Data, in itself, has many characteristics and challenges, especially in the world of big data. Those who utilize or collect data know about the hardships it brings. That’s why there are some widely known descriptions and parameters of data where certain characteristics are described for better management. One of the best examples are 5 V’s of data. Those 5 V’s of big data are velocity, volume, value, variety, and veracity. We can all understand why each of them is important to understand and handle, but nowadays the bigger focus and importance is set on data veracity.
It is not a newly coined term, but it’s often overlooked by those who do not understand that all data is not good data. And far too many times people forget about establishing systems and rules that will enable data veracity.
In data veracity, we must trust
Data veracity is the quality, accuracy, consistency, and trustworthiness of data. That’s why we can’t equalize it to just data quality. Data veracity refers also to the level of complete trust in data origin, type, and great data governance. It’s about the processes and systems behind leading data from source to its final utilization.
Reliability of data is one of the most prominent traits of veracity, and it means that those who use data in analysis can trust the source and precision of information. Data scientists find this extremely important since all their work depends on it.
Being able to trust the data you’re working on, means that your future work, whether it’s data analysis, machine learning, or AI, will be more accurate and significant. The metrics or insights derived from the work will be of greater sustenance and value.
Data veracity can be double-natured as high veracity and low veracity. High veracity means that many records or data entries are valuable to analysis and delivering metrics and insights. Low veracity, on the other hand, also contains noisy and meaningless data.
Sources that influence data veracity
Data veracity can be negatively influenced for multiple reasons. Even the smallest of doubts will cause a lack of trustworthiness in data and data sources. Such things present possible future complications in data analysis, or data science and engineering processes and methods.
Statistical and data biases
Data bias happens when some data is given more value and weight than the rest. This data is taken into consideration in calculations or analytics and can thus offer wrong or misleading results. Bias is not only operational, it can be the product of human interpretation. When you give more validity to some data, you intentionally or intentionally produce biased results that aren’t always correct.
Lack of data lineage
To trust data, you have to be certain of the data sources it came from. It takes a lot of time to track down where some data originated, so that’s why data governance and lineage are extremely important in ensuring veracity.
Software bugs
Bugs can easily create wrong data or miscalculations and data transformations. This leads to the creation of bad data that will skew the results and the reliability of data sources.
Noise and abnormalities
A lot of time and effort is spent on cleaning data and removing noise, or invaluable data. Noise has to be removed to get better and more accurate insights. Missing or incomplete data, or even outliers all signal that something is wrong with data and should be addressed accordingly.
Untrustworthy data sources and falsifications
Having untrustworthy data sources means that we cannot rely on that data in analytics. Data veracity relies on trustworthiness and its level, so without complete trust in data, veracity can’t be achieved. Also, veracity is compromised if falsification is at fault. Inputting wrong data or manipulating data and sources alters results and influences security as well.
Uncertainty and ambiguity of data
Doubt in data that is not in line with expected or correct values undermines veracity. Imprecision, doubt, multiple interpretations, and misleading data can cause low data veracity and slow down the work of data teams.
Out-of-date and obsolete data
Obsolete data provides no true value in analysis. Metrics and insights lose value and validity if they have taken out-of-date data into account. If analysis is done with old data, subsequent decisions will be wrong and could end up being costly.
How to potentiate data veracity?
Maintaining and ensuring data veracity is not easy, but it’s integral to keeping your data at the highest quality. You can not fully utilize data and gain value from it if the sources are bad and untrustworthy. Not having that level of reliability in data means that you can’t trust the results and will take wrong actions if you go by it.
Proper data management, governance, and infrastructure are vital in providing and maximizing veracity. Establishing good and monitored data flow will provide easier observability and control.
Another essential thing is having data knowledge. Companies need to know where data originated, who created it, what’s going on with it, who’s using it, and how. Data users have to be familiar with sources of data and for what they will use it.
Validate sources and information before doing anything with it. If you are not sure of data sources and their correctness, you can not guarantee good results in future projects and utilization. Basically, if you don’t know what you’re getting, you can’t expect miracles to happen later on. Plan ahead. Do things preemptively to make sure data systems will support it. Cover every scenario so you can ensure the highest quality and reliability of data.
Data veracity dictates the quality of results
As seen throughout the whole article, trustworthiness and reliability are characteristics data must follow. It dictates the correctness of all procedures and projects with data, not to mention final results. Data is complex. There’s no doubt about it. So that’s why it needs to be approached with care and diligence.
In a world where data grows in volume each second, you must focus on the information that brings value. And ultimately, you can’t wait for it to happen organically. You have to deploy systems, rules, and procedures to draw only the best out of it. Focus on data veracity and the rest will follow.
