Evolution of Big Data ICT Business Breakfast Durban, 17 September 2014 Willy Govender

1. Evolution of Big Data
ICT Business Breakfast
Durban, 17 September 2014
Willy Govender

2. What is Big Data?
“Large volumes of a wide variety of data collected from various sources across the enterprise including transactional data from enterprise applications/databases, social media data, mobile device data, unstructured data/documents, machine-generated data and more.“ Source: IDG: Big Data – Growing Trends and Emerging Opportunities

3. Data Sources
Structured
•Spreadsheets
•Relational Databases
•ERP
•CRM
•Legacy systems
•File share
Unstructured
•Documents
•Machine Data
•Messaging
•Photographs
•Video
•Social Media
•Web traffic logs
"90% of all data ever created, was created in the past two years. From now on, the amount of data in the world will double every two years."
Enterprise
Cloud

4. The Evolution of Big Data
Big data is traditionally referred to as 3Vs (now 5V, 7V)
Volume (amount of data collected – terabytes/exabytes)
Velocity (speed/frequency at which data is collected)
Variety (different types of data collected)
Now experts are adding “veracity, variability, visualization, and value”
Big data is not new
Supercomputers have been collecting scientific/research data for decades
However, now its uses are being seen in commercial competitive advantages
And now we are able to collect a variety of data from multiple devices and sources
Is the evolution of the BI ecosystem from data warehousing
Does not make DW obsolete
Big Data approaches are reducing the costs of data management
Data still needs to be standardized, data quality maintained, and access provided to constituent communities.
Data management will continue to be an evolutionary process.
Big data is simply a new data challenge that requires leveraging existing systems in a different way

5. So, what does Big Data do?
Focuses on finding hidden threads, trends, or patterns which may be invisible to the naked eye
Data store of clusters of servers (eg. Apache Hadoop used for Amazon Cloud)
A set of tasks that processes the data in different segments of the cluster then breaks down the results to more manageable chunks which are
Requires mathematical and statistical expertise as well as creative, communicative, problem-solving, and business skills summarized
Obviates the need for Data alignment or Data migration, or the requirement to move data into one place for cross-referencing. This achieved through indexes and crawlers (like Google) which constantly mine data update the indexes.

6. Framework and Data Flows
Data Models, Structures, Types
•Data formats, non/relational, file systems, etc.
•Big Data Management
Big Data Lifecycle (Management)
•Big Data transformation/staging
•Recording, Storage, Archiving
Big Data Analytics and Tools
•Big Data Applications
•Target use, presentation, visualisation
Big Data Infrastructure (BDI)
•Storage, Compute, (High Performance Computing,) Network
•Sensor network, target/actionable devices
•Big Data Operational support
Big Data Security
•Data security in-rest, in-move, trusted processing environments
Collection and Registration
Filtering, Classification and Enrichment
Analytics, Modelling and Prediction
Presentation and Visualization

7. What challenges can you expect
Platforms
•High end data warehousing tools
•Open source technologies challenging with accessing data from multiple servers rapidly in native form
•Selection of Enterprise Search Tools
Skills
•Managing Data Volumes
•Ability to really understand what can be achieved
•Open source platforms not easy to use
•Data scientists now required
Leadership
•New territory for IT professionals, so planning, marketing, ROI etc is an issue
•Getting Data on the Board's agenda
Walmart analyses real-time social media data for trend to guide online ad purchases

8. Enterprise Search: Vendors
TCO
FEATURE SET
Low
High
Low
High
Niche Progressive
Niche Traditional
Niche Progressive
Niche Traditional

9. Challenges in Big Data
— Increasing Amount of Disorganized Data and Data Sources (structured & unstructured)
Provides greater opportunity for failure – lack of information can lead to wrong decisions
Limits productivity – more time and effort needed to find information
Frustrates search users –
information is expected to be readily available and complete
—
Not tackling Big Data in enterprises …
Marketing Data
Data Warehouse
Social Media
Research Databases
Office Files
Transactional Data
Acquisition Data
→
DIGITAL DATA VOLUME
2010
2012
2014
2016
2018
2020
Etc.

10. Opportunity in Big Data
Source: IDC
35 Zetabytes
DIGITAL DATA VOLUME
2010
2012
2014
2016
2018
2020
STATUS QUO
— Accessible Data Has Value
48% CAGR1
No Specific Solutions Too hard and expensive
Homegrown
Hard to maintain and insufficient
Traditional Solutions
Waste countless months on inflexible solutions
—
Solution Types

11. Q-Sensei Product – Aimed at bringing Big Data approach to all Enterprises
—
Traditional Approaches
— Q-Sensei Revolution
•Complex products
•Rigid delivery model
•Pre-defined usage
•Expensive
•Limited audience
•Exhausting implementation
•Disparate solutions
•Poor interaction design
•Simple
•Powerful
•Fast
•Flexible
•Broad application
•Interactive
•Easy delivery model
•For everyone

12. Case Study mention in Wall Street Journal in 2012
They were able to analyze traffic details for various devices, spot problem areas and add network throughput to help prepare for future demand. Netflix was also able to get more insight into the type of content customers preferred, which enabled them to make more accurate suggestions as to what subscribers might like.

13. Case Study
—
Overview
•Premiere Internet subscription service for streaming media and DVD-by-mail services
•Over 50 million subscribers in 40+ countries; Revenue 2013: $4.37 billion
•Contract Management: Permission/licensing agreements with content creators
•Leader in interactive, contextual search changing the way companies search and analyze data
•Patented powerful multidimensional search and index capability
•Gives developers full access to award- winning technology and empowers them to built robust search and analytics applications for all data needs
World's Leading Internet television network (ITN)

14. Case Study – Search in Contracts
—
Goals and Key Challenges
1.Make searching their copious contract documentation better manageable and easier to use for end users
2.Integrate and unify their highly structured metadata with their unstructured content data
3.Incorporate Optical Character Recognition (OCR) of scanned documents during data ingestion process
4.Integrate with in-house, Drupal-based content management system
5.Flexibility to consume the data from their custom system
6.Data model that meets various needs of personnel
7.Timeline of only 3 month

15. Case Study – Search in Contracts
— Solution and Successes
1.In 3 month Q-Sensei conceptualized and deployed a solution for contract search needs using Fuse (including usability testing)
2.Addition of further capabilities based on end user feedback:
•n-gram phrase search
•date range search
•multi-sort of facets
•grid view of results
3.The flexibility and modular architecture of Fuse enables customer to implement the platform for further use cases (knowledge base search, log analysis, usage analysis, etc.)

16. Demo
— Q-Sensei Medical Demo
•Unified Access to Publications, Grants, Patents, Office Files, Person
•Content-Based Faceted Auto Complete
•Dynamic Faceting
•Search-within-a-search capability
•Data Interaction and deep Data Correlations
•360-degree view of information
•Multi-Dimensional Visualization
•Customizable Search Interface
•Integrated Data Sources (21m Publications, 1,8m Grants, 1,5m Patents, Office Files (DOC, XLS, PPT, PDF,…) , Person DB )
Set-up (Harvesting, Importing, Data Transformation, Indexing) in 5 days

17. Performance Metrics
Sample System
System Configuration
Performance
Based on Sample System
•Intel Ivy Bridge Quadcore 3.4GHz
•32GB RAM
•1TB HD
•64-bit Linux
•Up to 80 million documents can be indexed
•Up to 20 million records can be uploaded per hour (more than 5,000/sec)
•100,000 search queries can be processed per minute per million documents; a query includes:
•processing of search expression (including fulltext)
•computation of eight (8) standard facets
(Latest test: September 2013)

18. Contract Management Search
•Create a more accurate and efficient contract search by exposing all metadata and using facets
•Search scanned documents with advanced OCR capabilities Knowledge Base / Support Center Search
•Increase the efficiency of finding answers by utilizing more metadata in your knowledge base
•Embrace tags and faceted search over hierarchy to find answers more quickly Enterprise Search
•Unify your company’s information by searching all sources simultaneously
•Increase the productivity of everyone with better data accessibility
Usage Analysis
•Increase speed and agility of customer activity analysis by embracing a multidimensional view of your data
•Drive dynamic visualizations and build complex queries Structured Data Analysis
•Understand the composition of data, find relationships, and identify trends
•View data more accurately by analyzing all attributes simultaneously E-Commerce Faceted Navigation
•More accurately represent your products with dynamically updating facets that perform at scale
•Power more meaningful recommendations with the capability to use more metadata
Further Use Cases
—
A Single Platform for Everything

19. Other Examples East London Rural Mapping