Data is the lifeblood of any organization. More than ever before, infrastructure leaders are turning to data to make better and more informed decisions. However, data is only valuable when accurate, complete, and consistent. In its most recent Data Trust Survey, analyst firm IDC reports that just over a quarter (27%) of data practitioners fully trust the data with which they routinely work. As enterprises forge ahead with new data initiatives, quality remains a top concern among C-level data executives.

In its Data Integrity Trends report, the study found that 82% of respondents believe data quality concerns represent a barrier to their data integration projects. Over 50% reported that data quality is “very challenging” in their organizations, coming in at #1 on the list of data integrity concerns among the survey respondents.

Data is critical for success

Data quality metrics and reporting strategies are essential for businesses to measure and improve data quality. From an IT perspective, data quality is critical for IT operations to ensure accurate decision-making, improve efficiency, reduce risk, ensure compliance, and enable data integration from various sources.

Let’s explore the top five data quality metrics and reporting strategies businesses can use to measure and improve data quality.

1. Data Accuracy

Data accuracy is a critical quality metric that businesses must measure and improve. Data accuracy measures the extent to which data is free from errors or inaccuracies. Companies can measure data accuracy by comparing data with known or expected values. Data accuracy reporting can involve identifying and reporting inaccuracies, inconsistencies, and errors in the data.

To improve data accuracy, businesses can implement data cleansing and validation processes. These processes can identify and correct errors, inconsistencies, and inaccuracies in the data, resulting in more accurate and reliable data.

1. Data Completeness

Data completeness is another vital data quality metric businesses must measure and improve. Data completeness measures the extent to which data is complete and contains all required information. Companies can measure data completeness by comparing data with expected values and identifying missing data.

Data completeness reporting can involve identifying and reporting missing data, incomplete records, and gaps in the data. To improve data completeness, businesses can implement data entry and validation processes to ensure that all required data is collected and recorded.

1. Data Consistency

Data consistency is a data quality metric measuring the extent to which data is consistent across different data sources or sets. Data consistency reporting can involve identifying and reporting inconsistencies in the data, such as different spellings or naming conventions.

To improve data consistency, businesses can implement data standardization processes to ensure data consistency across different sources or data sets. Data standardization can transform data into a consistent format, structure, and naming convention.

1. Data Timeliness

Data timeliness is another critical quality metric businesses must measure and improve. Data timeliness measures the extent to which data is available when needed. Data timeliness reporting can involve identifying and reporting data availability or updates delays.

To improve data timeliness, businesses can implement data collection and processing processes that prioritize the timely collection and processing of data. This can involve automating data collection and processing processes or implementing real-time data collection and processing technologies.

1. Data Integrity

Data integrity is a critical data quality metric that measures how reliable and trustworthy data is. Data integrity reporting can involve identifying and reporting errors or inconsistencies in the data that could impact data quality.

To improve data integrity, businesses can implement data governance processes that ensure data is reliable and trustworthy. Data governance can involve implementing data security and privacy policies, ensuring data accuracy and completeness, and implementing data validation and cleansing processes.

Final Thoughts

As you navigate an expanding IT ecosystem, data quality metrics, and reporting strategies are essential for businesses to measure and improve data quality. The top five data quality metrics and reporting strategies companies can use include data accuracy, completeness, consistency, timeliness, and integrity. TFORM takes a unique approach to data quality. TFORM is API-based and agentless, so it integrates seamlessly into the infrastructure. Technical leaders can quickly utilize the detailed information provided by the suite of tools to establish a depiction of the current landscape and the ability to make data-driven decisions that are consistently updated. Regarding data quality, you always have control of your data streams and eliminate unintended drift.

Businesses can make more informed decisions, improve operational efficiency, and gain a competitive advantage by measuring and improving data quality.