Searching for "big data"

Big Tech and Their Metaverse

Big Tech Needs to Stop Trying to Make Their Metaverse Happen

https://www.wired.co.uk/article/metaverse-big-tech-land-grab-hype

The Metaverse is a fuzzy concept: It entered dictionaries via Neal Stephenson’s 1992 dystopian sci-fi novel Snow Crash, where the Metaverse is the virtual refuge from an anarchy-laden world controlled by the Mafia, and was brought back by a series of blogposts by VC Matthew Ball.

The morality of the Metaverse project is the least of its problems. Unlike Google Glass, the gold standard of tech blunders, it is not an overhyped (and ill-conceived) product: It is pure hype, without a product—except for some hypothetical “building blocks.”

letter by the CEO of Japanese game developer Square Enix, in which the executive expounded on his interest in NFTs and drew an odd distinction between people who “play for fun” and those who “play to contribute” was also badly received.

AI data and infodemic

AI progress depends on us using less data, not more

A minimal-data practice will enable several AI-driven industries — including cyber security, which is my own area of focus — to become more efficient, accessible, independent, and disruptive.

1. AI has a compute addiction. The growing fear is that new advancements in experimental AI research, which frequently require formidable datasets supported by an appropriate compute infrastructure, might be stemmed due to compute and memory constraints, not to mention the financial and environmental costs of higher compute needs.

MIT researchers estimated that “three years of algorithmic improvement is equivalent to a 10 times increase in computing power.”

2. Big data can mean more spurious noise. 

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more on infodemic in this IMS blog
https://blog.stcloudstate.edu/ims?s=infodemic

Turn Bad Data Into Good Data

How to Turn Bad Data Into Good Data

https://events.edsurge.com/webinars/how-to-turn-bad-data-into-good-data

Date: Wednesday, January 22, 2020  Time: 1:00 pm CT

a panel of data and education experts about how to make the most of your education data. In this webinar you’ll learn about:

  • How rapid data turnover can hurt you (and your bottom line)
  • How to access “good‘‘ data and what it looks like
  • Opportunities open to you when your data is clean 
  • Avoiding the pitfalls of using outdated or irrelevant data and making decisions that are not data informed
  • Navigating the unique challenges of working in education, such as privacy regulations that might hinder communication 

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more on big data in this IMS blog
https://blog.stcloudstate.edu/ims?s=big+data

Data driven design

Valuing data over design instinct puts metrics over users

Benek Lisefski August 13, 2019

https://modus.medium.com/data-driven-design-is-killing-our-instincts-d448d141653d

Overreliance on data to drive design decisions can be just as harmful as ignoring it. Data only tells one kind of story. But your project goals are often more complex than that. Goals can’t always be objectively measured.

Data-driven design is about using information gleaned from both quantitative and qualitative sources to inform how you make decisions for a set of users. Some common tools used to collect data include user surveys, A/B testing, site usage and analytics, consumer research, support logs, and discovery calls. 

Designers justified their value through their innate talent for creative ideas and artistic execution. Those whose instincts reliably produced success became rock stars.

In today’s data-driven world, that instinct is less necessary and holds less power. But make no mistake, there’s still a place for it.

Data is good at measuring things that are easy to measure. Some goals are less tangible, but that doesn’t make them less important.

Data has become an authoritarian who has fired the other advisors who may have tempered his ill will. A designer’s instinct would ask, “Do people actually enjoy using this?” or “How do these tactics reflect on our reputation and brand?”

Digital interface design is going through a bland period of sameness.

Data is only as good as the questions you ask

When to use data vs. when to use instinct

Deciding between two or three options? This is where data shines. Nothing is more decisive than an A/B test to compare potential solutions and see which one actually performs better. Make sure you’re measuring long-term value metrics and not just views and clicks.

Sweating product quality and aesthetics? Turn to your instinct. The overall feeling of quality is a collection of hundreds of micro-decisions, maintained consistency, and execution with accuracy. Each one of those decisions isn’t worth validating on its own. Your users aren’t design experts, so their feedback will be too subjective and variable. Trust your design senses when finessing the details.

Unsure about user behavior? Use data rather than asking for opinions. When asked what they’ll do, customers will do what they think you want them to. Instead, trust what they actually do when they think nobody’s looking.

Building brand and reputation? Data can’t easily measure this. But we all know trustworthiness is as important as clicks (and sometimes they’re opposing goals). When building long-term reputation, trust your instinct to guide you to what’s appealing, even if it sometimes contradicts short-term data trends. You have to play the long game here.

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more on big data in this IMS blog
https://blog.stcloudstate.edu/ims?s=big+data

data interference

APRIL 21, 2019 Zeynep Tufekci

Think You’re Discreet Online? Think Again

Because of technological advances and the sheer amount of data now available about billions of other people, discretion no longer suffices to protect your privacy. Computer algorithms and network analyses can now infer, with a sufficiently high degree of accuracy, a wide range of things about you that you may have never disclosed, including your moods, your political beliefs, your sexual orientation and your health.

There is no longer such a thing as individually “opting out” of our privacy-compromised world.

In 2017, the newspaper The Australian published an article, based on a leaked document from Facebook, revealing that the company had told advertisers that it could predict when younger users, including teenagers, were feeling “insecure,” “worthless” or otherwise in need of a “confidence boost.” Facebook was apparently able to draw these inferences by monitoring photos, posts and other social media data.

In 2017, academic researchers, armed with data from more than 40,000 Instagram photos, used machine-learning tools to accurately identify signs of depression in a group of 166 Instagram users. Their computer models turned out to be better predictors of depression than humans who were asked to rate whether photos were happy or sad and so forth.

Computational inference can also be a tool of social control. The Chinese government, having gathered biometric data on its citizens, is trying to use big data and artificial intelligence to single out “threats” to Communist rule, including the country’s Uighurs, a mostly Muslim ethnic group.

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Zeynep Tufekci and Seth Stephens-Davidowitz: Privacy is over

https://www.centreforideas.com/article/zeynep-tufekci-and-seth-stephens-davidowitz-privacy-over

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Zeynep Tufekci writes about security and data privacy for NY Times, disinformation’s threat to democracy for WIRED

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more on privacy in this IMS blog
https://blog.stcloudstate.edu/ims?s=privacy

European Data Sharing Space

“Towards a European Data Sharing Space” BDVA Position Paper

BDV Big Data Value Association

April, 2019. Position paper: http://www.bdva.eu/node/1277

This position paper is meant to i) support the dialog among European and national policy makers, industry, research, public sector and civic society in the definition of a common roadmap for the development and adoption of a pan-European Data Sharing Space, and ii) guide public and private investments in this area in the next Multiannual Financial Framework.

http://www.bdva.eu/sites/default/files/BDVA%20DataSharingSpace%20PositionPaper_April2019_V1.pdf

Microsoft BrightBytes DataSense

Microsoft Takes a Bite Out of BrightBytes, Acquiring Its DataSense Platform and Team

Tony Wan     Feb 5, 2019

https://www.edsurge.com/news/2019-02-04-microsoft-takes-a-bite-out-of-brightbytes-acquires-its-datasense-platform-and-team

From launching new tablets to virtual-reality curriculum, Microsoft has added plenty to its educational offerings

DataSense, a data management platform developed by Brightbytes.

DataSense is a set of professional services that work with K-12 districts to collect data from different data systems, translate them into unified formats and aggregate that information into a unified dashboard for reporting purposes.

DataSense traces its origins to Authentica Solutions, an education data management company founded in 2013.

A month later, BrightBytes acquired Authentica. The deal was hailed as a “major milestone in the industry” and appeared to be a complement to BrightBytes’ flagship offering, Clarity, a suite of data analytics tools that help educators understand the impact of technology spending and usage on student outcomes.

Of the “Big Five” technology giants, Microsoft has become the most acqui-hungry as of late in the learning and training space. In recent years it purchased several consumer brand names whose services reach into education, including LinkedIn (which owns Lynda.com, now a part of the LinkedIn Learning suite), Minecraft (which has been adapted for use in the classroom) and Github (which released an education bundle).

Last year, Microsoft also acquired a couple of smaller education tools, including Flipgrid, a video-discussion platform popular among teachers, and Chalkup, whose services have been rolled into Microsoft Teams, its competitor to Slack.

Tackling Data in Libraries

Tackling Data in Libraries: Opportunities and Challenges in Serving User Communities

Submit proposals at http://www.iolug.org

Deadline is Friday, March 1, 2019

Submissions are invited for the IOLUG Spring 2019 Conference, to be held May 10th in Indianapolis, IN. Submissions are welcomed from all types of libraries and on topics related to the theme of data in libraries.

Libraries and librarians work with data every day, with a variety of applications – circulation, gate counts, reference questions, and so on. The mass collection of user data has made headlines many times in the past few years. Analytics and privacy have, understandably, become important issues both globally and locally. In addition to being aware of the data ecosystem in which we work, libraries can play a pivotal role in educating user communities about data and all of its implications, both favorable and unfavorable.

The Conference Planning Committee is seeking proposals on topics related to data in libraries, including but not limited to:

  • Using tools/resources to find and leverage data to solve problems and expand knowledge,
  • Data policies and procedures,
  • Harvesting, organizing, and presenting data,
  • Data-driven decision making,
  • Learning analytics,
  • Metadata/linked data,
  • Data in collection development,
  • Using data to measure outcomes, not just uses,
  • Using data to better reach and serve your communities,
  • Libraries as data collectors,
  • Big data in libraries,
  • Privacy,
  • Social justice/Community Engagement,
  • Algorithms,
  • Storytelling, (https://web.stcloudstate.edu/pmiltenoff/lib490/)
  • Libraries as positive stewards of user data.

data is the new oil in Industry 4.0

Why “data is the new oil” and what happens when energy meets Industry 4.0

By Nicholas Waller PUBLISHED 19:42 NOVEMBER 14, 2018

Why “data is the new oil” and what happens when energy meets Industry 4.0

At the Abu Dhabi International Petroleum Exhibition and Conference (ADIPEC) this week, the UAE’s minister of state for Artificial Intelligence, Omar bin Sultan Al Olama, went so far as to declare that “Data is the new oil.”

according to Pulitzer Prize-winning author, economic historian and one of the world’s leading experts on the oil & gas sector; Daniel Yergin, there is now a “symbiosis” between energy producers and the new knowledge economy. The production of oil & gas and the generation of data are now, Yergin argues, “wholly inter-dependent”.

What does Oil & Gas 4.0 look like in practice?

the greater use of automation and collection of data has allowed an upsurge in the “de-manning” of oil & gas facilities

Thanks to a significant increase in the number of sensors being deployed across operations, companies can monitor what is happening in real time, which markedly improves safety levels.

in the competitive environment of the Fourth Industrial Revolution, no business can afford to be left behind by not investing in new technologies – so strategic discussions are important.

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more on big data in this IMS blog
https://blog.stcloudstate.edu/ims?s=big+data

more on industry 4.0 in this IMS blog
https://blog.stcloudstate.edu/ims?s=industry

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