When customer data is riddled with gaps and errors it hinders productivity and sales. Whereas companies that use accurate datasets successfully perform better. Data-driven organisations see 23 times more acquired customers, 6 times more retained customers, and are 19 times more likely to be profitable than organisations that don’t prioritise data.
The reason for this is that accurate, high-quality data adds business value – contributing towards better decision-making and a positive relationship between customers and stakeholders.
With that said, read on to find out the most common reasons for data errors and learn actionable techniques to improve data quality standards.
The Most Common Data Quality Issues
To improve data quality you must understand what kind of issues pose the biggest threats to your business and fix them.
One of the biggest root causes of poor data quality problems occurs in the data collection stage, from using limited data sources to human errors in data entry to systematic errors in data migration.
These create inaccurate data, resulting in poor communications, underperforming marketing campaigns, and ultimately, lost leads. Inaccuracies are likely to appear over time as people move home, switch email providers, and so on.
Another common cause is duplicate data. It happens when users input their information via multiple sources, for instance via different web forms, or multiple times through different departments. The company runs the risk here of contacting a lead or customer more than once, which is not only a drain on resources but also impacts the customer experience as company communications start to seem like spam.
Data silos cause further issues for companies. When departments fail to share information, gaps start to appear in customer profiles. Only with a complete picture of your customers can you market to them effectively and make informed business decisions.
Why You Need a Clear Data Quality Strategy
Data is vital for business growth. You should be analysing the quality of your data and using it to gain a deep understanding of your target market and customer base. This allows you to streamline logistics, personalise communications, monitor performance, and more. But it’s only possible if you have a good data management strategy in place.
It’s important to recognise that data quality means something different for every company. Different industries, and even the organisations within them, have distinct objectives and therefore need different data quality metrics. For example, a health insurance company needs to build a complete picture of a customer’s medical history. Objectives such as this should govern your data quality strategy.
But, as a rule of thumb, the following criteria can be used to assess data quality,
- Completeness – There is no missing data.
- Accuracy – Data is correct and organised in the right categories.
- Consistency – There isn’t contradictory data in the database.
- Uniqueness – There are no duplicates.
- Timeliness – You have the most up-to-date information for a customer.
Examples of Data Validation Techniques
Data validation techniques ensure databases meet a company’s quality criteria. These should be performed as regular maintenance for data hygiene purposes.
This process assesses the legitimacy and quality of data. Data profiling involves reviewing, analysing, and summarising data. It’s usually done at the start of a project and can also be used to identify data trends, risks, and other useful insights. Data profiling tools like Hopewiser help you merge duplicates and purge data points according to your chosen quality criteria.
This technique removes unwanted data from your database to keep it up to date. Suppression is also necessary for data governance and compliance as GDPR states that companies must maintain real-time accurate data. For example, inactive customers or contacts may be removed, along with deceased individuals, those with incorrect data, and users who have asked not to be contacted further.
How to Clean and Maintain Data
Regular data cleansing and maintenance are necessary to preserve data quality. Use the following tasks as a checklist for data maintenance:
- Verify contact data – Use address matching to ensure the accuracy of contact details. Root out misspellings and colloquialisms for example.
- Remove duplicate data points – Purge exact matches and locate close matches for further assessment.
- Enrich data – Fill in data gaps to flesh out a customer profile. Geographic data enrichment for example adds in missing points so you can carry out location analysis and intelligence.
- Remove inaccurate data – Use a suppression tool to flag data that is incorrect, no longer needed, or when users have opted out.
How to Train Employees to Be Data Literate
When employees understand the need for quality data assets and how to maintain data integrity, a company reduces security risks, stays compliant, and operates efficiently.
Data quality improvement requires initiative throughout your whole organisation, starting from the very top.
The first thing to do is implement training across the board and regularly share reminders about the importance of data accuracy. Ensure everybody understands the data quality criteria and set up a clear framework that employees can refer back to when they need to.
Make it easy for all employees to maintain data quality. Using a tool like Hopewiser, for instance, makes the data cleansing process simple for employees with different levels of tech-savvy. Only then, your organisation will thrive with a data-driven culture.
Improving data quality is essential for making astute business decisions, forward planning, and maintaining relationships with customers. You may not have realised your database has issues such as duplicate data points and data silos.
However, these issues are easily fixed with employee training, a clear data quality strategy, and the right software. Use Hopewiser to implement data validation techniques such as data profiling, data enrichment, and data suppression. Sign up for a free trial and start optimising your database today.