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Definition of big data from PC magazine Encyclopedia:
Big Data refers to the massive amounts of data collected over time that are difficult to analyze and handle using common database management tools. The data are analyzed for marketing trends in business as well as in the fields of manufacturing, medicine and science. The types of data include business transactions, e-mail messages, photos, surveillance videos, activity logs and unstructured text from blogs and social media, as well as the huge amounts of data that can be collected from sensors of all varieties.
My Definition of big data:
Nowadays, due to the high volume of data and content produced by all kinds of organizations, people and equipment we have faced to the phenomenon of “data explosion”. Development of communication technologies such as (Internet technologies) and information (such as a variety of electronic services) has encountered world “data deluge” phenomenon. Big Data originates from these emerging phenomena. For definition of big data three major indexes are used: 1.Volume data: Big Data in the least amount of terabytes of data (Terabyte) is. 2.Variety of data: fusion of massive data into structured, semi-structured and non-structured. 3.Data speed: the speed of Big Data, frequency of use of the data is much higher than the traditional approach.
Some articles related big data:
Extra Work For Missing a Lecture
Review of pages 0-90 killer apps book
Ethics in/of big data
Big data revolution brings lots of ethical issues related to privacy, confidentiality, transparency and identity. In this cutting age technology era, our ability to reveal new knowledge from raw or unexamined data moving faster than our current ethical guidelines can manage. What are your organization’s policies for generating and using huge datasets full of personal information? We should learn how to align our actions with explicit company values and preserve the trust of customers, partners, and stakeholders.
According to Ethics of Big Data Balancing Risk and Innovation book(Kord Davis and Doug Patterson), Both individuals and organizations have legitimate interests in understanding how data is handled. Your use of data can directly affect brand quality and revenue—as Target, Apple, Netflix, and dozens of other companies have discovered.
- Review your data-handling practices and examine whether they reflect core organizational values
- Express coherent and consistent positions on your organization’s use of big data
- Define tactical plans to close gaps between values and practices—and discover how to maintain alignment as conditions change over time
- Maintain a balance between the benefits of innovation and the risks of unintended consequences
Kord Davis: Big data itself, like all technology, is ethically neutral. The use of big data, however, is not. While the ethics involved are abstract concepts, they can have very real-world implications. The goal is to develop better ways and means to engage in intentional ethical inquiry to inform and align our actions with our values. There are a significant number of efforts to create a digital “Bill of Rights” for the acceptable use of big data. The White House recently released a blueprint for a Consumer Privacy Bill of Rights. The values it supports include transparency, security, and accountability. The challenge is how to honor those values in everyday actions as we go about the business of doing our work.
Jonathan H. King & Neil M. Richards say: From our perspective, we believe that any organizational conversation about big data ethics should relate to four basic principles that can lead to the establishment of big data norms:
- Privacy isn’t dead; it’s just another word for information rules. Private doesn’t always mean secret. Ensuring privacy of data is a matter of defining and enforcing information rules – not just rules about data collection, but about data use and retention. People should have the ability to manage the flow of their private information across massive, third-party analytical systems.
- Shared private information can still remain confidential. It’s not realistic to think of information as either secret or shared, completely public or completely private. For many reasons, some of them quite good, data (and metadata) is shared or generated by design with services we trust (e.g. address books, pictures, GPS, cell tower, and WiFi location tracking of our cell phones). But just because we share and generate information, it doesn’t follow that anything goes, whether we’re talking medical data, financial data, address book data, location data, reading data, or anything else.
- Big data requires transparency. Big data is powerful when secondary uses of data sets produce new predictions and inferences. Of course, this leads to data being a business, with people such as data brokers, collecting massive amounts of data about us, often without our knowledge or consent, and shared in ways that we don’t want or expect. For big data to work in ethical terms, the data owners (the people whose data we are handling) need to have a transparent view of how our data is being used – or sold.
- Big Data can compromise identity. Privacy protections aren’t enough any more. Big data analytics can compromise identity by allowing institutional surveillance to moderate and even determine who we are before we make up our own minds. We need to begin to think about the kind of big data predictions and inferences that we will allow, and the ones that we should not.
Mapping the ethical issues in digital disease detection: