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Highlights
- In a world flooded with data, it’s not how much you collect; it’s how much you can trust.
- A Data Quality Framework ensures your data is accurate, consistent, and reliable – powering better decisions, smoother operations and measurable business outcomes.
- High quality data drives strategic and financial gains by enabling better decision-making, reducing compliance risks, improved customer experiences and measurable ROI across the business.
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Data Quality Frameworks
In today's digital age, data has become an integral part of every organization, fueling decision-making processes, powering analytics, and driving innovation.
However, the value of data is directly proportional to its quality. Poor-quality data can lead to inaccurate insights, flawed decision-making, and compromised business outcomes. This is where data quality frameworks come into play, offering a systematic approach to ensure that data is accurate, reliable, and fit for purposes. Data quality frameworks provide a structured methodology for assessing, monitoring, and improving the quality of data within organizations. These frameworks encompass a set of principles, standards, processes, and tools aimed at addressing various aspects of data quality, including accuracy, completeness, consistency, timeliness, and reliability.
With a DQF, businesses can define their data quality goals and standards as well as the activities which are going to be taken to meet those goals. A DQF template is basically a roadmap that can be used to build their own data quality management strategy. Data quality management is too important to just leave up to chance. If data quality is negatively impacted, it could have massive consequences on not only the systems that rely on that data but also on the business decisions made.
The need for DQFs
While intuitively understanding the difference between good and bad data, formalizing data-handling processes helps in eliminating the guesswork. It provides a structured approach to assess, monitor and improve data quality across the organization. This framework guarantees that data is accurate, complete, consistent, reliable and relevant, to make informed decisions which in turn help in optimizing operations and achieving strategic goals.
A comprehensive data quality strategy helps to:
- Identify and rectify issues in the data proactively.
- Prevent potential errors and inefficiencies in the processes and outputs.
- Identify and act against biases that could harm business decisions.
- Promote continuous improvement and compliance with regulatory requirements.
- Define roles and responsibilities in managing data quality as part of data governance.
Variables
Many factors can have an impact on how robust the DQF is.
- Data Freshness: (Data Freshness must be checked before starting any processing)
- Decision-making accuracy: Fresh data is critical for making informed decisions. Stale data can lead to decisions that are based on outdated information, affecting the overall outcomes.
- Performance optimization: Ensuring data freshness optimizes performance by allowing up- to-date data to be used in processes and applications, thereby improving efficiency and effectiveness.
- Customer satisfaction: Timely and accurate data contributes to improved customer satisfaction by providing reliable and up-to-date information, enhancing the customer’s experience.
- Data Reconciliation: (Data Reconciliation must be done to compare the data flow statistics)
- Data inconsistencies: Data inconsistencies can arise from various sources and can lead to significant errors in data reports. Ensuring accurate data is crucial to maintain system integrity.
- Scalability issues: As data volume grows, ensuring that systems can scale efficiently becomes a challenge. Addressing scalability helps in maintaining performance and service continuity.
- Real-time processing: Processing data in real- time is demanding as it requires quick and accurate data handling. Real-time processing is essential for time- sensitive applications.
- Data Quality Check: (Data quality will be checked before ingesting the data to target)
- Accuracy: Accuracy ensures that the data correctly represents real- world values. High accuracy is critical for making reliable decisions based on the data.
- Consistency: Consistency ensures uniformity and coherence in the data. Inconsistent data can cause discrepancies and mistrust in the data's reliability.
- Completeness: Completeness means that all required data is present. Incomplete data can lead to incorrect analyses and insights, affecting business outcomes.
- Timeliness: Timeliness refers to having the data available when needed. Delays in data availability can hinder decision-making processes and response times.
Solution
We have discussed the problems and impacts of not having quality data. How can we solve this?
DQF can be divided into three steps – Pre Check, Data Quality Check & Post Check.
- Pre Check:
- This step involves verifying the data at the source. There can be various sources involved like SAP and Non-SAP (MSSQL, Oracle, PostgreSQL etc.) Apart from the Relational Database Management Systems, there are other sources such as files or other web applications that require data consumption.
- For RDBMS sources, data freshness will be checked by identifying the latest updated time of source tables, based on source ingestion schedule timings. For source files, validations can be done to check if the file is in order, for example, empty file check, wrong naming conventions, header mismatch, wrong format etc., which can be checked using scripting like Python or Shell.
- Data Quality Check:
- Once we have made sure that the source data is in order, this phase starts with the data load to target entities with technical and business data quality checks. This step involves two types of checks:
- Schema compliance check: The scope of this activity is to check the schema level integrities like, data type, null data, columns order, data length etc.
- Data quality check: Once the schema compliance check is cleared then data quality is checked. This phase checks for different data quality rules set by business and some generic data quality practices such as checking for date column formats, unwanted characters in data etc.
- Once we have made sure that the source data is in order, this phase starts with the data load to target entities with technical and business data quality checks. This step involves two types of checks:
- Post Check:
- Once the data load is done, this phase involves verifying the data that is loaded. In this phase we count reconciliations between source and target.
- We read audit data from the database where at run time Extract, Transform, Load (ETL) has generated source and target counts based on ETL capabilities. Then we reconcile the data between source and target based on the reconciliation rules set.
Reference DQF
A generic “reference data quality framework” provides a structured approach to ensure data accuracy, consistency, and reliability across various domains.
It outlines principles, processes, and tools to manage data quality from source to destination. This framework typically includes data profiling and assessment, defining quality dimensions (accuracy, completeness etc.) and establishing metrics and thresholds for data quality.
Figure 1:
Schematic Representation of Reference Data Quality Framework:
Figure 1:
Implementation
The solution follows a metadata driven approach.
All the job level metadata and settings are maintained in a metadata table. This table will help in initializing the jobs, executing the process, and even auto restart, recovery from point of failure, auditing and incremental loads. One-time initialization of the whole process, before implementation to any environment is required. There is minimal/no further manual intervention after implementation. The process will manage its execution paths and initiations based on the parameters and settings given, without multiple manual interventions.
All checks are performed during each phase of the quality framework approach and the actions are designated based on the settings done or requirement specific to any process. Once the process parameters are passed then the framework will alert with success mails or any other alerting system, which needs to be integrated into the framework. At any stage, if there are failures, the framework will alert the respective team based on a metadata-configured alert list and will create one ticket or incident to respective support team for debugging.
Schematic reference
As discussed earlier, a three-step approach towards the implementation of the Data Quality Framework includes different technical details such as:
- Checking for different files properties like empty files, naming convention, header mismatch, wrong file type etc.
- Identifying latest source data availability.
- Technical and business quality checks such as duplicate data check, not null check, schema compliance check.
- Data completeness check and reconciliation along with rejection of bad data based on requirements.
- Notification and incident management based on use cases.
- Process and log maintenance for auditing and controlling the jobs purview.
- Metadata management for auto process control and minimally manual interventions.
Figure 2:
Reference Architecture:
Figure 2:
Benefits
- Improved data accuracy and reliability: Ensures data is reliable for business decisions and reporting.
- Reduced costs: Minimizes the cost of data errors, rework, and data quality issues.
- Enhanced data governance: Establishes clear guidelines for data quality management.
- Better compliance: Helps organizations meet regulatory and compliance requirements.
- Increased efficiency: Streamlines data processing and improves data quality management processes.
- Proactive resolution: Detect and resolve data quality issues proactively.
Apart from the above benefits, DQFs also deliver value over existing traditional approaches, for instance:
- Improved data integrity.
- Performance optimization.
- Removed data inconsistencies.
- Enhanced data accuracy.
- Data completeness.
- Increased customer satisfaction.
To conclude, as data continues to be a strategic asset, maintaining high data quality is critical to achieving operational excellence and regulatory compliance, and gaining a competitive advantage. Hence, a robust DQF is not just a technical necessity but a strategic enabler that drives values across the entire organization by transforming raw data into reliable information that businesses can use for decision-making, operational efficiency, and strategic growth.
