5 Ways to Enable Business Agility with Data Monitoring in '24

Data is an important guide for businesses to make data-driven decisions and get a realistic view of the industry they operate in. Data consistency is critical for analyzing and observing the status of businesses. That’s where data monitoring comes in. It offers businesses the opportunity to continuously check the quality of their data.

What is data monitoring?

Data monitoring refers to the process of measuring and evaluating data to avoid degradation in quality over time. Data monitoring, for example, assesses the quality of your data to ensure that it meets or fulfills pre-determined business purposes.

What are the benefits of data monitoring?

Data monitoring enables business agility by checking the quality of data at the time of creation. Here are some key benefits of data monitoring for businesses: 

• Solve issues faster: Data monitoring ensures that data problems are identified as soon as they occur and enables businesses to intervene and resolve them.
• More accurate data-driven decision-making: Maintaining the quality of data enables businesses to make accurate decisions based on data.

In addition, data monitoring is essential for tracking the performance of machine learning models. In order to get optimal outcomes from ML models over time, companies should monitor data used to train ML models. It allows businesses to analyze the accuracy of model prediction and detect data issues early.

1. Detect Existing Problems

Data monitoring helps with detecting existing problems in data. Some examples of how data quality issues arise include:

• Data duplication: It commonly occurs because of human error. If not detected, it can cause skewed metrics.
• Missing data: These are missing values in databases. Missing values can occur due to a lack of observation or due to human error.
• Ambiguous data: It means that two separate data points in a database cannot be distinguished from each other. These types of data are vague and open to multiple interpretations.
• Data drift: Changes in data over time due to changing environments.
• Data mismatches: It is also called ‘data match error’, and causes inconsistencies in the data. This is a situation where data types don’t match input values.

Recommendation: Leverage data monitoring to ensure that data problems are identified as soon as they occur. This enables you to intervene and resolve them faster.

2. Predict  potential issues

Not only detecting existing problems but also predicting potential problems, such as data security and optimization issues, is a factor that improves the data monitoring process. Predicting potential problems saves businesses resources and time by allowing them to be more flexible and plan for the long term.

Recommendation: Enable more accurate data-driven decision-making by using data monitoring. Maintaining the quality of data enables businesses to make accurate decisions based on data.

3. Track the performance of machine learning models

Data monitoring is essential for tracking the performance of machine learning models. In order to get optimal outcomes from ML models over time, companies should monitor data used to train ML models. It allows businesses to analyze the accuracy of model prediction and detect data issues early.

4. Utilize alerts and dashboards

Monitoring is a process and good planning of the process can improve it. For instance,

• You can use dashboards that provide a summarized view of the system in the form of charts and graphs and contain historical data.
• You can set up alerts responding to the changes to your data and notify you when a problem occurs.

Recommendation: 

• Use dashboards that provide a summarized view of the system in the form of charts and graphs and contain historical data.
• Set up alerts responding to the changes to your data and notify you when a problem occurs.

5. Choose the right tools

It is important that you evaluate each situation you monitor in its own context. By interpreting the information you receive through the data monitoring process in its own way, you can better identify problems. At this stage, you can get more accurate and consistent results by choosing the right tool for your business.

You can also check our article on model monitoring.

If you have questions about which solutions to choose, we are happy to help: