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  • Writer's pictureDan Beck

Overcoming the Data Quality Challenge: How Poor Data Quality Can Ruin Your Firm's Automation Efforts


Here at PT 2.0, we work with professional services firms to realise the potential of their data and technology. One of the things that keep us awake at night is how preventable poor data quality can hinder automation efforts. This article will look at strategies to address and maintain data quality in the long term and help us all sleep better!


The Importance of Data Quality

In most firms, as much as 32% of the data is missing, incomplete, incorrect, or inconsistent. According to Ovum Research, companies lose about 30% of their revenues due to poor data quality. Moreover, 77% of businesses believe their bottom line is affected by bad data. In the United States, data errors cost businesses around $3.1 trillion. With the increasing reliance on automation, having clean and accurate data has never been more critical.


The Problem with Poor Data Quality

Firms that we have worked with over the years have had between 20,000 and 400,000 easily identifiable data issues. For instance, a firm with 100 staff had over 140,000 data issues on their active client records. Poor data quality not only effects the efficiency and effectiveness of your firm's automation efforts, it can also damage your reputation.


Automation can be a double-edged sword. While it can help streamline processes and improve efficiency, it can also backfire when the data being used is of poor quality. There's nothing worse than sending an email to a client saying, "Dear [insert first name], we want to thank you for being a loyal customer for the past five years," when you don't even have their name correctly entered into your database.


The Innovation Paradox

The irony of this situation is that poor data quality within modern accounting firms hinders the process improvement process. Firms implement tools to automate and save time, hoping to devote more time to innovation and building advisory services. However, these automations don't work effectively due to poor data integrity, leading to a vicious cycle that hampers progress.


Five Strategies to Address Data Quality Issues

1. Ignoring the problem

Some firms choose to ignore the issue, believing that it's not affecting them at the moment. While this may seem like a legitimate strategy, it implies that the firm has no plans to increase efficiency and automation within the next few years, which isn't feasible in today's competitive market.


2. Changing systems

Most businesses think that getting a new system will magically solve their data quality issues. However, this approach fails to address the root cause of the problem - poor data habits. Instead, it would be best to clean the data and improve data management practices before considering a system change.


3. Manual cleaning

Many firms resort to producing massive spreadsheets and having their team members go through the list to clean the data manually. While this method can work, it's often time-consuming (and expensive!) and may not address all the data issues across the client base.


4. Using a tool

Acquiring a tool that helps identify missing data and continually cleaning data exceptions throughout the year can be a more efficient approach. Tools like the ones developed by PT 2.0 can help firms see their data integrity issues in their systems, making it easier to address them.


5. Implementing a data strategy

The ideal approach is to have a data strategy in place, where firms analyse the types of data they use, eliminate wasteful data, and clean the useful and critical data constantly through the use of exceptions.


Practical Steps to Keep Data Clean

Categorize data

Divide your data into three major categories:

  1. Crucial information (e.g., names, telephone numbers, addresses)

  2. Useful data (e.g., client preferences, engagement history)

  3. Wasteful data (e.g., outdated or irrelevant information)


Focus on maintaining the quality of critical and useful data.


Establish ownership

Assign data ownership to specific team members or departments. This will ensure accountability for data quality and make it easier to identify and resolve issues.

Develop clear data entry guidelines: Create a set of data entry standards and guidelines to ensure consistent data input. Train your team members on these guidelines and reinforce their importance regularly.


Implement data validation and checks

Integrate data validation rules and checks into your systems to minimise the chances of errors during data entry. This can include mandatory fields, character limits, and format requirements.


Regularly audit and clean data

Schedule routine data audits to identify and resolve data quality issues. Use tools like the PT 2.0 Data Integrity Tool to help automate the process and make it more efficient.


Monitor data quality metrics

Establish key performance indicators (KPIs) related to data quality, such as data accuracy, completeness, consistency, and timeliness. Regularly track and report these metrics to ensure continuous improvement in data quality.


Foster a data-driven culture

Encourage a culture that values data and recognises its importance in driving decision-making and business success. This will motivate team members to take data quality seriously and actively contribute to maintaining it.


Conclusion

In conclusion, poor data quality can significantly impact your firm's automation efforts and overall business performance. To overcome this challenge, it's essential to implement a comprehensive data strategy that addresses data quality issues and promotes a data-driven culture within your organisation.


By categorising data, assigning ownership, developing clear guidelines, implementing checks and validations, regularly auditing data, monitoring quality metrics, and fostering a data-driven culture, you can improve data quality and fully leverage the power of automation for your firm.


Have questions or would like to share your thoughts on data quality? Reach out to us at info@pt20.com.au.


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TL;DR

Poor data quality can hinder automation efforts and impact business performance in professional service firms. To address this issue, implement a comprehensive data strategy that includes categorizing data, assigning ownership, developing data entry guidelines, implementing data validation checks, conducting regular audits, monitoring data quality metrics, and fostering a data-driven culture. This approach will improve data quality and enable firms to fully leverage the benefits of automation. Make it easy on yourself, give us a call at PT 2.0 and we can help you solve the data integrity issue.

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