What is data quality, and why does it matter? A simple definition explains that data quality refers to the overall ...
What is data quality, and why does it matter? A simple definition explains that data quality refers to the overall condition of a dataset based on several key characteristics. The quality or veracity of each characteristic determines the dataset’s quality level for its intended purpose or use, such as decision-making, analytics, or regulatory compliance.
Data quality matters because it plays a critical role in operational efficiency. Low-quality data is often useless and doesn’t contribute actionably to organizational outcomes or goals. Many times, poor or low-quality data hinders progress and stifles efficiency efforts like cloud migration. To ensure your company data is high quality and that it contributes rather than hinders progress, you must understand its characteristics and importance.
Data quality is a measurable asset. Understanding how to measure data quality requires the knowledge of several factors or characteristics that go into analyzing it.
Data accuracy refers to the correctness of the dataset. Data is accurate if it represents its real-world counter or the object it describes. For example, a customer’s address is valid if it matches their physical location, an email address is accurate if it corresponds to the intended recipient, or a product price is accurate if it reflects the current selling price.
Inaccurate data can lead to incorrect analysis, wrong conclusions, and misplaced or uncategorized emails, which can cause compliance issues and penalties. Data migration efforts should always perform tests and checks to ensure no information is unintentionally excluded from the transfer.
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Data completeness differs from accuracy because it focuses on data containing information in all necessary fields. Data should not have missing values or gaps because this can complicate migration.
Missing data can also be a compliance problem. For example, medical records should contain patient histories, tests performed, diagnoses, treatments, and other verifiable information. Missing any crucial data on a patient’s records could cause HIPAA violations.
What is data quality regarding consistency? Data consistency refers to the consistent formatting and representation across company systems, including email and legacy archives. For example, if email records establish dates in an MM/DD/YYYY format, all data and systems should use the same approach. If some datasets or files use DD/MM/YYYY or DD/MM/YY, datasets may become disorganized, possibly mucking up cloud migration efforts.
Reliable data is trustworthy and provides accurate insights when used for analysis or decision-making. A few common examples of reliable data include audited financial data, rigorously validated scientific research, or data from authoritative sources.
Timeliness refers to up-to-date datasets that reflect current situations, like real-time stock market prices, current weather forecasts, or up-to-date inventory levels. Regarding email and legacy archives, timeliness can refer to existing email threads or historical emails that correlate to project decisions.
In terms of uniqueness, what is data quality? Uniqueness refers to the singular appearance of each record. It is the elimination of redundancies and duplicates. You can often find duplicate data in email archives when a system copies an email from a sender’s outgoing mailbox and the same message in the receiver’s incoming box.
Duplicate data is problematic because it takes up storage space and can complicate eDiscovery or regulatory processes. Also, duplicate information can skew business decisions.
Data validity means that data conform to predefined rules, formats, and data types. For example, social security numbers should match the expected patterns. Validity can ensure compatibility and data integrity when transferring legacy systems to a cloud archiving solution.
Data quality is crucial to many business operations and domains, including:
Business owners and boards don’t make operational decisions by throwing a dart at a goal board. Their strategic decisions and long-term goals typically stem from analyzing high-quality datasets. If data quality is lacking, executives base decisions on wrong assumptions.
Data quality affects business operations more than you may like to admit. Datasets provide information on operational flows and processes. Poor-quality data can lead to:
In data migrations, low-quality data can increase the timeline.
The quality of your company’s data can also affect customer relationships. Modern consumers favor businesses that manage their data correctly and repay these companies with loyalty. Quality data practices make consumers happy because they mean accurate and consistent communications and interactions, resulting in reliable experiences. Inaccurate or low-quality data can lead to billing errors and data-driven mistakes that erode consumer trust and damage brand reputation.
Finally, data quality plays a significant role in regulatory compliance. Depending on the industry, data errors can lead to substantial fines and legal consequences. Typically, the strictest industries, like healthcare and finance, have the harshest penalties. Learning how to improve data quality throughout archiving and migration processes can reduce the risks of non-compliance.
Regarding the question of what data quality is and its impact on data migration efforts, it is necessary to highlight several profound ways data quality affects migration outcomes.
Data quality affects the success or failure of the process. Poor data quality leads to migrating inaccuracies and incomplete or inconsistent datasets, effectively transplanting flaws into the new system. High-quality data is clean and reliable, resulting in an effective data transfer and a streamlined process that increases the likelihood of the migration meeting predefined goals.
Data quality can affect the time and cost of the migration process. Dirty data, with its inaccurate field mappings, formatting inaccuracies, and invalid entries, must get cleaned and fixed, which causes delays. Data cleansing can be a massive and time-consuming undertaking, which can increase migration costs exponentially.
Data integrity can determine the level of trust users have in the new system. Poor data, including incomplete records, mismatches, and inaccurate data, erodes confidence in the new system, and mistrust can lead to user resistance, diminishing the project’s value.
Data quality can affect the smoothness of the migration. Low-quality data can lead to potential operational delays and a longer migration timeline. High-quality data reduces the risks of post-migration fixes and minimizes disruptions to your company’s daily operations.
Data quality affects compliance risks. Low-quality data may include unmanaged or incorrect datasets. When a company handles sensitive and regulated data, these flawed datasets can lead to fines and penalties. Your company can limit these risks before migration efforts by taking a proactive approach to data cleansing and quality checks.
Cloudficient can help you assess your data, email, and legacy archive system to ensure they are ready for the cloud migration process. Our team has the tools to cleanse and validate datasets, ensuring a smooth transition.
Data quality is an asset and a necessity for company datasets and archives, especially before a cloud migration. Cloudficient uses specialized tools and a team of experts to ensure cloud migration success. We analyze existing data and define quality standards. We monitor and supervise the entire migration process while continuing clear communication with our clients. If you want to learn more about data quality and how Cloudficient can help, check out our website or contact our team.
With unmatched next generation migration technology, Cloudficient is revolutionizing the way businesses retire legacy systems and transform their organization into the cloud. Our business constantly remains focused on client needs and creating product offerings that match them. We provide affordable services that are scalable, fast and seamless.
If you would like to learn more about how to bring Cloudficiency to your migration project, visit our website, or contact us.
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