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The true measure of an effective data warehouse is how much key business stakeholders trust the data that is stored within. To achieve certain levels of data trustworthiness, data quality strategies must be planned and executed.

It’s clear that data quality ultimately determines the usefulness and value of a data warehouse. But achieving high-quality data is no small task, especially in larger enterprises. This guide offers 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 crucial part of data governance that guarantees organizational data is fit for purpose. It is the metric 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 store of data amassed from a vast range of company sources; it is mainly used for decision support. A data warehouse is a non-operational system that merges data from operational systems and delivers optimized data for users. This type of data storage solution can deliver a single source of truth to an organization.

How to improve data quality in a data warehouse

Proactively implement measures to handle data quality issues

To ensure that trustworthy data is available, organizations should implement frameworks that capture and streamline data quality issues automatically. Both data cleansing 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 should happen early in the data integration process to flag data issues. Data profiling should also be a part of these frameworks because it is a pillar of building confidence in data. It helps organizations understand their business needs further and assess the quality of their data to uncover any gaps.

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

Scrutinize data quality shortcomings

Proactive measures do not guarantee safety from bad data. When bad data bypasses proactive measures and is reported by business users, such bad data needs to be investigated to ensure that user confidence is maintained. These investigations need to be prioritized.

Failure to investigate data quality shortcomings in a data warehouse will lead companies to deal with recurrent errors. Continuously correcting these kinds of data errors can be complex and time-consuming in the long run. Therefore, organizations should seek to identify errors and prevent similar errors from recurring in the future.

Company leaders should consider building data lineage and data control frameworks into their platforms to help them rapidly identify and remediate data issues. Where organizations are using commercial tools for their data integration pipelines, they need to consider installing mechanisms that assist in maintaining data quality.

Incorporate data governance

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

Various departments should collaborate to establish security, retention and collaboration policies for their data that are in line with legal and business requirements. Companies often end up fostering a culture of high data quality when they involve business users and data teams in data governance best practices.

Establish data auditing processes

Any processes and plans that businesses use to create and maintain data quality should be regularly measured for efficacy. Auditing data within data warehouses is a useful approach to building trust in data. Data auditing enables users to check for instances of subpar data quality such as incomplete data, data inaccuracies, poorly populated fields, duplicates, formatting inconsistencies and outdated entries.

Business leaders should also determine how frequently these audits should be conducted for optimal results. Having lengthy periods between audits means that ineffective processes and errors may proliferate for an extended period of time before they’re discovered. This also means that it may 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 fashion whenever possible. Some organizations opt to do a third-party audit so external professionals can determine any weak spots in the data warehouse.

Make data quality an enterprise-wide priority

Stakeholder buy-in is key to ensuring that high-quality data is available across an organization. When all stakeholders understand and take responsibility for data quality, they show commitment to upholding data quality. Every level of management needs to support data quality initiatives and cultures.

Take advantage of the cloud and cloud data warehouses

The continued growth of big data is leading many companies to forego more traditional on-premises data warehouses with their complexities 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 advantages to companies, but they aren’t always the easiest infrastructures to set up. Selecting the right vendor will determine how quickly and effectively your cloud data warehouse gets up and running. To help with your data warehouse selection process, reference this cloud data warehouse guide and checklist.

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Learn the latest news and best practices about data science, big data analytics, artificial intelligence, data security, and more. Delivered Mondays and Thursdays