Description: In the real world, your personal life is a private space. But in tech, your personal data is a ripe resource for businesses to harvest in their own interests.
Date:May 1. 2017
When it comes to data collection, services like Unroll.me and Uber are small fry compared with internet giants like Google and Facebook, which have a wealth of information about people. And then there are large data brokers like Acxiom, CoreLogic, Datalogix and ID Analytics, which collect, analyze and sell billions of details about consumers’ online activities for marketing purposes.
For consumers, giving up some data has become part of the trade-off of receiving compelling, personalized services. But that doesn’t mean you have to be caught by surprise. Here are some tips from privacy experts on protecting yourself from tricky data collection.read rest of story
1. Should we, as consumers have to give up personalized information to use a digital service? Why or why not?
2. How does one make sure that their personal data is being protected and is secure?
Description: At its height back in 2000, the U.S. cash equities trading desk at Goldman Sachs’s New York headquarters employed 600 traders, buying and selling stock on the orders of the investment bank’s large clients. Today there are just two equity traders left.
Source: MIT Technology Review
Date: Feb 7, 2017
The experience of its New York traders is just one early example of a transformation of Goldman Sachs, and increasingly other Wall Street firms, that began with the rise in computerized trading, but has accelerated over the past five years, moving into more fields of finance that humans once dominated. Chavez, who will become chief financial officer in April, says areas of trading like currencies and even parts of business lines like investment banking are moving in the same automated direction that equities have already traveled.
Today, nearly 45 percent of trading is done electronically, according to Coalition, a U.K. firm that tracks the industry. In addition to back-office clerical workers, on Wall Street machines are replacing a lot of highly paid people, too. READ REST OF STORY
Questions for discussion:
1. Do you feel that that Computerized trading and AI will make the financial industry almost a employee less industry ? Why or Why not?
2. Do you feel that Government industries are susceptible to this sort of computerization and AI to reduces Public service employees in Canada by a significant amount? explain
Description: People have made fortunes selling cars and trucks. For many of us, a car is the second most expensive thing we’ll ever buy. But experts say the value of vehicles will likely pale in comparison to the riches from our cars’ data.
“Data is the currency of the digital age,” said Jim Barbaresso, who leads Intelligent Transportation Systems at HTNB. “Vehicle data could be the beginning of a modern day gold rush.”The gold rush analogy is a common one, made by everyone from Barbaresso to the CEO of Daimler. Here’s why there’s so much potential:
Cars increasingly have sensors and cameras to track their performance and their surroundings. Vehicle sensors, for example, can better tell when an engine part is in need of replacement. A back-up camera doesn’t just help us park, it can tell how many pedestrians or vehicles are on a block.
These sensors generate data, which can be analyzed to make money. (If you doubt the way data can be turned into money, just look at the success of Google (GOOG) and Facebook (FB, Tech30). They offer free services to billions, and make a fortune off the data they collect.)
Description: Many of us never pause to consider what that means, but data is growing exponentially — with no end in sight.
Date:May 27. 2016
Have you ever tracked all the ways you use datain a single day? How many of your calories, activities, tasks, messages, projects, correspondences, records and more are saved and accessed through datastorage every day? I bet you won’t be able to stop once you start counting.
Many of us never pause to consider what that means, but data is growing exponentially — with no end in sight. There are already more than a billion cellphones in the world, emitting 18 exabytes (1 billion gigabytes) of data every month. As more devices continue to connect to the Internet of Things, sensors on everything from automobiles to appliances increase thedata output even more. read rest of story
1. Why do we need storage in the era of Big Data?
2. What do you feel will be the effective way for small business to keep track of their data storage? Why?
Description: Imagine walking into a shopping mall on a mission to buy something very specific — the right tie for a job interview, the perfect handbag for a wedding — and knowing immediately where to look. Instead of endless hours wandering from store to store, combing the aisles for the right purchase, you know immediately which shops have exactly what you’re looking for and which has the best price.
Date: Dec 15, 2015
Products are becoming to become intelligent, too, as more items and packaging start to come with low-energy Bluetooth tags that will guide smartphone-equipped shoppers to the exact location of the item they’re looking for. Combine that with customers logging in to good-old fashioned Wi-Fi networks and the retail environment becomes a rich mine of data for retailers who choose to build the supporting infrastructure to capture, analyse and interact with it. Read the Rest of the Story
1. What is the promise of Cloud Computing for the Retail Industry?
2.. What do you see as the two biggest benefits of cloud computing in the retail industry ? Why?
Description: The White House recently released a report about the danger of big data in our lives. Its main focus was the same old topic of how it can hurt customer privacy.
Date: March 2, 2015
Federal government regulators must ask themselves: Should data that only one company owns, to the extent that it prevents others from entering the market, be considered a form of monopoly?
The search market is a perfect example of data as an unfair barrier-to-entry. Google revolutionized the search market in 1996 when it introduced a search-engine algorithm based on the concept of website importance — the famous PageRank algorithm. But search algorithms have significantly evolved since then, and today, most of the modern search engines are based on machine learning algorithms combining thousands of factors — only one of which is the PageRank of a website. Today, the most prominent factors are historical search query logs and their corresponding search result clicks. Studies show that the historical search improves search results up to 31%. In effect, today’s search engines cannot reach high-quality results without this historical user behavior. Read the rest of the Story
Questions for discussion:
1. Do monopolies in the information markets hurt competition? yes or no — explain.
2. Do you see a lot of new entries into this marketspace in the future? is that important? explain
Due to the issues raised by its volume, velocity and variety, Big Data requires new technology solutions. Currently leading the field is an open-source project from Apache called Hadoop. This is developing a software library for reliable, scalable, distributed computing systems capable of handling the Big Data deluge, and provides the first viable platform for Big Data analytics. Hadoop is already used by most Big Data pioneers. For example, LinkedIn currently uses Hadoop to generate over 100 billion personalized recommendations every week.
What Hadoop does is to distribute the storage and processing of large data sets across groups or “clusters” of server computers using a simple programming model. The number of servers in a cluster can also be scaled easily as requirements dictate, from maybe 50 machines to perhaps 2000 or more. Whereas traditional large-scale computing solutions rely on expensive server hardware with a high fault tolerance, Hadoop detects and compensates for hardware failures or other system problems at the application level. This allows a high level of service continuity to be delivered from clusters of individual server computers, each of which may be prone to failure. Processing vast quantities of data across large, lower-cost distributed computing infrastructures therefore becomes a viable proposition. READ REST OF STORY
Questions for discussion:
What is Big Data and why is it important?
What potential applications do you see for Big Data and in what industries will this add the greatest value?