The document discusses big data analysis requirements and tools. It covers where big data comes from both internally and externally. It then discusses tools for analyzing big data such as BI tools, in-database analytics, Hadoop, decision management, and discovery tools. Techniques for analyzing big data like classification tree analysis, genetic algorithms, regression analysis, machine learning, and sentiment analysis are also covered. The key benefits and a successful implementation roadmap for big data in an organization are summarized.

1. Big Data
Tools
Yo u r C o m p a n y N a m e

2. Big Data Analysis Requirements
Where does Big data come from?
Tools for Analysing Big Data
Techniques for Analysing Big Data
Key Benefits Derived from Big Data Analytics
Successful implementation of Big Data in your Organization
Big Data Implementation Roadmap
Big Data Use Cases
2
Content
BIG DATA

3. Big Data Analysis Requirements
3
› Involves conserving computing resources
› As the volume of data grows, data movement becomes increasingly less efficient
› It makes more sense to store and process the data in the same place
› With new data and new data sources comes the need to acquire new skills
› Existing skillset will determine where analysis can and should be done
› When the requisite skills are lacking, a combination of training, hiring and new tools will address the problem
› Data security processes are often lacking with unstructured data sources and open source analysis tools
› Attention needs to be given to the security and data governance requirements of each analysis project
› Add text here
Attend to Data Security
Minimize Data Movement
Use Existing Skills
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4. Where does Big data come from?
Internal Data Sources External Data Sources
Majority of the Big data efforts are focused on analyzing internal data to extract insights.
Only a few times organization looks for data through external sources like social media.
4
88% 73% 57%
Transactions Log Data Emails Social Media
43%
Audio
38%
Photos & Video
34%

5. Tools for Analysing Big Data
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1 Discovery tools
› Useful throughout the information lifecycle for rapid, intuitive exploration and analysis of information
› These tools permit analysis alongside traditional BI source systems
› Important for reporting, analysis and performance management, primarily with transactional data from data warehouses and
production information systems
› BI Tools provide comprehensive capabilities for business intelligence and performance management
› This covers a variety of techniques for finding patterns and relationships in your data
› These techniques are applied directly within the database to accelerate information cycle times and reduces total cost of ownership
› Covers Predictive modeling, business rules, and self-learning to take informed action based on the current context
› Enables individual recommendations across multiple channels, maximizing the value of every customer interaction
› Useful for pre-processing data to identity macro trends or find nuggets of information,
› It enables businesses to unlock potential value from new data using inexpensive commodity servers
BI tools2
In-Database Analytics4
Decision Management5
Hadoop3
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6. Techniques for Analyzing Big Data
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Classification Tree Analysis
› Automatically assign documents to categories
› Categorize organisms into groupings
› Develop profiles of students who take online courses
Genetic Algorithms
› Schedule doctors for hospital emergency rooms
› Generate “artificially creative” content such as
puns and jokes
Regression Analysis
› Levels of customer satisfaction affect customer loyalty
› Number of supports calls received may be influenced
by the weather forecast given the previous day
Machine Learning
› Distinguish between spam and non-spam email messages
› Determine the best content for engaging prospective customers
Sentiment Analysis
› Improve service at a hotel chain by analyzing guest comments
› Determine what consumers really think based on opinions
from social media
Analyzing
Big Data
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7. 40%
56%
56%
60%
63%
64%
65%
65%
67%
68%
76%
0% 10% 20% 30% 40% 50% 60% 70% 80%
Key Benefits Derived from Big Data Analytics
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Improving Quality & Speed of Decision-making
Better planning & forecasting
Improving efficiency of internal processes
Better customer service
Reducing operating or capital expenditures
Improved ability to predict trends
Meeting compliance requirements
Better quantification of risk
Faster detection of fraud
Automated decisions for real-time processes
Segmentation of customers, users or citizens
These are the
reasons due to
which agencies are
pursuing data
analytic projects
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8. Successful implementation of Big Data in your Organization
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1. Define the Big Data Analytics Strategy
› Company first needs to define a clear strategy in synchronization with their core business objectives for the big data implementation
› Business strategy should align itself with the enterprise quality and performance goals
› should focus on measurable outcomes
› Identify right kind of data which tackles the business problem
› Data acquisition is carried out in two phases: Ingest & Transformation
• Ingest: includes gathering data from various sources and to bring them on a common platform
• Transformation: pon acquisition of data, it is refined and organized to help analysts apply the right data science tools for further analysis
2. Choosing the right data
3. Using the right data science tools
› Choosing the right tool is important to address the business problem
› Apache Hadoop is a popular framework designed to process petabytes of unstructured and structured data efficiently
4. Define an analytical process
› After figuring out on the programming tools, organizations must define a process, analytical models in order to establish critical success factors
› Advanced analytical models are needed to enable data-driven optimization
5. Align with the cloud operating model
› Entire process must be synchronized with private and public cloud provisions backed with data security
› Advantage of cloud-based operating models is that it can be scaled-up based on business requirements
6. Run a pilot project
› Pilot projects make it possible to test the outcomes before making a firm investment
› Data should be accessible, safe and familiar
› Project must have data privacy and security policies into the plan
7. Embed analytics into decision-making
› In order to gain a competitive advantage, companies need to embed analytics into business decision-making
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Implementationof
BigData

9. Big Data Implementation Roadmap
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1-2 weeks
Identify business owners,
Analyze the strategy, Develop
team, Estimate Cost
3-4 weeks
Deploy business application,
refine the big data
requirements, perform after
action assessment
7-9 weeks
Identify big data sources, choose
the affected business processes,
define the desired business
outcomes, use existing skills
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10. Big Data Use Cases
10
Machine-
Generated Data
As per research firms, there will be
excluding Phones, PCs & Tablets in the world in 2020, up from
12.5
billion
“smart” connected devices
today1.3
billion
2
The Amount of Data generated by machines will one day outstrip the amount of data
generated by humans
1
More & more devices are manufactured with sensors that monitor their own operation and
log the results for troubleshooting and analysis
3
Studying these data streams allows them to improve their products and devise more
accurate service cycles
4
The opportunities become even more compelling once data is analyzed in aggregate form
5
Big data plays a pivotal role in analyzing these trends into predictions
& taking corrective or pre-emptive action
6
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11. Big Data Analytics Icons Slide
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12. Additional Slides
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Product 01
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Product 02
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Product 03
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14. Column Chart
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Product 01
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and changes automatically based
on data. Just left click on it and
select “Edit Data”.
Product 02
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and changes automatically based
on data. Just left click on it and
select “Edit Data”.
Product 03
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and changes automatically based
on data. Just left click on it and
select “Edit Data”.

15. 15
Financial
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Dollar
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Euro
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Rupees

16. Comparison
16
VS
40%
People user Television
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70%
People user Mobiles
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17. 17
Mission
& Vision
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Vision
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Mission
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18. Our
Team
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Jack Lee
Managing Director
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Julie Star
Managing Director
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19. Puzzle
19
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20. 20
Thank
You
Address
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