Star models are popular in data warehousing for their efficiency in handling large amounts of data. However, with the increase in unstructured data, using an open text field in a star model is now a common practice.
While open text fields in a star model provide users with the flexibility to enter free-form text, they can as well be challenging.
This article will explore the best practices for open-text fields in a star model and provides a guide on how to implement them.
Let’s get started.
What is a Star Model in Power BI?
Before diving into the best practices for open text fields, let us first explain what a star model is. What is a star model? A star model is a data modeling technique that is used in data warehousing.
The star model consists of a central fact table connected to multiple dimension tables. While the fact table contains numeric data, the dimension tables include descriptive data (to provide context for that data).
This allows for efficient querying and analysis of large datasets.
What is an Open Text Field?
Open text fields are areas in a database where users can enter free-form text, such as notes or comments. This means that the user can enter any text they want, without any restrictions on length or format.
Unlike structured data, which is organized into defined categories, open text fields allow for more flexibility in the type of data that can be entered. However, the data can be difficult to analyze as it is unstructured.
Open text fields are commonly used in surveys, feedback forms, and other types of data collection forms.
Best Practices for Open Text Fields in a Star Model
When working with open text fields in a star model in Power BI, it is important to follow best practices to ensure success. The following are some of the best practices to effectively manage open text fields in your data analysis:
Limit the Use of Open Text Fields
You should only use open-text fields when necessary. If a question can be answered with a limited set of options, then you may make use of a multiple-choice or dropdown list. This helps to reduce the amount of noise and unstructured data in the model.
Example: In a customer feedback survey, instead of using an open text field for “Reason for Dissatisfaction,” use a dropdown list with options such as “Product Quality,” “Customer Service,” or “Delivery Time.”
Use Predefined Formats
The unstructured nature of open text fields in a star model can be seen as both a blessing and a curse. While the ability to enter free-form text is a great feature, it can also lead to inconsistencies, typos, and other data quality issues.
However, to mitigate these risks, it is important to establish predefined formats for the data entered into open text fields. Predefined formats are a set of rules and guidelines that dictate how data should be entered into open-text fields.
For example, a predefined format for a customer feedback field may specify a 1–5 rating and a 500-character comment. Thus, ensuring consistent, structured data entry and easy data analysis.
Also, in a survey, instead of using an open text field for “Age,” use a predefined format such as “18-24,” “25-34,” or “35-44.”
Finally, a predefined format can help with data validation and quality control. For instance, you can use data validation rules to ensure open text field data formats properly. This can help identify errors and inconsistencies in the data.
Use Text Analysis Tools
Text analysis tools, such as Natural Language Processing (NLP) – an artificial intelligence that focuses on the interaction between computers and human language, have become increasingly popular in recent years due to the eruption of unstructured data.
In many cases, a large portion of an organization’s data is unstructured, such as emails, chat messages, social media posts, customer feedback, etc. While extracting valuable insights from this data can be a challenge, NLP offers a solution to this problem.
Also, NLP can help identify patterns and trends in the data that would be difficult to identify otherwise. For example, by analyzing customer feedback, NLP can identify the most common issues or complaints and help to prioritize improvements.
It can also identify sentiments, such as whether customers are generally positive or negative about a product or service.
Clean and Normalize Data
During analysis, open text fields can be messy, with users entering text in different formats and using different terms to describe the same thing. This can create a lot of noise in the data, making it difficult to identify trends and patterns.
However, to solve these challenges, it is important to clean and normalize the data before loading it into the model. To clean the data, the process involves identifying and removing irrelevant details, such as punctuation, special characters, stop words, etc.
It also involves standardizing the format of the text, such as converting all text to lowercase, removing spaces, correcting spelling errors, and so on.
Normalization involves the process of formatting the data. For example, if the open text field contains a product name, normalization involves making sure that the name is consistent across all entries.
While all these can be time-consuming, it is essential for ensuring that the data is accurate and consistent. Once you clean and normalize the data, you can load it into the star model for analysis.
For example, in a survey, users may enter “USA,” “U.S.A.,” or “United States” to describe their country. So, you should clean and normalize this to ensure its consistency.
Examples of Open Text Fields in a Star Model
Open text fields in a star model can include a wide range of information, such as customer feedback, survey responses, comments on social media posts, etc.
While these fields allow for a more detailed analysis of customer behavior and preferences, they can also pose a challenge in terms of data management and analysis.
FAQs
Why follow best practices when managing open text fields in a star model?
Open text fields are often unstructured and messy, with inconsistent and inaccurate data entry. However, you can achieve data consistency and accuracy by following the best practices.
What are the challenges of managing open text fields in a star model?
The challenges of managing open text fields include the unstructured nature of the data, data inconsistencies, and time-consuming cleaning of the data.
How can organizations benefit from managing open text fields in a star model?
Effective management of open text fields can provide valuable data-driven insights and can lead to improved customer satisfaction, revenue, and competitive advantage.
Conclusion
Managing open text fields in a star model is no easy feat, but with the right tools and best practices, you can turn messy and unstructured data into valuable insights.
Remember, a little bit of data cleaning goes a long way in unlocking the full potential of your open text data.
If you enjoyed reading this, you can also read how to fix stuck creating connections in a Power BI Model.
Happy analyzing!