Introduction:
In cQube events are input with dimension values that are used as filters for creating KPIs. One of the main challenges is handling inconsistent or erroneous dimension values. In this document, we'll discuss different approaches to address this issue and their respective pros and cons.
Approach 1: Standard Set of Dimension Values
Definition: Use a standard set of dimension values that are predefined and agreed upon.
A standard set of dimension values refers to a predefined and standardized list of values for each dimension. These values are agreed upon by all stakeholders involved in data collection, analysis, and reporting. The purpose of having a standard set of dimension values is to ensure consistency and accuracy of data across all data sources and to facilitate data integration, aggregation, and analysis.
For example, in the context of education, a standard set of dimension values for the subject dimension can include Math, English, Science, Social Studies, and so on. Similarly, for the grade dimension, it can include Grade 1, Grade 2, Grade 3, and so on. The standard set of dimension values can be determined based on the specific needs and requirements of the domain and the stakeholders involved.
To implement a standard set of dimension values, a dimension master table is created for each dimension, and the standard set of values is populated in the table. Whenever new data is ingested, the system checks the dimension master table to determine if the dimension value already exists. If the value exists, the data is associated with the existing value. If not, it raises an error and later based on the context Dimension table can be updated.
In summary, a standard set of dimension values can be a useful approach to ensure consistency and accuracy of data in a domain-specific context. However, it requires careful planning and ongoing maintenance to be effective.
Pros:
Provides consistency in dimension values across all states and stakeholders and data sources.
Enables easy integration and aggregation of data from different sources
Facilitates data analysis and reporting by providing a standardized and structured way of organizing data
Reduces the likelihood of errors and inconsistencies in data analysis and reporting
Cons:
Might not be feasible to create a standard set of dimension values for all dimensions.
Requires a significant upfront effort to create and maintain the dimension master tables
May not capture all possible values, leading to data loss.
Approach 2: State-wise Standard Set of Dimension Values
Definition: Use a standard set of dimension values for each state that is predefined and agreed upon by the stakeholders in that state.
State-wise standard set of dimension values refers to having a predefined set of dimension values for each state. This means that each state will have its own set of standard dimension values that are specific to its region.
For example, let's consider the dimension value "Subject" for schools in different states. In one state, the standard set of dimension values for "Subject" might include "Mathematics", "Science", and "Social Studies". However, in another state, the standard set of dimension values for "Subject" might include "Maths", "Physics", and "Geography".
However, managing a state-wise standard set of dimension values can be challenging, as it requires a lot of effort and resources to maintain. This is especially true for large-scale applications like cQube, which need to manage a large number of dimension values across multiple states.
Pros:
Provides consistency in dimension values within each state.
Helps in data analysis, as it provides a consistent set of filters that can be used to drill down and analyze data.
Cons:
Might not capture all possible values, leading to data loss.
Might lead to inconsistency across states.
Approach 3: Dynamic Dimension Values
Definition: Allow for dynamic creation of new dimension values based on the input data.
Dynamic dimension values refer to the approach of allowing new dimension values to be added dynamically to the system, rather than using a pre-defined set of values. In this approach, new dimension values are added to the system as and when they are encountered in the data.
For example, let's consider the "Subject" dimension for schools. With a dynamic approach, if a new subject name such as "Artificial Intelligence" appears in the data, it will automatically be added to the list of dimension values. This allows for greater flexibility and adaptability in the system, as new values can be easily incorporated without requiring any manual intervention.
One advantage of dynamic dimension values is that it allows for greater flexibility in the data model, as the system can adapt to changes in the data without needing to update the schema or data model. It can also help avoid data loss, as new values that are not present in the standard set of dimension values can still be captured and analyzed.
However, one major disadvantage of dynamic dimension values is that it can lead to inconsistencies in the data, as there is no predefined set of values that are being used. For example, different users may use different variations of the same dimension value, leading to confusion and difficulty in analysis. It can also lead to the creation of duplicate dimension values, which can further complicate the data.
To mitigate these issues, it is important to have some level of validation and normalization of the dynamic dimension values. This can include tools like fuzzy matching algorithms to identify and group similar dimension values together, as well as user validation to ensure that new values are accurate and relevant to the data being analyzed. Additionally, having a standard set of dimension values to compare against can help in identifying and correcting errors in the data.
Pros:
Captures all possible values.
Easy to implement.
Cons:
Can lead to inconsistent dimension values.
Can lead to data redundancy.
Difficult to maintain.
Approach 4: Fuzzy Matching
Definition: Use fuzzy matching algorithms to match input dimension values with standard dimension values.
Fuzzy matching is a technique that allows for finding approximate matches between two strings or values that may not be identical but are similar. It is often used to handle situations where there may be variations or errors in the data, such as spelling mistakes or inconsistencies in formatting.
In the context of dimension values, fuzzy matching can be used to match values that are similar but not identical to a standard set of values.
For example, if the standard set of dimension values includes "Mathematics" and an incoming event has a dimension value of "Maths", fuzzy matching can be used to identify that "Maths" is a close match to "Mathematics" and map the value accordingly.
Fuzzy matching can be implemented using various libraries or tools such as Python's fuzzywuzzy or Java's Apache Lucene. However, it is important to note that there may be trade-offs between accuracy and performance when using fuzzy matching, especially with large datasets.
Pros:
Allows for matching similar but not identical values, which can improve accuracy of data analysis.
Can handle variations and errors in data that would be difficult to account for with strict matching rules.
Can be automated and integrated into data processing pipelines.
Cons:
May result in false positives or mismatches if the algorithm is not tuned or the similarity threshold is set too low.
May be computationally expensive, especially with large datasets, and may require optimization or parallelization.
Can be more difficult to interpret and audit compared to strict matching rules.
Conclusion:
Each approach has its own pros and cons, and the choice of approach depends on the specific requirements of the stakeholders. A combination of approaches might also be needed to handle the dimension values effectively. For example, a standard set of dimension values could be used as a baseline, and fuzzy matching could be used to capture variations.