Internal Data Assessment for IT Consulting Firm
Execution of a structured data quality assessment to evaluate Salesforce data within a management consulting firm, aimed at enabling future analytics, reporting, and AI initiatives.
Consltancy Client
2025
Client Description
An European IT consulting firm with global operations, using Salesforce as its primary platform to manage clients, consultants, and projects. The client wanted to increase internal data transparency to support dashboards, internal tooling, and potential AI applications.
Problem
Limited Visibility Into Internal Data Quality and Structure
Despite relying on Salesforce as a central system, the firm lacked clarity about how well its data was maintained and populated across objects like Accounts, Contacts, Tasks, and Time Tracking. Inconsistencies, null values, and unclear field usage made it difficult to scope dashboards or AI tools. Without structured insight, data-driven development was blocked by uncertainty and risk.
Solution
Systematic Profiling and Field-Level Analysis in Snowflake
A targeted data assessment was conducted using the firm's Snowflake data warehouse, where Salesforce data was already ingested. Using Python scripts and SQL queries, key quality metrics such as null rates, duplication, field usage frequency, and value distributions were analyzed across core objects. The findings were summarized in a stakeholder-ready presentation, highlighting both risks and opportunities for data use. This enabled the client to confidently plan next steps toward AI readiness and internal tooling.
Technologies & Frameworks
Salesforce Data Profiling with Snowflake and Python
Snowflake – cloud data warehouse used to query structured Salesforce exports
Python (pandas, matplotlib) – used to automate field-level data profiling and generate insight visualizations
SQL – applied to explore data relationships, aggregates, and schema-level metrics
Jupyter Notebooks – provided a reproducible environment for analysis and documentation
PowerPoint – used to communicate key data insights and quality risks to business stakeholders
More Works
Internal Data Assessment for IT Consulting Firm
Execution of a structured data quality assessment to evaluate Salesforce data within a management consulting firm, aimed at enabling future analytics, reporting, and AI initiatives.
Consltancy Client
2025
Client Description
An European IT consulting firm with global operations, using Salesforce as its primary platform to manage clients, consultants, and projects. The client wanted to increase internal data transparency to support dashboards, internal tooling, and potential AI applications.
Problem
Limited Visibility Into Internal Data Quality and Structure
Despite relying on Salesforce as a central system, the firm lacked clarity about how well its data was maintained and populated across objects like Accounts, Contacts, Tasks, and Time Tracking. Inconsistencies, null values, and unclear field usage made it difficult to scope dashboards or AI tools. Without structured insight, data-driven development was blocked by uncertainty and risk.
Solution
Systematic Profiling and Field-Level Analysis in Snowflake
A targeted data assessment was conducted using the firm's Snowflake data warehouse, where Salesforce data was already ingested. Using Python scripts and SQL queries, key quality metrics such as null rates, duplication, field usage frequency, and value distributions were analyzed across core objects. The findings were summarized in a stakeholder-ready presentation, highlighting both risks and opportunities for data use. This enabled the client to confidently plan next steps toward AI readiness and internal tooling.
Technologies & Frameworks
Salesforce Data Profiling with Snowflake and Python
Snowflake – cloud data warehouse used to query structured Salesforce exports
Python (pandas, matplotlib) – used to automate field-level data profiling and generate insight visualizations
SQL – applied to explore data relationships, aggregates, and schema-level metrics
Jupyter Notebooks – provided a reproducible environment for analysis and documentation
PowerPoint – used to communicate key data insights and quality risks to business stakeholders
More Works
Internal Data Assessment for IT Consulting Firm
Execution of a structured data quality assessment to evaluate Salesforce data within a management consulting firm, aimed at enabling future analytics, reporting, and AI initiatives.
Consltancy Client
2025
Client Description
An European IT consulting firm with global operations, using Salesforce as its primary platform to manage clients, consultants, and projects. The client wanted to increase internal data transparency to support dashboards, internal tooling, and potential AI applications.
Problem
Limited Visibility Into Internal Data Quality and Structure
Despite relying on Salesforce as a central system, the firm lacked clarity about how well its data was maintained and populated across objects like Accounts, Contacts, Tasks, and Time Tracking. Inconsistencies, null values, and unclear field usage made it difficult to scope dashboards or AI tools. Without structured insight, data-driven development was blocked by uncertainty and risk.
Solution
Systematic Profiling and Field-Level Analysis in Snowflake
A targeted data assessment was conducted using the firm's Snowflake data warehouse, where Salesforce data was already ingested. Using Python scripts and SQL queries, key quality metrics such as null rates, duplication, field usage frequency, and value distributions were analyzed across core objects. The findings were summarized in a stakeholder-ready presentation, highlighting both risks and opportunities for data use. This enabled the client to confidently plan next steps toward AI readiness and internal tooling.
Technologies & Frameworks
Salesforce Data Profiling with Snowflake and Python
Snowflake – cloud data warehouse used to query structured Salesforce exports
Python (pandas, matplotlib) – used to automate field-level data profiling and generate insight visualizations
SQL – applied to explore data relationships, aggregates, and schema-level metrics
Jupyter Notebooks – provided a reproducible environment for analysis and documentation
PowerPoint – used to communicate key data insights and quality risks to business stakeholders
More Works