Written By: Sudeshna Ghosh
Key Takeaways
- Quantzig’s Automated Data Quality Management framework using low-code platforms and Automated DQM tool helped a US-based retail client increase their overall data quality management efficiency by 20-30%.
- Implementing a robust data quality monitoring solution is challenging due to problems originating beyond data teams, lack of data expertise, data source complexity, and limited data visibility.
- Data quality monitoring ensures useful, accurate, and reliable data, enhancing compliance, risk mitigation, decision-making, customer satisfaction, cost reduction, and competitive advantage while fostering seamless data integration and continuous improvement.
Introduction to Data Quality Monitoring
As data becomes increasingly important in the digital age, the significance of data quality monitoring in making effective data and machine learning systems has increased immensely. The entire process involves the validation and ongoing assessment of data to ensure consistency, reliability, and accuracy. If you neglect continuous data quality monitoring, it can lead your B2B business to significant damages, such as compliance issues, poor application performance, revenue loss, and customer churn.
Are you accurately validating and checking the accurate data? How can you ensure the reliability of your present data pipelines? This case study will shed light on how Quantzig’s innovative data quality management solution helped a US-based retail client increase their overall data quality management efficiency by 20-30%.
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Quantzig’s Expertise in Data Quality Monitoring for a Retail Brand
| Category | Details |
|---|---|
| Client Details | A leading US-based retail company with a diverse product portfolio. |
| Challenges Faced by The Client | Inconsistent data quality and data lineage, and manual data quality control framework. |
| Solutions Offered by Quantzig | We built Automated Data Quality Management framework using low-code platforms like Power Automate and implemented Automated DQM tool to solve the data lineage issue. |
| Impact Delivered | The Automated DQM framework improved stakeholder control with an interface for rule management, cut manual data quality checks by 60-70%, increased data team efficiency by 20-30%, and boosted insights consumption by 70-80% due to greater data trust. |
Client Details
A leading US-based retail company with a diverse product portfolio.
Challenges Faced by the Client
One of our US retail clients’ two main data quality monitoring challenges was inconsistent data quality and data lineage. Due to frequent data problems, business stakeholders found it difficult to trust the daily business data, which also decreased their use of the insights produced by data analysis. The group used a quality control framework that was entirely manual and did not have any automation.
Solutions Offered by Quantzig
With the use of pre-established guidelines established by data teams, Quantzig’s Automated Data Quality Management framework assisted in both automatically detecting and fixing the majority of data problems. Using low-code platforms like Power Automate, the solution was constructed in a matter of weeks, saving development time and resulting in a low-cost solution.
Having a stakeholder approval process in place for all suggested data corrections made by the Automated DQM tool also solved the data lineage issue. Within a few weeks, the solution was constructed, and the business was able to customize it by adding more rules for data quality checks and setting up a threshold-based alert system for the designated data columns that exceeded the threshold.
Impact Delivered
- The Automated DQM framework helped in reducing the manual effort involved in Data Quality checks by 60-70%.
- It gave business stakeholders more control over data and included an interface where rules for data correction and alert systems could be added and changed.
- Data teams were the custodians of any rules being modified/added and were providing approvals to ensure there were no incomplete or wrong rules applied.
- It increased the overall efficiency of the data management team by 20-30%.
- The insights consumption increased by 70-80% as trust in data increased after the reduction in data errors.
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Start your Free Trial TodayWhat is Data Quality Monitoring?
Data quality monitoring is the measurement, assessment, and management of the entire organization’s business data in terms of consistency, accuracy, and reliability. This process utilizes numerous techniques to recognize and resolve data quality problems, making sure high-quality data is utilized for decision-making and business processes.
The significance of real-time data quality monitoring and data engineering services cannot be overstated because poor-quality data may result in inefficient operations, inaccurate conclusions, and a lack of trust in the information provided by the organization’s systems. It can ensure that data quality issues are early detected before they can hugely impact an organization’s customers and business operations.
The Necessity of Data Quality Monitoring
To identify why you should monitor your data quality, you should know where data quality issues stem from in your whole data lifecycle process and the types of data quality issues you’ll probably encounter at each stage. Inaccurate analyses, erroneous business decisions, widespread financial collapse, and ultimately detrimental effects on reputation have all resulted from poor data quality.
What are the Key Dimensions of Data Quality?
The following are the main dimensions of data quality that are properly recognized by data quality monitoring:
| Dimensions | Details |
|---|---|
| Accuracy | This dimension assesses how accurately values are compared to their true representation. |
| Completeness | It assesses how much of the necessary data is available and present. |
| Consistency | This refers to how data is the same from one source or system to another. |
| Timeliness | It evaluates the information’s currentness with respect to its intended use. |
| Validity | This is the process of making sure that every attribute in a dataset follows established formats, guidelines, or standards. |
| Uniqueness | This guarantees that a dataset contains no duplicate records. |
| Integrity | This ensures that there are no broken links in the referential relationships between datasets. |
What are the Key Metrics to Monitor Data Quality?
Apart from the data quality dimensions, some metrics can recognize your data quality problems. Tracking those metrics can enable early resolution and identification of issues before they impact customer experience or business decisions.
1. Error ratio
It measures the proportion of dataset records with significant errors. If the error ratio is high, it underlines poor data quality and could lead to faulty decision-making or inaccurate insights. Divide the number of records with errors by the total number of entries to calculate the error ratio.
2. Duplicate record rate
When multiple entries are made for a single entity due to human or system errors, duplicate records may result. In addition to taking up storage space, these duplicates distort analysis results and impair sound decision-making. The percentage of duplicate entries in a dataset relative to all records is determined by the duplicate record rate.
3. Recognize validity percentage
For companies that depend on location-based services, like delivery or customer service, having an accurate address is essential. The percentage of valid addresses in a dataset relative to all records having an address field is called the address validity percentage. It’s critical to routinely validate and clean your address data to preserve high data quality.
4. Data time-to-value
The rate at which value is extracted from data after it has been gathered is referred to as data time-to-value. A faster time-to-value suggests that your company processes and analyzes data for decision-making effectively. By keeping an eye on this metric, you can pinpoint any bottlenecks in the data pipeline and make sure business users have access to timely insights.
Data Quality Monitoring Challenges
It can be challenging to put a real-time data quality monitoring system into practice. The following are some potential data quality monitoring challenges you may run into:
1. Data source complexity:
Managing the massive amount of data that comes from multiple sources can be challenging. It is challenging to manage and keep an eye out for quality problems due to the sprawl of data.
2. Data quality issues originating beyond data teams:
Data quality problems can come from the data team and from other parts of the company. This can make determining an issue’s underlying cause challenging.
3. Lack of data expertise:
It’s possible that data engineers lack the time and means to oversee the quality of every piece of data. Furthermore, it can be challenging to locate individuals possessing the specialized knowledge needed to oversee data quality.
4. Limited data visibility:
It is possible that the data team does not have access to all of the metadata—the information that describes the actual data—that they require. The lack of visibility makes it challenging to interpret the data and spot issues with quality.
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Effective data quality management techniques include leveraging data quality monitoring software and tools for real-time data monitoring, which helps in promptly identifying and addressing data quality indicators and issues. Integrating machine learning with data analytics enhances the ability to detect anomalies and reduce data fragmentation, ensuring smooth data consumption processes. A robust data strategy and data governance framework support these techniques, providing the necessary structure and oversight to maintain high data standards. To keep an eye on the quality of your data, try these popular methods for data quality monitoring:
1. Data profiling
The process of examining, evaluating, and comprehending the relationships, content, and structure of your data is called data profiling. Using this method, data is examined at the column and row levels to spot trends, discrepancies, and abnormalities. Data profiling offers useful information, including data types, unique values, lengths, and patterns, which can help you understand your data quality.
Three primary forms of data profiling exist: dependency profiling, which finds relationships between attributes, redundancy profiling, which finds duplicate data, and column profiling, which looks at individual attributes in a dataset. You can obtain a thorough understanding of your data and spot possible quality problems that require attention by employing data profiling tools.
2. Data auditing
The process of evaluating data completeness and accuracy by comparing it to predetermined guidelines or standards is known as data auditing. This method assists companies in locating and monitoring problems with data quality, such as missing, inaccurate, or incomplete data. Data auditing can be done automatically with tools that scan and highlight data inconsistencies, or manually by going through records and looking for mistakes.
Establishing a set of guidelines and requirements for data quality that your data must follow is the first step toward performing an efficient data audit. After that, you can use accurate audit tools to find any problems and differences by comparing your data with these guidelines and standards. Lastly, you should review the audit’s findings and put any found issues with data quality into practice by taking corrective action.
3. Data cleansing
Finding and fixing mistakes, inconsistencies, and inaccuracies in your data is the process of data cleansing, sometimes referred to as data cleaning or data scrubbing. To make sure that your data is comprehensive, accurate, and dependable, data cleansing techniques use a variety of techniques, including data transformation, data validation, and data deduplication.
The steps involved in data cleansing generally include the following: identifying problems with data quality, figuring out the underlying causes of these problems, using the methods on your data, choosing suitable cleansing methods, and validating the outcomes to make sure the problems have been fixed. You can provide high-quality data that helps with effective decision-making and efficient business operations by putting in place a strong data cleansing procedure.
4. Metadata management
The process of arranging, preserving, and utilizing metadata to raise the caliber, consistency, and usability of your data is known as metadata management. Organizations can better understand and manage their data by using metadata, which is information about data. Examples of this information include data lineage, data definitions, and data quality guidelines. Ensuring that your data is easily comprehensible, accessible, and useful for your organization can be achieved by putting strong metadata management practices into place.
Establishing a metadata repository that consistently and systematically stores and arranges your metadata is the first step toward implementing efficient metadata management. Subsequently, as your data and data processing systems change, you can utilize accurate tools to preserve, record, and update your metadata. Lastly, to support efforts related to data governance, data integration, and quality monitoring, you should put best practices and procedures into place.
5. Data performance testing
This is the process of evaluating the effectiveness, efficiency, and scalability of your business data processing infrastructure and systems. This technique helps organizations ensure that their existing data processing systems can handle increasing data complexity, volumes, and velocity without compromising data quality.
You must first set benchmarks and performance goals for your data processing systems before you can conduct data performance testing. The performance of your systems can then be compared to the predetermined benchmarks and targets by simulating different data processing scenarios, such as large data volumes or intricate data transformations, using data performance testing tools. Lastly, you should review the outcomes of your data performance tests and make any required infrastructure and data processing system upgrades.
How to Implement Data Quality Monitoring
Whether you’re planning for a new data quality monitoring approach or addressing current issues, there are several important steps required to implement data quality monitoring.
1. Address significant data quality problems in your present data systems
- Start by identifying the phases where problems routinely arise using a root-cause analysis to solve major data quality concerns in your current data systems.
- To comprehend the schema and statistical characteristics of the problematic dataset, profile it. Examine all dependencies, including pipelines and workflows, to determine the downstream locations of crucial data ingestion, transformation, and consumption.
- Determine which important data points need to be monitored based on concerns found, then choose a continuous monitoring strategy that works for your company.
- Put the monitoring system into practice and adjust it so that it tracks and informs on problems without producing false positives or negatives.
- Establish management procedures for reports, including who should get them, how they should be formatted, and what should be done with the information they include.
2. Plan for a new problem
- To guarantee that your data is appropriate for solving the company challenge, begin by comprehending the technological, business, and data requirements. This entails figuring out the essential service-level agreements (SLAs), service-level objectives (SLOs), and service-level indicators (SLIs) to run the system efficiently, as well as identifying crucial metrics that correspond with these needs, such as precise output durations for data pipelines.
- To determine which critical data flows need to be monitored, relate these metrics to your data assets and dependencies. With this knowledge, you can create data validation tests for potential problems and select the best data quality & governance solutions system to enable prompt identification and resolution of data quality issues while addressing the major concerns previously identified.
Conclusion
In conclusion, data quality monitoring approach provides a response to the query of your data’s trustworthiness and dependability: To what extent is the data your pipeline is consuming from your whole data system dependable? To guarantee that the systems you are developing are dependable and won’t malfunction and harm business, it is imperative for engineers to comprehend the quality of the product—in this case, data—that they are working on. Inaccurate insights and bad decisions can be caused by a lack of control or visibility into the quality of the data, which can lead to lost revenue or a bad customer experience.
