Data quality and data quantity—what’s the difference? The former describes the accuracy, completeness, consistency, and relevance of your data; the latter the amount of data you have to work with. Both of these are crucial if you want to build comprehensive data-powered engines. Learn more.
Data Quality vs. Data Quantity
To begin with, we need to define both data quality and data quantity and discuss why they matter. Take a look at the comparison below.
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Data Quality |
Data Quantity |
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Data quality refers to the accuracy, completeness, consistency, and relevance of your data. It defines how well-prepared your information is for further processing, depending on the intended purpose. |
Data quantity refers to the amount of data you have. It describes how many “cases” were included in the data rather than the amount of information per data chunk. |
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Data quality is critical for ensuring that you do not make mistakes. It reduces the risk of bias, overfitting, and misleading results. Moreover, it can accelerate data processing as it involves cleansing your data from irrelevant pieces of information. |
Data quantity is the backbone of any machine-learning model. It enables you to teach AI by portraying multiple different sets of data and the results/consequences they led to. It also increases the diversity and representativeness of your data set. |
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Ensuring data quality is a time-consuming process that can consume a lot of resources. It may also require expertise and working with data scientists. |
Large volume of data may lead to a lot of noise and errors. It may also slow down your systems. |
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Data quality is ensured by cleansing your data: standardizing it, eliminating entries that may generate bias, and ensuring that it is error-free. It requires manual effort. |
Data quantity is increased simply by feeding your systems with more data. It might require acquiring relevant data from other sources if your organization has insufficient information. |
Finding the Golden Means
As you can see, both data quality and data quantity have their advantages and risks. Therefore, you typically need to find the middle ground. How do you do this?
First, you have to determine the expected data quality. What information do you need and how long does it take to prepare the data to meet your expectations? This will define the amount of resources you need to achieve the expected data quality for a certain size of data sets.
Afterward, consider your resources—how much data can you realistically process in a single sprint? This will be the cornerstone of your data quantity plan.
Naturally, you need to also add your data quantity needs into the equation. Does the above enable you to diversify your data enough for you to create an unbiased analytics engine? If not, you have to look for compromises—either find ways to reduce the quantity of your data or an aspect in which you can allow slightly lower data quality for the sake of efficiency.
The WealthArc Advantage
At WealthArc, we offer you a wealth management platform that helps you maintain balance. With our solution, you don’t have to choose between data quality vs. data quantity. Why is it so?
Our portfolio consolidation module is an automated (but manually reviewed!) feature that enables you to integrate portfolios from over 130 custodians. As part of your onboarding, we ensure that the data maintains over 99,7% accuracy. At the same time, our platform integrates all the data available, so you don’t have to worry about working on insufficient data quantity.
The standardized data, which is securely delivered to you via our REST API, can then become the backbone of your wealth management system. Thanks to this, you can make data-driven decisions in your investment strategies, offering clients faster, better, and more customized services than ever before.
The Takeaway
Both data quality and data quantity play a pivotal role in any data-driven system. The former is crucial to ensure that your decisions aren’t misled or biased, while the latter builds sufficient representation for you to extract valuable insights. Finding the golden means between them might not be easy. Yet, you’ve got WealthArc to support you—with our platform, you won’t have to make compromises between data quality and data quantity; you will also embrace the power of data in your decision-making and reporting!
Did you like this article? You may also read: How Can AI Transform the Wealth Management Industry for the Better?
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