Information has become superabundant. Along with the great benefits of information comes a greater need to manage it well. With the vast amount of data we have, the goal is now more insight to valid information and better accuracy.
The projected growth of data from different sources is mind-boggling. A recent Pew Research study participant theorized, “Big data is the new oil. The companies, governments, and organizations that are able to mine this resource will have an enormous advantage over those that don’t.” This is becoming more evident every day.
With so much data, how can we determine what to keep and what use?
The answer lies in the 4th “V”. The original 3 “V’s” – Volume, Velocity, and Variety – are the defining properties of what constitutes data, each just as important as the next.
Veracity is the 4th “V”, upending the original triad to form a solid foundational block. Veracity is the truth and accuracy in data. It serves as a comparison point – another validation for what you think you know. Strong veracity in data has the power to make stronger decisions.
In a recent McKinsey Global Institute study, 1 in 3 business leaders cited they did not trust the information they used to make decisions. Almost 1/3 of respondents were unsure how much of their data was inaccurate. In total, poor data quality costs the U.S. economy around $3.1 trillion a year.
The information age has yielded more data points that not only stand on their own, but also can confirm and validate other data points you already have. Data by itself is limited, but when matched with other unique data assets like alternative data, it can provide actionable insights for businesses and help them gain an edge over competitors.
Veracity is exactly what alternative data is doing – adding truth in data. The value of alternative data is complimentary to rich credit bureau data, combining to provide a more holistic picture.
Lenders today have a solid baseline of high-quality, pure data in credit bureau reports. Historically, credit bureau data sets are the foundational mechanics used to answer the question of past consumer credit behavior. We know past actions in credit usage are the best indicator of future behavior, but we also have to be sure there is truth in the volume and variety of data we are working with.
Additional data points provide lenders with more information to match back to for even stronger confidence in lending decisions. With alternative data we now have a comparison point – another validation for what you think you know.
- Volume: Amount of data collected and retained
- Velocity: Storing, processing & analyzing data
- Variety: Types and sources of data
- Veracity: Truth and accuracy in data
Studies show that 70% of people still have the first credit card they ever received. Lenders want to be first in wallet for long-term relationships with their customers. By using alternative data assets to validate information, lenders can gain a more holistic view of consumers, beyond traditional credit data.
Alternative data helps provide lenders with validation to approve with more confidence, allowing for more financial inclusion – putting new-to-credit consumers on the path to meeting their financial objectives.
Modern lenders can gain a clearer view of true risk by verifying truth in their data. Alternative data can add more insight into past consumer credit behavior for additional comfort in lending decisions. As lenders increase the amount of data they use to determine creditworthiness and gain confidence the data is correct, they are more likely to get a better understanding of a consumer’s risk than with traditional credit data only.
Data matching and accuracy of data provides actionable insights and an edge over competitors. In order to be first in wallet, top of wallet, or to stay in wallet, lenders must consider veracity of data. In a time of evolving consumer behaviors, it is critical to know how to match your data back against a strong answer sheet. For a complete approach, partner with an information and risk solutions provider that has a deep history and experience in matching data sets.
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