Join Barton Poulson for an in-depth discussion in this video Challenges with data quality, part of Big Data Foundations: Techniques and Concepts.
- In any data project the quality of…the data play a critical role.…In big data, however, quality is even harder to…deal with because the data often originated outside…of the immediate project and will…have a life of its own beyond it.…The researcher doesn't have complete control over…the data in the same way that they would…with a standard small data project.…Consider for a moment that much of the data that…goes into a big data project may…have once come from spreadsheets.…Some researchers have found that nearly 95% of…spreadsheets they examined had errors in them.…
And it falls back on the familiar expression from…computer science GIGO, for garbage in, garbage out.…Data can have these problems, and even if you…have Hadoop, even if you've got this tremendously…sophisticated analysis, if you're starting with…bad data you're going to end up with bad results.…Now, this gets back to our concept.…We want to make sure we have good data to start with…so we can have a defensible and informative result.…Let me go over very quickly some of the things that…
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- What makes big data "big"
- Understanding how big data impacts consumers, businesses, and research
- Exploring the intersection of data science and big data
- Facing big data's ethical challenges
- Understanding the sources and structures of big data
- Storing big data
- Prepping big data for analysis
Skill Level Beginner
What Is Big Data?
2. How Is Big Data used?
3. Big Data and Data Science
4. Ethics in Big Data
Challenges with anonymity5m 51s
5. Sources and Structures of Big Data
6. Storing Big Data
7. Preparing Data for Analysis
8. Big Data Analysis
Next steps1m 58s
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