Data example sentences

Related (11): information, statistics, facts, records, analytics, metrics, dataset, database, evidence, figures, details

"Data" Example Sentences

  1. The company collected data from thousands of customers to understand buying patterns.
  2. I spent all morning organizing the data in spreadsheets for the presentation.
  3. She couldn't believe how much personal data was being sold without her consent.
  4. The data shows that people are working longer hours than they did five years ago.
  5. My phone keeps asking me to back up my data to the cloud.
  6. We need more data before we can make any real conclusions about the problem.
  7. The researcher analyzed the data and found something completely unexpected.
  8. Honestly, the amount of data they're collecting on us is kind of terrifying.
  9. He lost all his data when his laptop crashed last week.
  10. The data doesn't lie — our sales are down across every region.
  11. Can you send me that data file? I need it for the report.
  12. She's been staring at data all day and her eyes are killing her.
  13. The government wants access to our data, which obviously nobody's happy about.
  14. I hate how apps track your data without really explaining what they're doing with it.
  15. The data revealed patterns nobody had noticed before.
  16. After months of collecting data, we finally had enough to publish.
  17. Why does every website need so much of my personal data?
  18. The data contradicted everything the marketing team had been saying.
  19. He felt violated when he realized how much data had been collected about him.
  20. Data breaches happen constantly now — it's just part of life.
  21. The team spent weeks cleaning up bad data before the analysis could begin.
  22. I'm drowning in data and none of it makes sense.
  23. She understood that data was the new currency of the digital age.
  24. The data painted a picture of a country deeply divided.
  25. Nobody reads the terms and conditions, so they don't realize what data they're giving away.
  26. Data privacy is becoming impossible to maintain.
  27. He watched as the data came in, confirming his worst fears.
  28. The data was incomplete, which meant the whole study was basically useless.
  29. Can the data really tell us what people actually want?
  30. She'd spent her entire career learning how to interpret data correctly.
  31. The data suggested one thing, but common sense suggested another.
  32. I need to download this data before I lose internet connection.
  33. The data from last quarter was embarrassing — we all knew it.
  34. Why is it so hard to get accurate data on this topic?
  35. The company's entire business model depends on collecting and selling data.
  36. He realized too late that the data had been manipulated.
  37. The data shows we're making progress, which is something at least.
  38. She felt oddly exposed knowing that her data was sitting on some server somewhere.
  39. The data analysis took three times longer than anyone expected.
  40. What does the data actually tell us about what's really happening?

Common Phrases

  1. data collection
  2. data analysis
  3. data breach
  4. data privacy
  5. raw data
  6. personal data
  7. data set
  8. data point
  9. data-driven
  10. big data
  11. data entry
  12. data mining
  13. data visualization
  14. backup data

Examples by Level

A1
  1. I have data on my phone.
  2. The data shows ten people like pizza.
  3. My teacher wants the data from our test.
A2
  1. The data we collected yesterday was wrong, so we had to do it again.
  2. She looked at the data and said we needed more information.
  3. Without the data, we cannot make a decision about the project.
B1
  1. The data suggests people are happier when they work from home, though nobody really expected that.
  2. I've been staring at this data for hours and I still don't understand what it means.
  3. Once we analyzed the data, it became clear that our original idea wasn't going to work.
B2
  1. The data paints a more complicated picture than the headlines suggest — most people missed the nuance entirely.
  2. She'd built her entire argument on data that turned out to be flawed, which was embarrassing in hindsight.
  3. What strikes me about this data is not what it shows, but what people choose to ignore in it.
C1
  1. The data reveals a paradox at the heart of modern consumer behavior: we claim to value privacy while systematically surrendering it.
  2. One might argue that data has become the new currency, yet we remain largely unconscious of its true value.
  3. The interpretation of data is never neutral — it is always filtered through the lens of whoever is asking the questions.
C2
  1. There's a peculiar irony in how we've come to fetishize data as objective truth, when the act of collecting it is inherently subjective.
  2. The data tells us what happened; it is the stories we tell ourselves about that data which determine what we do next.
  3. What fascinates me is not the data itself, but the gap between what it actually says and what we desperately want it to mean.

Examples by Scenario

🏠 Everyday Life
  1. My fitness tracker keeps showing weird data about my sleep — apparently I was awake at 3 AM for two hours, but I don't remember that at all.
  2. The data on my electricity bill jumped by thirty percent this month and I have no idea why.
  3. I found old data from my high school grades and honestly, I was way smarter back then than I thought I was.
💼 Workplace
  1. The data from last quarter came in and it's not great — we're going to have a difficult conversation with the team tomorrow.
  2. My manager asked me to pull data on customer complaints, and let's just say there's a pattern nobody wants to talk about.
  3. Someone accidentally sent the salary data to the whole company instead of just leadership — awkward doesn't begin to cover it.
📊 Business
  1. The data shows our competitor's market share dropped 12% after they launched that rebrand, so clearly it backfired.
  2. We're investing heavily in data analytics because that's where the real competitive advantage is now.
  3. The data on customer retention suggests we're losing people in the first thirty days — we need to fix onboarding immediately.
🛒 Buying / Shopping
  1. I checked the data on these two laptops and the specs are almost identical, but one costs three hundred dollars more.
  2. The reviews have data showing this mattress lasts longer than the cheaper option, but is it really worth double the price?
  3. I looked at the nutritional data on these cereals and they're basically just sugar with a few vitamins thrown in.
✈️ Travel
  1. The data on flight prices shows they're cheapest on Tuesdays, so I always book then, and it actually works.
  2. I used data from travel blogs to plan our route through Japan, but nothing prepared me for how crowded it actually was.
  3. The hotel's data said they had availability, but when I arrived they claimed they were fully booked — never again.
📚 Education
  1. The teacher showed us data from a study about how students learn better with breaks, so now she lets us take them every twenty minutes.
  2. My daughter's school sent home data on test scores and apparently she's in the top percentile, which surprised everyone including her.
  3. The data on college graduates' salaries made me reconsider whether a degree is actually worth the debt anymore.
❤️ Relationships
  1. He kept bringing up data about how much time I spend on my phone, which felt less like concern and more like he was building a case against me.
  2. We looked at data about divorce rates and it made us both anxious, so we stopped and just talked instead.
  3. The data showed we'd been together for exactly ten years, and somehow that number made it feel more real than it ever had before.
📰 News / Politics
  1. The data on voter turnout this year was the highest in decades, though the media barely mentioned it.
  2. Politicians love citing data that supports their position while completely ignoring data that contradicts it.
  3. The government released data showing unemployment dropped, but critics argue the methodology was manipulated to look better.
💻 Technology
  1. The app collects way too much data about your location and browsing habits — I deleted it after reading the privacy policy.
  2. AI systems are trained on data that reflects historical biases, so they end up perpetuating the same discrimination.
  3. My phone's data usage jumped to five gigabytes last month because I didn't realize the app was syncing videos in the background.
📝 Formal Writing
  1. The data presented in this study demonstrates a statistically significant correlation between sleep deprivation and cognitive decline in adults aged thirty to sixty.
  2. Analysis of the data reveals three distinct patterns in consumer purchasing behavior across demographic groups.
  3. The methodology employed in data collection adhered to established ethical guidelines and was approved by the institutional review board.

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