The business decisions you make are only as good as the data they’re based on. Kate Marxkors spoke at the Marketing Analytics Summit to share the importance of data integrity monitoring and how to roll out the process in your organization.

At Concord, we use data integrity monitoring to ensure your company is using consistently accurate and reliable data throughout its lifecycle. It’s not the sexiest of topics per se, but good, quality data and all that it encompasses drives the modern world. Data integrity monitoring includes detecting data corruption, monitoring for unauthorized data modifications, and ensuring data accuracy through various validation techniques.

Last week at the Marketing Analytics Summit, Senior Manager of Data Science Kate Marxkors taught us about the importance of data integrity monitoring. Here’s what we covered:

Poor Data Quality is a Risk

The volume and complexity of data are increasing over time, leading to a rise in data quality incidents, longer detection times, and increased time to resolution. Data downtime refers to periods when data is partial, erroneous, missing, or otherwise inaccurate. According to Wakefield’s 2023 State of Data Quality survey, data downtime has worsened, doubling year over year due to a 166% increase in the average time to resolution. The consequences of data downtime significantly impact revenue; survey respondents indicated that poor data quality affected 31% of revenue, and 74% reported that they were impacted by data quality issues that were not caught.

Data integrity monitoring is a key solution for building more reliable, scalable data systems and reducing data downtime and its associated costs.

The Benefits of Data Integrity Monitoring

Ensuring and maintaining data integrity can save your organization time, effort, and money that might otherwise be spent making decisions based on incorrect or incomplete data. After all, data-driven decisions are only as strong as the data they rely on. One effective method for achieving and maintaining data integrity is through monitoring.

Data integrity monitoring involves routinely checking for accuracy, consistency, and reliability. This process helps you detect and resolve potential issues before they escalate, ensuring that your data remains trustworthy and reliable over time. Monitoring can include validating data entries, checking for unauthorized changes, and ensuring that data transformations and migrations don’t introduce errors.

Establishing your Data Integrity Monitoring Requirements

Before you get started with data integrity monitoring, it’s important to establish the following:

• ‍Frequency: Determine how quickly you need to be alerted about an issue and how often the data you are monitoring is updated. This will help set up timely and effective alerts.
• ‍Level of detail: Identify the types of issues you will monitor. This can include data accuracy, data freshness, changes in volume or values, and incremental changes versus full dataset updates.
• ‍Responsibility for defining data quality: Identify your data subject matter experts (SMEs). Determine who should define the expected standards for your data and identify potential problems. Identify who is responsible for implementing a data quality monitoring tool and your data consumers.
• ‍Responsibility for monitoring: Decide who should be alerted if an issue arises. Establish a clear process for addressing quality concerns, including assigning roles for monitoring and resolution.
• ‍Tool considerations: Review the tools your teams currently use for reporting and monitoring. Evaluate existing processes and consider how data monitoring will integrate into your team's workflow.

Key Considerations for Implementation

Once your framework is set, you should consider the following steps before implementation:

1. Document dependencies

• Confirm that the data to be monitored is available and accessible for your monitoring solution.
• Define your validation methods.
• Outline the onboarding process to monitor additional data.

2. Define roles and responsibilities clearly

• Assign roles to data SMEs, data engineers, data consumers, and a project manager.

3. Integrate into team tools and processes

• Be mindful of your team’s existing tools and processes and ensure your monitoring solution fits into your workflow. For instance, if your team already uses dashboards, consider adding one specifically for detailed data quality analysis.
• Implement alerting mechanisms via email, Slack, or other communication tools.

Building your monitoring solution

Drilling into the details of alerted quality issues is vital for effectively monitoring, analyzing, and addressing data quality issues. Interactive visualizations and time series analysis make data understandable and manageable, allowing users to drill down into specific anomalies and track system health over time. It's also important to design your data monitoring system to scale with growing data volumes and complexity. Scalability ensures consistent performance and prevents system failures due to unexpected data increases, supporting continuous and reliable operation.

Concord develops data integrity monitoring solutions tailored to our clients' needs. For a financial services client, we created a solution that offered:

• Marketing data: Monitored the ingestion of data to the CDP, as data integrity is critical to marketing campaigns.
• Email alerts and dashboard: Sent email alerts when the data was refreshed and provided a dashboard for detailed analysis.
• Team collaboration: Enabled the marketing team to monitor for incidents, with a clear process in place to address issues with technical teams and SMEs.
• Daily monitoring: Refreshed data four times a day.

As a result, the client experienced a 50% increase in incident detection over three months year-over-year. Additionally, data incidents that previously took months to identify now only take days or hours.

Next Steps

By implementing a data integrity monitoring strategy, you can safeguard your data, ensuring it remains uncorrupted and accessible throughout its lifecycle—from initial input to storage, retrieval, and processing.

Here's a guide compiled by our experts that can help you leverage marketing mix modeling to optimize your marketing budget and prove the impact of your investments.

If you're interested in building a data integrity monitoring solution but unsure where to start, we're here to help. We offer a data integrity monitoring workshop with Kate that will guide you through creating a long-term data reliability strategy. Contact us to learn more!