “veracity” has been around since the early 17th century. It derives from the Latin term “verax”, which means “truthful” or “true”.
Thus, veracity in big data refers to the truthfulness of the data. In other words, how precise and accurate the information is. It describes data quality.
The veracity in big data is measured on a scale of highness and lowness. The higher the veracity of the data, the more usable it is for further analysis. Conversely, the lower the veracity, the higher the percentage of non-reliable data there is.
If certain data is high on the veracity scale, it means it has a lot of records that are valuable to analyze. Such data contributes to the overall results in a meaningful way. On the other hand, if the data is low on the veracity scale, it means it contains a high percentage of non-valuable, meaningless data.
Why Is Data Veracity Important?
Now that you know what data veracity is and the veracity scale, let’s move on to the next question. Why is it important?
Veracity in big data is important because organizations need more than just massive amounts of data. Organizations need data that is reliable and valuable.
Insights gained from big data are only meaningful if they come from reliable and valuable data. If the data is not reliable and valuable, the insights won’t be meaningful, let alone actionable.
Let’s use an example. Let’s say an organization has made decisions regarding how it will communicate and do targeted marketing. Unfortunately, the organization is leveraging low veracity data that is unreliable and not valuable.
Since the attempt uses data that is unreliable and not valuable, it ended up with wrong communications and targeting the wrong customers. Since the communications and targeted customers are wrong, there are no sales made. This ultimately leads to loss of revenue.In this case, for communications and targeted marketing to succeed, reliable and valuable big data is required. This is why veracity in big data is important. Without veracity, making good decisions will be difficult.
Sources of Data Veracity in Big Data
What are the sources of veracity in data? There are several sources, such as
1. Statistical biases
Data can become inaccurate i.e. low veracity due to statistical biases. These are a type of error wherein some data elements have more weightage than others. If an organization calculates biased values, the result is inaccurate data, which is not reliable at all.
There may be meaningless data in a given dataset. This type of data is referred to as noise. The more noise a dataset has, the more data cleaning process will be necessary to remove meaningless data.
The next source of veracity in big data is uncertainty. In big data, uncertainty refers to ambiguity or doubt in data. Even after taking necessary measures to ensure data quality, there is still the possibility that discrepancies exist within the data.
These discrepancies can come in the form of duplicate data, obsolete or stale data, or incorrect values. All these lead to uncertainty.
4. Anomaly or outliers
When data deviates from normalcy, it affects the veracity of data. This can happen even with the most meticulous of tools. The probability may be small, but it is not zero. This is why you may find anomalies or outliers from time to time.
5. Bugs in software or applications
While software and applications can help us process big data, they can also be a source of veracity in big data. Bugs in software or applications can miscalculate or transform data, thus leading to data veracity.
6. Data lineage
These sources of veracity in big data lead to data preprocessing and cleaning. With these processes, incorrect and non-valuable data can be removed, thereby leaving reliable valuable data that can provide meaningful insights.
How to Make Sure of Low Data Veracity
Organizations must possess data knowledge. That is, they have to know about not only what’s in the data but also data origin (i.e. where the data comes from), where the data is going, who’s using it, who’s manipulating it, the processes applied to it, which data is assigned for which project, and so on.
The right data management, along with a suitable platform for data movements, can help organizations build data knowledge.
Validating data sources
Volume-wise, big data is massive. Not only that, data comes from various sources well. For example, the internal databases of the organization, Internet of Things devices, and so on.
To make sure of low veracity in big data, it is important to validate the sources of data. Ideally, organizations should validate the data and its sources before collecting and merging it into their central database.
Input alignment can help make sure low veracity in big data as well. Let’s say an organization collects the personal information of its customers. The data collection process is done via a form on the organization’s website. If a customer entered their personal information incorrectly, the collected data will be useless.
The organization can correct this by performing input alignment. For example, if the customer entered the right information in the wrong field, the input alignment will put the information in the right field. This is done by matching the input with the field and the organization’s database.
Lastly, data governance. The term data governance refers to a set of standards, metrics, roles, and processes that ensure data quality, security, and processes used in an organization. It improves not only the integrity but also the accuracy of the data.
Use Cases of Veracity in Big Data
Regardless of the industry, poor quality or inaccurate data always gives a false impression. This shows just how important data veracity is. If an organization wants to get accurate results, which will help it makes data-driven decisions, data high in veracity is practically a must.
Here are two use cases of veracity in big data that show how consequential veracity in big data is.
If you want to know the best big data example, look no further than the retail industry. In this industry, a vast quantity of data is continuously gathered. Not only that, but the type of information gathered is also diverse.
From modes of payments used by customers, products bought, to customers’ behavior when shopping online. The scope and potential of big data in the retail industry are enormous. And so is the opportunity to improve decision-making.
Each time a retailer plans to implement a project that involves big data, important questions about data veracity come up. Here are several examples.
- What is the data collected?
- Where is it collected from?
- Is the data trustworthy?
- Can I rely on the data for making decisions?
If an organization wants to gain correct and meaningful insights from data analysis, reliable and valuable data is a must.
The data needs to be high-quality, accurate, up-to-date, and well-organized.If it is of low quality, not up to date, inaccurate or not organized properly, the veracity in big data is reduced significantly. To prevent this, organizations have to leverage a solid validation process that keeps the integrity of data in mind.
The next use case of veracity in big data is in the healthcare industry. Many doctors, hospitals, laboratories, and private healthcare centers continuously identify and improve new healthcare opportunities.
These healthcare providers leverage data from patient records, equipment, surveys, medicines, and insurance companies, gaining meaningful and valuable insights from it.
Like in the retail industry, the veracity of the data matters. In the case of the healthcare industry, evidence-based data will help increase efficiency, define best practices, reduce costs, and more. But to obtain these benefits, the data that is leveraged must be reliable and valuable i.e. has high veracity.
The Other “V”s of Big Data
One of the original “V”s of big data, volume refers to the amount of data in big data. Not too long ago, the amount of data used for data analysis was not that many. Nowadays, we are dealing with petabytes. It probably won’t be long until we are dealing with zettabytes, thanks to the advance in technology.
If you are wondering what the “big” in big data refers to, now you know.
The next “V” is variety. Also a part of the original “V”s of big data, variety refers to the formats the data come in. When it comes to big data, data can be unstructured or structured.
For example, information such as text (which includes messages, emails, tweets, PDFs, etc.), audio, images, and video data is considered unstructured data.
On the other hand, information such as names, addresses, dates, geolocation, and credit card numbers is considered structured data.
The last “V” of the original three is velocity. In the context of big data, the term refers to the rate or speed at which information is generated. So, the data in big data is not only enormous in volume and diverse in variety, but it is also fast in velocity.
Unsurprisingly, legacy tools can’t handle big data efficiently. For that, new and advanced tools methods are required.
Unlike volume, variety, and velocity, value is a later addition to the “V”s of big data. Here, value refers to the worth of the data. Not all data is equal. Some data are more valuable than others. Some are worth storing, cleaning, and processing. Some others, not so much.
Big data is valuable. Virtually all organizations leverage big data to make better decisions. To make the most of it, organizations must make sure of the sources and veracity of the data. Veracity in big data is important as it refers to the truthfulness of the data.
The higher the data in the veracity scale, the more reliable and valuable the data is. Likewise, the lower the data in the veracity scale, the less reliable and valuable it is. Ideally, organizations should strive for reliable and valuable data, without which making good decisions will be difficult.