Transcript of "Intelligent Big Data analytics for the future."
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.
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?
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.
Facebook AI analysis system
Google’s Deep Learning
Big Data Analytics in Medical Field
IBM Watson Labs
Facebook aims to
Use AI to analyze the profile semantically from the activities.
A data scientist would limit the pace by finding which pattern
The engine will use its computational power to find a pattern,
learns that pattern and apply the same pattern to other profiles.
16000 computers, 10 million images from YouTube video frames
and three days to see a cat?
“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.
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.
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
How much does your organization spent on Data Scientists?
200 TB = Need 50 Data Scientist
Average of $120,000 - $180,000
Total Cost = 50 x 150,000
= $7,500,000 ($7.5 million)/annum
Forbes Survey on 211 Senior Marketers
Need to Get
84% of agencies and non-agencies
indicated it as critical for the success of
their marketing campaigns
Fast-automated systems collect and
analyze data critical for:
Maintaining Data Quality
Generating Good Return on Investment
How much time and cost to destination?
Impact after taking a particular route?
Report on route congestion
• Branch of Artificial Intelligence
• Self aware and self learning system
• Solves complicated problems where multiple predictions are
Image Annotation Retrieval
Identifies right time and
communication medium to market
Performs real time analysis on big
data and accounts variable change
Utilizes streaming analytics
techniques to identify data for
Takeaway: Imagine, cost involved if
data scientists carry all these tasks
Increased Computational Power
Large Hadron Collider-LHC generates
5 trillion bits of data every second
Increasing computational is NOT about
Use past data sets to train system for
future data sets
Chop data into bits and distribute
across fixed processors for machine
Takeaway: Imagine, ROI and
performance on achieving even 5% of
computational power similar to LHC
• 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.