In the era of digital transformation, where organizations rely on DATA for EVERYTHING, whether it may devising strategies, making decisions, or gaining a competitive edge, QUALITY of DATA becomes paramount. Poor data can cost billions to businesses and become a roadblock to their success. So, ensuring the accuracy, reliability, and consistency of data is essential. This article will cover the key dimensions of data quality, data quality management, challenges faced by organizations, and best practices to overcome these hurdles.
Data is the asset of an organization. Data quality management is the process of ensuring that data is accurate, complete, consistent, and relevant to an organization’s needs through pre-defined standards and rules. Quality data ensures that it meets the needs of an organization and its intended purpose. If data quality is not maintained, it drastically impacts meeting core objectives of businesses such as:
The goal of improving data quality is to assess and enhance various dimensions of data quality.
As per Gartner, Data Quality is assessed depending on 6 key dimensions: accuracy, completeness, uniqueness, timeliness, validity (also called integrity), and consistency.
Let’s delve into each dimension.
Accuracy
Data must be consistent with real-world scenarios, reflect real-world objects or events, and align with independently verified data sources. Analysts must use verified sources to verify the accuracy of the data. This is determined by the degree of agreement between the values and the correct information sources. Ensuring data accuracy depends on effective data governance practices and is crucial for industries with stringent regulations.
Completeness
Completeness is defined as making sure all needed information is present. For product data, this can be tricky because different products need different details. To address this, Set clear data collection standards and guidelines for each category, check it regularly and use automation to catch and fix missing information.
Uniqueness
Uniqueness ensures that each data entry is represented only once. Ensure data collection follows company rules, formats, and acceptable ranges. Duplication often occurs when data integration is performed. To prevent this, apply proper rules to consolidate records correctly. A high uniqueness score boosts trust in the data, and improves data governance and ensures compliance.
Timeliness
It ensures that data is always available and up-to-date. It involves real-time synchronization and updates to keep data current. Use monitoring tools to track data availability and identify delays. Establish regular refresh protocols and understand the frequency of data changes to meet business needs effectively.
Validity (Integrity)
Validity, also known as data integrity, ensures that data meets specified business requirements in terms of type, range, format, and precision. Invalid data can impact completeness. So, it’s crucial to define validation rules and automated checks to maintain data validity. In addition, it requires maintaining consistent relationships between entities and properties. Implement relational DBMS (Database Management Systems) and perform regular integrity checks to maintain data consistency.
Consistency
Data consistency ensures that information remains uniform across applications, networks, and multiple sources. It is always linked with data accuracy. Point-in-time consistency refers to the ability to ensure that all elements in a system are identical at a particular moment in time. This helps prevent data loss during network crashes and improper shutdowns. Inconsistent data is a roadblock to data assessment and requires thorough testing across all datasets.
Many companies face challenges with bad data. The problem is often more serious than they realize. Organizations may neglect to implement data quality assurance procedures such as standards and criteria to speed up data collection and optimize programs in real-time. This can lead to an overreliance on incorrect, incomplete or redundant data. It can also create a domino effect of incorrect numbers and metrics. These are common data challenges.
The consequences of poor data quality include increased costs, poor decision-making, strained customer relationships, reduced efficiency, missed revenue opportunities, security risks, regulatory compliance issues, inaccurate reporting, wasted resources, and damaged reputation.
On the other hand, organizations are working with large amounts of big data, and many don't have the data science resources to correlate these data. They will not be able to make time-sensitive optimizations if they don't have the right Data Quality Tools and analysts to sort these data.
It is not surprising that all organizations are focused on improving data quality. These are 12 practices that your company can take to improve your data quality and increase your business' effectiveness and efficiency.
1. Conduct a data quality assessment
It’s like a diagnostic phase where existing data sets are evaluated for the quality and issues that may exist are identified to understand its organizational impact. Audit data sets for inconsistencies, inaccuracies, or incompleteness.
2. Define clear data governance policies
Create clear data governance policies and procedures for data collection, storage, and usage. Define roles and responsibilities for data management to ensure accountability for data quality across your organization. Establish a comprehensive framework detailing rules, policies, and procedures, including data ownership, access protocols, security and privacy guidelines, and quality standards.
3. Data harmonization
Regularly review and clean your data to correct errors, remove duplicates, and resolve inconsistencies. Use automated data harmonization tools to streamline this process, while also incorporating human oversight to ensure accuracy. Data harmonization should be an ongoing effort to keep your data accurate and current.
4. Data standardization & validation
Implement standard data formats, naming conventions, and validation rules to minimize inconsistencies and errors. This simplifies data entry and ensures clarity for users. Use data validation rules, such as format checks, range checks, and cross-field validation, to catch errors and maintain data integrity. Use data integration tools to automate the process and maintain consistency across datasets.
6. Define acceptable data quality
It is also important to determine acceptable data quality for your organization. How accurate and relevant can data be if they are not 100% accurate? Different data quality standards (DQ) may be needed for different types of data and different uses.
7. Define & Monitor KPIs
Define key performance indicators (KPIs) to measure data quality, such as missing values, duplicate records, and data entry errors. Regularly monitor these KPIs and address any issues promptly. Where feasible, use real-time data quality monitoring to detect and resolve problems as they occur.
8. Protect Your Data
It is your responsibility to protect valuable data from unauthorized access. You must comply with all privacy regulations to ensure that customer data is not misused. This is particularly important for protecting against cyberattacks and data breaches and ensuring that the data cannot be edited or compromised by unauthorized users. You need to use multiple data security methods while still allowing access to authorized users within your organization.
9. Promote a data-driven culture
Effective data quality improvement requires participation from all employees, including the C-suite and administrative pool. Educate employees on the significance of data quality and equip them with the tools and training needed to uphold it. Offer workshops, e-learning courses, and hands-on sessions. Foster a data quality culture by involving employees in data management and acknowledging their efforts in maintaining high standards.
10. Designate a Data Steward
A data steward is someone who oversees data quality management in your company. The data steward should be responsible for analyzing your data quality and conducting regular DQ reviews. They also need to implement new DQM methods. Your data steward must also be able to train your staff in DQM techniques and improve DQ over time.
11. Do regular DQ reviews
You should also conduct periodic reviews of your organization's data quality to ensure that they remain effective. These reviews will let you know if your organization is making progress and where you need to improve. These reviews should fall under the control of your data steward.
12. Use a robust data quality management solution
Last but not least, an automated data monitoring system is one of the best ways to improve your company's data quality framework. Automated DQM platforms automatically analyze your data and identify any issues. Then, they cleanse or delete bad data. This DQM platform is much faster and more efficient than manually doing it.
In today’s data-driven world, ensuring data quality is critical to business success. By focusing on aforementioned key dimensions like accuracy, completeness, and consistency, organizations can improve their data management practices and minimize costly errors. Overcoming common data challenges such as silos, duplication, and inconsistent data, requires robust governance, automation, and continuous monitoring. Implementing best practices helps ensure reliable, actionable data. Don't wait. Start enhancing your data quality today to drive better decisions and fuel business growth.
