Good, Bad, and Outdated: How to Improve Data Quality
In the age of big data, analytics play a major role. However, the results are only as good as the quality of the data that you gather. As the marketing and sales departments generate more and more data, the importance of cleansing it becomes key to maintaining top-notch operations.
Every year, bad data costs companies around $12M. Besides reducing the revenue, this data leads to poor decision-making and increases the complexity of data ecosystems.
Many companies don’t have a clear understanding of what data quality is. This keeps them from streamlining business processes and obtaining new market share. Let’s take a moment to dive deeper into data quality and ways to improve it.
What Is Data Quality?
Data quality describes the accuracy, completeness, consistency, and reliability of data. It encompasses the overall condition of the data and its ability to make a positive contribution to company operations.
The quality of data directly impacts decision-making, operational efficiency, customer satisfaction, and overall business performance. If the data quality is low, the organization suffers multiple adverse consequences.
High-quality data is:
- Accurate – how well the data reflects reality or truth. Accuracy assesses the correctness of the data and ensures that it’s free from errors, inconsistencies, or biases.
- Complete – whether all required data has been collected and if any values are missing. Completeness guarantees that all necessary data is present and accurate.
- Consistent– the extent to which data values adhere to rules and standards. Consistency also identifies conflicts or discrepancies between different versions of data.
- Relevant – the appropriateness and usefulness of the data for its intended purpose. Relevancy ensures that the data is suitable and valuable for the specific use case.
- Timely – whether the data is available in a timely manner and whether it is up-to-date within a relevant context. Timeliness ensures that the data remains current and suitable for the intended purpose.
- Valid – whether the data conforms to the rules and constraints of the data model. Validity verifies that the data is structurally sound and meets the defined requirements.
Good data quality means that the data is accurate and reliable. But most importantly, quality data is consistent. Consistent data follows standardized formats across different systems within the company. Reliability must be free from errors and duplications.
For example, good data quality means having accurate and up-to-date information about customers. This can include their contact details, preferences, and purchase history. Leveraging this data allows your business to personalize marketing campaigns, provide better customer service, and make informed business decisions.
On the other hand, poor data quality can have severe consequences. Inaccurate data may lead to faulty analysis, misguided decision-making, and wasted resources. For instance, if your company’s inventory data is unreliable, you could face overstocking or stockouts. This can lead to financial losses and unhappy customers.
Inconsistencies in data, such as different spellings or variations in formatting, can make it challenging to analyze datasets accurately. This can hinder data integration efforts and cause delays in reporting.
Duplicate records within the database can lead to confusion, wasted storage space, and inefficient operations. Imagine a scenario where a customer receives multiple identical marketing emails due to duplicate entries in the database. This could cause your reputation to suffer.
Consequences of Bad Data Quality
Poor data quality can have significant consequences for organizations. Without the right approach to data cleansing and maintenance, it’s easy to lose market share. Here are just a few issues the bad quality of data can lead to.
Data is the foundation for making informed decisions that are often called data-driven decisions. When data quality is compromised, decision-makers may rely on inaccurate or incomplete information. This usually leads to flawed judgments and misguided strategies.
Poor decisions result in missed opportunities, financial losses, and setbacks for the organization.
Inaccurate data can hinder productivity by causing delays or errors in various business processes. Employees may waste time searching for correct information or rectifying unexpected mistakes. This can disrupt your workflow and have an impact on overall productivity.
Bad data quality can harm an organization’s reputation. Incorrect customer information can lead to frustrating experiences for buyers. For example, an error in the mailing address could cause you to send a product to the wrong recipient.
Such data errors damage the customer’s perception of the organization. After leaving the company, the customer is likely to share their bad experience with friends and family. In fact, 62% of customers share their bad experiences with others. This could hurt your reputation significantly.
Higher Marketing Costs
Poor-quality customer data can lead to wasted marketing efforts. For example, sending promotional materials to the wrong email address or using outdated customer information for personalization purposes isn’t just ineffective. It’s expensive.
Organizations may end up spending resources on marketing campaigns that yield poor results. As marketing ROI decreases, so does the revenue.
Low data quality can lead to compliance issues. This is a huge problem in industries with strict regulations, such as healthcare or finance. Non-compliance with legal requirements due to bad data quality can lead to penalties and legal repercussions.
Data analytics relies heavily on the quality of input data. If the underlying data is inaccurate or inconsistent, the insights derived from analytics will be. This can hinder organizations’ ability to gain meaningful insights and make data-driven decisions.
Data Security Risks
Data quality issues can also pose security risks. Inaccurate data can lead to unauthorized access, breaches, or misuse of sensitive information. Organizations must ensure data quality to maintain the integrity and security of their data assets.
This doesn’t just relate to customer data. If you have poor internal data quality, you could cause employees to make serious mistakes. Since 95% of cyber breaches stem from human error, inaccurate data could lead to a major breach.
Best Practices for Improving Data Quality
Data quality is a critical aspect of any organization’s success. To ensure the accuracy of the data you use, you have to implement top practices that integrate data quality into the business processes. Here are a few tactics to follow to improve data quality.
Identify Data Quality Metrics
The first step to data quality improvement is figuring out the specific parameters that define high-quality data for your organization. These parameters may include the above-mentioned accuracy, completeness, consistency, timeliness, relevance, and validity.
Keeping these parameters in mind, you can establish benchmarks and standards against which to measure data quality. This allows your team to focus its efforts on addressing specific data quality issues and implementing targeted improvements.
Implement a Data Auditing Process
By implementing a data auditing process, you regularly assess the quality of different data types while measuring them against the data quality standards.
While conducting routine checks, your team can identify any data issues that may affect accuracy, completeness, or consistency. This allows you to take proactive measures to rectify these issues and improve data quality.
Data audits don’t just ensure that the data quality aligns with the organization’s requirements. They give your team an extra instrument for data quality management.
Cleanse Data Regularly
Regular data cleansing is a fundamental practice that allows you to improve data quality and eliminate outdated information. It involves:
- Identifying and rectifying errors
- Removing duplicates
- Updating outdated information
For example, you have an email address list that you use for email marketing purposes. With time, this list becomes outdated as customers lose passwords or change addresses. By using an email validator, you can cleanse the email list to eliminate invalid addresses and delete spam traps.
By implementing automated data cleansing routines, you can streamline data management processes. Regular data cleansing improves the reliability and usability of data so your team can make informed decisions and achieve better operational efficiency.
Eliminate Data Silos
When data is stored and managed separately in different systems, it’s easy to encounter inconsistencies and duplication. To improve data quality, you have to eliminate siloed data completely.
This allows for better data governance, reduces redundancy, and promotes consistency. By eliminating data silos and establishing a unified data management approach, you don’t just make data accessible to everyone who needs it. You ensure consistency and reliability.
Regulate Data Access
Controlling and regulating data access can do wonders for data quality improvement. Organizations should establish clear protocols and permissions to ensure that only authorized personnel have the right to modify data.
By implementing strict access controls, organizations can minimize the risk of unauthorized changes or data manipulation. This helps improve data quality and maintain its integrity.
Regulated data access is an essential part of data security. Even smaller organizations should consider setting up relevant controls.
Keep Data Secure
Data security is paramount for maintaining data quality. Organizations should implement robust security measures to protect data from unauthorized access, corruption, or loss.
Tactics that ensure high-quality data security include:
- Regular backups
By prioritizing data security, organizations can safeguard the integrity of their data and streamline data quality improvement. If you work in an industry that requires strict data security measures, your efforts can contribute to compliance.
Use Data Cleansing Tools
Leveraging data cleansing tools is an effective way to improve data quality. Depending on the tools you choose and the budget you can allocate, you can identify errors and correct them in real time. AI-driven data cleansing instruments can play a major role in your data quality improvement efforts.
By utilizing such tools, your team can save time and effort while ensuring high-quality data. Leveraging such instruments allows you to streamline the data cleansing process.
Implement a Data Quality Improvement Culture
Creating a data quality culture within the organization can be highly useful for maintaining consistent data quality standards. This involves establishing data quality policies and guidelines that help your employees understand tactics for improving data quality.
By fostering a culture that values data quality, employees will be more inclined to prioritize accuracy, completeness, and consistency in their data-related activities. This collective commitment to quality ensures that everyone takes ownership of data integrity.
Educate Employees About Data Quality
Educating your employees about the importance of data quality includes teaching them about:
- Data entry best practices
- The impact of poor data quality
- Significance of their role in maintaining data accuracy
By raising awareness and providing ongoing training, you can empower your staff to contribute to improving data quality. When employees understand the value of quality data, they can actively participate in ensuring its accuracy.
Establishing a Data Quality Improvement Plan
To achieve top results with data quality improvement, you need to design a comprehensive data quality improvement plan. This plan serves as a roadmap for identifying, addressing, and preventing data quality issues. It can include the following steps:
- Assess current data quality – conduct a thorough assessment of your organization’s existing data quality. Identify any recurring inconsistencies or inaccuracies in your data. This assessment will provide a baseline understanding of the areas that require improvement.
- Set clear objectives – define specific objectives for your data quality improvement efforts. They should align with your organization’s overall data strategy and address the specific challenges identified during the assessment.
- Implement data governance – establish a robust data governance framework to ensure accountability for data quality. This includes assigning roles and responsibilities for data management, defining data ownership, and establishing processes for data validation.
Remember that data quality is an ongoing process. Regularly monitor set metrics and performance indicators to identify areas for improvement and maintain data quality. Make sure to implement feedback loops and mechanisms to capture data quality issues.