Unleashing the Power of Veracity in Big Data: Real-Life Examples to Follow
When it comes to big data, veracity is an essential element that refers to the quality and truthfulness of the data. In other words, it’s vital that the data is accurate and reliable for businesses to make informed decisions. Unfortunately, achieving veracity in big data can be challenging, especially when dealing with a massive amount of information.
In this blog post, we’ll take a closer look at the importance of veracity in big data and some real-life examples that businesses can follow to ensure the veracity of their big data.
The Importance of Veracity in Big Data
As mentioned earlier, veracity is crucial for big data. If the data is inaccurate or unreliable, it can lead to wrong conclusions, decisions, and actions. Veracity is particularly important for industries such as healthcare, finance, and telecommunications, where the repercussions of wrong decisions can be severe.
Ensuring the veracity of data can be challenging since data comes from various sources, including customers, vendors, and third-party data providers. Additionally, data can be incomplete, inconsistent, or outdated, making it difficult to draw any insights from it.
Real-Life Examples of Veracity in Big Data
Here are some companies that have successfully achieved veracity in their big data:
As one of the largest technology companies globally, Google has vast amounts of data stored on its systems. For example, Google’s search engine processes over 40,000 searches per second, generating petabytes of data every day. To ensure the veracity of its data, Google employs a team of data quality analysts who manually examine the data, looking for errors and inconsistencies. Additionally, Google uses machine learning algorithms to flag any data inaccuracy or quality issues.
Facebook is a social media giant with over 3 billion users worldwide. To ensure the veracity of its data, Facebook employs several strategies. Firstly, the company has a team of data scientists who work to identify and fix any data quality issues. Secondly, the company uses automated systems to detect and flag any data quality issues. Moreover, Facebook uses human-in-the-loop verification to verify any data before it’s used to make critical business decisions.
Amazon
As one of the world’s largest online retailers, Amazon generates a vast amount of data on customer shopping behaviour. To ensure the veracity of this data, Amazon has built a data quality team responsible for verifying the data accuracy and completeness. The team uses a combination of automated tools and manual checks to validate the data.
Conclusion
In conclusion, veracity is an essential factor in big data. It’s crucial to ensure that data is accurate and reliable to make informed business decisions. While achieving veracity in big data can be challenging, businesses can follow the examples set by Google, Facebook, and Amazon to ensure the veracity of their data. By employing strategies such as manual checks, automated systems and human-in-the-loop verification, businesses can ensure that their big data is accurate and reliable.
