@Data_Ox
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Delivering data that leads since 2015. We extract data on demand from any public source - even complex websites. DM us
Joined September 2020
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Since 2015, we've helped businesses turn data chaos into advantage.
We build custom data pipelines around your goals — not templates. Tailored to your workflow, transparent and scalable as you grow.
Need help with web scraping or data extraction? DM us: bit.ly/499XEIM
Read the full story on Startup Rise:
startuprise.co.uk/how-dataox…
A successful Discord API request doesn't guarantee a usable dataset.
Incomplete coverage, missing context, and API limitations can affect data quality.
We break down 5 common challenges and how to address them.
Read the full article: data-ox.com/resources/blog/h…
Downloading 100 competitor product pages doesn’t give you 100 competitor prices.
A website ripper creates a local copy. It doesn’t create a structured dataset or track changes.
Where does a website ripper stop being enough? Read the comparison:
data-ox.com/resources/blog/w…
Annual comp surveys are 12 months stale before they reach your desk. Meanwhile, your competitors post salary ranges on Indeed & LinkedIn every day. Scraping that data = real-time benchmarking.
data-ox.com/?utm_source=X&ut…
#CompensationData #HRTech #JobMarket #TalentStrategy
Same logic applies to data pipelines. Nobody cares about the architecture debate - they care whether the data is there when the decision needs to be made. Latency in your pipeline = latency in your strategy.
People talk about local-first as a data-ownership debate.
To me it's a latency and feel decision, and the UX difference is the whole point.
The "philosophy" framing glosses over what it actually changes in Spondr: every read operation, search, thread load, body fetch, runs against local SQLite. 50ms, not 500ms. No spinner. No API in the critical path.
Users don't care about the philosophy. They care about speed.
This is exactly why real-time competitor data matters. If your pricing strategy depends on what you think competitors are doing - you're already behind. The companies that spotted Amazon's moves early had one thing in common: automated data monitoring, not manual checks.
#eCommerce 💻AMAZON accusé de manipuler les prix : un rapport accablant lève le voile sur les pratiques commerciales du géant
comment Amazon aurait mené des stratégies pour augmenter les prix chez ses concurrents juste avant le Prime Day, soit une période de soldes organisée par Amazon. L’entreprise américaine aurait également incité ses fournisseurs à rendre certains produits en rupture de stock, ou indisponibles à un prix inférieur, chez ses concurrents.
buff.ly/z7eBzAE
HR is still doing manual tracking in 2026.
Job boards move fast. Your data shouldn’t lag.
Practical guide: automated data collection for talent sourcing, salary benchmarking & competitor intelligence.
Link → data-ox.com/industries/job-a…
#HR #Recruitment #B2B #WebScraping
You're not slow at hiring. You're slow at getting the data to hire. 3+ boxes = data collection is quietly killing your speed: look in thread for list!
#TalentAcquisition #HR #Recruitment #HRTech
If data delays are slowing you down, automated collection can change the game.
Follow @Data_Ox for practical breakdowns.
Which point hits your team hardest? Comment below.
#WebScraping #TalentSourcing #DataDrivenHR
data-ox.com/?utm_source=X&ut…
Most companies pick the wrong one. Then waste 6 months fixing it. Here's the actual decision framework - 3 scenarios, one clear answer for each.
Which one fits your team right now?
#AI #DataQuality #DataEngineering #MachineLearning #DataStrategy
B2B teams lose deals talking to the wrong people. Stale lists. Outdated contacts. Dead databases. Web scraping fixes it: fresh targeted prospects from job boards & directories auto-matched to your ideal buyer. Short practical guide → Link in comments #B2BSales #LeadGeneration
Most people use “scraping” and “crawling”. They’re not the same. One discovers. The other collects.
We wrote a short guide breaking down how each works and when you need both together for real results. Link in comments!
#WebScraping #DataScraping #WebCrawling #DataExtraction
The problem isn't your AI. It's what you're feeding it. 🧵
#AI #DataQuality #DataEngineering #MachineLearning #DataFirst #BigData
Follow @Data_Ox we cover what getting the data foundation right actually looks like in practice.
#DataFirst #BigData
data-ox.com/resources/genera…