Ryan Consulting Group, LLC’s Post

🌟 Why Clean Data Matters 🌟 Clean data leads to better decisions. Raquel Analyst for Hire, I prioritize data cleaning to ensure accuracy and reliability. #DataCleaning #BusinessAnalysis #DataDriven When writing checks or wires for payments, even small errors can lead to big problems. Here are some common mistakes to watch out for: Typographical Errors: Entering “1243” instead of “1234” or adding an extra space like " (space)1234". Transposition Errors: Swapping numbers, such as entering “32” instead of “23”. Misclassified Data: Placing information in the wrong category or field. Stay vigilant and double-check your entries to ensure accuracy and avoid costly mistakes! #BusinessTips #PaymentProcessing #DataAccuracy #BusinessAnalysis

Make data entry easier by using data validation to create drop-down lists in cells. 𝗦𝘁𝗲𝗽𝘀: Select the cell or range where you want the drop-down. Go to Data > Data Validation. Under Criteria, select "List of items". Enter your list items, separated by commas. Click "Save". Use Case: This is perfect for status updates (e.g., "Pending", "In Progress", "Completed"), categories, or any other situation where you want to ensure consistency in data entry.

If you’ve heard a thousand times that you need to know how to clean “dirty data”, but you don’t really know what “dirty data” is, this post is for you. The term “dirty data” refers to data that is inaccurate, incomplete, inconsistent, or contains errors. This can take the form of missing values, duplicate records, inconsistent formats, and other issues. This begs the question: “How did the data get this way?” Answer: humans are human. For a lot of data in the real world, at some point the data has been entered by a real person, and people make mistakes. For example, let’s say you manage sales data manually entered by salespeople when they make a sale including name, phone number, gender, dollar amount of sale. For phone number you’ll get a lot of inconsistent formatting, for gender you’ll get different answers for the same thing (male, man, M, etc), and for dollar amount of sale you may get non numeric values making you unable to run calculations using that column. It’s your job as a data analyst to clean this data and make it usable. There are various techniques to work through these issues in SQL, Excel, and data viz tools, and once you get the hang of it, it’s not bad at all. What are some examples of dirty data you’ve run into?

Data Collection When I first considered looking for a side income, I dreamed of becoming a Data Analyst. I didn’t realize that Data Analysts have to do a lot of tasks before providing a summary or analysis report. What surprised me the most was that Data Analysts need to do coding, which is an activity that always stresses me out! Now, as a Unit Trust and PRS consultant, I get to experience being a Data Analyst occasionally. Haha! To perform due diligence, determine which funds are performing well, and which ones to avoid, I have to analyze everything myself. So, the first step to take is data collection. Yesterday, I managed to do Data Cleaning and Matching. I only filtered for Shariah-compliant funds because those are the ones I will recommend to my clients. There’s a shortcut to finding out which funds perform well—just ask a Financial Planner 🤣. Or, we can attend a webinar organized by my FP to gain knowledge together.

"Unlocking the power of data, one entry at a time! 💼 As a dedicated data entry specialist, I ensure accuracy and organization in every keystroke. Let's optimize workflows and drive success through meticulous data management. Connect with me to explore how we can elevate your data processes! #DataEntry #DataManagement #Efficiency"

One of the main reasons I use Excel is for data cleaning. If you want to be a data analyst, you’ll have to embrace the data-cleaning aspect of the job. Here are some data-cleaning terms you should become familiar with in Excel: - Data Validation - Find and Replace - Trim - Left/Mid/Right - Concat - Power Query - Text to Columns - Remove Duplicates - Conditional Formatting Anything else you’d add?

MY 2-STEP DATA ANALYSIS WORKFLOW 1️⃣ Identify the business the problem 2️⃣ Build the technical solution(s) to solve the business problem and deliver business critical benefits Technical skills combined with expert domain knowledge is a killer combination. You can have the best technical skills in the world, but if you don't have the necessary domain knowledge, you will never be able to do step 1️⃣ and will always have to wait until someone else tells you what to do / look for. You don't want to be a step 2️⃣ person solely. You also don't want to be a step 1️⃣ person solely. You want to be the complete package. You want to be the step 1️⃣ and 2️⃣ person -- someone who can talk the talk and walk the walk. Make Money with My Data Analysis Resources ▶️ https://lnkd.in/eHgNijzY ◀️

Day 2 - Data Cleaning Task: Import the data into Excel and perform basic data cleaning tasks such as removing duplicates, handling missing values, ensuring data types are correct and other data cleaning tasks. 2nd day of #7daysofExcelwithDigitaleydrive

I specialise in assisting business owners with their data entry needs, allowing them to focus on more critical tasks. My objective is to simplify the process and offer a dependable solution for maintaining efficiency and organization. Let me take care of the data entry so you can dedicate your time to expanding your business. #LetMeHelp #StressFreeZone #AdminExpert #VirtualPA #BusinessSupport

🚀 Hey ! LinkedIn family, I am Excited to share my latest project : "Credit Card Transaction & Customer Analysis" 🎯 Project Objective: Developed a comprehensive credit card weekly dashboard to provide real-time insights into key performance metrics and trends, empowering stakeholders to effectively monitor and analyze credit card operations. 🔧 Process: Prepared CSV files and created tables in MySQL. Imported CSV data into tables for ad hoc analysis. Connected the database to Power BI to streamline data processing and analysis. Used DAX queries to create necessary measures and added custom columns to the data. Developed a detailed dashboard to visualize insights. Shared actionable insights with stakeholders to support decision-making processes. 📊 Insights (53rd Week) : 📈 WoW Change: 1. Revenue increased by 28.8%. 2. Total Transaction Amount increased by 2.22% compared to the previous week. 3. Customer count increased by 1.80% compared to the previous week. 📅 Overview YTD: 1. Overall revenue: $57M 2. Total interest: $8M 3. Total transaction amount: $45.5M 4. Revenue contribution: Male customers - $31M, Female customers - $26M 5. Blue & Silver credit cards contribute to 93% of overall transactions. 6. Top contributing states: TX, NY, CA - 68% 7. Overall activation rate: 57.5% 8. Overall delinquent rate: 6.06% This project was a great opportunity to leverage MySQL and Power BI to deliver impactful data-driven insights. GitHub Link : https://lnkd.in/gdftEABG Guided By : Rishabh Mishra #DataAnalytics #PowerBI #MySQL #CreditCardOperations #DataDrivenInsights #StakeholderEngagement Feel free to connect and reach out for any collaborations or discussions on data analytics and business intelligence!