Here's how you can navigate incomplete or missing data in a business intelligence project.

Asking the right questions to the appropriate st stakeholders is crucial. Applying the 5 W's can significantly aid this process: 1. What data is missing and what is the impact? 2. Where in the dataset is the data missing? 3. When did the data go missing – in the raw or modified dataset? 4. Who is responsible for or can answer questions about the missing data? 5. Why is the data missing? In situations where stakeholder access is limited, such as with open-source or secondary data, imputation strategies like mean, median, and mode can be utilized. However, it’s important to note that these methods can introduce biases into the data.

Know which data elements are critical for project success. This allows you to conduct quick data profiling analyses where you shortlist all of the data attributes that must be accurate to drive reporting value. Once you come across core fields that have values missing it's important to come up with a data imputation strategy that makes sense for downstream reporting.

Missing data can also trigger three-value-logic issues. Even though 3VL makes sense, like that person of an unknown age is not exactly as old as another person of unknown age, it can cause a lot of confusion when NULL does not equal NULL.

Leverage imputation techniques like feature engineering to manipulate, add, remove, or replace missing data with approximate values. This allows you to smooth the overall dataset for better BI processing and downstream reporting. This also better prepares your dataset for more advanced modeling techniques like supervised learning.

Data Imputation is crucial in ensuring the integrity of BI projects. Imputing missing values enables us to maintain the robustness of our analysis without discarding valuable data. Simple imputation methods, like using the mean or median, are effective for datasets with minor gaps. However, for more complex scenarios, advanced techniques like predictive modeling can provide more accurate estimates. It's essential to choose a method that fits the nature of your data and the objectives of your BI project. Always validate the imputed data to ensure it doesn't introduce bias or distort your insights.

In Power BI you can implement imputation techniques using built-in functions, DAX, Power Query, or integrating with Python for more complex tasks. ▪️Power Query: "Replace Values" or "Fill Down/Up" options for forward/backward fill. Custom M scripts can implement more complex imputation logic. ▪️DAX: Create calculated columns using functions like IF, AVERAGE, MEDIAN, MODE, ALLEXCEPT, and custom logic to handle missing values. ▪️Embed Python scripts within Power BI to apply advanced imputation techniques like KNN (using k-nearest data points) and regression (to predict missing values). Power BI also provides options at the visualisation level: exclude null or blank values, custom visual settings to display placeholder or warning messages.

Good data is useless if you can't tell a compelling story with it. Advanced BI tools (e.g. Power BI, Tableau) give you the capability to shape your data in a way that communicates a data-driven narrative to your audience. You can also use these tools to monitor critical KPI metrics that spark discussion amongst stakeholders and drive actionable planning.

Engage your data analytics stakeholders directly and communicate ongoing issues. This helps maintain a window of transparency for the team to collaborate with you and improve data collection processes. This will help reduce future instances of missing data from core datasets that drive organizational operations.