Data warehouse quality is crucial for delivering reliable and actionable insights to your business. But how do you measure it? What are the best indicators that tell you if your data warehouse is performing well, meeting your expectations, and satisfying your users? In this article, we will explore six key aspects of data warehouse quality and how to assess them.
Data accuracy refers to how well the data in your warehouse matches the reality it represents. It is essential for ensuring that your analysis and reporting are based on valid and consistent information. To measure data accuracy, you need to compare your data warehouse data with the original source data, or with a trusted external reference. You also need to check for any errors, inconsistencies, or anomalies that might affect the accuracy of your data.
The best indicator of data warehouse quality is business adoption. Adoption can be easily measured. A quality data warehouse reduces silos as business develops dashboards and other data products on top of it. A warehouse could have great data quality but still lack adoption if it does not meet their business needs or if backlog for new features is too long.
If you look around you will see a lack of measurement for data quality in data warehouses. It is easy to detect - you don't see any measures included in dashboards or reports. It typically comes down to two issues; it takes a lot of effort to build and maintain data quality measures, and transparency can be embarrassing. If you don't show measures, you don't have anybody chasing your tail to improve the quality, and the business users just assume perfect quality.
Yes in my view error-free records that can be used as a reliable source of information is the base for any data warehouse which further enables the superior decision making.
The best indicators include data freshness by measuring the time it takes for new data to be available, query performance to ensure efficient data retrieval and it's retention and archiving abilities through appropriate data retention policies
Along with Accuracy, completeness, timeliness, consistency, relevance, accessibility, Other major thing to consider is Metadata.Ensure that metadata, including data definitions and relationships, is accurate and up-to-date. Check for consistency in metadata across different components of the data warehouse. Scalability, Security and Usability also need to be considered.
Data completeness measures how much of the relevant data is available in your warehouse. It affects the coverage and scope of your analysis and reporting. To measure data completeness, you need to identify the data requirements and expectations of your users, and compare them with the actual data in your warehouse. You also need to monitor the data loading and integration processes, and ensure that they are timely, reliable, and error-free.
Completeness in absolute sense is a myth. As much the world is interconnected to infinity, are their information. So the data that is relevant to a business entity spans well beyond internal operations and processes and entities. Customers and their customers, suppliers and the public and consumers, governing bodies and environment - it all has some relevance. Hence, completeness to be defined by what part of your data strategy is achieved through data warehouse ir lake, at a given point in time. How much of the data needs of the stakeholders (org personas, customers etc) is fulfilled, as they see it and experience it - let's say collected through practical survey, would also help get a sense of completeness
Sometimes, data loss doesn’t due to poor data inputs, but also poorly designed data fields/structure. Eg, use time granularity in seconds will save places compared to microseconds, but it constrains some detailed analysis like stock prices changes.
Measuring data warehouse quality in terms of data completeness involves assessing the presence of all data elements in an organization's data landscape. It evaluates whether the data warehouse contains comprehensive information without significant gaps or missing values. It ensures that the dataset is thorough and representative of the intended scope, allowing for robust and reliable analyses. Achieving high data completeness involves regular validation processes to identify and address any missing or incomplete data, ensuring that the warehouse provides a comprehensive foundation for accurate and insightful decision-making.
Data completeness will come only when we have data reconciliation steps and audit framework designed to validate source and target records of any mismatch data pipelines failure has to send email to prod support team so they can validate and fix the issues.
In my experience, Data completeness depends on the requirements of who is accessing this data. Hence the scale of completeness of data changes with time. With time the relevance of data also becomes a factor in its completeness. Older data might not be of use to the new requirements.
Data timeliness measures how current and up-to-date the data in your warehouse is. It affects the relevance and usefulness of your analysis and reporting. To measure data timeliness, you need to define the frequency and latency of your data warehouse updates, and compare them with the business needs and user expectations. You also need to track the performance and availability of your data warehouse, and ensure that it can handle the data volume and velocity.
Timeliness needs to be evaluated against the timeliness of the source. There is limited technology constraints to having a warehouse be near real-time in today's environments. So the timliness and relevance should really be measured as they relate to the source. If the source is a processed mainframe batch then the timeliness is that plus processing time to update the warehouse. If the business wants updates every half hour but the core job can only execute every evening at midnight you can't measure the warehouse timliness against the requirement.
Most data warehouses use batches to stay up to date. Implementing streaming is a good way of storing data in real-time. This might not be feasible for all types of data.
Timeliness depends of reporting needs of Organization. Most companies are happy with daily refreshes. Some complex financial measures can be only derived during a month-end process. More often than daily, typically is better handled by integrating operational systems, outside data warehouse.
Incorporate the frequency of updates to your source data in your integration strategy prior to establishing a schedule. This will ensure that you are meeting the point-in-time expectations of your user base. Not all data needs to be refreshed in real-time. Create methods for data validation and set a review schedule (e.g. quarterly, annually) with your customer. Track the history of your updates.
Timeliness should be according not to facts (well yes!) but to business cycle. Sometimes focus on data update, data capture, etc. and we forgot the the time should fit the needs for the subsequent analysis and decision.
Data consistency measures how coherent and harmonious the data in your warehouse is. It affects the quality and credibility of your analysis and reporting. To measure data consistency, you need to ensure that the data in your warehouse follows the same definitions, standards, and formats across different sources, domains, and dimensions. You also need to verify that the data transformations and calculations are accurate and consistent.
It is a bit like Data Quality and Data Accuracy, but in this case problems here drive to deadlocks. At first, ad-on verifications are needed, to at the end, review the acquisisiton, definicions or processes. Consistency checks are a tool to adjust systems, and impressive if we are able also to discard pieces of data. Less is more.
I've found it most effective when, as a practice, the data governance team has published and maintains definitions, standards and rules. It simplifies data quality control. These days standards and rules can be easily applied to your integration tools. Just be sure to keep up on documenting changes. It's a tedious task, but should be one that is mandatory.
Data Engineer
-Consistency in loose terms is a measure of lag/difference between data updates(write) in a database/warehouse and data read performed by a user. - Good understanding of data staleness and CAP theorem can help in achieving required consistency. -A proper agreement between stakeholders is necessary to understand the need for consistency in the systems. -Some use cases might require strong consistency such as in financial services, where accuracy is of prime importance, whereas some might require eventual consistency where analytics and historical processing at scale is desired than immediate data accuracy.
Data relevance measures how appropriate and useful the data in your warehouse is for your analysis and reporting purposes. It affects the value and impact of your insights and decisions. To measure data relevance, you need to align the data in your warehouse with the business goals and user needs. You also need to evaluate the quality and frequency of your analysis and reporting outputs, and solicit feedback from your users.
I'm lost regarding how to measure data relevance. Usually it is subjective to the business user. When the process to take decisions are too complex, it is difficult to measure how good was the decision, so the analysis and data behind it. When the process is simple, for instance, if you propose an item to a potential buyer based on data analysis, the measure could be the success of this action, but it is not lineal. So, for me the best is maintain people discussions around the table and drive it to this precise concept, data relevance.
I usually look at how Organization does financial reporting. Data Warehouse needs to be aligned, first to how a company managed its own P&L, followed by how company is organized.
Data accessibility measures how easy and convenient it is for your users to access and use the data in your warehouse. It affects the user satisfaction and adoption of your data warehouse. To measure data accessibility, you need to provide a user-friendly and secure interface for your data warehouse, and support various data formats, tools, and platforms. You also need to document and communicate the data in your warehouse, and provide training and support for your users.
It involves assessing how easily and efficiently users can retrieve the necessary information. It encompasses factors such as the design of data structures, query performance, and the availability of tools and interfaces for users to interact with the data. High-quality data regarding data accessibility ensures that users, including data analysts and decision-makers, can efficiently retrieve, manipulate, and analyze the data without unnecessary delays or complications. It involves optimizing database design, implementing effective indexing, and providing user-friendly interfaces, all of which contribute to a seamless and accessible data environment.
Data accessibility needs to be aligned with organization. Some companies allow most of their management see performance company-wide, others want row-level security and allow line-of-business manager only to see their performance.
The most important part to me is issue-to-feature time. The best product imaginable is flawed the moment anything changes. If you are not structured to allow for rapid changes in response to an ever more rapidly changing business environment your investment in building out that project is now little more than tech debt.
I think it's important to have clear data definitions and ownership to support all of these other ideas. I once had a conversation about data latency. The requirement was to have the data refreshed on an hourly cadence. The source that was defined for part of the calculated field was only updated daily. So the definition has to be agreed to or the source has to be altered. I think with modern architectures its almost better to worry about a granular data protection/privacy scheme, an open data environment with finer grain data, and user tooling than to pre-build a lot of dimensions and facts.
Smaller solutions are easier to deliver. Scoping and building out subject-area specific solutions instead of enterprise-wide tends to work best.
Elaborate the total cost of the solution, including licensing, hardware, and support for multiple years by estimating data growth. Pay attention the cost implications of scaling the solution as data volumes grows. Assess encryption, access controls, and compliancy with data protection regulations. Solution should come with robust auditing capabilities for tracking user activities. As final word be sure the data warehouse supports effective data modeling for complex analytics and reporting requirements with easy modification of data schemas as business requirements evolve by the time.
Great contribution by experienced professionals. Many growing companies face the challenge of scaling the data warehouse. A good data warehouse architecture accommodates growth vertically and horizontally. Verticle growth is increasing storage requirements, tabluar structure should be able to handle fast query execution despite increment in rows. Horizontal growth is more synonymous with adding columns to existing tables. A well planned data warehouse can support company growth by adding more data points and yet maintain performance.