Farah Kim on Muck Rack

Farah Kim

London, United Kingdom
As seen in: Medium, Business 2 Community, HackerNoon, ReadWrite, DEV Community, DZone, KDnuggets, Jeff Bullas's Blog, BigDATA Wire, Data Science Central and
Covers:  Data quality, martech, content marketing, B2B, SaaS
Doesn't Cover: Finance, Politics, COVID-19

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Farah Kim’s Biography

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Human-centric content for tech startups, SaaS, and enterprise-level service providers. Also a classic literature nerd with an affinity for everything profound and philosophical.

Farah Kim’s Journalist Portfolio

Data Analysts Aren't Janitors

Data Analysts Aren't Janitors

Data analysts should work with data to derive insights for a business, but too often they spend most of their…

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Identity Resolution With Data Matching - WinPure

Identity Resolution With Data Matching - WinPure

Businesses are drowning in customer data - most of it, is duplicated and scattered across multiple data sourc…

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Still Using SQL, Python, & Excel for Data Deduplication? Here's Why You Need Better Tools.

Still Using SQL, Python, & Excel for Data Deduplication? Here's Why You Need Better Tools.

I talk about no-code solutions for solving data quality challenges. Currently representing WinPure, a revolut…

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Articles

Why Data Cleansing Shouldn't Be an Afterthought

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By Farah Kim
| DZone
My development team lead was three weeks into building a slick React dashboard for a client when everything fell apart. The app looked great in demos with test data. We were ready to connect it to our production database. Then all hell broke loose. Suddenly, the charts didn't seem to align. Tables displayed incorrect records, and insights were off the mark.
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How Dirty, Duplicate Data Prevents Businesses from Being Data-Driven

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By Farah Kim
| DATAVERSITY
It’s a data-driven world, yet most businesses are struggling with dirty data; worse, many are still unable to perform basic tasks like deduplication and record linkage efficiently (and affordably). A recent report from my company, “Inside 2024’s Data Quality Challenge: Insights from the Frontlines,” reveals some eye-opening insights drawn from real-world conversations with customers.
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The Dark Side of Building AI Agents on Poor Data Quality

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By Farah Kim
| DEV Community
AI agents are the rage these days. Everyone's racing to build these agents that can supposedly automate tasks we humans are too lazy to do. Great, except that these agents are being built entirely on the foundations of flawed data. Most dev teams are excluded from conversations around data quality, until it's too late. An AI agent is fundamentally a pattern-recognition machine, gobbling up every piece of data you feed it.
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See all 31 licensed articles (32 Total)

Interview

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What was your first job as a journalist?

Freelanced and interviewed startup founders

Have you ever used a typewriter?

I learned typing using a typewriter!

What's the funniest news-related #hashtag you've seen?

#CareerEndingTwitterTypos

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