The impact of poor data quality is vast—with research suggesting it can cost businesses millions of dollars per year. Not only can this have a significant hit on profitability and financial stability, but it can also leave firms in a sticky situation if their data is not telling the full story, particularly where customers are concerned.
With so much at stake for businesses that don’t invest in their data quality, it’s crucial that businesses are informed about what makes high-quality versus low-quality data—and that they are confident in implementing strategies needed to collect, collate and analyse data.
Here, we will be taking a closer look at the importance of data quality and sharing our tips for assessing and improving your data quality.
What is data quality?
Data quality is a critical aspect of data management, defined as the degree to which data is accurate, valid, complete, and consistent—particularly with regards to the company’s expectations of these markers.
While data quality may sometimes be referred to as data integrity, there are slight differences between the two. Data quality ensures overall accuracy, completeness, consistency and timeliness of data which enables it to be fit-for-purpose while data integrity more broadly looks at overall data accuracy and security. Simply put, data quality makes data useful, and data integrity keeps it safe and ensures reliability in a business’ system or database.
What are the top data quality issues?
In an ideal world, all businesses would be producing and utilising high-quality data, but for business owners and data managers, there are some data quality challenges that can stop them from being able to maintain high data quality. Some of the most common are:
- Data inaccuracy: If businesses aren’t fully harnessing the power of intelligent automation, there’s every chance that their datasets contain typo’s, outdated or misclassified information.
- Incomplete data: If your business hasn’t found time or resource to prioritise data, you may find you have missing values or records. This can skew results and makes it difficult to draw accurate, complete conclusions.
- Duplicate data: Repeat, redundant information that’s stored in multiple places can cause confusion, increase storage costs, and lead to inconsistent and incorrect reporting.
- Data inconsistency: Having different values for the same data element can create misleading insights.
- Data inaccessibility: Isolated data systems can hinder the integration and accessibility of data across the organisation.
- Data obsolescence: Outdated data can misrepresent the actual facts or realities of your business.
- Lack of standardisation: Integrating and analysing datasets from different sources can be challenging, so having standardised formats for data entry and storage is key.
What are the main benefits of good quality data?
Prioritising good quality data can help enhance various aspects of operations, while also simplifying and streamlining the decision-making process—meaning leaders can make better decisions. The benefits are vast, and include:
- Better decision-making: High-quality data ensures decisions are based on accurate, reliable, and timely information, creating better outcomes.
- Improved operational efficiency: Enables an increase in self-service with quicker access to trustworthy data and improved cross functional collaboration.
- Increased regulatory compliance: High-quality data ensures organisations meet regulatory requirements and avoid non-compliance penalties.
- Improved customer experience: Accurate insights from data helps in better understanding customer needs and preferences, ultimately leading to better service and increased customer loyalty.
- Enhanced accuracy of analytics and reporting: Reliable data generates meaningful insights and analytics, which can be used to shape strategic initiatives and further innovation.
- Reduced cost: By reducing data errors and inconsistencies, firms prevent the need for rework and corrections, saving time and resources.
- Facilitates AI and ML models: For meaningful and useful outputs, data needs to be fit for purpose and is a pre-requisite for implementing GenAI.
6 ways to improve data quality
The benefits of improved data quality can have a widespread impact, from operations to employee morale and customer satisfaction. So, continuously thinking of ways to improve data quality should be high up the priority list of leaders and data managers.
Data quality improvement requires a structured approach with some best practices. These include:
1. Establishing a data governance function
To streamline and strengthen data, firms need to ensure they have an appointed function that can not only govern best practices for data within your organisation but also define what constitutes high-quality data for your specific company. This function will also be responsible for setting standards for aspects including accuracy, completeness, consistency and reliability, as well as implementing policies for data entry, storage, and usage.
2. Standardising data creation
A lack of standardisation is one of the main causes of low-quality data, and therefore the incorrect or misleading insights that come from analysis of this data. In using well-defined meta-data protocols, your data can be given context that helps prevent misinterpretation, improve discoverability, ensure consistency, and enhance data accuracy. Firms can also benefit by using automated tools that validate data at the point of entry, helping to catch errors early and maintain data integrity.
3. Improving data integration
Preventing incorrect data and eliminating duplicate records is crucial for maintaining data integrity, and improving your data integration can help with this. Not only does it ensure better consistency between different databases, applications, and systems which reduces discrepancies, but integrated data systems also often include validation and cleansing processes to correct errors and fill in missing information.
4. Introducing master data management
Data silos can create many issues for all parts of the business, limiting accessibility and visibility of correct, or important data. You can tackle this by creating and implementing a master data management system. This will create a single source of truth for all critical data, ensuring your teams have access to the correct and up-to-date information as and when they need it.
5. Monitoring and managing continuous improvement
Data improvements shouldn’t be treated as a tick-box, once-and-done exercise. Instead, you will gain much greater insights, and better customer and employee satisfaction if you continually monitor and improve your data as you go. Monitoring is a crucial part of ensuring high-quality data, and there are now many tools that can help you do this that provide dashboards to track data quality metrics, and mechanisms to continuously review and clean-up your data.
6. Leveraging AI and ML
Rapid technological advancements are making it easier for high-quality data to be created and maintained. With figures showing that 48% of businesses are already relying on AI to utilise big data effectively, it’s clear that implementing the latest automation trends can help drive higher quality data, better insights, and ultimately, enhance growth. AI can help identify patterns in data errors, automate components like data classification, while machine learning (ML) models can be used to predict and prevent data quality issues.
Data is integral for business success, providing insights into everything from customer satisfaction to employee motivation, and productivity. So, when you’re dealing with low-quality data, it can have a knock-on effect to every corner of your organisation. But, with the right preparation, reliable systems, and continuous monitoring, you can ensure you’re working with reliable, accurate, and complete data.
Struggling with data issues? Our Asset and Wealth Management specialists are experts in supporting businesses just like yours with improving your data quality. Get in touch to have a chat and find out what we can do for you.
