How can you determine the completeness of BI data?

Completeness refers to the presence of all required data elements. It ensures that all necessary data values are available without any missing or null values. For example, in a customer database, if a required field like email address is missing for some records, it affects the completeness of the data.

The real measure of Data Completeness should be a binary percentile i.e.: 0% or 100%. In between percentile are meaningless and superfluous.

Two words- More Data. Remember this problem cannot be solved by “ more programming “. Example: The larger ride share and full self driving data collectors will be only successful in this effort. Again it is only because of the “ DATA “ they have. The algorithmic breakthrough is just not possible when you compare it with the full data set. This can be easily understood if you really think why from your own steps as a baby you are easily able to cross the road now!

For a food distribution company, BI data completeness means real-time inventory levels, accurate supplier performance metrics, and comprehensive order fulfillment data. Ensuring that every SKU is tracked and supplier data reflects current lead times is crucial. One can adopt DAMA-DMBOK standards to meet these needs, ensuring BI data paints a complete picture for decision-making.

To gauge BI data completeness in our supply chain, we employ both quantitative and qualitative metrics. We quantify the level of data integrity by analyzing the percentage of missing values, detecting duplicates, and evaluating the distribution of values across our supply network. Qualitatively, we assess how the data's granularity aids in forecasting demand or evaluating supplier reliability. Tools like data profiling and auditing are indispensable in this process, and advanced data visualization allows us to identify trends and gaps in real time. By actively engaging with our data users for feedback, we ensure our metrics are aligned with operational realities and business objectives.

We pinpoint these gaps by examining discrepancies between actual and expected data. Such gaps may stem from manual data entry errors, delays in data synchronization from disparate sources, or misalignment between data capture methodologies and policy requirements. Root cause analysis often reveals that real-time data is not being captured at some points, or that integration points between systems are failing. Impact analysis then helps us understand the operational ramifications of these gaps, guiding us to prioritize fixes that have direct effects on service levels and risk mitigation.

In addressing completeness gaps, actions vary from straightforward corrections to intricate system enhancements. Immediate steps like rectifying incorrect entries or purging duplicates are initiated, while more complex measures—like overhauling data capture processes or improving system integrations—are systematically planned. We integrate project management principles to structure these improvements and apply change management practices to ensure smooth transition, minimizing disruption to operations. Engaging stakeholders is key; their input and buy-in are essential for successfully enhancing data models and governance policies, thereby fortifying the foundation of our BI system's data completeness.

We track improvements using data quality dashboards that display real-time completeness metrics, highlighting progress and flagging areas needing attention. Comparing current data against historical benchmarks, we assess enhancements in forecasting accuracy, waste reduction, and supply chain resilience. By measuring how these changes positively impact operational efficiency and decision-making, we can directly correlate improved data quality with increased business value. Regular reporting and setting up alerts for discrepancies ensures that stakeholders stay informed and can act promptly to maintain data integrity.