The document discusses the evolution and challenges of big data and artificial intelligence, highlighting historical milestones and the increasing complexity of data management. It emphasizes the necessity for a data-driven culture in enterprises and introduces the AI company, Igenius, which aims to simplify interactions between humans and data through its virtual advisor, Crystal. The document showcases Crystal's capabilities in data processing and accessibility, demonstrating how it streamlines data analysis and enhances user experience.

1. AI to Z
How Artificial Intelligence is
changing the relationship
between people and data

2. Gabriel Cismondi
COO @ iGenius

5. Once upon a Data
The 3 Big Data challenges
From Big Data to AI
Big data and AI applied today
New challenges in a data-driven world
Simplifying the relationship between humans and data

6. The big data timeline

7. 1990
Peter J. Denning
“…rate and volume of information flow
overwhelm our networks, storage devices
and retrieval systems, as well as the
human capacity for comprehension… it is
possible to build machines that can
recognize or predict patterns in data
without understanding the meaning of the
patterns. Such machines may eventually
be fast enough to deal with large data
streams in real time..."

8. 1997
Peter J. Denning
Michael Lesk,
“How much information there is
in the world?”
2000 Peter Lyman and Hal R. Varian,
“How much information?”

9. 2005
Peter J. Denning
Roger Magoulas from O’Reilly
media, refers to Big Data as a
wide range of large data sets
almost impossible to manage and
process using traditional data
management tools not only due to
their size, but also their
complexity
2005 Yahoo creates Hadoop based on
Google’s MapReduce

10. 2008
Peter J. Denning
“Google reaches the milestone of
processing over 20 petabytes of
data per day through an average
of 100,000 MapReduce jobs spread
across its massive computing
clusters.”
2010
Eric Schmidt - Techonomy
conference
“…there were 5 exabytes of
information created by the
entire world between the dawn of
civilization and 2003. Now that
same amount is created every two
days."

11. Data growth
over time

12. Data types and
segmentation

13. Big Data, Big challenges

14. The storage challenge

15. In 1937 Franklin D. Roosevelt’s administration created
the first data project. With the Social Security Act, the
government had to keep track of contribution from 26
million Americans and more than 3 million employers.
IBM got the contract to develop punch card-reading
machine for this massive bookkeeping project.

16. In 1965 the US Government built the first data centre to
store over 742 million tax returns and 175 million sets
of fingerprints by transferring al those records onto
magnetic computer tape that had to be stored in a
single location. The project was later dropped out of
fear for ‘Big Brother’, but it is generally accepted
that it was the beginning of the electronic data
storage era.

17. The processing power challenge

18. In 1943 the first data-processing was developed by the
British to decipher Nazi codes during World War II.
This device, named Colossus, searched for patterns in
intercepted messages at a rate of 5.000 characters per
second. Thereby reducing the task from weeks to merely
hours.

19. We are developing a super-computer that will do more
calculating in a second than a person with a hand-held
calculator can do in 30,000 years.
1996, Clinton's Speech - New York Times Archives

20. We’re entering the
“second half of the
chessboard”
2013,Ray Kurzweil
Moore’s law

21. The data-science challenge

22. Algorithmic methods are used on huge amount of data to
produce desired results and to find trends, patterns and
predictions. Complex analytical tasks, faster than
human imagination, are done on Big Data.

23. The next frontier for innovation, competition, and
productivity, states that in 2018 the USA alone will face
a shortage of 140,000 – 190,000 data scientist as well as
1.5 million data managers.
2011, the McKinsey report on Big Data

24. From Big data to AI

25. Processing
Power
Data
models
Big
Data
AI
In the past, AI wasn’t
developed because of
small data sets and
low processing power

27. Big Data and AI applied

28. Banks and insurances are
becoming smart and solving
problems in less time and
with zero hassle.

29. AI and Big Data are
in almost all the
products we use on a
daily basis.

30. Healthcare is one
of the sectors
where data can
have a disrupting
impact

31. Agricultural Robots
Crop and Soil Monitoring
Predictive Analytics

32. New challenges in a
data-driven world

33. Enterprises today have many types of data, the resources
to invest on data processing and, most of all, a big
necessity of shifting to a more data-driven culture, but
data shows that they still struggle

34. The success rate
of Big Data
initiatives isn’t as
good as expected

35. Creating a
data-driven
culture seems to
be one of the
hardest challenges

36. Which kind of mindset and tools do we need to
succeed in this quest?

37. Simplifying the relationship
between humans and data

38. There are too many BI tools, they are complex and
extracting valuable data is time consuming and many times
requires data-science skills.

39. Extracting
information
should be easy
and painless

40. That’s why we started iGenius,
an AI company on a mission to
simplify the relationship betwee
people and data.
That’s why we started
iGenius,
an AI company on a mission
to simplify the relationship
between people and data.

41. Artificial Intelligence can revolutionise they way we
access data

42. We leverage AI to create a seamless user experience, based
on the idea of transparency.
The technology should be transparent toward the customer
providing a seamless technology-human interaction.

43. crystal is the first virtual advisor
for business intelligence

45. Turn in data into insights
Be advised with proactivity
Ask, explore, share

46. This is the greatest
title ever written
Followed by this oh-so-interesting subtitle
crystal connects to
the data sources and
listens proactively to
important data changes
and updates.
Connects to customer’
enterprise data sources
Learn from customer’
data in real time/streaming
Provide customer’
with actionable advice
crystal’ virtual
advisor access the
connected data and
process it right away
in real time/streaming.
crystal makes data
accessible via voice
and chat, smart real
time dashboards and
unlimited reports.
1 2 3
Fast data retrieval Deep Learning AI Virtual Advisor
+
How it works with crystal
Data

47. Data analysis done in seconds
instead of days
Increase data driven
processes to 100%
Accelerate/replace complex
BI tools with crystal’s advisor

48. This is the greatest
title ever written
Followed by this oh-so-interesting subtitle
Comparison table Traditional BI crystal
Custom Integrations - Included
Natural Language Processing - Included
Sentiment Analysis - Included
Conversational AI - Included
Data Visualization Classic Modern
Information Search Classic Conversational AI
User Experience Classic Modern Apps
Collaboration features Included Included
Installation model SaaS / Server SaaS as a Service
Pricing model Basic + extra services All inclusive license

49. (big) Data can be engaging

50. Thanks for listening!
gabriel@igenius.ai
@gbcis