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Fuzzy matching is a technique used to compare and match data that is similar but not exactly the same. It helps identify records that are likely referring to the same entity, even if there are minor differences such as typos, formatting variations, or extra spaces.

It's useful when dealing with:

Typos (e.g., "Jhon" vs "John")
Abbreviations (e.g., "Corp." vs "Corporation")
Extra/missing spaces
Case mismatches

This technique is essential in data cleansing, record linkage, and data integration tasks where data comes from multiple inconsistent sources.

Example of Fuzzy Matching:

Origins in Computer Science: Fuzzy matching has roots in approximate string matching, developed for search engines, AI, and bioinformatics.

Algorithms: Classic algorithms include:

Levenshtein Distance (edit distance)
Jaro-Winkler Distance
Soundex (phonetic matching)
TF-IDF with cosine similarity (used in document comparisons)

These methods helped search engines, credit card fraud detection, and CRM deduplication from the 1970s onward.

In Business Intelligence: The need to merge messy datasets (e.g., customer names, addresses) brought fuzzy matching into tools like SQL Server Integration Services (SSIS), Excel, and laterPower BI.

Logical Concept of Fuzzy Matching

Fuzzy matching works by:

Comparing strings using asimilarity algorithm.
Scoring how similar two values are (e.g., 0.0 to 1.0).
Returning matches above a certain threshold.

Example:

"Acme Inc" vs "ACME Incorporated" might score0.85 similarity, and if your threshold is 0.8, it's considered a match.

Where and When to Use Fuzzy Matching

Use Cases:

Customer Deduplication ("Jon Smith", "John Smith")
Address Matching ( "123 Main St", "123 Main Street")
Company Merging ("ABC Ltd", "ABC Limited")
Data Consolidation (Joining datasets from different systems with inconsistent naming)

When NOT to Use:

When your data is already standardized.
When exact match is critical (e.g., invoice numbers).
In large datasets with poor quality: fuzzy matching can be resource-heavy.

Things to Consider

Performance: Fuzzy merges are slower than exact merges.
Quality: Review results—false positives/negatives may occur.
Data Volume: Use on smaller datasets or pre-filtered data.
Maintainability: Use transformation tables for better control.

https://powerappbuilder.blogspot.com/2025/05/say-goodbye-to-messy-joins-fuzzy.html

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New Release: Discover What’s New with Text SMS in Dynamics 365

Most sales teams are managing CRM SMS conversations the wrong way! Sales teams today manage dozens of SMS conversations across Leads, Contacts, and Opportunities, but CRM systems haven’t caught up to how real salespeople work. Here’s what typically happens: You get a reply from a lead, but you’re busy. You can’t reassign the chat without… Read More »

The post New Release: Discover What’s New with Text SMS in Dynamics 365 first appeared on Microsoft Dynamics 365 CRM Tips and Tricks.

https://www.inogic.com/blog/2025/05/new-release-discover-whats-new-with-text-sms-in-dynamics-365/

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.PBIX Demystified: Structure, Usage, and Best Practices

A .PBIX file is the primary file format used by Power BI Desktop. It contains the data model, visualizations, queries, and report layout. Internally, it is a renamed ZIP file. You can change the extension to .zip and extract it to view its internal structure, including report metadata, model data, and resources. Understanding Power BI file types is important for effective development, sharing, and management of reports. Among these, the .PBIX format is the most commonly used and essential for report development. Power BI Desktop Developer Mode (currently in preview) is a new feature that enhances collaboration among developers and teams. It supports source control integration and helps automate deployments across development, test, and production environments.

PBIX (Power BI XML) file is a file created by Power BI Desktop that includes:

Data model (imported data or direct query definitions)
Data transformations (Power Query M code)
Relationships between tables
Visualizations (charts, graphs, slicers, etc.)
Measures and calculated columns (DAX expressions)
Report layout and metadata

The file format is essentially a ZIP container holding multiple components (models, queries, settings, visuals) stored in a structured way.

Logical Purpose

Self-contained analytics solution:

Everything needed to create and view a report (data, logic, visuals) is packaged into a single `.pbix` file.
Useful for sharing or deploying to Power BI Service (app.powerbi.com).

Version-controlled design:

Developers can treat PBIX files like development artifacts.
Teams can maintain different PBIX files for Dev, Test, and Prod stages.

Technical Use and Features

Model Storage Stores a tabular model built with DAX or Power Query
Import/DirectQuery Support Allows both imported data or real-time query connections
Embedded Queries Contains Power Query M scripts
Relationships Defines how tables are connected
Measures/Columns Supports DAX calculations
Visual Layout UI/UX design and visuals are embedded
Parameters Allows parameterization of queries or connections

Why is it Used?

All-in-one file: Consolidates your data model, queries, visuals, and logic.

Portability: Can be saved, emailed, uploaded to Power BI Service.

Custom Analysis: Allows business users and developers to define KPIs, build dashboards, and tell data stories.

Gateway Integration: Supports refresh schedules via gateways when published to Power BI Service.

Challenges with PBIX Files

Large file size Importing large datasets leads to large PBIX files, affecting performance.
Binary format Not friendly for version control (e.g., Git) because it's not text-based.
Limited collaboration Unlike Power BI Service reports, PBIX files are harder to co-develop in real-time.
No modularization Difficult to separate logic (data model) from presentation (report).
Change tracking Hard to track incremental changes unless using external tools (e.g., ALM Toolkit).

https://powerappbuilder.blogspot.com/2025/05/pbix-demystified-structure-usage-and.html

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