A peek into the next level of big data analytics. Artificial Intelligence and machine learning combined with big-data.

2. 
The current process of Big data analytics involves considerable presence of human element
in form of data scientists and analysts who are
Difficult to find because of their unique skill set.
Expensive.
Prone to errors common with any human and only work on principles of limited but well
definite set of rules and algorithms that operate within limited scope of learning.
Can we reduce the involvement of data scientists
and analysts by using Artificially Intelligent
systems for big data processing?

3. 

An intelligent big data engine can…
Process and predict based on huge volumes of data.
Learn from the data.
Identify patterns and cause and effect relationship.
Utilize Combinatorics computational model to overcome the
limitations of a human working on the same problem.

4. 
 Facebook AI analysis system
 Google’s Deep Learning
 Big Data Analytics in Medical Field
 IBM Watson Labs

5. ’
Facebook aims to
Use AI to analyze the profile semantically from the activities.
A data scientist would limit the pace by finding which pattern
to apply.
The engine will use its computational power to find a pattern,
learns that pattern and apply the same pattern to other profiles.
Image: www.theverge.com

6. ’
16000 computers, 10 million images from YouTube video frames
and three days to see a cat?

7. ’
“We don’t understand how our deep-learning decision-making
computer systems have made themselves so good at recognizing
things in photos. This means that we may need fewer experts in
future as it can instead rely on its semi-autonomous, semi-smart
machines to solve problems all on their own.”
--Quoc V. Le, Google software engineer, Machine learning
conference San Francisco.
Source: http://www.theregister.co.uk/2013/11/15/google_thinking_machines/

8. ’
What it means for you?

9. 
 NuPIC :: www.numenta.org
 GROK :: www.numenta.com
 Quill
:: www.narrativescience.com
 Yseop :: www.yseop.com

10. 
Image: www.groksolutions.com

11. 
Case-study to monitor cloud servers
•Stream data from Amazon CloudWatch.
•It builds hundreds of models automatically and
identifies the best model.
•Get Insights, take action.
Image: www.groksolutions.com

12. 
How much does your “conventional” Big Data Solution Cost?
$740 million to Implement
Enterprise Data Warehouse
on Hadoop in 5 years for
500TB of data !!
“$219 spent on Analysis”
Image: Big Data: What Does It Really Cost? A WinterCorp Report

13. 
How much does your organization spent on Data Scientists?
200 TB = Need 50 Data Scientist
Average of $120,000 - $180,000
= $150,000/annum
Total Cost = 50 x 150,000
= $7,500,000 ($7.5 million)/annum
Image: http://www.kdnuggets.com/2013/02/salary-analytics-data-mining-data-science-professionals.html

14. 
Forbes Survey on 211 Senior Marketers
Need to Get
84% of agencies and non-agencies
Smarte
indicated it as critical for the success of
r
Faster…
their marketing campaigns
Fast-automated systems collect and
analyze data critical for:
Maintaining Data Quality
Optimizing Processes
Generating Good Return on Investment
(ROI).
Image: http://www.creditcards.com/credit-card-news/consumers-getting-smarter-about-credit_scores-1270.php

15. 
Leverage
How much time and cost to destination?
Predictive
Impact after taking a particular route?
Prescriptiv
e
Report on route congestion
Proactive
Analysis
Image: http://www.wired.com/autopia/2009/03/fedex-gets-mad/

16. 
• Branch of Artificial Intelligence
• Self aware and self learning system
• Solves complicated problems where multiple predictions are
required
Image Annotation Retrieval
Scenario
Image: http://www.ngdata.com/wp-content/uploads/multi_target_prediction.pdf

17. 
Advertising Campaigns
 Identifies right time and
communication medium to market
product
 Performs real time analysis on big
data and accounts variable change
(feedback mechanism)
 Utilizes streaming analytics
techniques to identify data for
advertisement targeting
 Takeaway: Imagine, cost involved if
data scientists carry all these tasks
Image: http://thedesigninspiration.com/articles/40-clever-advertising-campaigns-of-mcdonalds/

18. 
Increased Computational Power
 Large Hadron Collider-LHC generates
5 trillion bits of data every second
 Increasing computational is NOT about
adding processors
 Use past data sets to train system for
future data sets
 Chop data into bits and distribute
across fixed processors for machine
learning
 Takeaway: Imagine, ROI and
performance on achieving even 5% of
Image:http://www.govtech.com/computing/Baltimore-Weaves-New-Infrastructure-with-Fabric-Based-Computing.html
computational power similar to LHC

19. 
• Akshay Wattal: Analyzing cost effectiveness and efficiency of working
with Intelligent Big data with fewer data scientists.
• Mohana Kumaran S: Present Big Data infrastructure and justifying the
need for Intelligent Big Data systems.
• Mohul Kaila: Introduction to Big data and its evolution.
• Shashank Garg: Identifying solutions to achieve intelligent big data
systems and current state of art.

20. 
QUESTIONS