How Symfa can help you transform raw data into valuable insights

In today’s data-driven world, the ability to turn raw data into actionable insights is more crucial than ever. With vast amounts of information being generated daily, businesses must navigate the complexities of data cleaning, processing, and standardisation to uncover trends, patterns, and opportunities. This is where Symfa, an innovative software development company, excels — helping businesses turn fragmented and inconsistent data into reliable, insightful, and valuable information.

In today’s data-driven world, the ability to turn raw data into actionable insights is more crucial than ever. With vast amounts of information being generated daily, businesses must navigate the complexities of data cleaning, processing, and standardisation to uncover trends, patterns, and opportunities. This is where Symfa, an innovative software development company, excels — helping businesses turn fragmented and inconsistent data into reliable, insightful, and valuable information.

Symfa specialises in transforming complex datasets into valuable insights through cutting-edge solutions. Their expertise spans across front-end, back-end, and mobile development, with a unique focus on data management, analysis, and standardisation.

The firm recently took on a challenging project from one of the largest freelance platforms in the world.

The task: to clean, standardise, and process a massive database containing thousands of job postings — rich in project details, required skills, geographical information, and company-specific metrics.

The end goal was to make this data usable for in-depth analysis, discovering hidden trends and creating predictive models.

The journey began with a raw, fragmented dataset that required careful handling to unlock its potential. Here’s how Symfa approached the challenge of transforming this data into valuable insights, using their powerful data pipeline and industry-leading tools.

A modest beginning: Laying the foundation for data processing

The first step in the journey was collecting the raw data, which was stored in MongoDB, a NoSQL database known for storing complex, nested objects.

While MongoDB is excellent for unstructured data, it presented a challenge: the data wasn’t standardised, which led to inconsistencies and difficulty in analysis.

To overcome this, Symfa devised a solution to transfer the data from MongoDB to Snowflake, a relational database designed for data warehousing and analytics.

This transformation was crucial, as relational databases offer the structure necessary for more reliable data analysis.

Using Python and DVC (Data Version Control), Symfa broke down the large dataset into smaller, manageable CSV files, allowing them to process the information efficiently.

However, despite this progress, the raw data still contained duplicated columns, fragmentation, and inconsistencies that could undermine the accuracy of any analysis.

Making sense of a messy dataset

Cleaning a massive dataset can often feel like trying to untangle a mess of wires — it’s time-consuming, but it’s a task that’s essential for ensuring the data’s reliability. Symfa approached this stage methodically, breaking the cleaning process into several key phases.

The first issue they encountered was the inconsistent naming of cities. For instance, “New York City” appeared in at least five different variations across the dataset, including “NYC,” “New York,” and even “Big Apple.” This inconsistency made it difficult to properly identify and analyse the cities.

Symfa used GeoNames, a global geographical database, in combination with the Levenshtein distance algorithm, which measures the difference between two strings, to standardise city names.

This automated matching process covered 90% of the dataset, with the remaining 10% handled by a language model-based solution (LLM), ensuring a high level of accuracy in the final data.

Once the names were standardised, Symfa enriched the dataset with additional demographic and economic data, such as GDP per capita and population statistics. This step was crucial for providing numeric parameters that could be used for deeper analysis and uncovering meaningful patterns.

Eliminating the irrelevant and redundant

Another challenge Symfa faced was dealing with empty and redundant columns that added little value to the dataset.

Empty fields were replaced with relevant data or marked for exclusion, while redundant columns were consolidated. This ensured that the dataset remained focused and didn’t contain unnecessary or repeated information.

Additionally, complex lists of skills were restructured into clean, searchable formats, making it easier to identify trends and analyse the data more efficiently. The result was a lighter, more insightful dataset, one that was ready to be explored further.

Ensuring relevance and value

Cleaning the dataset is only part of the process — ensuring that the data is relevant and truly valuable is the final test.

Before finalising the dataset, Symfa conducted a thorough review of every parameter to ensure it served a specific purpose. Some fields, while interesting, were removed due to their lack of practical utility, while others were given more prominence.

This step ensured that every column in the dataset contributed to the overall analysis, laying the groundwork for deeper exploration and valuable insights. Though some patterns remain to be discovered, the clean and structured dataset is now a solid foundation for future analysis and modelling.

Read the full blog from Symfa here.

Copyright © 2025 InsurTech Analyst

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