The document discusses how companies often approach big data wrong by having data scientists manually collect and clean data instead of using big data as a service solutions. It provides the example of a data scientist named Jackie at a publishing company who needed pricing data but found manual and scraper methods too slow, expensive, and difficult to scale. While good for small volumes, these approaches did not meet the needs of her large company. The document advocates for using big data as a service which can provide specialist skills, scalable data sources, predictable costs, and reclaim over 50% of data scientists' time spent on data preparation instead of analysis.

1. Why you’re doing
Big Data wrong
Ma#hew Painter
CTO at import.io

2. Data: Supply and Demand
Volume
Electronic
Databases
Demand
Supply
Big Data
Business
Intelligence
1980s 1990s 2000s 2010’s

3. A case study

4. This is Jackie

5. she is a Data Scien:st
working for a global
publishing house

6. She wanted data for
pricing books

7. She started manually

8. She tried scrapers

9. Both of these
weren’t feasible

10. Slow & Expensive

11. Hard to resource

12. GeCng data off the
web can be fast

13. Data in minutes

14. Data in seconds

15. Perfect for data
journalists…

16. or small companies

17. Why’s it not right
for Jackie?

18. Scale.

19. Big-­‐Data-­‐as-­‐a-­‐Service
“the forecast value of the BDaaS market could be $30 billion” -­‐ Forbes

20. Benefits of BDaaS
data supply

21. Data scienNst ready:
maximum value

22. Specialist skills:
web insights quicker

23. Known cost:
predictable ROI

24. Scalable data:
supply your demand

25. Do Big Data right…

26. reclaim >50% of your
Data ScienNsts’ Nme