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Introduction to Veracity in Big Data

Big data is fuel for businesses and today’s analytical applications. Companies increasingly try to use all data to make better decisions and strategies. Before big data, analytical tools used only a small portion of data stored in relational databases. But, the data that did not fit in relational databases were left unused.

Earlier, there were very famous 3V’s attached to big data, i.e., Velocity, Variety, and Volume. But there is, one very challenging problem in big data is Veracity in big data, which refers to the accuracy and quality of data. The other V’s than veracity have well-defined measurements, but Veracity is very complex and theoretical with no standard approach for measurement.

  • According to Gartner’s research, an organization’s average cost is $9.7 million per year due to poor data quality.
  • According to IBM, businesses lose $3.1 trillion in the US only due to poor data quality.
  • 80% of companies believe they lose revenue due to poor data quality challenges, identified by Cio.com.
  • Employees spend half of their time juggling with managing the data accuracy tasks.
  • 21% of businesses faced reputation issues and lost 30% of their revenue due to insufficient data, leading to mail delivery issues reported by Econsultancy.com.
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What is Veracity in Big Data?

Veracity is a big data characteristic related to consistency, accuracy, quality, and trustworthiness. Data veracity refers to the biasedness, noise, abnormality in data. It also refers to incomplete data or the presence of errors, outliers, and missing values. To convert this type of data into a consistent, consolidated, and united source of information creates a big challenge for the enterprise.

While enterprises’ primary focus is to use the total potential of data to derive insights, they tend to miss the problems faced by poor data governance. When we talk about the accuracy of big data, it's not just about the quality of data but also depends upon how trustworthy your data source and data processes are.

Let’s discuss an example to know the effects of data veracity—communications with customers that fail to convert to sales due to incorrect customer information. Poor data quality or incorrect data can result in the targeting of wrong customers and communications, which ultimately cause a loss in revenue.

Validity

Every organization wants accurate results, and valid data is the key to making accurate results. Validity refers to the question, “Is the data correct and accurate for the intended use?”

Volatility

Volatility refers to the rate of change and lifetime of data. Organizations need to understand how long a specific type of data is valid. For example, sentiments frequently change in social media and are highly volatile. An example of low volatile data is weather trends which are easier to predict.

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What are the sources of Data Veracity?

Veracity is the degree to which data is accurate, precise, and trustworthy. Let’s have some sources of veracity in data.

  • Biasedness: Bias or data bias is an error in which some data elements have more weightage than others, resulting in inaccurate data when an organization decides on calculated values that are suffering from statistical bias.
  • Bugs: Software or application bugs can transform or miscalculate the data.
  • Noise: The non-valuable data in the datasets is known as noise. High noise will increase data cleaning work as they have to remove the unnecessary data to get better insights.
  • Abnormalities: Abnormality or Anomaly in the data is the data point that is not normal or the data point that stands out from the actual data. For example: detecting credit card fraud based on the amount spent.
  • Uncertainty: Uncertainty can be defined as doubt or ambiguity in the data. The uncertain data contains noise that deviates from the correct, intended, or original values.
  • Data Lineage: Organizations collect data from multiple sources, but sometimes an inaccurate source is discovered without historical reference. It would be complicated to track the data source from which it has been extracted and stored.
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How to ensure low data veracity?

  • Data Knowledge: Companies must know about their data, where it comes from, where it is going, who is using it, who is manipulating it, processes applied to the data, which data is needed for which project. It is always good to have proper data management, and companies must create a platform that provides complete knowledge of the data movements.
  • Input Alignment: Suppose we collect data of your customer’s personal information through contact details form on the website where each field represents a piece of information about your customer. Now, if a customer wrongly fills the fields, that information will be of no use unless we correct it on our own.
  • The company can match the information with the field and the overall database. The company will make sure the correct information is flowing in, and it can be possible by adopting the best practices of data integrity security in the organization.
  • Validate your source: The data will come from multiple sources in any organization. These sources can be IoT devices, internal databases, or other sources. Before extracting and merging the data in the central database, the organization must validate the information and the sources.
  • Give Preference to Data Governance: Data governance is the collection of processes, roles, standards, metrics that ensure the quality and security of data and processes used in the organization. The data governance practices improve the accuracy and integrity of data quality.
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Use Cases of Data Veracity

Inaccurate or poor-quality data always gives a false impression of insights in any industry. It signifies that data veracity is incredibly consequential to get accurate results which help in data-driven decisions.

Health care

Many hospitals, labs, pharmaceuticals, doctors, and private healthcare centers constantly improve and identify new healthcare opportunities. The data from patient records, surveys, equipment, insurance companies, and medicines collected and analyzed bring valuable insights and daily breakthroughs in the medical industry to improve diagnostics and patient care. The use of big data analytics to provide information based on evidence will help define best practices, increase efficiency, decrease costs, and many other benefits.

Data veracity is a big challenge the healthcare industry faces and other challenges. Veracity refers to whether the big data collected and insights obtained can be trusted. The Healthcare industry relies on reliable data; they can not utilize the insights derived from biased, noisy, and incomplete healthcare data. They cannot compromise with patients' health. With the help of data governance frameworks and healthcare quality standards, organizations can ensure clean, ready, unbiased, and complete data.

Retail

The retail industry is the best example of big data. A massive amount of data is collected from products bought by customers to different modes of payments, from searching for products online, comparing them with others, and putting them in a cart. There is a lot of potential and scope in big data to learn and improve decision-making.

Whenever a big data project is planned to be implemented by a retailer, what and from where the data is collected is always an important question. Equally, an important question arises, “Is the collected data trustworthy? Can I rely on this data for making important decisions for my business?” Correct insights from data analysis require high-quality, clean, and accurate data.

If the data is inaccurate, not up to date, or poorly organized, the veracity of big data decreases drastically. Retailers must adopt a robust validation process that enables access to data needed for data-driven decisions keeping data integrity in mind.

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Conclusion

Nowadays, every business or organization uses Big Data and implements the new digital culture as their primary tool for development and successful performance. It faces the issue of ensuring high-quality data. To find the solution to these challenges, companies should look into the veracity of data and adapt data governance practices. It will help the companies achieve high-quality trustful, fast data and motivate them to use the full potential of data and achieve the best from the data.

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