Database

Introduction

In database management, ensuring that data is normalized and structured correctly is crucial for maintaining data integrity and usefulness. Sometimes, you may encounter tables that lack the necessary relationships between data points, leading to a disorganized database structure. A common scenario involves a table that records customer locations without a dedicated field for company names.

This post aims to address a specific example where someone has been handed a table consisting of approximately 18,000 rows with a single field for “Location Name,” and no field for “Company Name.” The situation presents challenges due to the absence of a proper company designation for multiple locations operated by the same company, potentially leading to complications in data retrieval and analysis.

In this blog, we will explore a systematic approach to normalizing such a table, generating a company list based on distinct location descriptions, and making the database efficient again.

Understanding the Current Table Structure

The existing location table has a simple structure:

 ID  Location_Name     
 1   TownShop#1        
 2   Town Shop - Loc 2 
 3   The Town Shop     
 4   TTS - Someplace   
 5   Town Shop,the 3   
 6   Toen Shop4

What we aim for is a more structured output that includes a “Company_ID” for each location:

 ID  Company_ID   Location_Name     
 1   1            Town Shop#1       
 2   1            Town Shop - Loc 2 
 3   1            The Town Shop     
 4   1            TTS - Someplace   
 5   1            Town Shop,the 3   
 6   1            Toen Shop4

In tandem with this location table, we will also create a separate company table:

 Company_ID  Company_Name  
 1           The Town Shop

Generating Company Names

Since there is no existing list of company names, we need to generate it from the provided location names. Here’s a step-by-step approach to accomplish this:

Step 1: Identify Candidate Company Names

  • Extract Location Names: Create a list of Location Names that are primarily made up of alphabetic characters.
  • Use Regular Expressions: To filter out irrelevant entries (like locations with numerical or special characters), employ regular expressions to parse the data.

Step 2: Manual Review

  • Sort the List: Sort the filtered list of location names alphabetically.
  • Select Company Names: Manually review the sorted list to determine which locations serve best as representative company names.

Step 3: Match Scoring

  • Software Algorithm for Matching: Utilize the Levenshtein distance or any similar string comparison algorithm to assess closeness between each potential Company Name and the various Location Names.
  • Create a Score System: Store these results in a new table reflecting CompanyName, LocationName, and their corresponding MatchScore.

Step 4: Implement Thresholds

  • Filter Matches: Define a threshold score; any match falling below this predetermined score will be excluded from further consideration.

Step 5: Manual Vetting

  • Review the Data: Manually check each entry listed by CompanyName, LocationName, and MatchScore and finalize which names genuinely represent each company.
  • Organize for Efficiency: Order results by MatchScore to streamline the review process and reduce workload.

Conclusion

While the process outlined might seem time-consuming, it leverages automation and algorithmic techniques to manage the complexity of handling around 18,000 records. This structured approach not only saves time but helps in confidently categorizing data, ultimately leading to better database integrity and meaningful analyses in the future.

By employing this method, you should find it much easier to normalize tables with low integrity and improve the usability of your database. Always remember: the goal of normalization is not only to structure data but to enhance its accessibility and reliability.