Sathish - Data Analyst / Data Scientist / Cyber Security Analyst / Salesforce
About Me
I am a data-driven professional with experience in data analysis, business intelligence,
reporting automation, dashboard development, data validation, and stakeholder reporting.
My background includes finance, healthcare, and enterprise operations, with hands-on work
using SQL, Python, Power BI, Tableau, Excel, Alteryx, and Salesforce reporting concepts.
My portfolio focuses on Data Analytics, Data Science, Cybersecurity Analytics, Salesforce
Analytics, Financial Analytics, and Supply Chain Analytics projects. I use data to identify
trends, improve reporting accuracy, support business decisions, and solve real operational
problems through dashboards, automation, and analytical storytelling.
Situation: Finance and operations stakeholders needed reliable portfolio reporting, KPI tracking, and performance visibility to support business reviews and decision-making.
Task: My responsibility was to analyze financial datasets, improve reporting accuracy, automate recurring reports, and create dashboards that helped leadership monitor risk, performance, and operational trends.
Action:
Analyzed large financial and operational datasets using SQL, Excel, and Python.
Created dashboards and reports for portfolio performance, risk monitoring, and KPI tracking.
Built Power BI and Tableau reports for leadership reviews and stakeholder presentations.
Performed reconciliation, validation checks, and quality reviews to improve reporting accuracy.
Automated recurring reporting workflows using SQL scripts and Python-based data preparation.
Identified data discrepancies through root cause analysis and worked with teams to resolve issues.
Partnered with finance, risk, operations, and technology teams to gather reporting requirements.
Standardized KPI definitions, reporting logic, and documentation for governance and audit readiness.
Improved stakeholder confidence by delivering consistent, validated, and timely reports.
Result: Improved reporting accuracy, reduced manual reporting effort, strengthened data quality controls, and helped leadership make faster decisions using clear financial and operational insights.
CVS Health | Data Analyst
Situation: Healthcare operations teams needed accurate reporting for claims, pharmacy activity, operational metrics, and KPI performance across business teams.
Task: My role was to analyze healthcare datasets, develop Power BI dashboards, validate data, support UAT, and provide insights that improved reporting quality and operational visibility.
Action:
Analyzed healthcare claims, pharmacy, and operational datasets using SQL, Python, and Excel.
Built Power BI dashboards to track KPIs, claims trends, pharmacy utilization, and operational performance.
Used Python for data preparation, trend analysis, duplicate checks, and validation support.
Performed data cleansing and transformation using SQL, Alteryx, and Excel.
Validated data mappings, reporting logic, and dashboard outputs against business requirements.
Supported UAT testing by comparing expected results with actual reporting outputs.
Conducted root cause analysis on reporting discrepancies and data quality issues.
Collaborated with business users to gather requirements and define reporting metrics.
Prepared KPI summaries, trend reports, and executive-ready reporting views.
Documented business rules, metric definitions, and reporting standards for future use.
Result: Improved healthcare reporting accuracy, reduced manual validation effort, increased visibility into operational KPIs, and supported better decision-making for business and operations teams.
Tata Consultancy Services | Data Analyst
Situation: Enterprise clients needed reliable reporting, data migration validation, KPI dashboards, and improved data quality across business and operational processes.
Task: My responsibility was to gather requirements, develop SQL reports, support ETL validation, create dashboards, and deliver analysis for client-facing business teams.
Action:
Worked with stakeholders to gather business requirements and understand reporting needs.
Developed SQL queries, Excel reports, and Tableau dashboards for business users.
Supported ETL processes by validating source-to-target data movement.
Performed data quality checks, reconciliation, and business rule validation.
Created KPI reports for operations, customer trends, and business performance tracking.
Used Power Query and Excel to clean, transform, and structure reporting datasets.
Prepared ad hoc analysis for client reviews and project decision-making.
Documented reporting logic, business rules, and metric definitions.
Worked with cross-functional teams to resolve reporting defects and improve data accuracy.
Supported dashboard enhancements based on stakeholder feedback and changing business needs.
Result: Improved reporting delivery, reduced rework from data issues, strengthened client reporting accuracy, and helped stakeholders make better operational decisions.
Projects
Data Analyst Project 1: Sales Performance Dashboard
Situation: Sales teams needed better visibility into revenue, regional performance, customer segments, and underperforming products.
Task: Build a dashboard that helped stakeholders monitor KPIs, compare performance, and make faster sales decisions.
Action:
Collected sales, customer, product, and regional data from multiple sources.
Cleaned and transformed raw data using SQL, Excel, and Power Query.
Created KPIs for revenue, profit, order volume, average order value, and customer growth.
Built an interactive Power BI dashboard with slicers, drill-downs, and trend views.
Analyzed monthly performance, regional sales patterns, and product category trends.
Validated dashboard numbers against source files to ensure reporting accuracy.
Created business summaries explaining key trends and improvement opportunities.
Recommended actions for low-performing regions and product categories.
Result: Reduced manual reporting effort, improved sales visibility, and helped stakeholders quickly identify growth opportunities and performance gaps.
Data Analyst Project 2: Healthcare KPI Reporting
Situation: Healthcare teams needed accurate reporting to monitor claims volume, pharmacy activity, and operational performance.
Task: Build a KPI reporting solution that improved visibility into healthcare operations and reporting accuracy.
Action:
Analyzed claims and pharmacy datasets using SQL, Python, and Excel.
Checked missing values, duplicate records, inconsistent fields, and reporting gaps.
Created KPIs for claims volume, processing status, turnaround time, and pharmacy utilization.
Built Power BI dashboards for operational and leadership review.
Used Python for data preparation, validation, and trend analysis.
Performed reconciliation between source files and dashboard outputs.
Supported UAT testing by validating reporting results against business rules.
Documented metric definitions and dashboard logic for stakeholder use.
Result: Improved healthcare KPI tracking, strengthened reporting accuracy, and reduced manual validation effort for recurring reports.
Data Science Project 1: Customer Churn Prediction
Situation: A business needed to identify customers who were likely to stop using services so that retention teams could act earlier.
Task: Build a machine learning model to predict churn risk and identify the main factors driving customer loss.
Action:
Collected customer profile, usage, billing, and service interaction data.
Cleaned missing values, handled duplicates, and standardized categorical fields using Python.
Performed exploratory data analysis to identify churn patterns and high-risk segments.
Created features such as tenure, usage drop, payment delays, and service complaints.
Built classification models using logistic regression and decision tree methods.
Evaluated model performance using accuracy, precision, recall, and confusion matrix.
Created a churn risk score to rank customers by likelihood of leaving.
Visualized churn drivers using Power BI charts and Python analysis outputs.
Result: Helped identify high-risk customers, supported targeted retention actions, and improved understanding of churn drivers.
Data Science Project 2: Sales Forecasting Model
Situation: Business teams needed better forecasting to plan inventory, staffing, and future revenue targets.
Task: Create a forecasting model that predicted future sales based on historical trends and seasonality.
Action:
Collected historical sales data by date, region, product category, and customer segment.
Cleaned and structured time-series data using Python and SQL.
Performed exploratory analysis to identify seasonal patterns and revenue trends.
Created moving averages, monthly growth metrics, and lag-based features.
Built forecasting models to predict short-term sales performance.
Compared forecasted results with actual sales to evaluate accuracy.
Built Power BI visuals to show actual vs forecasted sales trends.
Prepared business recommendations for planning and inventory decisions.
Result: Improved sales planning visibility and helped stakeholders understand future demand trends for better decision-making.
Salesforce Project 1: Sales Pipeline Analytics
Situation: Sales teams needed better visibility into leads, opportunities, deal stages, and pipeline value.
Task: Analyze Salesforce CRM data and create a reporting dashboard for pipeline performance.
Action:
Used sample Salesforce-style data for leads, accounts, opportunities, and sales activities.
Cleaned CRM records by standardizing sales stages, account names, and missing values.
Created KPIs for lead conversion rate, opportunity value, win rate, and pipeline aging.
Used SQL to connect opportunity, account, and activity data.
Built a Power BI dashboard showing sales funnel, deal value, and regional performance.
Identified stale opportunities and low-conversion lead sources.
Created drill-down views by sales stage, owner, region, and product line.
Prepared insights to help sales teams prioritize high-value opportunities.
Result: Improved pipeline visibility and helped teams focus on stronger opportunities, weak stages, and delayed deals.
Salesforce Project 2: Customer Support Case Analysis
Situation: Customer support teams needed to understand case volume, response time, issue types, and SLA performance.
Task: Build a Salesforce case reporting dashboard to monitor support performance and repeated customer issues.
Action:
Analyzed case records by priority, status, owner, issue type, and resolution time.
Cleaned support data using Excel, SQL, and Power Query.
Created KPIs for open cases, closed cases, SLA breaches, and average resolution time.
Built Power BI dashboards for support managers and operational teams.
Identified repeated issue categories and delayed resolution patterns.
Created weekly trend views for case volume and support backlog.
Segmented cases by priority and team to identify workload imbalance.
Prepared recommendations to improve case routing and support efficiency.
Result: Improved support performance visibility and helped teams identify delayed cases, repeated issues, and SLA risk areas.
Financial Project 1: Budget vs Actual Analysis
Situation: Finance teams needed to understand why actual expenses differed from budgeted and forecasted amounts.
Task: Build a financial analysis dashboard to track budget variance and identify major cost drivers.
Action:
Collected budget, actual expense, vendor, department, and account-level data.
Cleaned and reconciled financial records using SQL and Excel.
Created variance calculations by month, department, cost center, and category.
Built Power BI dashboards for budget tracking and executive review.
Performed root cause analysis on large expense deviations.
Identified duplicate vendor entries, unusual spending patterns, and category-level variances.
Prepared summary reports explaining major budget variance drivers.
Created documentation for variance logic and financial KPI definitions.
Result: Improved budget monitoring, supported cost control, and helped finance teams understand key variance drivers faster.
Situation: Security teams in a SOC environment need to monitor alerts, incidents, severity levels, response time, and recurring threats from multiple systems.
Task: Build a cybersecurity analytics dashboard to support SOC analysts in tracking incidents, prioritizing high-severity alerts, and improving response visibility.
Cleaned and structured incident records using SQL and Excel for reporting and dashboard development.
Created KPIs for total alerts, open incidents, closed incidents, high-severity alerts, false positives, and average response time.
Built Power BI dashboards to monitor SOC alert volume, incident trends, and analyst workload.
Grouped incidents by severity levels such as Critical, High, Medium, and Low to support alert prioritization.
Identified recurring threat categories including failed login attempts, malware alerts, suspicious IP activity, and access violations.
Created weekly trend views to help SOC teams monitor spikes in security events.
Highlighted unresolved critical incidents and delayed response cases for faster escalation.
Documented alert classification logic, severity definitions, and incident status rules.
Prepared SOC-style summary insights to support security operations review meetings.
Result: Improved SOC incident visibility, helped prioritize high-risk alerts, supported faster escalation of unresolved incidents, and gave security teams a clearer view of response performance and recurring threat patterns.
Cybersecurity Project 2: Login Activity and Suspicious Access Analysis
Situation: SOC analysts need to monitor user login behavior to detect suspicious access patterns, repeated failed attempts, unusual locations, and possible account compromise.
Task: Analyze authentication logs and create a reporting solution that helps identify risky login behavior and supports SOC investigation workflows.
Action:
Collected sample authentication log data including user ID, timestamp, login status, IP address, device type, location, and application name.
Used Python and SQL to clean, structure, and analyze login activity records.
Created KPIs for successful logins, failed logins, repeated failures, after-hours logins, and unusual location activity.
Identified users with multiple failed login attempts within short time windows.
Flagged suspicious patterns such as repeated failures followed by successful login, unusual country access, and abnormal login time.
Built Power BI dashboards for SOC review, showing login trends by user, device, location, and time period.
Created filters for high-risk users, failed attempts, risky IP addresses, and after-hours activity.
Used Python to summarize login frequency, failure rates, and abnormal access patterns.
Documented detection logic for suspicious login behavior and access monitoring rules.
Prepared investigation-ready insights that could help SOC analysts review risky accounts faster.
Result: Improved visibility into suspicious login activity, supported SOC-style access monitoring, helped identify repeated failed login patterns, and enabled faster investigation of potential account security risks.
