Data Monitoring: Benefits, Best Practices, and Automation Opportunities

Imagine your company relies on inaccurate data to drive its strategies, only to realize too late that the information needed to be revised. The consequences could be devastating — missed opportunities, incorrect forecasts, and damaged customer relationships.

But by monitoring data, you can understand your company's digital ecosystem comprehensively, make informed decisions, optimize processes, and mitigate risks effectively.

To help you understand data monitoring, we've broken it down into the following chunks:

• What is data monitoring?
• How does data monitoring work?
• Improve data quality with data monitoring
• Best practices for data monitoring
• Automated data monitoring system

Let's learn more.

What is data monitoring?

Data monitoring is observing and tracking data to verify whether it's accurate, quality-ensured, and integrated. Doing so can help you identify and address issues, make better decisions, and maintain the reliability of data-driven processes.

You can monitor the following types of information to detect anomalies, trends, or patterns that may require attention:

• Metrics
• Performance indicators
• System logs

(Explore the differences between logs and metrics.)

Benefits of data monitoring

Monitoring data allows you to spot problems and fix them. It's like having a watchdog that looks for issues that might affect the company's operations or decision-making.

And constant monitoring helps maintain high-quality data and ensure that it meets previously established standards for formatting and consistency.

Here are some reasons why data monitoring is essential:

• Saves time and money: Monitoring stored data saves time and money by avoiding resource-intensive pre-processing.
• Improves data quality: Data monitoring helps maintain high data quality by detecting and correcting issues before they become significant problems.
• Supports governance initiatives: Data monitoring ensures adherence to standards and protocols while providing actionable insights through dashboards, alerts, and reports.
• Helps connect better with customers: Accurate information improves customer connections through reliable data monitoring.
• Identifies errors before they cause harm: Monitoring data quality helps prevent mistakes that can lead to wrong decisions, resource waste, and legal issues.
• Provides accountability: Monitoring helps track resources and results. It generates early warnings, recommends improvements, and evaluates program effectiveness.

How data monitoring works

Data monitoring works by reviewing data to check its accuracy and quality. It's an ongoing process that requires consistent attention and adaptation. So, here's how you can monitor data:

1. Identify what to monitor for your business goals. It could be sales figures, website traffic, customer feedback, or other relevant information.
2. Set up monitoring tools to track and collect data automatically. These tools can range from basic spreadsheets to advanced analytics platforms.
3. Establish monitoring parameters for normal or acceptable data. This helps in detecting any deviations or anomalies that require amendments.
4. Collect and analyze data weekly, bi-weekly, or monthly to identify patterns, trends, or irregularities that require further investigation.
5. Act upon insights when any issues or opportunities are detected. This will help you resolve problems and optimize processes to improve business outcomes.

Improve data quality with data monitoring

Improving your company's data means making it reliable and free from errors, duplicates, inconsistencies, and outdated information. By doing so, you'll have more accurate data to support your company's decision-making processes and operational activities.

Here's what you should do to improve the company's data quality with monitoring:

Detect and correct errors such as missing values, incorrect formatting, or outliers. Once you detect these issues, take appropriate actions to rectify the errors and ensure data accuracy.

Identify anomalies to understand data quality issues like entry errors or system malfunctions. By promptly identifying and investigating these anomalies, you can address underlying quality problems and prevent them from affecting decision-making processes.

Ensure data completeness by verifying that all required fields are populated and complete. This way, you can fill in any gaps and make sure that your data is comprehensive.

Maintain data consistency across different datasets and systems to identify and resolve discrepancies, harmonize data formats, and maintain a unified information view.

Track data quality metrics to assess the overall health of your data. These metrics could include data accuracy, completeness, timeliness, and integrity. By regularly monitoring these metrics, you can set benchmarks, identify areas for improvement, and establish data quality goals.

Implement data governance practices to uphold data quality standards throughout the organization. This includes defining data ownership, stewardship roles, and quality policies guiding data management practices.

Best practices for data monitoring

To establish an effective data monitoring strategy that helps you manage your data, detect issues early, and make decisions, here are some best practices to follow:

Define clear objectives

Start with defining your data monitoring objectives and goals. Identify the key metrics, performance indicators, or anomalies you want to track. This helps to focus on the most critical aspects of data and avoid unnecessary hurdles.

Select relevant data sources

Choose the data sources that are most relevant to your objectives. Determine which systems, databases, applications, or sensors provide the data you need to monitor. By selecting suitable sources, you can ensure that you're collecting meaningful and actionable information.

Set realistic thresholds

Set appropriate thresholds or benchmarks to define your data's normal or abnormal behavior. These thresholds should be based on historical data, industry standards, or predefined business rules. And this will help you identify deviations and abnormalities accurately.

Establish timely alerts

Configure alerts to notify you promptly when data anomalies or critical events occur. Make sure that the alerts are sent to the right people who can act against them because this will prevent potential issues from escalating.

Use visualization tools

Visualizations help you quickly identify trends, patterns, or outliers. They also make it easier for others to understand what you're representing. So, choose tools that provide suitable visualizations for data and make spotting anomalies or problems more manageable.

(Looking at outputs to understand the internal system is a backbone of good data practice — we call it observability.)

Regularly review and refine

Perform regular audits of your data monitoring approach to assess the following:

• Relevance of the monitored metrics.
• The effectiveness of alerts.
• The usefulness of the visualizations.

Stay open to adjusting and improving based on feedback and changing business needs.

Collaborate and communicate

Foster collaboration and communication between different teams involved in data monitoring. And encourage others to share insights, knowledge, and best practices.

You should also establish clear channels for communication and ensure that everyone understands their roles and responsibilities.

Ensure data security and compliance

Make sure to keep the data safe and only allow authorized people to access it when monitoring it. Follow the laws and privacy rules that apply to your monitoring practices and encrypt the data to protect it.

(Some organizations pursue compliance as a service.)

Monitor performance impact

Keep an eye on how monitoring your data affects the performance of your systems. Also, adjust the frequency and data collection methods to balance monitoring with system performance.

Automated data monitoring systems

An automated data monitoring system collects, analyzes, and reports data types in real-time or near real-time. It monitors and tracks data from multiple sources, such as:

• Databases
• Applications
• Servers
• Networks
• IoT devices

And an automated data monitoring system ensures the availability, performance, and integrity of data within an organization. It helps detect and resolve issues, optimize system performance, and ensure compliance with predefined business rules.

Here's what an automated data monitoring system can do:

• Collect data from various sources.
• Monitor data for anomalies, errors, or deviations.
• Generate alerts or notifications when an issue is detected.
• Analyze data to identify trends, patterns, or potential problems.
• Help to identify bottlenecks or performance issues and suggest optimizations.
• Ensure data handling and processing adhere to regulation and security standards.
• Maintain historical data repository for analysis and audit purposes.
• Integrate with existing systems and is scalable to handle increasing data volumes or sources.

How does it work?

The three core elements that make up an automated data monitoring system:

• Data collection
• Data analysis and monitoring
• Alerting and reporting

Combining these three core elements—the automated system will help your organization manage its data infrastructure and address issues promptly.

Here's how the system works:

Data collection

The data monitoring system collects and consolidates data from different sources for analysis. These sources could include databases, applications, servers, network devices, log files, APIs, and IoT sensors.

(Performance monitoring is different across systems — see how different network and application monitoring can be.)

Like data sources, there are diversified data collection methods, too. So the system will choose the one that suits its data source.

Some standard data collection methods are:

• Querying databases
• Capturing log files
• Using APIs to retrieve data from external systems
• Utilizing specialized connectors for specific applications

Data analysis and monitoring

Once the data is collected, the automated system analyzes it in real-time or near real-time. The analysis involves applying predefined rules, thresholds, or algorithms to detect anomalies, errors, patterns, or trends.

To do so, your system should have advanced analytics capabilities like statistical analysis, machine learning algorithms, and pattern recognition to process and interpret the collected data efficiently.

Alerting and reporting

The automated data monitoring system generates alerts or notifications when an issue or abnormality is detected during the data analysis phase. These alerts are sent to system administrators, IT support teams, or business users to take immediate action.

Alerting helps to ensure that responsible individuals are promptly informed about data anomalies that require attention. The system should have:

• Configurable alerting mechanisms.
• Ability to allow users to define thresholds.
• Notification channels (such as email, SMS, or instant messaging).
• Escalation procedures.

And the data monitoring system also generates reports or visualizations to provide insights into the monitored data. These reports can include dashboards, charts, graphs, or summary statistics to help stakeholders understand the current state of the data.

This further assists them with identifying trends and making informed decisions.

Data monitoring in a nutshell

Data monitoring is vital for businesses to ensure accurate and reliable data, make informed decisions, optimize processes, and mitigate risks. By implementing monitoring best practices, and utilizing automated data monitoring systems, companies can improve data quality, detect issues promptly and maintain a robust data infrastructure.

FAQs about Data Monitoring