Big Data is the lifeblood to business it provides an overview of the concept and application for business management practices in decision-making. Big data has now reached every sector in the global economy. The real value is in the analytics and the ability to use that intelligence to gain a desired business outcome. In this Education Transfer Project (ETP) an important, open question is: to what extent do larger data actually lead to better predictive models for decision-making for business to achieve the competitive advantage? IISME Fellowship provided an opportunity to work at Cisco San Jose, California campus in the global operations development, computer software group. My goal for the fellowship was to analyze the Cisco’s Global Operations university hiring data of interns, cooperatives, and new hires. The curated information provides factors that fulfill the need to educate students in Science, Technology, Engineering, and Math (STEM) are analyzed to address the challenges of university hiring in the global computer software group. The use case is not a big data set however, the potential for impact is big. It meets the need for stimulation purposes. The solution is to validate the corporate invest in the university hiring positions. The investment in university hiring positions Cisco in a competitive advantage amongst its competitors to hire the best candidates using data science and the data analysis. Major topics of discussion include data collection, model design, data analysis, and strategic management practices.
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Introduction To Big Data
1. Dr. Jeannice Fairrer Samani, MBA
Big Data Operations Business Strategy:
Business Intelligence Solutions
Fellowship 2015
2. Module Structure
• Big Data is set up as one of seven modules in
the Management Practice 101 course.
• Team interaction and individual
understanding
• In class lecture, resources in the portal
• Assignments: Proposal Due by 2nd
day (Tuesday
midnight) of the module, 3rd
session outline
and references (Friday), and final presentation
to follow the last class (Monday)
3. Content
• Introduction: What is Big data?
• What does it Matter?
• Big Data Ecosystem
• Machine generated Data: Use Case
– Limitations
– Opportunities
4.
5. Assignment: Team Interaction
• Identify a research question,
• Create a Proposal for identified need and use
for Big Data
– What is the benefit of using big data and data
science for analysis
– What is the impact on the business and/or market
value
• Resource articles on Schoology
• Submit on the portal
14. Session Two
• Objective: provide a connection with
Information Technology and Business
Development and Decision making
• Map and Reduce concepts
• Architecture
Assignment: Research of identified need and
reference to support your question, submit on
portal.
16. The Nature of Data Science Process
Raw Data
Collected
Data
Is Processed
Raw Data
Collected
Communication
Reports
Data
Products
Models &
Algorithms
Exploratory
Data
Clean
Dataset
Decisions are
Made
17.
18. Big Data: SWOT
Strengths
Lower Costs
One-stop Data Shopping
Productive
Weaknesses
Data management
Security
Unknown
Opportunities
Discovery
Advances Analytics
Application
Competitive Advantage
Threats
Status quo
Skills
19.
20. What is the Internet of Things?
• The Internet of Things: Implications for the
Enterprise
• The Internet Of Things (IoT) is already a reality
but getting value out of that is still in its
infancy. This session analyzes
the implications of IoT for the
enterprise with are examples
from the work that has done.
21. Why it Matters
• Most businesses are focused on getting
products to market more quickly,
• adapting to regulatory requirements,
increasing efficiency, and
• more importantly-continuing to innovate.
Your highly mobile workforce, customers, and
supply chain demand anytime, any where
business tools.
Products Solutions Services
22. Use Case: Cisco IoT
• Analytics
– Software
• Programming
• Statistical
• Business Intelligence: Change go to market--
• IT use data stored in Cisco’ Cloud Using
predictive Analytics.
Simulation: University Hiring Use Case. Use a data set for the students to analyze. It doesn’t have to be “Big Data.”
Key Objective: using data and it becomes information that is applied for business intelligence for decision-making
Simulation: University Hiring Use Case. Use a data set for the students to analyze. It doesn’t have to be “Big Data.”
Key Objective: using data and it becomes information that is applied for business intelligence for decision-making.
Session One
Facebook
When you click on links / “Like”-button, they are recorded
These click streams are analyzed
Analysis used to:
Build a better home page
Promote links / photos that you’ll likely to click
- Show ads ($$$)
Overview of the concept:
BD is……
BD is transforming the way we do business and is impacting out lives.
The basic ide behind the phase “Big Data’ is that everything we do is increasingly leaving a digital trace (or data), which we (and others) can use and analyze.
BD is a popular term to describe the exponential growth and availability of data, both structured and unstructured. And BD may be as important to business and society as the Internet has become.
BD therefore refers to our ability to make use of the ever increasing volumes of data.
Volume refers to the vast amounts of data generated every second. Just think of all the emails, twitter messages, photos, video clips, sensor data etc. we produce and share every second. We are not talking Terabytes but Zettabytes or Brontobytes. On Facebook alone we send 10 billion messages per day, click the "like' button 4.5 billion times and upload 350 million new pictures each and every day. If we take all the data generated in the world between the beginning of time and 2008, the same amount of data will soon be generated every minute! This increasingly makes data sets too large to store and analyze using traditional database technology. With big data technology we can now store and use these data sets with the help of distributed systems, where parts of the data is stored in different locations and brought together by software.
Velocity refers to the speed at which new data is generated and the speed at which data moves around. Just think of social media messages going viral in seconds, the speed at which credit card transactions are checked for fraudulent activities, or the milliseconds it takes trading systems to analyze social media networks to pick up signals that trigger decisions to buy or sell shares. Big data technology allows us now to analyze the data while it is being generated, without ever putting it into databases.
Variety refers to the different types of data we can now use. In the past we focused on structured data that neatly fits into tables or relational databases, such as financial data (e.g. sales by product or region). In fact, 80% of the world’s data is now unstructured, and therefore can’t easily be put into tables (think of photos, video sequences or social media updates). With big data technology we can now harness differed types of data (structured and unstructured) including messages, social media conversations, photos, sensor data, video or voice recordings and bring them together with more traditional, structured data.
Veracity refers to the messiness or trustworthiness of the data. With many forms of big data, quality and accuracy are less controllable (just think of Twitter posts with hash tags, abbreviations, typos and colloquial speech as well as the reliability and accuracy of content) but big data and analytics technology now allows us to work with these type of data. The volumes often make up for the lack of quality or accuracy.
Value: Then there is another V to take into account when looking at Big Data: Value! It is all well and good having access to big data but unless we can turn it into value it is useless. So you can safely argue that 'value' is the most important V of Big Data. It is important that businesses make a business case for any attempt to collect and leverage big data. It is so easy to fall into the buzz trap and embark on big data initiatives without a clear understanding of costs and benefits.
I have put together this little presentation for you to use when talking about or discussing the 5 Vs of big data:
Why more Data may lead to more accurate analyses. More accurate analyses may leas to more confident decision making. And better decisions can mean greater operational efficiencies and productive.
Connected Devices
Smart phones send a lot of data back
Navigation applications (Google Maps or Waze) send a lot of data
Back
Location, speed
Used to calculate traffic patterns ..etc
There are two tracks of Big Data. One is the software that is broken down into two additional tracks Python – expecting to be run on local machines (e.g. laptop or cloud) or Java which can be on a cloud (Future Systems (open Source), Amazon or Azrue or again on the laptop in less ambitious method.
Explain the application for Amazon.
Partnership in Business-Amazon’s / Pentaho using Hadoop data system
applications of BD:
The term ‘Big Data’ is a massive buzzword at the moment and many say big data is all talk and no action. This couldn’t be further from the truth. With this post, I want to show how big data is used today to add real value.
Eventually, every aspect of our lives will be affected by big data. However, there are some areas where big data is already making a real difference today. I have categorized the application of big data into 10 areas where I see the most widespread use as well as the highest benefits [For those of you who would like to take a step back here and understand.
1. Understanding and Targeting Customers
This is one of the biggest and most publicized areas of big data use today. Here, big data is used to better understand customers and their behaviors and preferences. Companies are keen to expand their traditional data sets with social media data, browser logs as well as text analytics and sensor data to get a more complete picture of their customers. The big objective, in many cases, is to create predictive models. You might remember the example of U.S. retailer Target, who is now able to very accurately predict when one of their customers will expect a baby. Using big data, Telecom companies can now better predict customer churn; Wal-Mart can predict what products will sell, and car insurance companies understand how well their customers actually drive. Even government election campaigns can be optimized using big data analytics. Some believe, Obama’s win after the 2012 presidential election campaign was due to his team’s superior ability to use big data analytics.
2. Understanding and Optimizing Business Processes
Big data is also increasingly used to optimize business processes. Retailers are able to optimize their stock based on predictions generated from social media data, web search trends and weather forecasts. One particular business process that is seeing a lot of big data analytics is supply chain or delivery route optimization. Here, geographic positioning and radio frequency identification sensors are used to track goods or delivery vehicles and optimize routes by integrating live traffic data, etc. HR business processes are also being improved using big data analytics. This includes the optimization of talent acquisition – Moneyball style, as well as the measurement of company culture and staff engagement using big data tools.
3. Personal Quantification and Performance Optimization
Big data is not just for companies and governments but also for all of us individually. We can now benefit from the data generated from wearable devices such as smart watches or smart bracelets. Take the Up band from Jawbone as an example: the armband collects data on our calorie consumption, activity levels, and our sleep patterns. While it gives individuals rich insights, the real value is in analyzing the collective data. In Jawbone’s case, the company now collects 60 years worth of sleep data every night. Analyzing such volumes of data will bring entirely new insights that it can feed back to individual users. The other area where we benefit from big data analytics is finding love - online this is. Most online dating sites apply big data tools and algorithms to find us the most appropriate matches.
4. Improving Healthcare and Public Health
The computing power of big data analytics enables us to decode entire DNA strings in minutes and will allow us to find new cures and better understand and predict disease patterns. Just think of what happens when all the individual data from smart watches and wearable devices can be used to apply it to millions of people and their various diseases. The clinical trials of the future won’t be limited by small sample sizes but could potentially include everyone! Big data techniques are already being used to monitor babies in a specialist premature and sick baby unit. By recording and analyzing every heart beat and breathing pattern of every baby, the unit was able to develop algorithms that can now predict infections 24 hours before any physical symptoms appear. That way, the team can intervene early and save fragile babies in an environment where every hour counts. What’s more, big data analytics allow us to monitor and predict the developments of epidemics and disease outbreaks. Integrating data from medical records with social media analytics enables us to monitor flu outbreaks in real-time, simply by listening to what people are saying, i.e. “Feeling rubbish today - in bed with a cold”.
5. Improving Sports Performance
Most elite sports have now embraced big data analytics. We have the IBM SlamTracker tool for tennis tournaments; we use video analytics that track the performance of every player in a football or baseball game, and sensor technology in sports equipment such as basket balls or golf clubs allows us to get feedback (via smart phones and cloud servers) on our game and how to improve it. Many elite sports teams also track athletes outside of the sporting environment – using smart technology to track nutrition and sleep, as well as social media conversations to monitor emotional wellbeing.
6. Improving Science and Research
Science and research is currently being transformed by the new possibilities big data brings. Take, for example, CERN, the Swiss nuclear physics lab with its Large Hadron Collider, the world’s largest and most powerful particle accelerator. Experiments to unlock the secrets of our universe – how it started and works - generate huge amounts of data. The CERN data center has 65,000 processors to analyze its 30 petabytes of data. However, it uses the computing powers of thousands of computers distributed across 150 data centers worldwide to analyze the data. Such computing powers can be leveraged to transform so many other areas of science and research.
7. Optimizing Machine and Device Performance
Big data analytics help machines and devices become smarter and more autonomous. For example, big data tools are used to operate Google’s self-driving car. The Toyota Prius is fitted with cameras, GPS as well as powerful computers and sensors to safely drive on the road without the intervention of human beings. Big data tools are also used to optimize energy grids using data from smart meters. We can even use big data tools to optimize the performance of computers and data warehouses.
8. Improving Security and Law Enforcement.
Big data is applied heavily in improving security and enabling law enforcement. I am sure you are aware of the revelations that the National Security Agency (NSA) in the U.S. uses big data analytics to foil terrorist plots (and maybe spy on us). Others use big data techniques to detect and prevent cyber attacks. Police forces use big data tools to catch criminals and even predict criminal activity and credit card companies use big data use it to detect fraudulent transactions.
9. Improving and Optimizing Cities and Countries
Big data is used to improve many aspects of our cities and countries. For example, it allows cities to optimize traffic flows based on real time traffic information as well as social media and weather data. A number of cities are currently piloting big data analytics with the aim of turning themselves into Smart Cities, where the transport infrastructure and utility processes are all joined up. Where a bus would wait for a delayed train and where traffic signals predict traffic volumes and operate to minimize jams.
10. Financial Trading
My final category of big data application comes from financial trading. High-Frequency Trading (HFT) is an area where big data finds a lot of use today. Here, big data algorithms are used to make trading decisions. Today, the majority of equity trading now takes place via data algorithms that increasingly take into account signals from social media networks and news websites to make, buy and sell decisions in split seconds.
For me, the 10 categories I have outlined here represent the areas in which big data is applied the most. Of course there are so many other applications of big data and there will be many new categories as the tools become more widespread.
What do you think? Do you agree or disagree with this data revolution? Are you excited or apprehensive? Can you think of other areas where big data is used? Please share your views and comments.
Soft
How is data collected: Human generate data
- critical for operation (email footprint and archive
Email. Blogs, clicks (Google), pictures, tweets
Ecosystem; Collection / Process / use
SWOT
Application for BI
Cisco’s IoT
The Internet of Things is here
The Internet of Things (IoT) is increasing the connectedness of people and things on a scale that once was unimaginable.
Growth in analysis and cloud computing
Increasing interconnectivity or machines and personal smart device ’the proliferation of applications connecting supply chains, partners, and customers
http://video.cisco.com/detail/video/4303516120001/iot-empowering-the-enterprise:-part-15?autoStart=true&linkBaseURL=http%3A%2F%2Fvideo.cisco.com%2Fdetail%2Fvideo%2F4303516120001%2Fiot-empowering-the-enterprise%3A-part-15%3FautoStart%3Dtrue
Change go to market
IT use data stored in Cisco’ Cloud Using perdictive Analytics.
http://video.cisco.com/detail/videos/technology-trends/video/4289320896001/fanuc-embraces-iot-through-cisco-intercloud?autoStart=true&linkBaseURL=http%3A%2F%2Fvideo.cisco.com%2Fdetail%2Fvideos%2Ftechnology-trends%2Fvideo%2F4289320896001%2Ffanuc-embraces-iot-through-cisco-intercloud%3FautoStart%3Dtrue
The missing dimension to your graph visualization
Q & A for the presentation
Simulation: University Hiring Use Case. Use a data set for the students to analyze. It doesn’t have to be “Big Data.”
Key: using data and it becomes information that is applied for business intelligence for decision-making.