We make sense of the mess
We help founders clean, understand, and use their data to make better decisions.
About
About DataSharpener
DataSharpener helps organizations move from raw data to clear direction. We simplify how data is collected, handled, and understood, so it can actually be used to make better decisions.
Our focus is not analysis for its own sake. We work with you to identify what matters, interpret the signals, and define the next steps that improve performance, efficiency, and outcomes.
We are a team of data analysts, data scientists, and data engineers working alongside you at every stage, from structuring data and building pipelines to analysis, insight, and execution. Our role is to bring clarity, reduce uncertainty, and support strategies that work in the real world.
From data models and pipelines to dashboards and decision playbooks, we stay embedded with your team so insights turn into outcomes.
Data that moves teams
Why data matters for every business
When data is cleaned, modeled, and delivered where people already work, it unlocks faster decisions: marketing sees which channels pay back, sales spots accounts at risk before renewal, finance forecasts with confidence, and ops automates the busywork slowing growth. The result: a team that trusts its numbers and moves sooner.
What’s new
Latest updates across the site
Fresh articles and case studies so you can see the work, the approach, and the results.
Insights
New article: Attribution, MMM, and Lift Analysis
When to use each measurement model and why the wrong choice can be worse than no model at all.
Read the article
Case Studies
New stories on fraud and churn
Dive into a credit card fraud detection model and a churn dashboard that keep revenue healthy.
Read the case studiesCase Studies
5 projects
Case Study
Credit Card Fraud Detection - ML
Random Forest classifier for highly imbalanced credit card transactions; focused on precision, recall, and MCC.
Case Study
Global Sales & Profit Analysis Dashboard
Deep dive into a global sales and profit dashboard.
Case Study
S&P 500 Portfolio Analysis
End-to-end Python analysis of historical S&P 500 stocks (AAPL, MSFT, NVDA, CHK), covering returns, moving averages, volatility, correlations, and portfolio performance.
-- churn risk by cohort SELECT cohort, COUNT(*) AS users, SUM(churned) AS churned FROM customers GROUP BY cohort;
Case Study
Customer Churn Analysis Using SQL & Tableau
A complete end-to-end analytics project
trials = fetch_trials(condition="oncology")
clean = trials.dropna(subset=["status", "phase"])
agg = clean.groupby("phase").size()Case Study
Clinical Trial Insights: Automated Web Scraping, Data Engineering, and Tableau Analytics
Analyzing global medical research trends using real data from ClinicalTrials.gov
Services
- KPI Dashboard Audit & Redesign
- SQL Query Optimization
- Data Cleaning & Transformation Package
- Dashboard Performance Boost
- End-to-End Dashboard Development
- Data Modeling & Star Schema Setup
- Self-Service BI Setup
- Embedded Analytics Setup
- ETL Pipeline Setup (Python or Power Query or Fabric)
- Database Setup & Optimization
- Data Warehouse / Lakehouse Lite Build
- Monthly / Weekly Reporting Automation
- Financial Reporting Automation
- Marketing Analytics Reporting
- Sales Forecasting with Python
- Power BI Training for Teams (Beginner to Advanced)
- Data Strategy Consultation
- Custom Power BI Workshop
- Predictive Analytics
- A/B Experiment Analytics
- Data Governance Setup
Contact
Ready to make decisions with clarity?
Book a quick demo and we'll map your data, dashboards, and quick wins.
Prefer email? hello@datasharpener.com