The document discusses challenges with big data and how to create meaningful insights from large datasets. It notes that while humans have been collecting and storing data for thousands of years, it is only recently that we have begun processing large amounts of data to discover patterns and hidden meanings. When data reaches high volumes, velocities, and varieties, it is considered "big data." The document emphasizes that data does not inherently create meaning on its own - humans must use critical thinking skills to properly analyze and interpret big data in order to develop meaningful insights and conclusions. Some challenges with big data include differing contexts for similar words, variability in how humans discuss topics, and the influence of personal biases on data interpretation.

1. What do we do with all this
BIG DATA??

2. INTRODUCTION

3. Since the dawn of
Agricultural
Revolution, we
have :
NUMBERS
That is 5500
years ago!!

4. Humans have come a long way since then, BUT…
Until recently, we were only COUNTING and STORING data

5. We have now
started Processing
the data
Discovering
patterns and
hidden meanings
from it

6. We can now have
Meaningful
Conversations
with data
And not just use it
passively

7. When the data has,
increasing amounts of :
 Volume
 Velocity
 Variety
We call it BIG DATA

8. CREATE
MEANING
FROM DATA

9. Data does
not
create
meaning.
We Do.

10. We need to
improve our
Critical
Thinking
Skills
Critical thinking, the ability to identify
and spot problems in arguments, is a
crucial skill to succeed in creating
meaning from Big Data.

11. Tendency to interpret data
such that it conforms one’s
beliefs.
Confirmation Biases
False Correlations
Emotional Appeal
Awareness of
our own
biases
When we wrongly pinpoint a
correlation to some other
factor.
When the result makes us
feel happy and successful.

12. We need to
use our
Human
Expertise
We need to be knowledgeable about
humanities and social subjects,
including Philosophy, Sociology,
Rhetoric, etc.

13. Understand Our
Methodology
Data Sources01
02
03
Will give us
Confidence in
our results
Methodologies
Hypothesis
Clarity on :

14. BIG DATA
CHALLENGES
Similar words have
different context.
Humans, talk about the same
thing in different ways
Factual data, is not
always correct
2
5
3
1
4
Our language and
interactions are fluid
Our biases, have an effect on
our interpretation of the data

15. References
• www.flickr.com/photos/jimkaskade/15871675688/
• www.flickr.com/photos/7728345@N06/4841959292/
• www.flickr.com/photos/7728345@N06/4841959292/
• www.flickr.com/photos/140442672@N04/26284600950/
• www.flickr.com/photos/86083886@N02/9570381016/
• www.flickr.com/photos/64588740@N00/59308573/
• www.flickr.com/photos/95222905@N02/10997835056
• http://sci2s.ugr.es/sites/default/files/files/TematicWebSites/BigData/v3
.png
• www.flickr.com/photos/torontoatheist/6044308539/
• www.flickr.com/photos/mikeion/8035916054/
• www.flickr.com/photos/44501831@N02/4191948275/
• www.flickr.com/photos/32723260@N00/283298853/
• www.flickr.com/photos/jwalsh_/2466239241/
• www.flickr.com/photos/perfect_photography/5661202986/

16. THANK YOU!!