How do you measure your data's health?

1 Data quality dimensions

One of the most important aspects of data health is data quality, which can be assessed by using different dimensions, such as accuracy, completeness, consistency, timeliness, validity, and uniqueness. Each dimension reflects a different aspect of how reliable, relevant, and error-free your data is. For example, accuracy means how well your data reflects the real-world facts, completeness means how much of the required data is available, and uniqueness means how well your data avoids duplication. You can use various techniques and tools to measure each dimension, such as data profiling, data cleansing, data validation, and data deduplication.

3 Data usability and value

The third aspect of data health is data usability and value, which means how well your data supports your business needs and objectives. Data usability depends on factors such as the relevance, clarity, and accessibility of your data, the alignment with your data strategy and architecture, and the integration and interoperability with other data sources and systems. Data value depends on factors such as the impact, efficiency, and innovation that your data enables, the return on investment (ROI) and total cost of ownership (TCO) of your data projects, and the customer satisfaction and loyalty that your data enhances. You can use methods and tools such as data analysis, data visualization, data storytelling, data literacy, data cataloging, and data valuation to measure these factors.

4 Data health assessment methods

To measure your data's health, you need to use a systematic and comprehensive approach that covers all the aspects and dimensions mentioned above. There are different methods and frameworks that you can use to conduct a data health assessment, such as a Data Quality Assessment (DQA), Data Maturity Assessment (DMA), Data Audit, and Data Scorecard. DQA is a process that evaluates the quality of your data using predefined criteria, standards, and indicators, and identifies the root causes and impacts of data quality issues. DMA is a process that evaluates the maturity of your data capabilities, processes, and culture using a maturity model that defines the levels and characteristics of data excellence. A Data Audit is a process that evaluates the compliance of your data with the legal, ethical, and contractual obligations and requirements, and identifies the risks and gaps in your data management practices. Finally, a Data Scorecard is a tool that measures the performance of your data against the strategic goals and objectives of your organization, and tracks the progress and improvement of your data initiatives.

5 Data health improvement strategies

Once you have measured your data's health, it is important to take action to improve and maintain it at a high level. To do this, consider data quality management (DQM), data governance, data architecture, and data culture. DQM involves planning, implementing, monitoring, and controlling the data quality activities and processes. Data governance defines, implements, and enforces the roles, responsibilities, policies, standards, and procedures for data management. Data architecture designs, develops, and maintains the data structures, models, and systems that support the data management and usage. Finally, data culture fosters a data-driven mindset and behavior among the stakeholders while promoting data literacy, collaboration, and innovation.

6 Data health monitoring tools

To measure and improve your data's health, you should utilize the right tools to collect, analyze, and visualize data health indicators and metrics. There are various types of tools that can be used for data health monitoring, such as data quality tools like Informatica Data Quality, Talend Data Quality, and IBM InfoSphere QualityStage. Additionally, there are data governance tools like Collibra Data Governance, Alation Data Catalog, and IBM Data Governance. For data architecture, Erwin Data Modeler, Snowflake Data Platform, and AWS Data Services are viable options. Lastly, Tableau Data Visualization, Microsoft Power BI, and Google Data Studio are great for enhancing data literacy and collaboration.