How can null values be handled in a dimensional model?

In my experience, using a single "null surrogate" can make it difficult to distinguish between various reasons for a null value. For example, we wouldn't have the ability to differentiate between a value that's simply missing but expected and a value that's not applicable. Also, if not handled well, null surrogate key propagation can cause unwanted spreading of null placeholder records throughout the database.

Según mi experiencia depende de los requisitos de negocio 1.- Es un valor nulo, pero no tiene valor analítico, es equivalente al valor predeterminado. Se puede solucionar con un valor desconocido. 2.- Tiene valor analítico, pero solo para distinguir entre los que son nulos o no. Se puede solucionar usando una mini-dimensión que lo categorice 3.- tiene valor analítico, y hay que distinguir casuísticas distintas de ese valor y de otros en una tabla de hechos o dimensión. Hay que ver las combinaciones y categorizar los hechos, ya no en un valor binario sino en lo que corresponda 4.- Tiene valor analítico puesto que influye en un distinct-count. Usar Hash o cualquier valor que unifique el campo para poder resolver el problema

Los valores nulos obtenidos en el proceso ETL, deben ser tratado con sumo cuidado, ante todo la recomendación es "explorar y conocer tus datos". Toma en cuenta que el tipo de dato del campo: 1. Nulos en campos numéricos: El escenario más común de tratamiento es reemplazarlo con 0, pero cuidado con medidas que incluyan promedios y mínimos. 2. Nulos en campos de texto: Una solución común es reemplazar nulos por "No asignado", "No aplica", "Sin Respuesta", u otro indicio del dato faltante y su significado. 3. Nulos en campos fecha: Cuidado, suele ser el más complicado; valora bien si puedes reemplazarlo por la fecha actual, y podrás para calcular días en espera, pero llevado de la mano con un campo de texto que te ayuda a procesar las medidas.

In this case, the null value has two potential meanings. Either the value did not exist, or our measurement system failed to capture the value. Either way, we generally leave the value as null because most database products will handle nulls properly in aggregate functions including SUM, MAX, MIN, COUNT, and AVG. Substituting a zero instead would improperly skew these aggregated calculations.

Employ surrogate keys in your dimensional model to uniquely identify records, even when the business key is null. This facilitates consistent relationships across tables. For instance, in a sales dimension, assign surrogate keys to each product, ensuring accurate reporting and analytics even when the original product identifier is null.

when you're dealing with missing data in a dimensional model, a simple move is to toss in default values. For numbers, maybe go with 0, and for words, slap in something like "Unknown." When you're prepping data, swap out those blanks with your defaults. Use the same trick in your reports—swap nulls for defaults using COALESCE. Keep it real, and think about how this plays into your big picture. If something's seriously missing, throw up a flag. It's all about keeping your reports and analysis rock-solid in that dimensional model of yours.

I have learned a great method from Professor Bernie at UCI. In this method you keep 3 data quality rows names : -2 Not Applicable -1 Invalid 0 unknown For missing dimension values in fact tables , you would use of the value based on business rules and processes knowledge. Attributes within dimension can use the same logic.

NULL values in ERP/Supply Chain, especially in a dimension table usually indicates a problem with Master Data and the process feeding the data into the ERP. I use Default values. But I make sure it is a very explicit default value like “NOT AVAILABLE” or “NOT APPLICABLE”. The report should scream that there is a problem with master data so the process owners fix the master data. Understand that BI folks have very limited control of ERP and business owners, but by making the data gaps painfully obvious in the visuals, business leaders can be made aware of the underlying process issues and solve the problem at the source. I normally have a data quality dashboard so that the executives understand the assumptions behind the metrics.

The default value helps analysts understand gaps in the data. These default values need to be not one of valid values. These can be "unknown", "missing" , "invalid" etc. so that they can be easily distinguished from valid ones. This way the dimensions can be consumed using SQL joins and filters.

Consider assigning default values to attributes with null values. This is particularly useful for numeric or categorical attributes. Share an experience where setting a default value for a null attribute improved query performance or ensured that missing data didn’t disrupt reporting.

Add a flag or indicator field in your dimension to mark records with null values. This allows for easy identification and handling of these records during analysis

Implement null flags to explicitly indicate whether an attribute is null or intentionally empty. This enhances transparency in data interpretation. Share a scenario where null flags clarified the status of null values, preventing misinterpretation during analysis or reporting.

Devemos destacar os valores retornados como nulos para que n?o haja uma interpreta??o err?nea nos resultados, dessa forma, n?o impacta no indicador a ser demonstrado. Valores nulos devem ser diferenciados de valores vazios, é interessante apontar um valor padr?o se o preenchimento for nulos, assim, o modelo se torna mais eficiente e menos complexo.

Implementing a null flag in dimensional decision-making can enhance data integrity by explicitly representing the absence of a value, providing clearer insights into missing or undefined data points within a given dimension. This practice can contribute to more robust analyses and decision-making processes, helping to avoid misinterpretation of data due to undefined or overlooked values.

Em vez de substituir o valor nulo, pode-se adicionar uma coluna adicional para indicar se o valor original é nulo. Isso pode ser útil para manter a integridade dos dados, permitindo que os usuários distingam entre um valor real de '0' e um valor nulo substituído por '0'.

We generally encounter dimension attribute nulls due to timing or dimension sub-setting. For example, perhaps not all the attributes have been captured yet, so we have some unknown attributes for a period of time. Likewise, there may be certain attributes that only apply to a subset of the dimension members. In either case, the same recommendation applies. Putting a null in these fields can be confusing to the user, as it will appear as a blank on reports and pull-down menus, and require special query syntax to find. Instead, we recommend substituting an appropriately descriptive string, like “Unknown” or “Not provided.”

Cette approche hybride offre une flexibilité adaptative aux besoins spécifiques du contexte. Cependant, elle présente des défis tels que l'augmentation de la complexité et de la variabilité du modèle dimensionnel, nécessitant une logique et une documentation supplémentaires pour la mise en ?uvre et la maintenance. De plus, la possibilité de confusion chez les utilisateurs finaux souligne l'importance d'une communication claire sur la logique et les règles sous-jacentes à ces solutions hybrides. Le choix de cette approche dépend de la balance souhaitée entre la qualité, l'exactitude, la cohérence, la simplicité et les performances des données dans un contexte donné.

In my experience, it depends on the data-warehouse tool we use. I have worked extensively on SAP products and it uses extended star schema. This helps in dealing with null values because dimensions with null values can be addressed using the hybrid option and at the same time fixed in the seamlessly connected ERP source system.

m dimens?es como 'tempo' ou 'geografia', onde valores nulos podem ser mais significativos, pode-se adotar estratégias específicas, como a cria??o de registros de 'dimens?o desconhecida' que podem ser usados para substituir valores nulos.

Combine strategies based on the context. For example, use surrogate keys for entities where a unique identifier is critical, default values for attributes where a sensible default exists, and null flags for indicating intentional nulls. Share a case where a hybrid approach effectively handled null values in a complex dimensional model.

The issue of nulls and missing data extends beyond just reporting and querying challenges; in the AI age, it can often impact the quality and performance of machine learning models. Addressing this problem requires a proactive approach at the data's source, rather than just 'handling' it downstream. Merely replacing NULL with generic placeholders like 'Unspecified' or 'NA' misses the point. Data engineers should help design database architectures and data flows with this in mind and if necessary employ creative and innovative strategies to ensure high-quality data enters their databases from the outset. It's important to remember: prevention is often better than cure!

One way to handle null values is using the ragged hierarchies, in PowerBI use the ISINSCOPE function in DAX, which returns true if a column is in the filter context of the current cell in a matrix or table visual. This function can help to determine the scope of the current node and hide the blank values that belong to nodes with no leaves. A ragged hierarchy is a type of hierarchy that has an uneven number of levels, meaning that some members do not have children at the lowest level of the hierarchy. For example, an organizational chart or a chart of accounts can be ragged hierarchies. Another way to handle null values in ragged hierarchies is to use a Inforiver (custom visual), which has multiple options to turn off the empty leaves.

Null handling in dimension modelling normally can set null to a default value e.g.-1, unknown etc. But we should also consider too many null values may indicate data quality in source system. We should consider to run some Data quality checking on a regular basis to detect the quality and establish a DQ process to rectify these issues in the source system if this is critical to analysis

Replacing null with default 0 or unknown is not a solution and rather inviting inaccuracy in reporting, leading to more corrective measures for the future. More efforts should be taken in fine tuning the input upstream data stream in not sending null's and regular dimension update is recommended.

Null as a Separate Category: Treat nulls as a separate category in analysis, especially when nulls represent a meaningful category on their own. Custom Business Logic: Implement custom business logic in ETL (Extract, Transform, Load) processes to handle nulls. This might involve complex transformations or calculations based on business rules. Coalesce Function: Use SQL’s COALESCE function or similar functionality in ETL tools to substitute nulls with a specified value. Each of the above strategies has its pros and cons, and the choice largely depends on the specific requirements and context of the data analysis task. It’s important to understand the implications of each approach on the integrity and accuracy of the analysis.