Data quality is essential for delivering reliable analytics that business users and decision-makers trust. Organizations should assess their data to ensure it meets their quality standards. Data quality management (DQM) is the practice of using data to serve an organization’s purposes with flexibility and agility. An assessment can also find gaps in data, such as missing information, that need to be filled in, to improve data quality. Here are seven ways to improve data assessments:

1. Assess completeness. Data completeness is the comprehensiveness or wholeness of a data set. It can be measured as a percentage of all required data that’s currently available in the data set. It’s important to note that non-essential information can be missing without making the data incomplete. For example, data that does not have a customer’s phone number will probably not impact email campaigns. Likewise, performing analytics on sales data within a certain time period will not be affected by missing information outside of those specified dates. However, for data to be complete, it must include values for all of the fields needed for the intended analytics.
2. Ensure consistency. Data should be the same across all uses and applications. This means that no matter where data is stored or used—on-premises, clouds, apps, or databases—it must be consistent. For example, customer data in the data warehouse needs to be the same as the customer data in a customer relationship management (CRM) system. Inconsistencies can be the result of data silos, outdated information, or information entered differently across users, such as a customer name entered with various spellings, like “John” and “Jonathan.” Testing multiple data sets helps determine consistency.
3. Confirm timeliness. Organizations want the most accurate data available at the time it’s being used. The right data must also be easily accessible when it’s needed, including for real-time or near-real-time use. The value and accuracy of data can depreciate over time. For example, data about buying habits prior to COVID-19 may no longer be relevant. Timely data that’s current and accurate helps stakeholders make the most informed decisions, uncovers new and emerging trends, and automates processes. This is where the right data platform delivers value—it makes integrated and timely data available to everyone who needs it.
4. Validate accuracy. Data must be correct, meaning it has the right information in all required fields, such as customer profile details or product specs. The fields can include everything from a customer’s date of birth and geographic location to sales numbers and corresponding sales dates. The data impacts business areas such as marketing, billing, and product design. Inaccurate data skews analysis, so it must be correct and complete. Data accuracy can be validated by confirming a data set against a verified or authentic source. Maintaining an effective data governance program helps ensure data accuracy.
5. Determine integrity. Data used for analysis should meet the organization’s data quality governance standards to ensure it maintains its integrity, which is the accuracy and consistency of data over its lifecycle. Each time data is duplicated or moved, the integrity can be compromised by information getting lost or attribute relationships becoming disconnected. For example, a CRM system that loses part of a customer profile, like a mobile phone number or email address, has data with compromised integrity. Data integrity allows organizations to trace and connect data. Data quality checks help verify its integrity.
6. Measure validity. Data must match the intended use for the data set, whether it’s for analytic insights or another purpose, and must also meet the organization’s defined rules for the data. Validated data can include information that fits into specific data types, forms, numerical ranges, or mandatory data fields, such as birth months that fall within the numbers one to 12 or zip codes that contain the correct number of digits. Data should be validated after a migration, like moving data sets from an on-premise infrastructure to the cloud. Implementing data validation rules helps ensure data meets the organization’s requirements.
7. Evaluate uniqueness. Uniqueness helps identify instances of data duplication by determining if the same information exists multiple times within the same data set. For example, if a list of 500 customers has data for more than 500 people, then data is duplicated. Data cleansing and de-duplication processes help resolve this problem.

Ensuring Quality Data Ensures Trustworthy Data Analytics

Assessing data is increasingly important as data volumes continue to grow and data sources expand. Having established processes in place to assess and govern data helps ensure the business can trust the results of its data analytics, including advanced analytics. Data that’s current, accurate, and complete also improves time to value. If it takes an unusually long time to get analytic results from a data set, there’s probably a data quality issue. Auditing and assessing data can identify issues and determine if a data set is fit for a specific purpose, such as advanced analytics. In addition, an audit can identify when changes were made to data, such as when a customer’s address, email, or phone number was updated.

Use a Modern Cloud Data Platform to Ensure Quality Data

One way to maintain data quality across the organization is to bring all data together on a single platform where it’s governed by established processes. Data governance ensures data meets compliance and quality standards. Data profiling also helps with data quality by identifying the structure, content, and formatting of data so it can be assessed and enhanced.

Actian offers modern, easy-to-use solutions for assessing and using data. The Avalanche Cloud Data Platform makes integrated data readily available to everyone who needs it. The trusted platform provides a unified experience for ingesting, transforming, analyzing, and storing data—and ensures data is complete and compliant using data quality rules.

Related resources you may find useful:

• What is Data Quality Management?
• Three Steps to Better Data Management in 2023
• What is Data Lifecycle Management?

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