<?xml version="1.0" encoding="UTF-8"?><rss xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:content="http://purl.org/rss/1.0/modules/content/" xmlns:atom="http://www.w3.org/2005/Atom" version="2.0"><channel><title><![CDATA[Bensportfoliowebsite]]></title><description><![CDATA[Bensportfoliowebsite]]></description><link>https://benlusic.wixsite.com/bensportfolio/home</link><generator>RSS for Node</generator><lastBuildDate>Tue, 01 Sep 2026 21:41:33 GMT</lastBuildDate><atom:link href="https://benlusic.wixsite.com/bensportfolio/blog-feed.xml" rel="self" type="application/rss+xml"/><item><title><![CDATA[Insurance Underwriting &#38; Risk Performance Dashboard- Power BI]]></title><description><![CDATA[ GitHub Repository: View Full SQL Script &#38; Reproducible Files on GitHub Building a P&#38;C Insurance Underwriting Intelligence Dashboard: A Journey in Iterative Design When building data analytics tools, it is easy to assume the hardest part is writing the initial data model or SQL scripts. But as any analyst will tell you, the real battle is won in user experience (UX) and visual hierarchy. Recently, I built a Property &#38; Casualty (P&#38;C) Insurance Underwriting Intelligence Dashboard in Power BI....]]></description><link>https://benlusic.wixsite.com/bensportfolio/post/insurance-portfolio-underwriting-risk-performance-dashboard-power-bi</link><guid isPermaLink="false">6a3425bc4ee6c699ede58a1d</guid><category><![CDATA[Power BI]]></category><pubDate>Thu, 18 Jun 2026 17:25:19 GMT</pubDate><enclosure url="https://static.wixstatic.com/media/9dcf9c_1b51a0103c9b4e0abf244a95d7adbc6a~mv2.png/v1/fit/w_1000,h_559,al_c,q_80/file.png" length="0" type="image/png"/><dc:creator>benlusic</dc:creator></item><item><title><![CDATA[Global Retail Sales &#38; Budget Intelligence Dashboard- Power BI]]></title><description><![CDATA[ GitHub Repository: View Full SQL Script &#38; Reproducible Files on GitHub Project Overview: Executive Sales &#38; Budget Analytics This project is a practical exercise in sales performance analysis and variance reporting using Power BI. Using a cleaned version of the public AdventureWorks retail dataset alongside created budget targets, I modeled the data to analyze sales performance against mock business goals. 1. Mismatched Timeframes &#38; Granularity The AdventureWorks sales data ended earlier...]]></description><link>https://benlusic.wixsite.com/bensportfolio/post/global-retail-sales-budget-intelligence-dashboard</link><guid isPermaLink="false">6a29dcaab2d71ad74fed7d09</guid><category><![CDATA[Power BI]]></category><category><![CDATA[SQL Server Studio]]></category><pubDate>Wed, 10 Jun 2026 22:07:12 GMT</pubDate><enclosure url="https://static.wixstatic.com/media/9dcf9c_04c0b757964d41dea03cf64571fe5478~mv2.png/v1/fit/w_1000,h_768,al_c,q_80/file.png" length="0" type="image/png"/><dc:creator>benlusic</dc:creator></item><item><title><![CDATA[Data Professionals Survey- PowerBI]]></title><description><![CDATA[Project Overview What does the global landscape look like for modern data professionals? This interactive Power BI dashboard visualizes data from a comprehensive industry survey to explore demographic trends, salary benchmarks, workplace sentiment, and the tools driving the field forward. By transforming raw survey responses into an intuitive, single-page executive summary, this project highlights key trends in how data professionals work, live, and progress in their careers. Link to...]]></description><link>https://benlusic.wixsite.com/bensportfolio/post/data-professionals-survey</link><guid isPermaLink="false">6a21fc5daaa8d363bc441f6f</guid><category><![CDATA[Power BI]]></category><pubDate>Thu, 04 Jun 2026 22:39:03 GMT</pubDate><enclosure url="https://static.wixstatic.com/media/9dcf9c_3bb63f36c9ce48c3a50fa040463e415e~mv2.png/v1/fit/w_1000,h_884,al_c,q_80/file.png" length="0" type="image/png"/><dc:creator>benlusic</dc:creator></item><item><title><![CDATA[Nashville Housing Data Cleaning Project- SQL Server Studio]]></title><description><![CDATA[ GitHub Repository: View Full SQL Script &#38; Reproducible Files on GitHub Executive Summary &#38; Business Impact Raw real estate transaction records frequently suffer from missing values, unformatted date strings, embedded delimited addresses, and duplicate rows that invalidate downstream market reporting. This project implements an end-to-end data cleaning pipeline in Microsoft SQL Server (T-SQL) to transform over 56,000 raw housing records into a standardized, production-ready relational...]]></description><link>https://benlusic.wixsite.com/bensportfolio/post/nashville-housing-data-cleaning-project</link><guid isPermaLink="false">6a1f563bd7053c3c7ea327ad</guid><category><![CDATA[SQL Server Studio]]></category><pubDate>Tue, 02 Jun 2026 22:58:26 GMT</pubDate><enclosure url="https://static.wixstatic.com/media/9dcf9c_779a9e8bf1a94ad382187ebc0fe3a039~mv2.jpg/v1/fit/w_1000,h_712,al_c,q_80/file.png" length="0" type="image/png"/><dc:creator>benlusic</dc:creator></item><item><title><![CDATA[Cleaning the Chaos: Taming a COVID-19 Layoffs Dataset with MySQL]]></title><description><![CDATA[Link to Github Data cleaning isn't usually the flashiest part of data analytics, but it’s undeniably the most critical. If the underlying data is a mess, any dashboard or predictive model built on top of it is built on sand. Recently, I decided to tackle a messy, real-world dataset containing global company layoffs during the COVID-19 pandemic. The goal? Transform raw, unformatted, and duplicate-ridden tracking data into a pristine, production-ready relational table.   Here is a look under...]]></description><link>https://benlusic.wixsite.com/bensportfolio/post/cleaning-the-chaos-taming-a-covid-19-layoffs-dataset-with-mysql</link><guid isPermaLink="false">6a31b89cd98cc147601ecc72</guid><category><![CDATA[MySQL]]></category><pubDate>Tue, 16 Jun 2026 21:12:27 GMT</pubDate><enclosure url="https://static.wixstatic.com/media/9dcf9c_69a4c492c22c4a13b3eb307d4cee099f~mv2.png/v1/fit/w_1000,h_1000,al_c,q_80/file.png" length="0" type="image/png"/><dc:creator>benlusic</dc:creator></item><item><title><![CDATA[Covid Dashboard- Tableau, SQL Server Studio]]></title><description><![CDATA[Project Overview When the pandemic hit, the sheer volume of global health data was overwhelming. For this project, I wanted to take those massive, complex datasets and turn them into a clear visual story. I built a data pipeline using SQL Server to clean and transform raw global health data, and then used Tableau to build an interactive, user-friendly dashboard that tracks infections, mortality rates, and vaccine rollouts worldwide. Link to Dashboard: Tableau The Technical Deep Dive (SQL)...]]></description><link>https://benlusic.wixsite.com/bensportfolio/post/covid-dashboard</link><guid isPermaLink="false">6a18b71e145da5e38307911e</guid><category><![CDATA[Tableau]]></category><category><![CDATA[SQL Server Studio]]></category><pubDate>Thu, 28 May 2026 22:06:49 GMT</pubDate><enclosure url="https://static.wixstatic.com/media/9dcf9c_0694fd9089f74521945d8547558c2edf~mv2.jpg/v1/fit/w_1000,h_576,al_c,q_80/file.png" length="0" type="image/png"/><dc:creator>benlusic</dc:creator></item><item><title><![CDATA[HR Analysis Project- PowerBI]]></title><description><![CDATA[A Human Resources project analyzing attrition rate by age, education, gender, Salary, etc. Project Overview This Power BI dashboard provides a strategic deep dive into workforce dynamics, it specifically focuses on identifying the root causes of employee attrition. By aggregating demographic, performance, and tenure data, the report allows HR leadership to move from anecdotal observations to data-driven retention strategies. Link to Dashboard: Power BI The Story the Data Tells This analysis...]]></description><link>https://benlusic.wixsite.com/bensportfolio/post/hr-analysis-project</link><guid isPermaLink="false">69bc473fcbf61f6be542ed07</guid><category><![CDATA[Power BI]]></category><pubDate>Sun, 22 Mar 2026 19:04:15 GMT</pubDate><enclosure url="https://static.wixstatic.com/media/9dcf9c_8dd61c64d9d9436d8985073ef310e4bd~mv2.jpg/v1/fit/w_1000,h_1000,al_c,q_80/file.png" length="0" type="image/png"/><dc:creator>benlusic</dc:creator></item><item><title><![CDATA[Customer Churn Rate Dashboard- Tableau]]></title><description><![CDATA[A dashboard showing customer churn rate within a cable company. Project Overview Customer churn is one of the most significant revenue drains in the telecommunications industry. The goal of this project was to analyze cable subscriber data to identify exactly when and why customers were leaving, allowing the business to refine pricing strategies and improve onboarding for new segments. Link to Dashboard: Tableau Key Insights Discovered The "New User" Price Sensitivity: My analysis revealed a...]]></description><link>https://benlusic.wixsite.com/bensportfolio/post/customer-churn-rate-dashboard</link><guid isPermaLink="false">69bc684506e9acd4d41ca7da</guid><category><![CDATA[Tableau]]></category><pubDate>Sat, 21 Mar 2026 21:22:12 GMT</pubDate><enclosure url="https://static.wixstatic.com/media/9dcf9c_8edbdda53686425b9baf56c9a391319a~mv2.jpg/v1/fit/w_852,h_683,al_c,q_80/file.png" length="0" type="image/png"/><dc:creator>benlusic</dc:creator></item><item><title><![CDATA[Profitability of Graphing Calculator- Tableau]]></title><description><![CDATA[Product Profitability Insights: A deep dive into sales of Texas Instruments Graphing Calculator. The Business Challenge To maximize ROI, a company must understand not just what is selling, but who is buying and where those sales are most profitable. This project analyzes the sales performance of Texas Instruments graphing calculators across the United States, specifically focusing on identifying the most lucrative customer segments and regional profit drivers. Link to Dashboard: Tableau Key...]]></description><link>https://benlusic.wixsite.com/bensportfolio/post/profitability-of-graphing-calculator</link><guid isPermaLink="false">69bc63b6d06d6deb3e8397d3</guid><category><![CDATA[Tableau]]></category><pubDate>Sat, 21 Mar 2026 21:15:14 GMT</pubDate><enclosure url="https://static.wixstatic.com/media/9dcf9c_b11b8b1c9b1c492e83a143d906056338~mv2.png/v1/fit/w_1000,h_1000,al_c,q_80/file.png" length="0" type="image/png"/><dc:creator>benlusic</dc:creator></item><item><title><![CDATA[IT Monthly KPI Dashboard- Tableau]]></title><description><![CDATA[On this dashboard we are building visualizations for our IT department on key KPI Metrics. Link to Dashboard: Tableau Project Overview This dashboard serves as a central monitoring hub for a Customer Service department, tracking agent performance through the lens of customer satisfaction ratings. It is designed to provide leadership with an at-a-glance view of service quality, response trends, and team efficiency. Key Features &#38; Analysis Dynamic KPI Tracking: High-level metrics focusing on...]]></description><link>https://benlusic.wixsite.com/bensportfolio/post/it-monthly-kpi-dashboard</link><guid isPermaLink="false">69bc4c2cbec251fc0ba8bc53</guid><category><![CDATA[Tableau]]></category><pubDate>Sat, 21 Mar 2026 19:24:12 GMT</pubDate><enclosure url="https://static.wixstatic.com/media/9dcf9c_e9c72dc035d44265af4aff0365693fb4~mv2.jpg/v1/fit/w_903,h_757,al_c,q_80/file.png" length="0" type="image/png"/><dc:creator>benlusic</dc:creator></item><item><title><![CDATA[US Household Data Cleaning Project- MySQL]]></title><description><![CDATA[In this project we walk through the process used for cleaning the raw household data. Link to GitHub Background: Received raw household data from a client and needed to transform and clean the data to be used in a Web Application. Process: Used MySQL to ingest the data, identified data inconsistencies, and normalized the data using processes shown below. First, let's take a look at the data:  We need to check for duplicates first. Let's do this by running a count on the id which should be...]]></description><link>https://benlusic.wixsite.com/bensportfolio/post/us-household-data-cleaning-project</link><guid isPermaLink="false">69bda20ae108eb64bf20f1bc</guid><category><![CDATA[MySQL]]></category><pubDate>Fri, 20 Mar 2026 22:40:35 GMT</pubDate><enclosure url="https://static.wixstatic.com/media/9dcf9c_c512b50b201f4b6a8600d78834b4a72a~mv2.jpg/v1/fit/w_1000,h_1000,al_c,q_80/file.png" length="0" type="image/png"/><dc:creator>benlusic</dc:creator></item></channel></rss>