What is Data Quality Monitoring?

As businesses become increasingly data-dependent, maintaining high-quality data is essential. Data Quality Monitoring is the continuous assessment of data against predefined rules to detect errors, maintain consistency, and improve reliability. 

A well-structured monitoring system enhances accuracy, supports compliance, and ensures that business decisions are based on clean, trustworthy data. 

Why is Data Quality Monitoring important?

Imagine a financial firm processing loan approvals. Without proper DQ monitoring, data discrepancies could misrepresent credit worthiness, resulting in faulty approvals, compliance violations, or significant financial losses.  With proactive data quality monitoring, errors can be detected and corrected in real time, ensuring accurate risk evaluation, regulatory compliance, and sound financial decision-making. 

From a stakeholder's perspective, Data Quality Monitoring ensures accurate, reliable, and actionable data across the organization. Business leaders and decision makers benefit from trustworthy data for strategic planning, while data analysts and data scientists rely on it to generate credible insights. IT teams use monitoring tools to quickly detect and resolve data issues, improving operational efficiency. Compliance officers leverage high quality data to meet regulatory standards, avoiding penalties and maintaining trust. 

Leveraging Datagaps DataOps Suite's DQ Monitor for Streamlined Data Quality Monitoring

DataOps DQ Monitor automates the testing of both data in motion and data at rest, whether at the source and ingestion stage or during the transformation process. 

DQ Monitor plays a vital role in validating key elements of data quality to ensure reliable and trustworthy data. The important aspects it monitors include: 

• Accuracy: Verifies that data accurately represents the real-world entities it is meant to reflect. 

• Completeness: Ensures all required data is present and accounted for, leaving no gaps. 

• Consistency: Checks for uniformity and coherence of data across datasets and systems. 

• Timeliness: Confirms that data is available when needed for timely decision-making. 

• Unicity: Detects and eliminates duplicate records within datasets. 

• Validity: Validates that data adheres to predefined formats, rules, and structures. 

• Conformity: Ensures compliance with organizational standards, business rules, and regulatory requirements. 

By addressing these critical dimensions, DQ Monitor helps organizations maintain high-quality data that drives better decisions and operational efficiency. 

How DQ Monitor works?

Following screenshot is the Data Quality module of the DataOps suite. If observed, we can find four crucial segments in this section. 

First, is the Data Quality Score which is automatically computed based on the Data Quality Rules for data at rest as well as for the data in motion (ETL). It computes Data Quality score for data assets and displays trend reports on a Data Quality Dashboard

We'll be looking at Data Models next, A data model is an abstract representation that organizes data elements and standardizes how they relate to each other and to real-world entities, serving as a blueprint for data storage and retrieval.  

Data models are crucial because they provide a structured framework for organizing, storing, and managing data efficiently. They can be designed in different ways depending on use cases and business needs. 

Following screenshot is a dashboard representing elements of a data model called “Customer Data Model”. 

We can see that there are 9 tables organized in this data model. If required, tables can be added or removed from the model. DataOps suite gives the flexibility to add a new table or import the existing tables either from data source or from existing data models. For this data model, the Data Quality score is tracked for the previous 5 runs to see how the model is changed in terms of data quality during this course. 

On clicking a table in a data model, we can observe that the columns are automatically identified by the model. If required, new columns can be added. 

Similarly, if there are any relations pre-defined between the tables, they will be detected, or users can add the required relations between the tables themselves as shown in the below screenshot. 

Moving on, the rules can be added to validate the columns of tables in the data model. Input is taken from the table dataset to apply rules on the whole table and individual columns. Rules have a definitive success criterion where users can define what rule gets passed or failed. Thus, allowing users to define the validation checks and further determine the Data Quality score for this table. These rules can be tested and saved.  

When multiple tables exist in a data model, their data can be periodically updated and validated. The Data Quality Score is aggregated from these tables, meaning any changes to the tables or the model itself can affect validation results, ultimately impacting the overall data quality. 

Moving on, we have Common Data Model module in Data Quality Monitor. This is where common entities of a subject area are grouped together. A subject area refers to a high-level organization of data representing a group of related concepts within a specific functional area of an organization.  

In the Common Data Model, the subject area can be added by providing the subject area name and selecting the data owner and data steward. To work on the subject area, the user first needs to import or add Entities(tables). After adding or importing, the user needs to map subject area to particular data model. 

From the above image, we can see that the rules for entities can be defined like how business rules can be applied for Email Address 

In the last module of Data Quality Monitoring, we'll be looking Data Analysis. Here, Data Observability comes into the picture where the suite uses AI and statistical methods to identify and report the anomalies of the data.  

Through this, the predictions are done based on the collected data measures through options like standard deviation, time series, Inter Quartile Range and others to count anomalies. These anomalies are reported when the values are out of the expected bounds. 

In Analysis, there are two important aspects, one is statistics and the other is prediction.  

From the above screenshot, we collected the incoming data for the dataset “Covid Data Analysis”. Measures are collected and from the status, we can see that few values say “Pass”. This indicates that these values fall between the expected or set bounds of values. The same status will reflect “Fail” if the incoming value is not within these bounds. If there are no set bounds, the status will reflect “collected”. These measures can be used for predictions if required. 

Based on the collected data, the predictions are made and reflected in the predictions section as shown below 

We can see that the “Predicted Value” along with Lower Bound and Upper Bound for measures are given in the predictions results. (In the screenshot, the predictions are made for 23rd May based on the data collected till 20th May.) 

Wrapping this up, Data quality is the foundation of reliable insights and effective decision-making. With Data Quality Monitor, organizations can proactively track, validate, and maintain high-quality data with ease