What are the most common BI metrics used in industry?

1 Data Quality

Data quality is the degree to which data is accurate, complete, consistent, and reliable for its intended use. Poor data quality can lead to erroneous or misleading BI results, reduced user trust, and wasted resources. Data quality metrics can help monitor and improve the data quality by measuring aspects such as validity, completeness, uniqueness, timeliness, and conformity. For example, a data quality metric can track the percentage of missing or invalid values in a data set, the number of duplicate records, or the frequency of data updates.

2 Data Governance

Data governance is the set of policies, standards, roles, and processes that ensure the proper management, security, and usage of data across an organization. Data governance metrics can help evaluate and improve the data governance by measuring aspects such as compliance, risk, accountability, and value. For example, a data governance metric can track the number of data breaches, the level of data access control, the alignment of data with business objectives, or the return on investment of data initiatives.

3 Data Usage

Data usage is the extent and manner in which data is accessed, analyzed, and consumed by users for their BI needs. Data usage metrics can help understand and optimize the data usage by measuring aspects such as frequency, volume, diversity, and satisfaction. For example, a data usage metric can track the number of data queries, the amount of data transferred, the variety of data sources or tools used, or the feedback from users.

4 Data Value

Data value is the impact and benefit that data generates for the organization in terms of revenue, cost, efficiency, or innovation. Data value metrics can help quantify and communicate the data value by measuring aspects such as profitability, productivity, growth, or competitiveness. For example, a data value metric can track the increase in sales, the reduction in expenses, the improvement in processes, or the creation of new products or services.

5 Data Visualization

Data visualization is the presentation of data in graphical or interactive formats that can help users explore, understand, and communicate the data insights. Data visualization metrics can help assess and enhance the data visualization by measuring aspects such as clarity, relevance, accuracy, and aesthetics. For example, a data visualization metric can track the number of charts, tables, or dashboards used, the alignment of visuals with data insights, the correctness of data representation, or the appeal of design elements.

6 Data Literacy

Data literacy is the ability and willingness of users to read, write, and communicate with data effectively. Data literacy metrics can help measure and improve the data literacy by measuring aspects such as knowledge, skills, confidence, and culture. For example, a data literacy metric can track the level of data education, training, or certification, the proficiency in data analysis or visualization tools, the comfort in using or sharing data insights, or the adoption of data-driven decision making.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?