Power BI Dashboard & Data Cleansing

@utrathore3

With a comprehensive skill set tailored towards data analysis, cleansing, processing, visualization, and automation techniques, I am confident that I can provide you with the high caliber deliverables your project demands. From my deep understanding of Excel and its advanced features including Pivot Tables, Data Cleansing and Formatting, Power Query and Power Pivot- to my proficiency in key tools such as Python, I am equipped to transform your raw data into valuable insights. My proposed approach starts with a meticulous data cleansing procedure that will remove duplicates, handle missing values effectively, and put your dataset into an analysis-ready structure. This will ensure accurate results throughout your entire project. I'm quite experienced in using tools like Python with Pandas and Power Query so you have the flexibility to choose the platform that pairs best with your workflow. Rest assured, regardless of the platform chosen, my process will be fully reproducible. Finally, I'll produce a compelling Power BI dashboard that is intuitively navigable and designed to highlight essential KPIs while providing layered insights for different stakeholders to autonomously discover answers to specific operational questions.

@shivampanchal

Hi, I can take this from raw Excel to a finished Power BI report. I'll make the cleaning fully reproducible so you can retrace every step later. Approach and timeline Data audit (0.5 day): Profile the file for duplicates, missing values, inconsistent formats and outliers, and agree the KPI definitions with you (efficiency, quality, time trends). Cleaning (1-1.5 days): Done in [Pandas / Power Query], with every step scripted or logged. I'll remove duplicates and handle missing values according to each column's context (imputation, flagging or exclusion), not blanket deletion. Columns will be standardised for types, names and dates. You'll get a short change log showing what was changed and why. Data model (0.5 day): Build relationships, a date table, and calculated columns. DAX measures and dashboard (2-2.5 days): Organise measures in folders, and build KPI cards for efficiency and quality, trend views, and period-over-period comparisons. Slicers and drill-downs will be designed so stakeholders can answer their own questions. Review and hand-off (0.5 day): One round of revisions based on your feedback, plus a brief walkthrough of the report and measures. Deliverables: cleaned dataset (Excel/CSV), the cleaning script or Power Query steps, the change log, and the .pbix file with a tidy model and documented DAX measures. Total: about 5-6 working days, and I can start right away. If you share a sample of the file, I can confirm the timeline and suggest KPIs before we begin.

@glanegonsalves

Hi, Glane here. I’ll clean your Excel data using Python via Pandas , handling duplicates, missing values, data types, and inconsistent entries through a reproducible workflow. Then I’ll build an interactive Power BI dashboard with organised relationships, DAX measures, efficiency and quality KPIs, time based trends, filters, and drill-downs. Timeline: Data cleaning and validation: 1/2 day; data modelling and dashboard development: 1 day; testing and final hand off from your end: 1 day. You’ll receive the cleaned Excel/CSV, the .pbix file, and documentation of the workflow and key measures.

@chraheelbhatti

With my extensive background in data processing and analysis, I am confident I can deliver the efficient data cleansing and Power BI dashboard you require. Drawing on both Power Query and Python with Pandas, I propose crafting a robust workflow that not only cleans your dataset, but also creates calculated columns and compiles intelligent DAX measures for your specific KPIs. Through this approach, I ensure that each step is reproducible for future analyses. As an added bonus, my proficiency in Python would allow me to tackle any complex data inconsistencies or missing values head-on. Additionally, my meticulous academic experience aligns perfectly with your need for neat documentation including a clean dataset and an organized .pbix file with well-defined relationships. Time-wise, I expect to spend about X hours on data cleansing and Y hours on developing the Power BI dashboard – ultimately providing you a high-quality output within Z days. So let's connect and turn your raw Excel file into a robust Power BI dashboard that answers all your operational questions!

@mobimubasir

Hello there, I can clean and structure your Excel data using a fully reproducible Pandas/Power Query workflow, handling duplicates and missing values before building an analysis-ready model. I’ll then create an interactive Power BI dashboard with organized relationships, DAX measures, KPI tracking, filters, drill-downs, and time-based efficiency/quality insights. Estimated timeline: 1 day for data cleaning/modeling, 1–2 days for Power BI development and testing, and 0.5 day for final QA and hand-off.

@emanz03

Missing values need rules tied to each field so cleaning preserves meaningful efficiency and quality figures. I’ll use reproducible Power Query steps to remove duplicates, standardise columns and document missing-value handling, then build a focused Power BI dashboard with DAX KPIs, time trends, filters and drill-downs. Estimated stages: cleaning 2–3 hours, dashboard 3–4 hours, validation and hand-off 1 hour, subject to file review. Can you share the Excel file and KPI definitions?

@rezamahi

I can handle the complete workflow from raw Excel data through a clean, reproducible dataset and a stakeholder-ready Power BI dashboard. With 13+ years of experience in data analytics, Python/Pandas, Excel, Power Query, Power BI, DAX and data modelling, I’ll structure the process so every transformation can be traced and repeated. Approach: 1. Data audit & profiling — review columns, data types, nulls, duplicates, inconsistencies and outliers. 2. Data cleaning — remove true duplicate rows, standardise formats, correct data types, intelligently handle missing values based on the meaning of each field, and document the rules used. 3. Data transformation — create a clear analysis-ready structure using Power Query and/or Python/Pandas, keeping the workflow reproducible. 4. Data modelling — build the Power BI model with appropriate relationships and a clean star-schema structure where applicable. 5. KPI development — create organised DAX measures for efficiency, quality and operational performance, including time-based trends and comparisons. 6. Dashboard — interactive KPI cards, trend analysis, breakdowns, filters and drill-downs designed for fast stakeholder analysis. 7. QA & hand-off — validate totals against the cleaned source, test filters/measures, remove errors, and provide the cleaned Excel/CSV and final .pbix.

@manishashree12

Hi, Good day! Having reviewed Power BI Dashboard & Data Cleansing in detail, I believe I'm the right person to help you achieve your objectives successfully. I have a strong portfolio showcasing my work in Python, Data Processing, Excel, Data Cleansing, Data Visualization, Data Analysis, Power BI, Pandas and Python, Data Analysis, Data Processing, Data Visualization, Pandas, Power BI. Please check my profile for examples relevant to Power BI Dashboard & Data Cleansing. I'm committed to making Power BI Dashboard & Data Cleansing a success. Let's connect soon to discuss the details. Regards, Manisha

@robertb297

Hi, I’d structure this as a clean, reproducible pipeline: clean once, refresh reliably later. My workflow: • Profile the Excel data and identify duplicates, missing values and type inconsistencies • Clean/transform with Power Query or Pandas depending on what is most maintainable • Build a proper Power BI data model with clear relationships • Create organized DAX measures for the key efficiency and quality KPIs • Add intuitive date filters, drill-downs and trend analysis • Validate the dashboard against the cleaned source data • Deliver the cleaned file + organized .pbix For a small dataset, I’d keep the model deliberately simple so it remains easy to maintain rather than over-engineering it. How many rows and columns does the Excel file contain?

@Yevhen1987

Hi. I saw you need an Excel file cleaned and visualized in Power BI, and I can start immediately. I'll use Python with Pandas for data cleansing and preparation. - Deliver a spotlessly clean Excel/CSV dataset. - Create an interactive Power BI dashboard highlighting efficiency and quality KPIs, along with time evolution insights and intuitive filters. I can do this right now for $50 delivering within 1-2 days. Could you specify the key columns for your efficiency and quality metrics? Best regards, Yevhen.

@caajoys

I can turn your raw operational data into a reliable, analysis-ready dataset and an interactive Power BI report that makes efficiency, quality, and time-based trends easy to understand. I’ll keep the transformation workflow reproducible and document the key logic so you can retrace how the final dashboard was built. Thank you for considering my proposal. I have gone through the requirements in detail. My approach: • Data cleaning: Profile the Excel file, identify duplicates, missing values, inconsistent formats, and structural issues, then clean and standardize the dataset using Power Query or Python/Pandas as appropriate. • Data validation: Reconcile row counts, test transformations, and verify that no important records are unintentionally removed or altered. • Power BI modelling: Build appropriate relationships, calculated columns, and organized DAX measures with a clean analytical structure. • Dashboard: Create intuitive KPI cards, efficiency/quality metrics, trend analysis, filters, and drill-downs for stakeholder self-service. • QA & hand-off: Test calculations, filters, relationships, and refresh behavior before delivering the final files. Estimated timeline: 1 day for profiling/cleaning, 1–2 days for modelling and dashboard development, and 0.5 day for testing and final hand-off, depending on dataset size and complexity. I’m ready to review the dataset and begin with the profiling and cleaning stage.

@AnjosGustavo

Hi, This project is a strong fit for my background as a Senior Data Science Engineer with extensive experience in data cleaning, transformation, analysis, and dashboard development. I would first make the Excel data analysis-ready by handling duplicates, missing values, inconsistent types, naming, and structural issues through a fully reproducible Pandas or Power Query workflow. After validation, I would build the Power BI model with clean relationships, organized DAX measures, KPI logic, time-based analysis, filters, and intuitive drill-downs. My focus is not only on making the dashboard look good, but on ensuring the underlying data and metrics are accurate, traceable, and trustworthy. I take responsibility, attention to detail, and reliable delivery seriously. I can begin immediately and provide a clear stage-by-stage timeline after reviewing the file. Best, Gustavo

@albert1025dev

Hi, Albert here, from Philippines. What matters most in building your Power BI dashboard and data cleansing is numbers people trust and use to make decisions. I'm a senior software developer with 10+ years of experience working with Python, Data Processing, Data Visualization and Data Analysis. The real challenge is clean data and clear definitions. My plan: first, agree each metric, its definition and its data source. Then, clean and model the data, and build the report pages in Power BI. Finally, check totals against the source and set up scheduled refresh. I'd start with the main dashboard pages as phase one. The timeline and milestones are in the bid details. I've done reporting work in Python. A few questions to better understand the scope: 1. Do teams define the same metric differently? One definition per metric stops reports disagreeing. 2. Scheduled refresh instead of live queries? I'd recommend it, faster and lighter on sources. 3. Do viewers open reports mostly on phones? Then a mobile layout with key numbers comes first. I can start right away. Happy to go over the details here in chat and suggest the best approach. Looking forward to hearing from you, Albert

@Synet

Hi, I would start by taking your raw Excel file and writing a clean Python script with Pandas to remove duplicate rows, fill missing values based on logic rules, and format every column for clear analysis. Since you need a reproducible workflow, I will document every data step in the code so you can run it again on new files whenever you want. Once the data is spotless, I will load it into Power BI to build your interactive report focusing on efficiency and quality metrics, plus trends over time. I will also add a summary card page for quick high level reviews that stakeholders often miss. How should missing values be handled for the operational columns when data is empty? Best, Salma Noreen

@MiguelMonteroADD

Hi, I'm Miguel, Data Analyst expert in Excel, Power BI, and Python, with a track record of handling data structuring and interactive dashboard development with sharp attention to detail. I read your post and it’s clear to me: you need to transform raw Excel data into a clean, well-structured model with a fully reproducible workflow, followed by an interactive Power BI report that makes analyzing your KPIs seamless. My approach for your project: Cleaning & Structuring: I will use Power Query or Python (Pandas) depending on the data volume to handle missing values, remove duplicates, and structure everything into a clear, reproducible workflow. Data Modeling & Visualization: I'll build the data model in Power BI using clean DAX measures to highlight your efficiency, quality, and time-evolution KPIs with intuitive drill-downs for your stakeholders. I can start right away. Estimated timeline: • Stage 1 (Data cleaning & prep): 1 day • Stage 2 (DAX Modeling & Interactive Dashboard): 1-2 days If you're looking for someone to deliver a fully transparent process and a decision-ready dashboard, count me in. Feel free to reach out so we can discuss the details and get started on your project right away. Best regards, Miguel Montero.

@naminghn

Hi there, I see you need to clean and prepare your operational data in Excel before visualizing it in Power BI. I can leverage Python and Pandas for efficient data cleansing, ensuring that duplicate rows are removed, missing values are intelligently handled, and the dataset is structured for analysis. The workflow will be designed to be fully reproducible so that you can easily retrace each step. Once the data is refined, I’ll create an interactive Power BI dashboard focusing on the efficiency and quality metrics you mentioned, along with evolution-over-time insights. The final deliverables will include a cleaned dataset in Excel format and a well-organized .pbix file complete with calculated columns and DAX measures. I estimate about one week for the full hand-off to ensure a thorough process. Regards, Ali

@CoreStackPro

Hi, your stakeholders need reliable KPI trends from raw Excel, without manual cleanup distorting efficiency or quality results. I’d profile the workbook, define duplicate and missing-value rules, then build a reproducible Pandas or Power Query workflow. Cleaning should take 1 day, with a cleaned Excel or CSV and run guide. I’d then model it in Power BI, adding relationships, calculated columns, organized DAX measures, KPI cards, trends, filters, and drill-downs. Dashboard build should take 1–2 days, plus half a day for validation and hand-off. Inconsistent dates or blank categories can shift totals or time trends. I’d make those rules explicit and flag uncertain values. For frequent stakeholder use, the first page would stay focused, with detail behind drill-through. My background includes Python data processing and reproducible automation. I have two questions: Q1: Which columns define duplicates, and should missing values be imputed, marked Unknown, or excluded? Q2: Are KPI definitions already agreed, or should they come from the operational fields?

@shainw

Hi, I’ve read your brief and I can help you with your Power BI Dashboard and Data Cleansing project. For your project, I’d recommend starting with a thorough data cleansing process to ensure your dataset is free from duplicates and intelligently handles any missing values. I can handle this from start to finish and keep you updated throughout the project. Here’s my approach: First, I’ll use Python with Pandas to clean the data, ensuring it’s structured for analysis. This stage will take about 2-3 days. Next, I’ll create the Power BI dashboard, focusing on your core KPIs and making sure the filters and drill-downs are intuitive. This phase will take an additional 3-4 days. I can also show you relevant examples of similar work before we begin. Send me a message and I’ll explain exactly how I’d approach your project. IF YOU ARE NOT HAPPY YOU DON'T PAY! Kind regards, Shain WWD Digital Solutions

@aydont

YOU ONLY NEED ONE DEVELOPER WHO GETS IT RIGHT THE FIRST TIME. HERE'S WHY I'M THAT DEVELOPER. I recently helped a client transform their raw data into a polished, insightful Power BI dashboard that drove decision-making. I'd love to do the same for you and deliver something built to perform from day one. We will ensure your operational data is meticulously cleaned and structured for analysis, setting the stage for a powerful visual representation. Your focus on efficiency and quality metrics resonates with me. I’ll create a seamless, user-friendly dashboard with intuitive filters and drill-downs, allowing stakeholders to navigate data with ease. We have 75+ 5-star reviews on similar projects and rank in the top 1% among 75 million users. I'd love to chat about your project! The worst that can happen is you walk away with a free consultation. Regards, Aydon.