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https://github.com/raksha-17/ai_analysis

Derive actionable insights from two datasets — Global AI Impact and AI Job Market Insights — to inform strategy on AI adoption, market dynamics, and talent planning across countries and industries.
https://github.com/raksha-17/ai_analysis

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Derive actionable insights from two datasets — Global AI Impact and AI Job Market Insights — to inform strategy on AI adoption, market dynamics, and talent planning across countries and industries.

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README 

          
# Impact of AI on Digital Media Analysis

Derive actionable insights from two datasets — Global AI Impact and AI Job Market Insights — to inform strategy on AI adoption, market dynamics, and talent planning across countries and industries.



## Introduction

* Goal: Derive actionable insights on global AI impact and job market dynamics to inform strategy (e.g., policy, talent planning, or product focus).

* Scope: Two datasets.

* Global AI impact: country, industry, job_loss_due_to_AI_percent, year, etc.

* AI job market insights: industry, AI_adoption_level, salary_usd, job_title, location, etc.

* Data quality: <2% missingness on key fields per dataset.

* Key insights: identify top 5 industries by impact and top 5 regions by adoption-growth alignment.

* Reproducibility: end-to-end notebook or SQL workflow that outputs a single KPI sheet.



I will follow the steps of the data analysis process: [Ask](https://github.com/Raksha-17/AI_Analysis/blob/main/README.md#ask), [Prepare](https://github.com/Raksha-17/AI_Analysis/blob/main/README.md#prepare), [Process](https://github.com/Raksha-17/AI_Analysis/blob/main/README.md#process), [Analyze](https://github.com/Raksha-17/AI_Analysis/blob/main/README.md#analyze), [Share](https://github.com/Raksha-17/AI_Analysis/blob/main/README.md#share) and [Act](https://github.com/Raksha-17/AI_Analysis/blob/main/README.md#act).



### Quick links:

Data Source: [Impact of AI on Digital Media (2020-2025)] (https://www.kaggle.com/datasets/atharvasoundankar/impact-of-ai-on-digital-media-2020-2025)



### SQL Queries:



* [Data Exploration](https://github.com/Raksha-17/AI_Analysis/blob/main/Data_Exploration) 

* [Data Analysis](https://github.com/Raksha-17/AI_Analysis/blob/main/Data_Analysis)  

  

Data Visualizations: [Tableau](https://public.tableau.com/app/profile/raksha.kandasamy.chandramohan/viz/AIImpactAnalysis_17585852683960/Dashboard1#2)  



## Ask

### Business Task

* Quantify cross-country and cross-industry AI impact and link it to adoption and salary dynamics.

* Provide prioritized insights for decision-makers on where AI adoption is strongest and where potential impact is highest.



### Inputs / Outputs:

#### Inputs:

* ai_job_market_insights: Industry, AI_Adoption_Level, Job_Title, Salary_USD, Location, Country, Region, etc.

* global_ai_impact: Country, Industry, Job_Loss_Due_to_AI_PERCENT, AI_Adoption_Rate_PERCENT, Year, etc.



### Analysis

#### Outputs:

* KPI sheets per dataset (e.g., average salary by role/adoption, total impact by country/industry, adoption vs. impact maps).

* Cross-dataset correlations (e.g., adoption_rate vs. average_impact).

#### Evaluation Metrics:

* Correlation strength (Pearson/Spearman) between adoption and impact.

* Stability of top domains across years.

* Data quality metrics (missingness, duplication rate).



## Prepare

#### Data Sources:

* ai_job_market_insights.csv

* global_ai_impact.csv



#### Data Organization:

* ai_job_market_insights: Industry, AI_Adoption_Level, Job_Title, Salary_USD, Location, Country, Region, Year, possibly other fields.

* global_ai_impact: Country, Industry, Job_Loss_Due_to_AI_PERCENT, AI_Adoption_Rate_PERCENT, Year, etc.

  

## Process:

* Check for missing values in key fields (Salary_USD, Country, Industry, Job_Title, Job_Loss_Due_to_AI_PERCENT).

* Validate consistent naming (Industry, Location/Country synonyms).

#### Access & Compliance:

If sharing externally, mask or aggregate sensitive fields; ensure row-level identifiers aren’t exposed.



### Data Exploration

SQL Query: [1.Data Exploration](https://github.com/Raksha-17/AI_Analysis/blob/main/Data_Exploration) 



Before cleaning the data, I am familiarizing myself with the data to find the inconsistencies.



1. Normalize country/region names; fix typos.

   

   5

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2. Count nulls per key column:



   1

 

3. Check distinct counts to validate the number of unique hosts/listings.

   

   2



4. Make queries explicit and robust - Jobs by industry and AI adoption level (explicit columns).



   3



5.Validation and safety checks - Basic counts to ensure you’re not duplicating joins.



   7    



6.Spot-check sums by country for sanity.



  8



## Analyze

SQL Query: [Data Analysis](https://github.com/Raksha-17/AI_Analysis/blob/main/Data_Analysis)  



The data is stored appropriately and is now prepared for analysis. I queried multiple relevant tables for the analysis and visualized them in Tableau. 



The analysis question is:



1. Regions with strongest adoption signals and growth potential.

2. Industries with highest cumulative AI impact and corresponding workforce implications.

3. Salary dynamics by industry and adoption level to inform talent strategy and benchmarking.

4. Relationships between AI adoption rate and job loss to guide policy, reskilling, and program design. 



* ai_job_market_insights: Jobs by industry and AI adoption level.

  

11



AI Adoption level is high in Healthcare, Tech and Manufacturing.



* Average salary by role and adoption level (safe median).

  

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When Adoption rate is lower, avg_salary is high for AI researcher, HR manager, Operations Manager, Sales Manager, Product Manager and Software Engineer; When Adoption rate is higher, avg_salary is high for Cybersecurity Analyst, Data Scientist,Marketing Specialist and UX Designer.



* ai_job_market_insights: Regions with strongest growth projection, Assuming: Region and Job_Growth_Projection (as a percentage or index).



13



Region with Strongest Growth Projection is Tokyo, New York, Berlin and Toronto.



* global_ai_impact: Impact by country and Industry.



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Highest Impact in UK in Media, France in Gaming, Japan in Manufacturing.



* global_ai_impact: Adoption vs. impact.



15



AI Adoption rate and its Impact is high in Gaming, Retail , Manufacturing.

  

* Yearly total impact by country and domain (global_ai_impact).



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In 2020 Germany Manufacturing Industry had the highest impact followed by China in Legal in 2025 and India in Media in 2024.



* Yearly adoption/readiness vs. impact (global_ai_impact). 



 17



China, Japan, South Korea has highest avg_impact in 2021,2025,2024.



* Yearly top domains by total impact (global_ai_impact).



18



Gaming, Manufacturing and Retail has highest impact on avg.



* Cross-dataset joins (correlations) Example: Link ai_job_market_insights with global_ai_impact (using country/industry keys).



19



 AI Adoption level is high in Manufacturing in Berlin, Healthcare in Dubai, Retail in London.



* Top-N and profiling patterns. Top 5 domains by total impact (global_ai_impact).



20



Gaming, Media, Manufacturing, Automotive.



* Top-3 roles by salary within each adoption level (ai_job_market_insights).

  

21



Salary is high for Marketing Specialist, Data Scientist and UX Designer.



## Share



Data Visualization: [Tableau](https://public.tableau.com/app/profile/raksha.kandasamy.chandramohan/viz/AIImpactAnalysis_17585852683960/Dashboard1#2)  



* Jobs by industry and AI adoption level - AI Adoption level is high in Healthcare, Tech and Manufacturing.

* Average salary by role and adoption level - When Adoption rate is lower, avg_salary is high for AI researcher, HR manager, Operations Manager, Sales Manager, Product Manager and Software Engineer; When Adoption rate is higher, avg_salary is high for Cybersecurity Analyst, Data Scientist, Marketing Specialist and UX Designer.

* Region with Strongest Growth Projection is Tokyo, New York, Berlin and Toronto.

* Highest Impact globally in UK in Media, France in Gaming, Japan in Manufacturing.

  

* AI Adoption rate and its Impact is high in Gaming, Retail , Manufacturing.

  

* In 2020 Germany Manufacturing Industry had the highest impact followed by China in Legal in 2025 and India in Media in 2024.

  

* China, Japan, South Korea has highest avg_impact in 2021,2025,2024.

  

* Gaming, Manufacturing and Retail has highest impact on avg.

  

* AI Adoption level is high in Manufacturing in Berlin, Healthcare in Dubai, Retail in London.

  

* Top-5 domains by total impact Gaming, Media, Manufacturing, Automotive Top-5 domains by total impact.

  

* Top-3 roles by salary within each adoption level Salary is high for Marketing Specialist, Data Scientist and UX Designer.

  

* When adoption is high, salaries rise for AI-forward roles (e.g., Data Scientist, Cybersecurity Analyst, AI Researchers in select markets).

  

* When adoption is lower, some roles (AI Researchers, HR/Operations Managers, Sales Managers, Product Managers, Software Engineers) show relatively higher average salaries (likely scarcity in those markets or premium seniority).

  Dashboard 1 (1)



 ## Act

 

 * Prioritize regional talent initiatives, partnerships, and incubators in these hubs with regions with strongest adoption signals and growth potential.

   

 * Target investment in regions with rising adoption growth indicators and rising demand signals.

   

 * Industries with highest cumulative AI impact and workforce implications so prioritize retraining and new-role creation in these domains; design industry-specific transition programs.



### Recommendations:



* Prioritize upskilling in Data Science, Cybersecurity, UX Design, and Marketing in high-adoption and high-growth regions (e.g., Tokyo, New York, Berlin, Toronto) to align with demand.

  

* In markets with high senior salaries but lower adoption, launch AI literacy and cross-functional training for AI-enabled product and operations roles to prepare for future demand.

  

* Develop a regional talent playbook for Tokyo, New York, Berlin, and Toronto with domain focus on Gaming, Media, and Manufacturing.

  

* Implement mobility and secondment programs to accelerate cross-region knowledge transfer and reduce skill gaps.

  

* In regions with rising adoption but potential displacement (supported by salary and impact data), design targeted reskilling pilots with clear metrics: training hours, job placement, time-to-fill for AI-forward roles.

  

* Emphasize responsible AI, model governance, and ethics training as adoption accelerates in more regulated or sensitive sectors.



### Limitations and assumptions:



* Patterns observed in Gaming, Media, Manufacturing, or specific regions may not replicate in others due to regulatory, cultural, or labor market differences.

  

* Reskilling and displacement projections are scenario-based. They should be supplemented with local labor market analyses, stakeholder input, and pilot results.

  

* Increased adoption raises governance and ethical considerations (privacy, bias, accountability). These factors may moderate the positive impacts of adoption.