What are some of the key metrics and indicators that you use to measure the success of a BI project?
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These are the metrics that align with the business goals and objectives of the BI project, such as increasing revenue, reducing costs, improving customer satisfaction, or enhancing decision making. Business value metrics can be quantitative, such as sales growth, profit margin, or customer retention rate, or qualitative, such as feedback, testimonials, or ratings. To measure these metrics, you need to establish a baseline before the BI project, define the target outcomes and milestones, and track the progress and impact of the BI project over time.
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Remember, that when it comes to business value metrics you are closest to overall business strategy. What you measure will be what the entire organization cares about. So, it is important to think broadly. How could a focus on a metric help or hurt essential innovation? Is your framework rigid or adaptable? What might the unintended consequences of your metrics be on incentives structures? Will your metrics serve corporate values AND the values of your customers, investors, and other key stakeholders? Next, because business value metrics are strategic, remember users could be board directors and execs who are not expert in the methodologies or governance. So, a metric of your success will be how well you explain benefits and limits.
These are the metrics that measure the accuracy, completeness, consistency, timeliness, and relevance of the data that is used and generated by the BI project. Data quality metrics can include error rates, missing values, duplicates, outliers, or validity checks. To measure these metrics, you need to set the data quality standards and rules, implement data quality controls and audits, and monitor and report the data quality issues and resolutions.
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Uniqueness is critical. Duplication of data renders reports inaccurate, generates spurious analysis, and destroys ML pipelines by skewing the training set. I recommend exception based governance, e.g. a revenue or cost category on a given day is detected as unexpectedly exceptionally large. Detecting, resolving, and openly communicating issue-detection-solution actions before BI end-users have a chance to even detect an issue builds trust and confidence.
These are the metrics that measure the usage and satisfaction of the BI project by the intended users, such as business analysts, managers, or customers. User adoption metrics can include user counts, frequency, duration, or actions, as well as user feedback, ratings, or suggestions. To measure these metrics, you need to collect and analyze the user data and behavior, conduct user surveys and interviews, and implement user training and support.
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With adoption, there is also the User Satisfaction piece. You shall conduct surveys or feedback sessions to gauge user satisfaction with the BI system. This can include factors such as ease of use, accessibility of data, and the ability to derive actionable insights.
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If the BI solution is useful for achieving a desired goal it will be embraced by end-users. Track usage relative to total Business Intelligence end-users who have access.
These are the metrics that measure the efficiency, reliability, scalability, and security of the BI project from a technical perspective. Technical performance metrics can include response time, availability, throughput, or error rate, as well as compliance, backup, or recovery status. To measure these metrics, you need to define the technical requirements and specifications, test and optimize the BI system and components, and monitor and report the technical performance and issues.
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You must include the Time to Insight best practices here. Evaluate the speed at which users can obtain meaningful insights from the BI system. A shorter time to insight suggests that the project is enabling quick decision-making and providing value in a timely manner.
These are the metrics that measure the execution and delivery of the BI project according to the project scope, schedule, budget, and quality. Project management metrics can include completion rate, variance, deviation, or change requests, as well as risk, issue, or stakeholder management. To measure these metrics, you need to follow the project management methodology and best practices, use project management tools and software, and communicate and report the project status and outcomes.
These are the metrics that measure the alignment and compliance of the BI project with the organizational policies, standards, and regulations. Governance metrics can include documentation, audit, or review status, as well as roles, responsibilities, or accountability. To measure these metrics, you need to establish and follow the governance framework and processes, assign and clarify the governance roles and responsibilities, and review and update the governance policies and procedures.
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Reduction in the number of employees or resources required to create and validate report data for accuracy. For repetitive data asks, employee time is spent on identification, creation, and validation of data. A self-serve approach and democratization of access to data, lead to standardized governance and automation of data updates in reports and dashboards. Thereby ensuring that less employees are involved, freeing up their time for more productive tasks.
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The fundamental success of a BI Project should include that - it fundamentally changes organizations opmechs (ex: becomes critical part of weekly reviews, decisions etc) - provides speed to insights (insights available more frequently, lesser latency than before, with higher confidence ) - insights meet SLA of available time, data quality - intuitive and ready to use (reduces or eliminates manual manipulations of data, establishes clear definitions of metrics - BI solution is well supported (continually adapts, scales and grows with organization)
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With all said above, there is one other important aspect that I follow, which is Decision-Making Accuracy. You must assess the impact of the BI system on decision-making quality. Measure the accuracy of decisions made based on insights from the BI system and compare them to previous decision-making processes.
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