Want to learn about what is veracity in big data? AAmcourses has the answer for you. Veracity is considered the most important of the 5 Vs in big data.
We all know business analytics has become the core of businesses. Before, it was just finance, marketing, and operations. Recently added HR, and now analytics. In business analytics, raw data is converted into information. This information is then used to analyze the business position.
There are many analytical tools that are used in business analytics. Each tool works in its way. The only thing required is data, that’s where big data comes in.
Before we look at the veracity in big data, let us understand what big data is.
What is Big Data?
Big data is exactly what it sounds like. Big refers to quantity. So, big data means a large amount of data that is too complicated and too much for old analytical tools to handle. Big data includes both – structured and unstructured data that is used for daily operations in business. It includes the sales number, the cost, financial statements, etc.
Big data is described by its 5 main characteristics – The 5 Vs of big data. The 5 Vs of big data are :
- Volume
- Value
- Variety
- Velocity
- Veracity
Out of these, veracity is the most complex one.
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Now that we know what big data is let’s move to the main course. Let’s study what is veracity in big data.
What is Veracity in Big Data?
Veracity is one of the most complex characteristics of big data. Veracity is used to study the consistency, preciseness, quality, and reliability of data. Veracity also helps to determine defective data, errors, and missing numbers in the data.
In simple terms, you can say that veracity in big data is analyzing the data. It is the process of analyzing the source of data to make sure the data is reliable. It includes the process of determining how accurate the data is. It is the process of checking the quality of the collected data.
It is almost impossible for this type of miscorrect data to be converted into consistent and consolidated data. But the business has to do it somehow, and veracity is the way.
Businesses that know the importance of analytics and big data tend to focus on correcting their data. They know that incorrect or missing data can cause huge problems. Such businesses try to make a trustworthy stream of data collection. Having good and reliable data can help to make analytics more accurate. This, in turn, would provide data useful to businesses.
Let’s take an example of a disc brake manufacturing company. Suppose ABC company got a tip that a new company is to be formed and they need disc brakes for their operations. The ABC company would then research this new company and make a call to sell their product. But the source of information provided only half information. And due to that half of the information, ABC missed their big sales opportunity.
So, missing, low-quality, and misleading data can result in loss of customers and business. This is where data veracity is used. Dot get me wrong, veracity in big data should always be used while perfuming analytics, not just when there is a problem.
Methods To Ensure Low Data Veracity
Data Investigation
Businesses collect information from various sources in the market. Businesses have to make sure that they can counter any upcoming problems and take advantage of every situation. They have several people, websites, and other channels that help them collect data. But due to the presence of multiple sources, sometimes false data is fed to the management. This data can result in a misguided business analytics process.
The management should make sure that they trace the information back to its source and double-check the information using other channels. Evaluate the information from different angles and perspectives to make sure it is reliable. This can help to minimize the veracity in big data.
Data Governance
Another way to ensure low veracity in big data is data governance.
Businesses should prioritize data governance. Data governance is the process of setting a standard approach for collecting and managing data. It sets up the principle for data from the time of procuring the data to disposing of the data.
Knowledge About The Data
One of the most effective ways to ensure low veracity in big data is by knowing about data.
Businesses must ensure that they are aware of everything with regard to the data. The management should know where the data is procured from. They should know who procured the data, who handles it, who uses it, where it is to be used, and everything. This ensures that management out the most reliable people and channels in the process to keep the data secured.
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FAQs
What does veracity of data mean?
In simple terms, veracity in big data is basically analyzing the data. It is the process of annoying the source of data to make sure the data is reliable. It includes the process of determining how accurate the data is. It is the process of checking the quality of the collected data.
What are the 5 V’s of big data?
Big data is described by its 5 main characteristics – The 5 Vs of big data. The 5 Vs of big data are:
Volume
Value
Variety
Velocity
Veracity
Closing Statement
Veracity in big data is an important characteristic of big data. It helps to ensure that the data is valid and reliable to be used. Even if there is a problem with the data, veracity helps the management to correct that data and proceed with its process smoothly.
I hope this veracity in big data is informative. If you have any doubts or suggestions, comment the data with low veracity in the comment box.