IISME_Fellowship_2015_JFS

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

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

6. What is Big Data?

8. Why does it Matter?

10. How do you think Big Data benefits business?

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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.

15. Session Two • Ecosystem • Big Data Meets Data Analytics • Application

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

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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

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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.

23. Showing the Story

24. Session Three: Use Case Presentations

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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 &quot;like&apos; 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 &apos;value&apos; 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.

IISME_Fellowship_2015_JFS

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