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Geethasree Naguboina's avatar
Geethasree Naguboina
proHyderabad, India
Excel & Data Analytics | Dashboards, KPI & Reporting
120
Followers
Excel & Data Analytics | Dashboards, KPI & Reporting
Cover image for I used to spend hours
I used to spend hours cleaning messy Excel files manually. Now? I use AI tools to cut that time in half — and deliver better results to my clients. Here's how AI has changed my freelance data workflow: 🔹 Faster data cleaning Instead of manually hunting for duplicates and formatting errors, I use AI to flag inconsistencies instantly. What used to take 2 hours now takes 30 minutes. 🔹 Smarter dashboard structure Before building any dashboard, I use AI to help me think through what metrics actually matter for that specific business — not just what looks good. 🔹 Better client communication I use AI to help me write clearer project summaries and delivery notes — so clients understand exactly what they're getting and why it matters. But here's what AI can't replace: ✔ Understanding what a CA firm actually needs vs what a D2C brand needs ✔ Knowing when a number looks wrong before the formula catches it ✔ The judgment that comes from actually working with real business data AI is a tool. The thinking is still yours. If you're a small business owner or CA firm drowning in Excel data — I can help you turn it into something you can actually use. DM me or check my portfolio 👇 geethasree-data-support-cr241o7.gamma.site (http://geethasree-data-support-cr241o7.gamma.site)
3
5
416
Cover image for The difference between a report
The difference between a report and a dashboard Most people use these words interchangeably. They're not the same thing. A report tells you what happened. A dashboard shows you what's happening. The difference: Report → static, detailed, built for documentation Dashboard → dynamic, visual, built for decisions The mistake I see most often: People build dashboards that are actually just reports. The result? — Decision makers scroll through pages of data — Numbers are outdated by the time anyone reads them — No one knows what to act on The fix is simple: A report answers: "What happened last month?" A dashboard answers: "What do I need to do right now?" When you design for the right purpose: Reports become clear records. Dashboards become decision tools. One looks back. The other drives forward.
2
3
277
Cover image for Most people think a spreadsheet
Most people think a spreadsheet and a dashboard are the same thing. They're not. A spreadsheet is where data lives. A dashboard is where decisions happen. The difference: Spreadsheet → raw, editable, flexible, built for input Dashboard → structured, visual, built for reading and decisions The mistake I see most often: People try to do both in the same sheet. The result? Decision makers see too much raw data Numbers get accidentally edited No one knows what to trust The fix is simple: Keep your data layer and your presentation layer separate. Raw data in one sheet. Dashboard in another. One is for building. One is for reading. When you separate them, updates become clean, mistakes become rare, and your reports actually get used. A spreadsheet stores your data. A dashboard tells its story.
4
6
293
Cover image for Executive Excel Dashboard for Business Reporting
Executive Excel Dashboard for Business Reporting
4
19
Business Analyst(4)
Taziem Uddin's avatar
Taziem Uddin
proLahore, Pakistan
I install done-for-you analytics in high-ticket businesses.
$10k+
Earned
3x
Hired
4
Followers
I install done-for-you analytics in high-ticket businesses.
Cover image for No More Mondays: $1M+ Traced, Deal by Deal
No More Mondays: $1M+ Traced, Deal by Deal
1
7
Cover image for No More Mondays: Built From Scratch, One Warehouse
No More Mondays: Built From Scratch, One Warehouse
1
47
Cover image for No More Mondays: Ask Your Data, Get Answers in Seconds
No More Mondays: Ask Your Data, Get Answers in Seconds
2
49
Cover image for No More Mondays: The Same Close Rate, Told Two Ways
No More Mondays: The Same Close Rate, Told Two Ways
1
52
Business Analyst(1)
Nguyet T.'s avatar
Nguyet T.
Finland
Business Research & Process Specialist | Insights & Analysis
$1k+
Earned
1x
Hired
5.0
Rating
7
Followers
Business Research & Process Specialist | Insights & Analysis
Cover image for Market Research for Contra
Market Research for Contra
0
9
Cover image for Outcome & Deliverable Library
Outcome & Deliverable Library
0
11
Cover image for Business Process Owner
Business Process Owner
0
9
Cover image for PrimeFlow Business Market Research
PrimeFlow Business Market Research
0
80
Business Analyst(1)
Andy Luo's avatar
Andy Luo
Washington, USA
Bridging the gap between data analysis and market
$50k+
Earned
1x
Hired
5.0
Rating
28
Followers
Bridging the gap between data analysis and market
Cover image for Budget Execution Analysis PowerBI Visualization
Budget Execution Analysis PowerBI Visualization
8
247
Cover image for Agile and Technical System Diagram Designing
Agile and Technical System Diagram Designing
2
44
Cover image for Recommendation System: Planning & Management
Recommendation System: Planning & Management
2
29
Cover image for Bitcoin Price Prediction Using ML
Bitcoin Price Prediction Using ML
1
26
Business Analyst(1)
Klaas Lieuwes's avatar
Klaas Lieuwes
proAmsterdam, Netherlands
ERP & product data support for manufacturers
$1k+
Earned
1x
Hired
3
Followers
ERP & product data support for manufacturers
Cover image for ERP parts data structuring for an electric vehicle manufacturer
ERP parts data structuring for an electric vehicle manufacturer
1
9
Cover image for Validating and correcting PII annotations
Validating and correcting PII annotations in synthetically generated Dutch-language conversations, covering both nl-NL and nl-BE (Flemish) locales. The work goes beyond simple labelling. Each conversation is reviewed for missing or incorrect entity spans, offset accuracy, and whether every instance of a PII entity is consistently tagged across turns. On top of that, the Dutch language quality is assessed — grammar, naturalness, locale-appropriateness, and conversation coherence — with corrections and explanations provided where needed. PII entity types covered include names, addresses, phone numbers, email addresses, IBANs, ID numbers, dates, usernames, and passwords, each validated against the correct Dutch or Belgian format. Quality targets: inter-annotator agreement above 95%, full locale validity, and self-QC accuracy of 98% or higher. Native Dutch speaker covering both standard Dutch and Flemish — not two separate skill sets, but one annotator who knows the difference.
1
47
Cover image for Transcribing and annotating Dutch-language audio
Transcribing and annotating Dutch-language audio for AI speech recognition training at Deepgram, one of the leading speech AI companies. Work involves listening carefully, producing accurate transcripts, and applying consistent annotation according to strict quality guidelines. This is specialist work — not bulk transcription, but quality-controlled annotation used directly to train production speech models. Accuracy and consistency matter more than speed. Native Dutch speaker with genuine familiarity with regional speech patterns, accents, and edge cases that a non-native annotator would likely miss or flag incorrectly.
1
51
Cover image for Dutch Website Translation & Technical
Dutch Website Translation & Technical Content Translating website copy, product descriptions, and technical documentation from English into Dutch for an international client operating in the technology sector. The work requires more than language skills alone — understanding the underlying product, its technical context, and the target audience is essential to producing translations that are accurate, natural, and usable. Generic machine output doesn't cut it here; the client needs a native speaker who can think along. What this project covers: Website UI copy and product pages Technical documentation and support content Ongoing collaboration on an hourly basis — not a one-off, but a sustained working relationship Good fit for clients who need: Native Dutch, not translated Dutch Someone who understands what they're translating, not just the words Reliable turnaround and consistent terminology across deliverables Hourly — ongoing availability.
1
62
Business Analyst(1)
Peter Chinaka's avatar
Peter Chinaka
proEdmonton, Canada
Project Management & Automation Specialist
13
Followers
Project Management & Automation Specialist
Cover image for Product Owner at ServiceRocket
Product Owner at ServiceRocket
0
31
Cover image for Agile Product Management at Mozilla MDN
Agile Product Management at Mozilla MDN
0
71
Cover image for Implementation of a HMIS for a major hospital
Implementation of a HMIS for a major hospital
0
30
Cover image for AI Lead Qualification & CRM
AI Lead Qualification & CRM Automation I built a production AI system that enriches, scores, and qualifies leads using Apollo, PDL, and Claude AI — writing decisions and talking points back to HubSpot. How It's Used: A Sales Rep creates a HubSpot contact. n8n detects it, enriches via Apollo/PDL, runs Claude AI scoring, writes results back in under 60s. Qualified leads trigger Slack alerts. Key Highlights – 5-agent pipeline: Research, Qualification, Routing, Engagement, Feedback – Parallel enrichment + Claude AI analysis – n8n: HubSpot trigger → Vercel → CRM writeback + Slack – Configurable ICP settings — no code needed Outcomes ✓ End-to-end HubSpot → AI → CRM in under 60s ✓ 4-dimension scoring with feedback loop ✓ Live ops dashboard Stack - Python · Vercel · Supabase · n8n · Claude API · Apollo · PDL · HubSpot · Slack · React
1
0
170
Business Analyst(3)
USMAN AKINTOBI's avatar
USMAN AKINTOBI
Nigeria
Making Sense of Data to Drive Decisions
5.0
Rating
6
Followers
Making Sense of Data to Drive Decisions
Cover image for Housing Dashboard
Housing Dashboard
1
8
Cover image for Energy Dashboard
Energy Dashboard
1
12
Cover image for DSP Asset Managers
DSP Asset Managers
2
30
Cover image for Context-Aware Fintech User Feedback Classification
Context-Aware Fintech User Feedback Classification & Urgency Detection Most feedback tools stop at sentiment; positive or negative. This project goes further by building a decision-driven NLP system that classifies fintech user feedback by issue type and detects operational urgency, enabling smarter triage and escalation. What was built: Two machine learning models trained on manually collected and annotated app store reviews from real fintech platforms like Revolut, Wise, and Monzo: Issue Classifier: categorizes feedback into 9 classes including transaction issues, account security, KYC, refunds, app performance, and fraud Urgency Detector: binary classifier that flags high-risk feedback requiring immediate attention, optimized for recall to minimize missed critical cases
1
138
Business Analyst(3)