The importance of data quality in your business

Achieve better data quality throughout your organisation and increase the effectiveness of your business decision-making.

4 minutes read | by Lee Smith | 23 June 2022

The importance of data quality.

Whether you are just starting a digital transformation initiative, or are nearing the endpoint, you’ll already know that one key benefit is the rich data sets that become available. This data can be analysed to identify any problem areas or improvements that can be made to enhance performance. However, analysing large data sets is only beneficial when the data is correct.

“As organisations accelerate their digital business efforts, poor data quality is a major contributor to a crisis in information trust and business value, negatively impacting financial performance.“
Ted Friedman, vice president at Gartner.

Data is becoming an ever more present player within business. Across industries, companies rely on data to know where they are and where they are going. The knowledge gained from this data is utilised to make business decisions, with huge implications.

With more and more data being collected and analysed, the room for error is growing. With this analysis so heavily relied upon, errors can be extremely costly. Gartner research has found that poor data quality is responsible for an average of $15 million per year in losses.

Understanding the most common data quality problems will help you identify issues with your data before it becomes a problem.

The benefits of data quality.

Streamlined business processes Improved decision making Better customer experiences
Streamlined business processes. Improved decision making. Better customer experiences.
Data validation and enhancements can be done automatically, leaving your team to focus on what matters most. With interactive reports providing greater accuracy and deeper insights your team will make the right decisions at the right time. Create better customer experiences by offering them the products and services matter to them, when and where they need them.


Common data quality issues.


Often the same data is inputted into many different systems. When the same data is stored in different formats it is difficult for systems to recognise that they belong to the same customer. This hampers customer analysis and can lead to potential GDPR nightmares, but it can also result in duplicate data records potentially causing poor customer experience.


Duplicate data is a challenge that every company deals with. It is estimated that 15% of leads contain duplicated data1. Often it occurs because of siloed processes spanning departments, with multiple systems recording the same information. When completing analysis on company wide data, this can significantly skew the results leading to ill informed decisions.

Incomplete information:

Data fields that have not been filled in are a significant issue. For example, when a customer’s postcode is not filled in bills go missing, or are not sent at all. This can also become an issue for analytical analysis, as it lacks important geographical information to help you spot trends.

Inaccurate data:

62% of organisations rely on marketing and prospect data that’s up to 40% inaccurate2. Attempting to contact customers using inaccurate data is a blatant waste of resources, while running analytics on inaccurate data can lead to costly decisions being made.

Tips to achieve better data quality.

It is important to be proactive when it comes to data quality. Data quality issues accumulate; not managing them or managing them through manual intervention with no automatic error correction is costly and libel to error. You don’t want to wait for costly issues to arise, before realising there is a problem.

Tip 1. Examine the current state of your data.

Tip 2. Define your business data needs and assess the impact of poor data on your business decisions.

Tip 3. Assess the potential impact of poor data on your business decisions.

Tip 4. Promote a data-driven business culture.

Tip 5. Build in automated data monitoring processes.

Tip 6. Increase engagement through interactive dashboards.

Tip 7. Establish data quality stewards and procedures.

Resolving data quality issues in multiple systems can be a time consuming and costly task. However, Low-Code platforms, like PhixFlow, can be utilised to quickly implement the process across an entire business.

Fix your data quality issues with PhixFlow.

Data quality is at the heart of the PhixFlow Low-Code platform. This ensures systems configured on PhixFlow deliver high quality data supporting any analysis, decision making and legal compliance requirements.

Our Low-Code Application Development Platform is designed to handle even the most rigorous of data quality and governance policies making it easy for citizen developers and IT to configure applications that support their business needs.

For more information on how to ensure you are getting the most from your data, please request a demo.




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