Hello guys, I am Bharat professional Bookkeeper where I take data from the client clean it and give the proper consistent and Structured Data Today I had Data of WellCare Health Clinic where I get the Messy and Inconsistent data of the clinic which I had fixed the problem What was the problem: 1.Was it Structured ❌ 2.Is (http://2.Is) the data provided Consistent and Not Broken ❌ 3.Is (http://3.Is) the data easily Recognisable by looking at it (like can we Identify it is expense, Utilities or Income ❌ 4.Is (http://4.Is) it maintained in proper format ❌ How I fixed it using Excel: 1.First, I categorize the data properly in a Structure ✅ 2.Then I put the data into proper format (Like 1/4/24 to 01-04-2024) ✅ 3.I just take out the Inconsistent, Broken and duplicate data into Consistent data ✅ 4.I made data easily Recognisable by looking it (Like We can easily identify expense utility and income by highlighting them. ✅ Left one is the messy and Inconsistent data and right one is Proper structured and Consistent Data If you are someone looking for Bookkeeping service, I will do it with proper analysis and with a proper disciplined structure. Thank you #Bookkeeping (https://www.linkedin.com/search/results/all/?keywords=%23bookkeeping&origin=HASH_TAG_FROM_FEED) #Dataentry (https://www.linkedin.com/search/results/all/?keywords=%23dataentry&origin=HASH_TAG_FROM_FEED) #India (https://www.linkedin.com/search/results/all/?keywords=%23india&origin=HASH_TAG_FROM_FEED) #USA (https://www.linkedin.com/search/results/all/?keywords=%23usa&origin=HASH_TAG_FROM_FEED) #Datacleaning (https://www.linkedin.com/search/results/all/?keywords=%23datacleaning&origin=HASH_TAG_FROM_FEED) #Australia (https://www.linkedin.com/search/results/all/?keywords=%23australia&origin=HASH_TAG_FROM_FEED) #UAE (https://www.linkedin.com/search/results/all/?keywords=%23uae&origin=HASH_TAG_FROM_FEED)

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Cover image for Hello Everyone This the Data

Hello Everyone This the Data of Vertex Media Agency which was Initially given Messy, Broken and Inconsistent Data What was the Problem: 1.Actually, Data was Broken 2.It (https://2.It) was Messy 3.Non-Structured 4.Inconsistent After Looking at the Data, I cleaned it and made it consistent, formatted, Structured Data What changed after Improving and cleaning the data: 1.The Data is properly Structured 2.It (https://2.It) is CRM Ready 3. (https://3.It)Consistent

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CRM Ready Data Cleaning

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The image is a visual comparison of the customer-lead data before and after cleanup for TechNova Solutions. Upper section: Messy data The upper section shows the information exactly as it was collected from business cards and signup sheets. It contains inconsistent capitalization, different phone-number formats, duplicate entries, incomplete fields, and possible spelling variations. For example, “ravi kumar” and “RAVI KUMAR” represent the same person, while Farhan Ali appears twice with slightly different company and phone formatting. Lower section: Organized and cleaned data The lower section is the CRM-ready version. Duplicate leads have been removed, names and company names have been standardized, email addresses have been converted to lowercase, and phone numbers have been formatted using the +91-XXXXXXXXXX pattern. A new Status column has also been added. Status Meaning Ready The lead has the required email and phone information. Needs Follow-up Important information, such as an email address or phone number, is missing or incomplete. The blank cells in the cleaned table are intentional. They indicate information that was not available in the original data; no details were invented. Overall, the image demonstrates how unstructured lead information can be transformed into a more consistent and usable CRM database.

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Bharat Bhat

Professional Data Cleaning and Bookkeeping services