Data Quality Management: Tools, Pillars, and Best Practices
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As companies gather and process increasing volumes of data, ensuring data quality becomes ever more important and challenging. Data is the lifeblood of every business today. And data quality management is a crucial approach that ensures valuable outcomes from data by integrating all the key elements of a data ecosystem, including culture and people.
Data quality is a measure of the health of all the data flowing through your company, including details such as data values. Data quality management is a situation-specific process that aims to enhance the fitness of data used for analysis and decision making. Gaining insights on data health by using diverse procedures and technologies is especially challenging when you’re dealing with large and complicated data sets.
How do you get started with data quality management? Keep reading to get a primer on all things related to the practical side of measuring and maintaining data quality.
What is data quality management?
Data quality management is a collection of processes that focus on ensuring high data quality. A good example of that is data quality testing. Data quality management includes everything from data collection to the deployment of modern data procedures to successful data delivery.
Effective data quality management is a critical part of any consistent data analysis process because it guarantees that insights used for decision-making are generated with the help of high quality data.
What does high data quality mean?
Data quality is the assessment of the data you have in relation to its purpose and capacity to serve that goal, for example, via a data quality framework. A good level of data quality is the level required to meet an organization’s operational, planning, and decision-making requirements.
Data quality is often measured using these six data quality metrics:
- Accuracy – a measure of how closely a piece of data corresponds to reality.
- Completeness – does the data meet your requirements for comprehensiveness? Is data saved in one location consistent with data kept in another? Is it there when you need it?
- Timeliness – a measure that defines the age of data in a database
- Consistency – this measures how effectively individual data points from two or more data sources synchronize. When two data points contradict each other, it indicates that one of the records is inaccurate.
- Validity – a measure that answers queries such as: Are data values in the correct format, type, or size? Is it compliant with the rules/best practices?
- Integrity – can you combine diverse data sets to produce a more comprehensive picture? Are relationships disclosed and carried out correctly?
|Data quality dimension
|Data’s readiness within a certain time frame.
|The amount of usable or complete data, representative of a typical data sample.
|Accuracy of the data values based on the agreed-upon source of truth.
|How much data conforms to acceptable format for any business rules.
|Compares data records from two different datasets.
|Tracks the volume of duplicate data in a dataset.
What is the purpose of data quality management?
Data quality management is a critical step in making sense of your data. Data quality management lays the groundwork for all business efforts. Outdated or untrustworthy data might lead to errors in decision making – and these errors might be costly!
Data quality management also saves money on unnecessary expenses. Poor quality data can result in costly errors and oversights, such as losing track of orders or expenditures. By having a good handle on your data, data quality management creates a data foundation that helps you comprehend your spending patterns.
A data quality management program creates a framework for all teams that defines and enforces data quality regulations. You also need data quality management for compliance purposes. Clear protocols and communication, as well as strong underlying data, are key to successful data governance.
Data quality management pillars
|Data quality management pillar
|Bulding a team that will bring your data quality vision to life.
|Creating insight into existing data and compare it to quality goals.
|Data quality rules
|Establishing quality rules (business or technical requirements) in line with corporate objectives and needs.
|Data quality reporting
|Eliminating and documenting any compromising data is called data quality reporting.
|Determining the most effective method of data correction and implementing changes.
Pillar 1: Team
You need the right team to bring your data quality vision to life:
- DQM Program Manager – this role assumes overall control of business intelligence efforts. They’re in charge of managing daily tasks concerning data scope, project budget, and program implementation. The vision for quality data and ROI should be led by the program manager.
- Organizational Change Manager – this person provides clarity and insight into data technology solutions used by the organization. The change manager plays an important role in the visualization of data quality.
- Business/Data Analyst – the business analyst defines the quality requirements from an organizational standpoint. These requirements are then translated into data models for data collection and delivery.
Pillar 2: Data profiling
Data profiling is a critical step in the DQM lifecycle, and it involves steps such as:
- Extensive data analysis
- Comparing data to its metadata
- Implementing statistical models
- Reporting on data quality
This process aims to create insight into existing data and compare it to quality goals. It helps teams develop a starting point for the DQM process and establishes a benchmark for how to improve information quality. Complete and accurate data quality metrics are critical for this stage.
Pillar 3: Data quality rules
The third pillar is about establishing quality rules in line with corporate objectives and needs. These rules are business or technical requirements that must be followed for data to be considered healthy and of high quality.
This pillar may prioritize business requirements, as important data items will vary depending on the industry. One example is setting a given data transformation error rate. Quality rules are very important to the success of any DQM process because they find corrupted data and stop it from hurting the whole set.
Data quality criteria will repair anomalies among useful data. When used with online BI tools, these rules will come in handy for forecasting trends and providing insights.
Pillar 4: Data quality reporting
The process of eliminating and documenting any compromising data is called data quality reporting. Once exceptions have been found and collected, they should be aggregated in order to identify quality trends.
The collected data points should be modeled and described depending on specific attributes (for example, by rule, date, source, and so on). Once this data has been gathered, it’s worth connecting it to an online reporting platform to report on the level of quality and exceptions on a data quality dashboard.
Pillar 5: Data repair
Data repair is a two-step procedure that involves determining the most effective method of data correction and implementing changes in line with data quality metrics.
The most important part of data repair is doing a “root cause” analysis to understand why, where, and how the data problem occurred. The remedial strategy should start once you complete this assessment.
Restarting data operations that relied on previously flawed data is almost always necessary, especially if the faulty data jeopardized or damaged their functionality. Reports, campaigns, and financial paperwork are examples of such procedures.
Data quality management lifecycle
The first step towards improving data quality is putting in place an effective structure for data cleansing and management. Here are a few example steps that are often part of data quality management lifecycles:
- Data collection – the process of gathering data from multiple internal and external sources
- Assessment – determining if the data you collected fulfills the quality standards
- Data cleansing – removing data that is duplicated, incorrectly formatted, or irrelevant to your goals
- Integration – bringing your data sources together to obtain a full picture of your information
- Reporting – using KPIs to check the quality of your data and prevent future problems
- Repairing – if your reports display corrupted data or require changes, apply them as soon as possible
How to implement data quality management in 4 steps
Step 1: Data profiling audits
Data profiling is an auditing process that identifies and corrects data quality concerns. Data duplication, as well as a lack of consistency, correctness, and completeness, are examples of issues among data values that directly relate to data quality metrics.
At the outset of each project, it’s worth evaluating the data quality to see if it is suitable for analysis. Before moving data to the target database, diagnose and correct any quality concerns in the source.
This is also the right moment to identify critical interdependencies and unexpected business rules that may impact the data profiling process and adjust the profiling procedures as necessary.
Step 2: Organizational structure
This part is about building a team, which is much more complicated than implementing data quality tools. You will need to create a specialized Data Quality Management team and allocate positions depending on the skills, expertise, and certifications held by team members.
Step 3: Rectifying errors
The process of repairing errors – also called data remediation – includes selecting the appropriate strategy for repairing data and implementing the modification in data collection, processing, and analytic processes.
This is also the time to examine and update data quality guidelines. Important business operations will work smoothly once you have enhanced your data quality requirements and have high-quality data.
Step 4: Reporting and monitoring data
DQM reporting is the practice of monitoring, reporting, and documenting exceptions in data and data-dependent business processes. These exceptions can be captured using business intelligence (BI) tools. This helps to spot data exceptions.
You need to regularly monitor your data with quality in mind. The software solutions you pick should provide you with monitoring capabilities through interactive data visualizations on handy dashboards.
Data quality management best practices
Make sure to involve all teams in your data quality program. A data quality manager, data analyst, and other positions play a key role in establishing and monitoring data quality requirements.
But for data quality to stay high, you need a culture change based on building a sense of accountability for data quality among other teams. This is also relevant to data governance – it’s advisable to hold everyone accountable for how they access and change data.
This is important to data management in general, but especially to master data management where business and IT collaborate to maintain the uniformity, correctness, ownership, semantic consistency, and responsibility of the company’s official shared master data assets.
Data governance is a collection of policies, responsibilities, standards, and key performance indicators (KPIs) that guarantee businesses use data effectively and safely. Implementing a governance structure is a critical step in defining data quality management roles and duties.
After establishing your DQM framework, you need to monitor compliance across two crucial areas:
- Check if the policies and standards outlined in the preceding sections are being followed internally
- Assess if the company is satisfying the regulatory rules for data usage in general
During the data’s lifecycle, you’ll need to archive, cleanse, retrieve, and destroy data from a variety of sources during the quality control process. This data may also require access by a lot of people.
That’s why you need to ensure that effective security measures are in place to avoid data breaches or abuse. To accomplish that, rely on current management solutions with top-notch security features.
Provide a high level of transparency to all important stakeholders throughout the process. Ensure that all data management standards and practices are communicated throughout the organization to avoid mistakes undermining your efforts.
Create a data glossary
Creating a data glossary as part of your governance plan is a smart move. Your glossary should include a compilation of all important phrases used to characterize the company data in an accessible and easy-to-navigate format. This builds a shared understanding of data definitions used throughout the company.
Invest in automation
Manual data input is regarded as one of the most prevalent reasons behind poor data quality due to human error. This becomes considerably more serious in businesses that require a large number of employees to enter data.
To avoid this, it is a good idea to invest in automation solutions that handle the input process. These solutions may be tailored to your policies and integrations, ensuring that your data is consistent across the board.
Establish KPIs for data quality
Data quality management is just like any other analytical process – it calls for establishing key performance indicators (KPIs) to analyze the success and performance of your efforts. Create quality KPIs that are in line with your overall organizational goals.
Data quality management tools
Here are five modern data quality tools that help teams keep track of the quality of their data and improve it:
1. Great Expectations
Great Expectations is an open-source data validation tool is simple to integrate into your ETL process and can help you avoid data quality concerns. To test data, you can use SQL or file interface – with a documentation format to create automatic documentation from the provided tests! After all, Great Expectations was built as a logging system.
The tool also lets you create a data profile and specify expectations for effective data quality management, which you may discuss throughout testing.
Deequ is an open-source metadata validation tool. AWS developed it to help engineers set up and manage metadata validation. Deequ is an Apache Spark-based tool for creating “unit tests for data,” which assess the quality of data in large datasets.
The tool is intended to deal with tabular data such as CSV files, database tables, logs, flattened JSON files, or anything else that can fit into a Spark data frame.
3. Monte Carlo
Monte Carlo offers a way to ensure observability (an essential data quality criterion) without using code to protect your data assets.
Monte Carlo uses machine learning to infer and understand the look of your data, discover and analyze data problems, and communicate alerts through connections with traditional operational systems. It also allows for the exploration of root causes. An essential pick among data quality management tools.
The automated data problem detection tool Anomalo helps teams keep ahead of data issues by automatically recognizing them as soon as they appear in the data and before they affect anyone else. Data practitioners may immediately connect Anomalo to their data warehouses and start monitoring the tables they care about.
Without the need to write rules or establish thresholds, the ML-powered application can automatically comprehend the historical structure and patterns of the data, alerting users to a number of concerns.
Lightup enables data professionals to easily apply and expand prebuilt data quality tests on massive volumes of data. Deep data quality checks may be carried out in minutes as opposed to months.
The solution also helps teams expand data quality tests across corporate data pipelines quickly and effectively using time-bound pushdown queries – without sacrificing speed. Furthermore, an AI engine can automatically monitor and discover data anomalies.
Data quality management is an essential initiative for any team looking to improve data quality, handle the most common data quality issues, implement solid data quality control standards, and provide data consumers with data products that are reliable and trustworthy. Ultimately, investing in data quality means building greater trust around data and enabling data-driven decision-making across the board.
Even if putting your data lake on object storage has advantages in terms of scalability and throughput, adhering to best practices and ensuring high data quality metrics remains tough.
How do you ensure data quality in this situation? The only hope is to include automation in the equation.
Continuous integration and continuous data deployment are automated procedures that rely on the ability to detect and prevent data errors from reaching the production environment. You can quickly achieve high data quality by building this feature with a range of open-source alternatives.
One of them is lakeFS. It offers hooks for zero-copy isolation, pre-commit, and pre-merge to aid in the automated process. lakeFS offers a solution for evaluating data quality technologies in accordance with the best practices outlined above.
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