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Capitalize On Social Media With Big Data Analytics
Hassan Keshavarz
Ph.D Candidate, MJIIT, UTM
Sep 27, 2017
2
The Power of PowerPoint | thepopp.com
Haassan Keshavarz
- Ph. D Candidate in MJIIT
- Microsoft certification holder in IT sector
- Member of IEEE Association
- Reviewer for Several Journals, and Conferences
- Currently, working in Xchanging Malaysia, a DXC-YTL Joint
Venture as Technical Lead in EDW Team, Big Data
Administrator/ Developer/Analytics
Agenda
Introduction
The Human impact of Technological Revolution
Big Data
Introduction to Hadoop Platform
Conclusion
3
4
Data is the new
oil” Clive Humby
“
5
Social media promises to accelerate innovation, drive cost savings and strengthen brands
through mass collaboration. Companies across every industry are using it to hype new
products and services, and also monitor what people are saying about their brand. And yet,
most struggle to measure the true value of social media engagement and few have the big
data analytic capabilities in place to deliver insights on how these activities impact the
bottom line.
To truly leverage social media as a tool for the organization, the entire business must be
aligned for effective interaction to be achieved.
“Employees need to respond in a proactive and timely manner on the social channel of
choice and be able to tailor the communication or content that they provide to different
audiences with the right reply, the right response, the right content and the right tone of
voice,” “Helena Schwenk, principle analyst at MWD Advisors, an IT advisory firm based in
the U.K.”
Introduction
6
Strategize for Success
As Facebook, Twitter, Pinterest and other social sites continue to unleash a torrent of data,
organizations need to not only turn the information generated into actionable intelligence,
but also to measure the business value.
Businesses often struggle to determine what social data is actually useful for them to
collect. By utilizing listening tools and sentiment analytics complemented with human
intelligence, companies can filter out noise and—with the help of machine-learning
technology—hone in on the critical data that advances the business.
Correlating social media strategies with key performance indicators (KPIs) is imperative for
getting a handle on Return On Investment (ROI). Counting the number of new followers on
Twitter doesn’t provide significant insights, but tying metrics and KPIs back to something
meaningful can deliver substantial benefits.
7
Maximize Product Performance
Measuring how social media sentiment correlates to an intention to purchase or the
likelihood a customer will churn provides insight that can be analyzed and acted upon.
For example, launching new products in the marketplace is a challenge, especially when,
according to a Nielsen Global New Products Report, nearly two out of every three new
products are destined to fail. To counteract that, companies can harness insights from
searches, blogs and social media data to reduce uncertainty and improve product
performance.
Big, diverse data is opening opportunities in every industry. By tracking search and social
data, companies can gauge interest pre-launch and use those insights to evaluate marketing
tactics and increase or reallocate advertising spending, if needed, to maximize product
performance.
8
Real Business Value
Social media marketing and analytics are in the early stages of maturity. Still, organizations
can make the channels and the information collected work to their advantage by instilling
the right company mindset, creating the right strategy and employing the right technology.
By knowing how to effectively measure the business value of social initiatives, companies in
every industry can gain critical insights that allow them to improve and promote their
products and services–and increase the bottom line.
9
How Technology Changed Over the Years.
10
The Human impact of Technological Revolution
Digital technology is transforming politics, businesses, economies and society, as well as
our day-to-day lives.
Technology is also changing the ways people work, and is increasingly enabling machines
and software to substitute for humans. Enterprises and individuals who can size the
opportunities offered by digital advances stand to gain significantly, which those who
cannot may lose everything
Digital technology has not only broken down the old, familiar models of organizations,
but has also created a broad set of new challenges.
• The most popular social media creates no actual content (Facebook),
• The fastest growing banks have no actual money (SocityOne)
• The world's largest taxi company own no taxis (Uber), and
• The largest accommodation provider owns no real estate (Airbnb)
• Anytime anywhere access to information.
• Machin and software substitute humans.
• How should we adapt????!!!!!!!
11
Today's Technologies Buzzwords
Big Data Internet of Things
Cloud Data Scientist
Apps,
Wearable Data Visualization
Business Intelligence Analytics
12
Big Data
• Learning
• Science
• Retail
• Entertainment
• Government
• Healthcare/Medicine
• Social Media
• Finance
• Transportation
Big Data Source
Social Media Big Data
400 Million Tweets sent per day
2.5 billion content items shared on FB
2.7 Billion Likes
300 Million photos uploaded
500+ TB data ingested.
100+ PB disk space in a single HDFS cluster
105 TB data scanned via Hive
Big Data Explosion on Social MediaSocial Media Data Supplements Existing Data Sources for new Marketing Capability
•Retail Data
•Point-of-sale,
Loyalty, Inventory
•Syndicate Data
•households,
Neilsen
•Social Data
•Twitter, Facebook,
Travel blogs,
Professional
organizations
•Trade Customers
Personal
•Family, Freinds,
Business/ loyalty,
leisure
- Understand
consumer
sentiment to
protect and
Improve brand and
corporate image
- Improve
consumer value
and loyalty through
trade promotion
optimization
- Creat Innovation
products and
services based on
consumer desire
New
Capabilities
How can Big Data Help?
The following are the key areas where Big Data can help in marketing
- Implement more targeted marketing campaigns for specific geographies or individual
consumers.
Track and respond to promotions in real time to ensure the most profitable outcomes
- Identify which promotion strategy will yield the best results in a specific chain or
cluster of stores.
- Determine which new product options are the most profitable and least risky to
pursue Better assess product price elasticity before implementing price changes
- Perform predictive analytics across all areas of the business to improve performance.
- Process larger volumes of data faster, including batch data provided by external
sources.
Challenges With Social Media Big Data
- One of the biggest challenge with s much data on social media is, deriving a meaningful
contextual information.
- Social media data is unstructured. Unlike other customer data from retail, banking etc.
which is structured, data on social media is unstructured.
Most organizations want to capture contextual conversations and other widely available
sources of unstructured data from social media, blog commentaries and other sources in real
time, and put them side by side with structured data in their information ecosystem for a
much clear picture of what is going on.
17
Predictive
Analytics
What will happen?
Prescriptive
Analytics
How can we make
it happen?
Regular Analytics Big Data
Descriptive
Analytics
What happened?
Diagnostic
Analytics
Why did it happen?
Hindsight Insight ForesightInformation Optimization
Analytics spectrum: from descriptive to prescriptive
18
7 Stage of Data Driven Decision Making
1- Framing the
Problem
2- Hypothesis
Development
3- Data
Collection
4- Data Analysis
5- Interpretation
6- Decision
Making
7-
Communication
strong
network
unique
data (Social
Media Data)
powerful
insights
smart
solutions
fast
iteration
customer
satisfaction
19
20
Necessary Skills Needed to Do the Job
Data
Management
Meta Data Repository
Policy Management Engine
Policy Enforcement Point
Data Anonymization
Consent Management
Platform
Data
Sourcing
Data Search Engine
Batch Data Router
Streaming Data Router
Access Management
System
Data Set
Creation
Data Ingestion
Hadoop
Offline Storage
Insight
Development
Machine Learning
Analytics
Collaboration and Sharing
Published Insights
InsightPortal
Data Wrangler
Data
Scientist
• To be successful in delivering Data Insights, team members require new capabilities to do their job.
• Required capabilities are provided as Technology Enablers
21
Big Data Process for Insight Development
TOP DOWN
Big Data selects project identified
by Business Unit
BOTTOM UP
Business Unit adopts findings
identified by Big Data
Big Data and Business Units
MUST collaborate in order to succeed
COLLABORATIVE
TEAM MADE UP OF
Business Unit Team
Taking action from results of analysis
Giving context to analysis
Identifying key sources of data
Explaining complexities with raw data
Big Data Team
Learning about business problem
Acquiring data necessary for analysis
Combining raw datasets for models
Developing explanatory and predictive
models Communicating what models mean
22
What is Hadoop
Hadoop is a hot up and coming big data technology and includes several tech skill such as
NoSQL databases, analytics and others. The great thing about this technology is that it is
affordable since it utilizes low-cost, ordinary hardware. Actually, huge data is not really a new
technology but a term used for several technologies. Although some of these technologies have
been around for some time, many pieces come together to make big data the thing for the future.
23
Major advantages of Hadoop
1.Scalable. It is a highly scalable storage platform since it could store and distribute very big data
sets across numerous inexpensive servers operating in parallel. Furthermore, it allows businesses
nodes that involve hundreds of thousands of data terabytes.
2.Flexible. Hadoop allows businesses to access new data sources easily and tap into various data
types, both structured and unstructured to generate value. This means that businesses could use
business insights from sources of data like email conversations, social media or clickstream data.
range of purposes, like log processing, data warehousing, recommendation systems, fraud
analysis.
3.Cost effective. Hadoop offers a storage solution that is cost-effective. It is designed as a scale-
out architecture that could affordably store all data of a company for later use. The cost savings
computing capabilities for hundreds of pounds per terabyte.
4.Resilient to failure. A major advantage of Hadoop use is its fault tolerance. When data is sent to
an individual node, data is replicated to other nodes, meaning that in case of failure, there’s
5.Fast. The unique storage is based on the distributed file system, which basically "maps" data
wherever its location in the cluster. The data processing tools often are on the same servers
much faster processing of data. When dealing with big volumes of unstructured data, Hadoop
within minutes and petabytes within hours.
24
Turning Big Data into knowledge
Hadoop Distributed File System
Unstructured Data
Structured Data
Data Sources
Internal
& External
Hadoop
MapReduce
Analytics
AnalyticsRelational
Database
Insights
25
From Technology To Leaders
26
Problem Solving & Decision Making
27
1. The system should have a facility to use review by using Goal-Question-Metric.
2. If the review goes in wrong way, it should be any way to iterate it again by
using any of these methods: Cascade methodology, Spiral methodology, and or
Agile method?
Fast Iteration
28
Most Important Source of 2016 Election News
After Hillary Clinton had led throughout most
of the campaign, she was also ahead in the BBC
poll of polls on Tuesday with 48% of the votes
to Donald Trump's 44%.
Number cruncher Nate Silver, of statistical
analysis website FiveThirtyEight, wrote that
morning that Mrs Clinton had a 71.4% chance
of winning. The results of course were quite
different.
2- An Artificial Intelligence system called MogIA called it for Trump. Mogia measures
engagement data from sites like Google, Facebook and Twitter. For example how many
people read a Trump tweet or watched a Trump Facebook Live? (BBC News)
1- False information
There is also a danger of using social media
when a lot of the information on it is unverified.
29
Use Social Data to Predict Consumer Confidence
We have unlocked the power of social analytics to predict consumer purchasing behavior
and confidence. Combined with other leading indicators such as sales data and economic
indicators, you can measure the consumer confidence index of a brand, product or the state
of the economy of a country.
Sentiment Analytics. Doing It Right.
- Measure public perception before a crisis happen.
Some crisis can be contained because it occurs within a similar interest group. Social
analytics allows companies to predict the likelihood of an event (or campaign) to turn into
one. We combine people, process and technology to obtain these insights from data science.
- Compare sentiment between two or more products.
Sentiment analytics was deployed to compare sentiment for each users (marked by each
colored bubbles) to measure the reaction between two products. Initial findings, revealed
overall sentiment from different groups of users was skewed towards positive.
30
Emotional Analytics
Social analytics research to assess the emotional level among airline passengers travelling
through selected airports in the Southeast Asian region is a potential case. The goal is to
determine whether the passengers and travelers had experienced joy during their travels.
The approach.
Extract the historical social data for one year from all social media channels. Applying the
right techniques for data extraction and data cleansing, you will be able to process raw social
data into the primary emotion categories (Joy, Love, Anger, Fear, Sadness, Surprise)
Get more insights from social data at a specific location.
Leading location-based social media monitoring technology provider to extract insights at a
specific location, area, district or building. Translate the social data at specific locations
using our sentiment and emotion analytics algorithm to measure the public mood at a
particular location. The outcome of this social science research is used to measure the impact
of leadership and policy changes.
31
- Because law and regulations are stable and designed to be long-lasting, whereas the
digital environment is changing rapidly.
- Thus leaders cannot afford to show fear or reluctance in implementing it.
Instead, they must embrace technology with a clear view of its potential.
- Analyzing large data sets, so called Big Data- will become a key basis of competition,
underpinning new waves of Productivity growth, Innovation, Consumer surplus,
Strategize for Success, Maximize Product Performance, Real Business Value
Leaders Should Turn their Attention Deep Data Analytics to Unlock Values
on Social Media
Conclusion
32
Thank You
Email:
hassan@mjiit.com
Think Globally, Act Locally. :)
33
Some References:
1- https://www.forbes.com/sites/teradata/2015/05/27/capitalize-on-social-media-with-big-data-
analytics/#6e00a05e8021
2- http://www.bbc.com/news/election-us-2016-37942842
3- The Impact of Big Data Analytics in Modern Leadership, Dr. Mohammad Reza Beikzadeh, KL, Malaysia- 11 Aug 2017

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Capitalize On Social Media With Big Data Analytics

  • 1. Capitalize On Social Media With Big Data Analytics Hassan Keshavarz Ph.D Candidate, MJIIT, UTM Sep 27, 2017
  • 2. 2 The Power of PowerPoint | thepopp.com Haassan Keshavarz - Ph. D Candidate in MJIIT - Microsoft certification holder in IT sector - Member of IEEE Association - Reviewer for Several Journals, and Conferences - Currently, working in Xchanging Malaysia, a DXC-YTL Joint Venture as Technical Lead in EDW Team, Big Data Administrator/ Developer/Analytics
  • 3. Agenda Introduction The Human impact of Technological Revolution Big Data Introduction to Hadoop Platform Conclusion 3
  • 4. 4 Data is the new oil” Clive Humby “
  • 5. 5 Social media promises to accelerate innovation, drive cost savings and strengthen brands through mass collaboration. Companies across every industry are using it to hype new products and services, and also monitor what people are saying about their brand. And yet, most struggle to measure the true value of social media engagement and few have the big data analytic capabilities in place to deliver insights on how these activities impact the bottom line. To truly leverage social media as a tool for the organization, the entire business must be aligned for effective interaction to be achieved. “Employees need to respond in a proactive and timely manner on the social channel of choice and be able to tailor the communication or content that they provide to different audiences with the right reply, the right response, the right content and the right tone of voice,” “Helena Schwenk, principle analyst at MWD Advisors, an IT advisory firm based in the U.K.” Introduction
  • 6. 6 Strategize for Success As Facebook, Twitter, Pinterest and other social sites continue to unleash a torrent of data, organizations need to not only turn the information generated into actionable intelligence, but also to measure the business value. Businesses often struggle to determine what social data is actually useful for them to collect. By utilizing listening tools and sentiment analytics complemented with human intelligence, companies can filter out noise and—with the help of machine-learning technology—hone in on the critical data that advances the business. Correlating social media strategies with key performance indicators (KPIs) is imperative for getting a handle on Return On Investment (ROI). Counting the number of new followers on Twitter doesn’t provide significant insights, but tying metrics and KPIs back to something meaningful can deliver substantial benefits.
  • 7. 7 Maximize Product Performance Measuring how social media sentiment correlates to an intention to purchase or the likelihood a customer will churn provides insight that can be analyzed and acted upon. For example, launching new products in the marketplace is a challenge, especially when, according to a Nielsen Global New Products Report, nearly two out of every three new products are destined to fail. To counteract that, companies can harness insights from searches, blogs and social media data to reduce uncertainty and improve product performance. Big, diverse data is opening opportunities in every industry. By tracking search and social data, companies can gauge interest pre-launch and use those insights to evaluate marketing tactics and increase or reallocate advertising spending, if needed, to maximize product performance.
  • 8. 8 Real Business Value Social media marketing and analytics are in the early stages of maturity. Still, organizations can make the channels and the information collected work to their advantage by instilling the right company mindset, creating the right strategy and employing the right technology. By knowing how to effectively measure the business value of social initiatives, companies in every industry can gain critical insights that allow them to improve and promote their products and services–and increase the bottom line.
  • 9. 9 How Technology Changed Over the Years.
  • 10. 10 The Human impact of Technological Revolution Digital technology is transforming politics, businesses, economies and society, as well as our day-to-day lives. Technology is also changing the ways people work, and is increasingly enabling machines and software to substitute for humans. Enterprises and individuals who can size the opportunities offered by digital advances stand to gain significantly, which those who cannot may lose everything Digital technology has not only broken down the old, familiar models of organizations, but has also created a broad set of new challenges. • The most popular social media creates no actual content (Facebook), • The fastest growing banks have no actual money (SocityOne) • The world's largest taxi company own no taxis (Uber), and • The largest accommodation provider owns no real estate (Airbnb) • Anytime anywhere access to information. • Machin and software substitute humans. • How should we adapt????!!!!!!!
  • 11. 11 Today's Technologies Buzzwords Big Data Internet of Things Cloud Data Scientist Apps, Wearable Data Visualization Business Intelligence Analytics
  • 12. 12 Big Data • Learning • Science • Retail • Entertainment • Government • Healthcare/Medicine • Social Media • Finance • Transportation Big Data Source
  • 13. Social Media Big Data 400 Million Tweets sent per day 2.5 billion content items shared on FB 2.7 Billion Likes 300 Million photos uploaded 500+ TB data ingested. 100+ PB disk space in a single HDFS cluster 105 TB data scanned via Hive
  • 14. Big Data Explosion on Social MediaSocial Media Data Supplements Existing Data Sources for new Marketing Capability •Retail Data •Point-of-sale, Loyalty, Inventory •Syndicate Data •households, Neilsen •Social Data •Twitter, Facebook, Travel blogs, Professional organizations •Trade Customers Personal •Family, Freinds, Business/ loyalty, leisure - Understand consumer sentiment to protect and Improve brand and corporate image - Improve consumer value and loyalty through trade promotion optimization - Creat Innovation products and services based on consumer desire New Capabilities
  • 15. How can Big Data Help? The following are the key areas where Big Data can help in marketing - Implement more targeted marketing campaigns for specific geographies or individual consumers. Track and respond to promotions in real time to ensure the most profitable outcomes - Identify which promotion strategy will yield the best results in a specific chain or cluster of stores. - Determine which new product options are the most profitable and least risky to pursue Better assess product price elasticity before implementing price changes - Perform predictive analytics across all areas of the business to improve performance. - Process larger volumes of data faster, including batch data provided by external sources.
  • 16. Challenges With Social Media Big Data - One of the biggest challenge with s much data on social media is, deriving a meaningful contextual information. - Social media data is unstructured. Unlike other customer data from retail, banking etc. which is structured, data on social media is unstructured. Most organizations want to capture contextual conversations and other widely available sources of unstructured data from social media, blog commentaries and other sources in real time, and put them side by side with structured data in their information ecosystem for a much clear picture of what is going on.
  • 17. 17 Predictive Analytics What will happen? Prescriptive Analytics How can we make it happen? Regular Analytics Big Data Descriptive Analytics What happened? Diagnostic Analytics Why did it happen? Hindsight Insight ForesightInformation Optimization Analytics spectrum: from descriptive to prescriptive
  • 18. 18 7 Stage of Data Driven Decision Making 1- Framing the Problem 2- Hypothesis Development 3- Data Collection 4- Data Analysis 5- Interpretation 6- Decision Making 7- Communication
  • 20. 20 Necessary Skills Needed to Do the Job Data Management Meta Data Repository Policy Management Engine Policy Enforcement Point Data Anonymization Consent Management Platform Data Sourcing Data Search Engine Batch Data Router Streaming Data Router Access Management System Data Set Creation Data Ingestion Hadoop Offline Storage Insight Development Machine Learning Analytics Collaboration and Sharing Published Insights InsightPortal Data Wrangler Data Scientist • To be successful in delivering Data Insights, team members require new capabilities to do their job. • Required capabilities are provided as Technology Enablers
  • 21. 21 Big Data Process for Insight Development TOP DOWN Big Data selects project identified by Business Unit BOTTOM UP Business Unit adopts findings identified by Big Data Big Data and Business Units MUST collaborate in order to succeed COLLABORATIVE TEAM MADE UP OF Business Unit Team Taking action from results of analysis Giving context to analysis Identifying key sources of data Explaining complexities with raw data Big Data Team Learning about business problem Acquiring data necessary for analysis Combining raw datasets for models Developing explanatory and predictive models Communicating what models mean
  • 22. 22 What is Hadoop Hadoop is a hot up and coming big data technology and includes several tech skill such as NoSQL databases, analytics and others. The great thing about this technology is that it is affordable since it utilizes low-cost, ordinary hardware. Actually, huge data is not really a new technology but a term used for several technologies. Although some of these technologies have been around for some time, many pieces come together to make big data the thing for the future.
  • 23. 23 Major advantages of Hadoop 1.Scalable. It is a highly scalable storage platform since it could store and distribute very big data sets across numerous inexpensive servers operating in parallel. Furthermore, it allows businesses nodes that involve hundreds of thousands of data terabytes. 2.Flexible. Hadoop allows businesses to access new data sources easily and tap into various data types, both structured and unstructured to generate value. This means that businesses could use business insights from sources of data like email conversations, social media or clickstream data. range of purposes, like log processing, data warehousing, recommendation systems, fraud analysis. 3.Cost effective. Hadoop offers a storage solution that is cost-effective. It is designed as a scale- out architecture that could affordably store all data of a company for later use. The cost savings computing capabilities for hundreds of pounds per terabyte. 4.Resilient to failure. A major advantage of Hadoop use is its fault tolerance. When data is sent to an individual node, data is replicated to other nodes, meaning that in case of failure, there’s 5.Fast. The unique storage is based on the distributed file system, which basically "maps" data wherever its location in the cluster. The data processing tools often are on the same servers much faster processing of data. When dealing with big volumes of unstructured data, Hadoop within minutes and petabytes within hours.
  • 24. 24 Turning Big Data into knowledge Hadoop Distributed File System Unstructured Data Structured Data Data Sources Internal & External Hadoop MapReduce Analytics AnalyticsRelational Database Insights
  • 26. 26 Problem Solving & Decision Making
  • 27. 27 1. The system should have a facility to use review by using Goal-Question-Metric. 2. If the review goes in wrong way, it should be any way to iterate it again by using any of these methods: Cascade methodology, Spiral methodology, and or Agile method? Fast Iteration
  • 28. 28 Most Important Source of 2016 Election News After Hillary Clinton had led throughout most of the campaign, she was also ahead in the BBC poll of polls on Tuesday with 48% of the votes to Donald Trump's 44%. Number cruncher Nate Silver, of statistical analysis website FiveThirtyEight, wrote that morning that Mrs Clinton had a 71.4% chance of winning. The results of course were quite different. 2- An Artificial Intelligence system called MogIA called it for Trump. Mogia measures engagement data from sites like Google, Facebook and Twitter. For example how many people read a Trump tweet or watched a Trump Facebook Live? (BBC News) 1- False information There is also a danger of using social media when a lot of the information on it is unverified.
  • 29. 29 Use Social Data to Predict Consumer Confidence We have unlocked the power of social analytics to predict consumer purchasing behavior and confidence. Combined with other leading indicators such as sales data and economic indicators, you can measure the consumer confidence index of a brand, product or the state of the economy of a country. Sentiment Analytics. Doing It Right. - Measure public perception before a crisis happen. Some crisis can be contained because it occurs within a similar interest group. Social analytics allows companies to predict the likelihood of an event (or campaign) to turn into one. We combine people, process and technology to obtain these insights from data science. - Compare sentiment between two or more products. Sentiment analytics was deployed to compare sentiment for each users (marked by each colored bubbles) to measure the reaction between two products. Initial findings, revealed overall sentiment from different groups of users was skewed towards positive.
  • 30. 30 Emotional Analytics Social analytics research to assess the emotional level among airline passengers travelling through selected airports in the Southeast Asian region is a potential case. The goal is to determine whether the passengers and travelers had experienced joy during their travels. The approach. Extract the historical social data for one year from all social media channels. Applying the right techniques for data extraction and data cleansing, you will be able to process raw social data into the primary emotion categories (Joy, Love, Anger, Fear, Sadness, Surprise) Get more insights from social data at a specific location. Leading location-based social media monitoring technology provider to extract insights at a specific location, area, district or building. Translate the social data at specific locations using our sentiment and emotion analytics algorithm to measure the public mood at a particular location. The outcome of this social science research is used to measure the impact of leadership and policy changes.
  • 31. 31 - Because law and regulations are stable and designed to be long-lasting, whereas the digital environment is changing rapidly. - Thus leaders cannot afford to show fear or reluctance in implementing it. Instead, they must embrace technology with a clear view of its potential. - Analyzing large data sets, so called Big Data- will become a key basis of competition, underpinning new waves of Productivity growth, Innovation, Consumer surplus, Strategize for Success, Maximize Product Performance, Real Business Value Leaders Should Turn their Attention Deep Data Analytics to Unlock Values on Social Media Conclusion
  • 33. 33 Some References: 1- https://www.forbes.com/sites/teradata/2015/05/27/capitalize-on-social-media-with-big-data- analytics/#6e00a05e8021 2- http://www.bbc.com/news/election-us-2016-37942842 3- The Impact of Big Data Analytics in Modern Leadership, Dr. Mohammad Reza Beikzadeh, KL, Malaysia- 11 Aug 2017

Editor's Notes

  1. Problem Solving Decision Making Judgment Communication Self Management Collaboration Value Clarification 1- Identify the Problem 2- Gathering the Information 3- Explore the Options 4- Choose the Options 5- Evaluation the Option 6- Implement the Decision 7- Monitor the Impact 8- Modify the Decision
  2. http://www.bbc.com/news/election-us-2016-37942842