Here's how you can impress your managers by identifying and solving data quality issues in Data Warehousing.

Analyze the impact of each issue on your business decisions. For instance, missing customer addresses in a sales report are more critical than slightly inconsistent product descriptions. Prioritizing lets you focus resources on areas with the biggest impact. Furthermore, suggest data quality metrics aligned with these priorities. Track these metrics over time to showcase the effectiveness of your data cleansing efforts and impress your managers with your data-driven approach.

When sizing a data warehouse, understanding usage patterns is crucial. Analyze data access needs for concurrent users or batch jobs, affecting size and architecture. Examine historical data growth to estimate future volumes. Cloud-based data warehouses like AWS Redshift and Google BigQuery offer elastic scaling, adjusting resources on-demand, preventing over- or under-provisioning. Impressing Managers with Data Quality Solutions: Identify data quality issues with profiling tasks. Analyze the impact on business decisions, prioritize critical issues, and define data quality metrics. Automate checks, engage stakeholders, and monitor continuously to maintain high data quality and align improvements with business goals.

Below is common in all large data warehouse setup ? Data is consumed from 50 to 100+ internal systems ? Each system can sync 3 to 5 data entities like order, shipment, invoice etc in DW ? Each entity can have 75 to 150 columns based on granularity of reporting used in the organization ? Required fields for one specific entity can be distributed in multiple source systems Building column level data quality check for all these entities can be a nightmare and will require big DW Engineering team presence just to manage data quality area. Usually data quality check jobs are configured on few key elements between staging and integration layer as periodic DQ check

Propose Data Governance. Don't just fix the immediate issues, propose a data governance framework to prevent future problems. This framework should define data ownership, establish data quality standards, and implement automated data validation rules. By involving stakeholders in creating the framework, you ensure their continued commitment to data quality. Studies show that strong data governance can improve data quality by up to 40% [source], leading to better decision-making and increased ROI from your data warehouse.

Proactive Monitoring. While defining standards is essential, ongoing vigilance is key. Implement data profiling tools to identify inconsistencies and anomalies within the warehouse. This can catch issues like missing values, outliers, and duplicate records before they impact analysis. Schedule regular data quality checks and reports to proactively address issues and demonstrate your commitment to data integrity. Remember, data quality is an ongoing process, not a one-time fix.

Below approach can be used in batch load ? 1:1 replica database is maintained by DBA team for each source system production database ? DW staging job connects to these replica database every hour and sync data in source specific staging schema in DW, either as a full load or incremental load based on last update time in source record ? DW integration jobs integrate data from all such staging schema in upsert mode, runs every 2 hours and fetch data received in staging layer in last 3 hours ? Data from integration layer is moved to DW reporting layer with associated task like dimension key generation, upsert, every 2 hour based data refresh etc ? BI layer follows next for dashboard and reporting

Utilize data profiling tools to unearth more nuanced data quality issues. These tools can detect inconsistencies like missing values, invalid formats, or outliers that might slip through basic checks. Schedule regular profiling reports to proactively catch potential problems before they affect data analysis. This demonstrates a data-driven approach to quality control, ensuring your managers receive trustworthy insights. Data quality is a continuous process, not a one-time fix. Regularly refine your checks and reports as your data sources and business needs evolve. This proactive management showcases your commitment to maintaining a high-performing data warehouse.

Usually data quality check jobs are configured on few key elements between staging and integration layer as below ? Hourly order count comparison between source and DW ? Shipments without order id count match between source and DW ? Percentage variance in daily order count and data quality check failure above 10% variance, if maintained as a business metric ? Delay in receiving vendor invoice above defined threshold ? Delay in shipment delivery after out for delivery status Above process ensures data is received and synced in DW incrementally throughout the day and only 75 to 100 data quality check job is sufficient to maintain data quality for a 1500 daily batch job based data warehouse

Proactive Prevention is Key. While monitoring identifies existing issues, go a step further by implementing preventative measures. Standardize data formats across source systems to minimize inconsistencies during integration. Enforce data quality rules at the point of entry, such as mandatory fields or valid value ranges, to catch errors early in the process. This proactive approach reduces the need for downstream data cleansing and ensures high-quality data from the outset. Remember, an ounce of prevention is worth a pound of cure, especially when it comes to data quality.

All progress associated with remediating a data quality incidents in data warehouse must be defined, planned and comment to all stakeholders; then all stakeholders can monitor and help you in evaluate how data is with these fixes. Define and share the plan of fixes with the verification schedule could generate a great impress on all stakeholders.