Business intelligence (BI) is the process of collecting, analyzing, and presenting data to help organizations make better decisions. BI can help businesses improve their performance, optimize their operations, increase their revenue, and enhance their customer satisfaction. But how can you measure the value and impact of BI for your business? How can you justify the investment in BI tools and solutions? How can you communicate the benefits of BI to your stakeholders and decision-makers?

In this article, we will explore some of the methods and models that can help you assess the value of BI for your business. We will also provide some examples and best practices for using BI metrics to drive business growth.

Why measure the value of BI?

Measuring the value of BI is important for several reasons:

• It helps you evaluate the return on investment (ROI) of your BI initiatives. You can compare the costs and benefits of different BI solutions and choose the best option for your business needs and budget.
• It helps you monitor and improve the effectiveness and efficiency of your BI processes. You can track the performance and quality of your data, analytics, and reports, and identify areas for improvement or optimization.
• It helps you demonstrate the business impact of your BI insights. You can show how your data-driven decisions have improved your business outcomes, such as sales, profits, customer satisfaction, or market share.
• It helps you align your BI strategy with your business goals and vision. You can use BI metrics to measure your progress towards your strategic objectives, and adjust your plans and actions accordingly.

How to measure the value of BI?

There are different methods and models that can help you measure the value of BI for your business. Depending on your business context and objectives, you might choose to focus on different aspects of BI value, such as cost, efficiency, or effectiveness. Here are some of the common models that you can use to measure the value of BI:

Total Cost of Ownership (TCO) Model

The TCO model calculates the total cost of owning a BI solution and compares it with the expected benefits. The TCO model considers not only the initial purchase price of a BI tool or solution, but also the ongoing costs of maintenance, support, training, upgrades, integration, and security. The TCO model helps you choose the lowest-cost option that meets your business requirements and expectations.

Worker Productivity Model

The worker productivity model measures the gains in worker productivity achieved with different BI solutions. The worker productivity model uses the cost of labor as a proxy for measuring the value of BI. The worker productivity model assumes that by using a BI solution, workers can save time, reduce errors, increase output, or improve quality. The worker productivity model helps you calculate the ROI of your BI investment by comparing the labor cost savings with the TCO of a BI solution. Know that realizing the bull potential of BI also involves a fair amount of change management. People and technology have to work in tandem.

Expected Impact Model

The expected impact model quantifies the value gained from improved decision-making across your organization as a result of using a BI solution. The expected impact model estimates how much revenue or profit can be generated or increased by applying the insights derived from a BI solution to your business processes or strategies. The expected impact model helps you evaluate the effectiveness and relevance of your BI insights for achieving your business goals.

Examples of BI metrics

BI metrics are specific indicators that help you measure and maximise the potential and impact of BI for your business. Keeping an eye on these metrics will give your BI project the best chance for success. There are many types of BI metrics that you can use depending on your business domain and objectives. Here are some examples of common BI metrics across different categories:

• Data quality metrics: These metrics measure the accuracy, completeness, consistency, timeliness, and validity of your data sources and datasets. Examples include data error rate, data completeness rate, data freshness rate, data duplication rate, etc. Remember the mantra “garbage in = garbage out”.
• Data processing metrics: These metrics measure the performance and efficiency of your data processing pipelines and workflows. Examples include data ingestion rate, data transformation rate, data loading rate, data processing time, etc. Inefficient models or data pipelines can cost a lot in wasted compute power over time.
• Data analysis metrics: These metrics measure the quality and effectiveness of your data analysis methods and techniques. Examples include data coverage rate, data correlation rate, data variance rate, data significance rate, etc.
• Data visualization metrics: These metrics measure the usability and attractiveness of your data visualization tools and dashboards. Examples include data visualization load time, data visualization refresh rate, data visualization interactivity rate, data visualization satisfaction rate etc.
• Data usage metrics: These metrics measure how often and how well your data products are used by your target audience or consumers. Examples include data product views, data product visitors data product engagement rate, data product feedback rate etc.
• Data impact metrics: These metrics measure how much value and benefit your data products generate for your business outcomes or objectives. Examples include revenue growth rate, profit margin customer retention rate, market share etc.

Best practices for using BI metrics

Using BI metrics to measure the value and impact of BI for your business can help you optimize your BI strategy and processes, and communicate your BI success stories to your stakeholders and decision-makers. Here are some best practices for using BI metrics effectively:

• Define your business goals and objectives clearly and align them with your BI strategy and vision.
• Choose the most relevant and meaningful BI metrics that reflect your business goals and objectives, and match your business context and domain.
• Establish a baseline and benchmarks for your BI metrics, and track your progress and performance over time.
• Use a combination of qualitative and quantitative BI metrics to capture the full spectrum of BI value and impact.
• Use data visualization tools and dashboards to present your BI metrics in a clear and compelling way, and highlight the key insights and trends.
• Share your BI metrics and insights with your stakeholders and decision-makers regularly, and solicit their feedback and input.
• Review and update your BI metrics periodically, and adjust your BI strategy and actions accordingly.

Conclusion

BI can provide tremendous value and impact for your business, but you need to measure it properly to realize its full potential. By using the appropriate methods and models, and selecting the right BI metrics, you can assess the value and impact of BI for your business, and use it to drive business growth.