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1. Turning Big Data
Analytics
to Knowledge
Your Company Name
1

2. Content
2
Big Data Analysis Requirements1
Where does Big data come from?2
Tools for Analyzing Big Data3
Techniques for Analyzing Big Data4
Key Benefits Derived from Big Data Analytics5
Successful implementation of Big Data in your Organization6
Big Data Implementation Roadmap7
Big Data Use Cases8
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3. 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
Minimize Data Movement
› 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
Use Existing Skills
› 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
Big
Data Analysis
Requirements

4. Where does Big Data come from?
4
INTERNAL
DATA SOURCE
EXTERNAL
DATA SOURCE
Transactions
Emails
Log Data
Audio
Photos & Videos
Social Media
88% 34%
73% 43%
57% 38%
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
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5. 5
› 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
› 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
› 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
Discovery
Tools
BI
Tools
Hadoop In-Database
Analytics
Decision
Management
Tools for Analysing
Big Data
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6. 6
Classification Tree Analysis Genetic Algorithms
Machine Learning Regression Analysis
› Automatically assign documents to categories
› Categorize organisms into groupings
› Develop profiles of students who take online
courses
› Schedule doctors for hospital emergency
rooms
› Generate “artificially creative” content such as
puns and jokes
› Distinguish between spam and non-spam email
messages
› Determine the best content for engaging
prospective customers
› Levels of customer satisfaction affect customer
loyalty
› Number of supports calls received may be
influenced by the weather forecast given the
previous day
Sentiment Analysis
› Improve service at a hotel chain by analyzing
guest comments
› Determine what consumers really think
based on opinions from social media
Techniques for
Analyzing
Big Data
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7. Key Benefits derived from Big Data Analytics
7
Improving quality and speed of Decision-Making
Better Planning and 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 76%
68%
67%
65%
65%
64%
63%
60%
56%
56%
40%
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
8
› 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
› 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
› 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
› 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
› 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
› In order to gain a competitive advantage, companies need to embed analytics into business
decision-making
› Add text here
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Define the Big Data
Analytics Strategy
Choosing the
Right Data
Using the right data
Science Tools
Define an Analytical
Process
Align with the Cloud
Operating Model
Run a Pilot Project
Embed Analytics into
Decision-Making

9. Big Data Implementation Roadmap
9
Identify Business Owners,
Analyze the Strategy, Develop
Team, Estimate Cost
Deploy Business Application,
refine the Big Data Requirements,
perform after Action Assessment
Identify Big Data Sources, choose
the affected Business Processes,
define the desired Business
Outcomes, use Existing Skills
1-2weeks weeks
3-4 weeks
7-9
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10. Big Data Use Cases
10
The amount of data generated by machines will one
day outstrip the amount of data generated by humans
As per research firms, there will be 12.5 billion “smart”
connected devices—excluding phones, PCs and
tablets—in the world in 2020, up from 1.3 billion today
More and more devices are manufactured with
sensors that monitor their own operation and log the
results for troubleshooting and analysis
The opportunities become even more compelling
once data is analyzed in aggregate form
Big data plays a pivotal role in analyzing these
trends into predictions & taking corrective or pre-
emptive action
Studying these data streams allows them to
improve their products and devise more accurate
service cycles
Machine-Generated Data
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11. Turning Big Data Analytics to Knowledge Icons Slide
11

12. 12
Additional Slides

13. Clustered Column
13
Product 01
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Product 02
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Category 1 Category 2 Category 3 Category 4
Product01
Product02

14. Area Chart
14
Product 01
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1.5
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Category 1 Category 2 Category 3 Category 4
Product 01
Product 02

15. 15
Our
Mission
Mission
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Vision
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Goal
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16. Puzzle
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17. OurTeam
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Name Here
Designation
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Designation
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Designation
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19. Timeline
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20182017 2019 2020
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20. Puzzle
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01
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21. Magnifying Glass
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22. 22
Thank
You
Email Address
emailaddress123@gmail.com
Contact Number
0123456789
Address
# street number, city, state