Navigating the Data Quality Conundrum: Strategies for Success

Data and analytics are vital for any organization to run operations, serve clients, scale, and develop revenue strategies. However, compromised data quality can have consequences such as revenue loss, increased expenses, client risk, deal delays, or the failure to deliver critical initiatives on time. The severity of these impacts varies based on size and complexity of the data problem and the organization, but there is no question about the negative effects.

Why is data quality important?

Maintaining good data quality is crucial for managing risk, enabling growth strategies, or meeting regulatory needs. Accurate data creates a guide for business strategies and enhances any organization's operations and outcomes. Poor data quality has negative business impacts. For instance, inaccurate data may lead to incorrect customer charges, misconceptions about profitability, and inaccurate sales forecasts. 

One of our clients was unable to accurately ringfence high-risk customers, increasing their risk of credit events. A client was also losing interest income due to incorrect interest rate being charged. Regulators have levied hefty fines against public companies mired with data quality challenges.

The data quality problem requires attention and solving it is not insurmountable. It requires proper execution and best practices for data management.

My data is incorrect and I need to fix it. Now what?

When data quality issues arise, organizations often resort to creating redundant processes, multiple 'Sources of Truth' for data, or implementing quick fixes, exacerbating data challenges over time. Poor data quality typically stems from existing People, Processes, and Technology Systems, or their absence. Preventing and minimizing the impacts of poor data quality is crucial.

To address data quality issues effectively, consider the following principles and strategies: 

1. The Data Quality Team. Involve those who create and maintain the data in your remediation efforts to provide insights such as the data’s history or its behavior in various business scenarios, and to establish data requirements.
   

2. Fix the Root Cause. Address underlying issues rather than just symptoms to improve data quality for the long-term. While “band-aid” quick fixes are appealing, can cost less, quicker to implement, and are sometimes appropriate, resolving underlying issues is essential to prevent recurrence.
   

3. Assess your People, Processes, and Technology Systems. Identify and rectify the sources of data quality issues in the trifecta of your People, Processes and Technology tools. 
   

   1. People. Automate data entry, avoid manually rekeying the same data in multiple systems, and implement effective people controls (the Four-Eyes principle is an effective data control to ensure accuracy).
      

   2. Process. Identify and streamline processes that impact the data, automate manual processes, implement approval mechanisms for data modifications, and ensure timely data updates across its lifecycle. For example, a client's data entry process included manually keying in a negative value in the system, and a decision was made to begin inputting it as a positive value. The report logic overlooked this input process change in signage, resulting in data errors.
      

   3. Technology Systems. Invest in automation and implement data quality rules and controls in the technology to improve data accuracy throughout its lifecycle. For instance, if the deal term requires a date for a fixed-term contract but not for an evergreen contract, then system validation controls must account for that scenario with a data quality rule. 
      

4. Control Your Data Effectively. Deploy data quality rules and control measures across all stages, from data input to modifications and outputs, to ensure accuracy and credibility. While data may move through multiple systems, maintaining alignment between the data source and output is the key.
   

5. Speak the Same Language. Maintain consistent data naming conventions, definitions, and structures across systems and tools to ensure data integrity.
   

6. Maintain a Data Map or Data Dictionary. Use data maps and dictionaries to provide transparency and traceability between data sources, downstream systems, and outputs. This is especially useful when there are variances in data naming conventions between the system of entry and the output, or during data transformation. To provide data transparency, we designed a reporting tool for a client that displayed the source of a data field, its formula, and a definition in plain English to all report users.
   

7. Audit Trail. Data often changes over time and an audit trail is critical to data management. Audit trails track changes in data, ensure accountability, and aid in resolving data quality issues.
   

8. Continuous Data Monitoring. Regularly monitor data for inaccuracies and resolve them to maintain trust and reliability.

Data quality is crucial in today's data-driven world. By adhering to key data management principles, involving the right people, addressing root causes, and implementing effective controls, organizations can mitigate poor data quality risks such as financial setbacks and ensure accurate, dependable, and trustworthy data. 

Payal Nevatia-Garland

Managing Partner, Canvas LLC

Do you need to solve your data quality and reporting challenges? Get in touch with us to see how we can help.

Book a consultation