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Best practices for data quality in data warehouses

Data quality is paramount in data warehouses, but data quality practices are often overlooked during the development process.

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The real measure of an effective data warehouse is how much key business stakeholders trust the data stored in it. To achieve certain levels of data reliability, data quality strategies must be planned and executed.

It is clear that data quality ultimately determines the usefulness and value of a data warehouse. But getting high quality data is no small task, especially in larger enterprises. This guide provides best practices for any data professional or leader who wants to learn how to optimize data quality in their organization’s data warehouses.

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What is data quality?

Data quality is a critical part of data management that ensures that the organization’s data is fit for purpose. It is the measure that measures usability when it comes to processing and analyzing a dataset for other uses. Data quality dimensions include consistency, completeness, conformity, integrity and accuracy.

What is a data warehouse?

A data warehouse is a large amount of data collected from a large number of business sources; it is mainly used for decision support. A data warehouse is a non-operational system that aggregates data from operational systems and provides optimized data for users. This type of data storage solution can provide an organization with a single source of truth.

How to improve data quality in a data warehouse

Proactively implement measures to address data quality issues

To ensure that reliable data is available, organizations must implement frameworks that automatically capture and streamline data quality issues. Both data cleaning and data profiling can be helpful at this point in the process.

SEE: Cloud Data Warehouse Guide and Checklist (TechRepublic Premium)

Since data cleansing involves analyzing the quality of data in a data source to determine whether or not to make changes, data cleansing must be done early in the data integration process to identify data issues. Data profiling should also be part of these frameworks, as it is a pillar to build trust in data. It helps organizations better understand their business needs and assess the quality of their data to spot any gaps.

Data sanitization and data profiling should go hand in hand to ensure that flaws revealed during data cleanup are addressed. These data quality frameworks may require an upfront investment. Despite the potential costs, organizations should assess the investment and consider making the investment based on the expected long-term benefits to the data warehouse.

Investigate data quality deficiencies

Proactive measures do not guarantee security against bad data. When bad data bypasses proactive measures and is reported by business users, such bad data must be investigated to ensure user trust is maintained. These investigations must be given priority.

Failure to investigate data quality deficiencies in a data warehouse will lead to businesses experiencing recurring errors. Continuously correcting these types of data errors can be complex and time consuming in the long run. Therefore, organizations should try to identify errors and prevent similar errors from recurring in the future.

Business leaders should consider building data lineage and data control frameworks into their platforms to help them quickly identify and resolve data issues. Where organizations use commercial tools for their data integration pipelines, they should consider installing mechanisms to help maintain data quality.

Integrate data management

It is useless to centralize data for analytics if the data is ingested into a poor quality data warehouse; the data warehouse will be ineffective for one of its main purposes: decision support. Implementing robust data governance guidelines can help organizations avoid such a fate.

Different departments must work together to establish security, retention and collaboration policies for their data that are consistent with legal and business requirements. Companies often foster a culture of high data quality when they engage business users and data teams in data governance best practices.

Setting up data audit processes

All processes and plans companies use to create and maintain data quality should be regularly measured for effectiveness. Auditing data in data warehouses is a useful way to build trust in data. Data audits allow users to check for instances of substandard data quality, such as incomplete data, data inaccuracies, poorly filled fields, duplicates, formatting inconsistencies, and outdated input.

Business leaders also need to determine how often these audits should be performed for optimal results. Long periods between audits means that ineffective processes and errors can multiply over a longer period of time before they are discovered. This also means that it can take much more time and effort to investigate and correct these errors and processes.

Audits should be continuous, automated, and structured in a periodic or incremental manner whenever possible. Some organizations choose to do a third party audit so that outside professionals can identify any vulnerabilities in the data warehouse.

Make data quality a company-wide priority

Stakeholder buy-in is essential to ensure that high-quality data is available across the organization. When all stakeholders understand and take responsibility for data quality, they demonstrate commitment to maintaining data quality. Every level of management should support data quality initiatives and cultures.

Take advantage of the cloud and cloud data warehouses

The continued growth of big data is driving many companies away from more traditional on-premises data warehouses with their complexity and latency issues. Cloud data warehouses enable data quality tools to live closer to data sources and users, which can result in more effective data quality practices.

The cloud also simplifies the process of integrating data quality and data integrity tools into a data warehouse. Finally, cloud data warehouses make it easier to access data as they efficiently ingest and prepare data from different sources in multiple formats.

Cloud data warehouses offer many data strategy benefits to businesses, but they are not always the easiest infrastructures to set up. Selecting the right vendor determines how quickly and effectively your cloud data warehouse becomes operational. Refer to this cloud data warehouse guide and checklist to help guide you through your data warehouse selection process.

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