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A Simple Explanation Of 'The Internet Of Things'

A Simple Explanation Of 'The Internet Of Things' | Data & Informatics | Scoop.it
The "Internet of things" (IoT) is becoming an increasingly growing topic of conversation both in the workplace and outside of it. It's a concept that not only has the potential to impact how we live but also how we work.  But what exactly is the "Internet of things" and what [...]
Stephen Dale's insight:

Simply put this is the concept of basically connecting any device with an on and off switch to the Internet (and/or to each other). This includes everything from cell phones, coffee makers, washing machines, headphones, lamps, wearable devices and almost anything else you can think of.


But why would we want to connect all of this stuff together? The idea is to automate life as far as possible, and to give us more information about our environment on which to make better (?) decisions. For example, if your car had access to your appointments diary and to satnav it could alert you to traffic delays, select a better route to take and maybe send an email or text to the people you are meeting to warn them that you might be late. 


One of the (many) issues with all of this is how we can effectively store, track analyse and make sense of this huge volume of data. And what about all the issues associated with security and privacy of your date? It's doubtful that anyone will know exactly what data is being collected, where it's being stored and who has access to it.


The article concludes by advising that regardless of whether we're excited or worried about the potential impact on our lives, we should at least try to understand what the IoT is all about. 


My view? What could possibly go wrong! 


 #bigadata #IoT

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Data & Informatics
The application and usage of data along with the interaction between people, organisations and technology
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Social Media Strategy: Why Insight and Evidence is So Important

Social Media Strategy: Why Insight and Evidence is So Important | Data & Informatics | Scoop.it

Via janlgordon
Stephen Dale's insight:

A timely call for a dispassionate, unbiassed and "agnostic" analysis of data to discover what it is really telling us, and then acting on this information. Sounds obvious? Then why are we so often misled through our ignorance of good and accurate data analysis? 

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Daniel Maina's comment, June 4, 2014 7:36 AM
Thanks for sharing:)
Daniel Maina's comment, June 4, 2014 7:36 AM
Thanks for sharing:)+
Daniel Maina's comment, June 4, 2014 7:37 AM
Thanks for sharing:)
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Rise of the Data Visualization Competency Center

Rise of the Data Visualization Competency Center | Data & Informatics | Scoop.it
You re already familiar with the business intelligence competency center (BICC). Here s how data visualization takes the competency center model to its next level.
Stephen Dale's insight:

Advocating the role of the recently formed " Data Visualizaruion Competency Centre", which promotes successful data visualization using the right kind of graphicacy to correctly interpret and analyze data, as well as employing the right combination of design principles to curate a meaningful story.

 

Reading time 6mins

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A little known hack from Japan to get your notebook organized

A little known hack from Japan to get your notebook organized | Data & Informatics | Scoop.it

The back of your notebook will act like a tag list or index. Every time you create a new entry at the front of the book you’re going to “tag” it.

For example let’s imagine you’re keeping a notebook for recipes and you just wrote down a Chinese recipe on the first page.

Next you’d go to the last page and create the tag ‘Chinese’ by writing it on the first line right next to the papers left edge.

Now you’d go back to the first page where the recipe is and on the exact same line as the ‘Chinese’ label you just wrote you’d make a little mark on the right edge.

You’d make this mark so that even when the notepad was closed the mark would be visible. After repeating this for various recipes you’d now have various tags visible on the notebooks edge.


Via Howard Rheingold
Stephen Dale's insight:

I love the simplicity and elegance of this. "Analogue tagging"

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Howard Rheingold's curator insight, July 9, 3:18 PM

Tagging is a great infotention tool. Why confine it to digital media? h/t @Bopuc

Chad Gorski's curator insight, July 10, 9:55 AM

Self-explanatory but simple way to organize an old-school notebook using index markings on the edge of each page.

Deanna Mascle's curator insight, July 10, 3:52 PM

Not little known among the #NWP teachers I know...

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Enterprises Don’t Have Big Data, They Just Have Bad Data

Enterprises Don’t Have Big Data, They Just Have Bad Data | Data & Informatics | Scoop.it
PayPal co-founder and venture capitalist Peter Thiel commonly harps on the tech community for overusing buzzwords like “cloud” and “big data.” He’s..
Stephen Dale's insight:

Enterprises have historically spent far too little time thinking about what data they should be collecting and how they should be collecting it. Instead of spear fishing, they’ve taken to trawling the data ocean, collecting untold amounts of junk without any forethought or structure. Deferring these hard decisions has resulted in data science teams in large enterprises spending the majority of their time cleaning, processing and structuring data with manual and semi-automated methods.


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An executive’s guide to machine learning | McKinsey & Company

An executive’s guide to machine learning | McKinsey & Company | Data & Informatics | Scoop.it
It’s no longer the preserve of artificial-intelligence researchers and born-digital companies like Amazon, Google, and Netflix. A McKinsey Quarterly article.
Stephen Dale's insight:

"A frequent concern for the C-suite when it embarks on the prediction stage is the quality of the data. That concern often paralyzes executives. In our experience, though, the last decade’s IT investments have equipped most companies with sufficient information to obtain new insights even from incomplete, messy data sets, provided of course that those companies choose the right algorithm. Adding exotic new data sources may be of only marginal benefit compared with what can be mined from existing data warehouses. Confronting that challenge is the task of the “chief data scientist.”


But the article does not make clear how to "choose the right algorithm". Are we becoming over-dependent and too trusting of machines and algorithms that mots of us don't understand?

 

Reading time: 15mins

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June 2015 Newsletter: Fostering a Data Ecosystem

June 2015 Newsletter: Fostering a Data Ecosystem | Data & Informatics | Scoop.it
Stephen Dale's insight:

A letter from the Development gateway CEO:


To foster a stronger data ecosystem, Development Gateway continues to engage with governments to support country ownership of data for better decision-making; with international organizations dedicated to improving data interoperability and uptake; and with innovative initiatives that work to close the feedback loop between governments and citizens.

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Inside NSA, Officials Privately Criticize "Collect It All" Surveillance - The Intercept

Inside NSA, Officials Privately Criticize "Collect It All" Surveillance - The Intercept | Data & Informatics | Scoop.it
“We in the agency are at risk of a similar, collective paralysis in the face of a dizzying array of choices every single day,” the analyst wrote in 2011. “’Analysis paralysis’ isn’t only a cute rhyme. It’s the term for what happens when you spend so much time analyzing a situation that you ultimately stymie any outcome …. It’s what happens in SIGINT [signals intelligence] when we have access to endless possibilities, but we struggle to prioritize, narrow, and exploit the best ones.”

Via Howard Rheingold
Stephen Dale's insight:

In an information-rich world, the wealth of information means a dearth of something else: a scarcity of whatever it is that information consumes. What information consumes is rather obvious: it consumes the attention of its recipients. Hence a wealth of information creates a poverty of attention and a need to allocate that attention efficiently among the overabundance of information sources that might consume it.


Reading time 10mins

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Howard Rheingold's curator insight, June 2, 1:02 PM

This article details the NSA's infotention problem. More information and sophisticated tools for slicing and dicing it don't mean that analysts will notice what is important. 

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How Numbers Can Lie

How Numbers Can Lie | Data & Informatics | Scoop.it
While the idea of “scientifically engineered” solutions sounds attractive, we should remember that science isn’t about certitude, but skepticism. There is never a magic formula that can solve all our problems.
Stephen Dale's insight:

The article discusses the  tendency of economists to cloak ideology in obscure equations to give their views a false appearance of rigour. 

When managers say they are data driven and ROI focused they are usually more intent on professing a belief than delivering results. They are, essentially, accidental theorists, putting their faith in an abstract idea rather than engaging in any true analysis of cause and effect. Despite what many will tell you, numbers can lie and only fools follow them blindly..

 

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Displaying Visual Information

Displaying Visual Information | Data & Informatics | Scoop.it

Visual displays are extremely powerful tools, which means manipulation of these tools can spread inaccurate information and influence public perception. The sheer volume of images making their way through the Internet requires viewers to have a higher level of visual literacy than in years before in order to prevent manipulation.

 

This article covers:

1. The types of displays that can be misleading
2. What can be done to make visual displays less misleading

 

Reading time: 10mins

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The Internet Of (Some) Things

The Internet Of (Some) Things | Data & Informatics | Scoop.it
In 2015, a modern-day gold rush has taken the technology sector by storm. A recent study discovered that 75 percent of executives surveyed said that Internet..
Stephen Dale's insight:

As many organisations currently undertaking IoT initiatives are discovering, the goal should not be to create the Internet of Everything; it should be to construct a “Network of Some Things” deliberately chosen and purposely deployed.

 

The IoT gold rush continues apace, driven by machines that decouple our awareness of the world from mankind’s dependency on consciously observing and recording what is happening. But machine automation only sets the stage. Real impact, business or civic, will come from combining data and relevant sensors, things, and people so lives can be lived better, work can be performed differently, and the rules of competition can be rewired.

 

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How we sold our souls – and more – to the internet giants

How we sold our souls – and more – to the internet giants | Data & Informatics | Scoop.it
Adapted from Data and Goliath by Bruce Schneier
Stephen Dale's insight:

Whether or not you care about your personal data privacy, you should read this. It will conform your worst fears, or at least open your previously half-closed eyes to what is really happening as you surf the interweb. One extract caught my eye: 

 

"Our relationship with many of the internet companies we rely on is not a traditional company-customer relationship. That’s primarily because we’re not customers – we’re products those companies sell to their real customers. The companies are analogous to feudal lords and we are their vassals, peasants and – on a bad day – serfs. We are tenant farmers for these companies, working on their land by producing data that they in turn sell for profit"

 

Article by Bruce Schneier @schneierblog

 

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Power to the new people analytics | McKinsey & Company

Power to the new people analytics | McKinsey & Company | Data & Informatics | Scoop.it
Techniques used to mine consumer and industry data may also let HR tackle employee retention and dissatisfaction. A McKinsey Quarterly article.
Stephen Dale's insight:

McKinsey have developed an approach to retention: to detect previously unobserved behavioural patterns, they combine various data sources with machine-learning algorithms. Workshops and interviews are used to generate ideas and a set of hypotheses. Over time they collected hundreds of data points to test. Then ran different algorithms to get insights at a broad organisational level, to identify specific employee clusters, and to make individual predictions. Finally they held a series of workshops and focus groups to validate the insights from our models and to develop a series of concrete interventions.

 

The insights were surprising and at times counterintuitive. They expected factors such as an individual’s performance rating or compensation to be the top predictors of unwanted attrition. But analysis revealed that a lack of mentoring and coaching and of “affiliation” with people who have similar interests were actually top of list. More specifically, “flight risk” across the firm fell by 20 to 40 percent when coaching and mentoring were deemed satisfying.

 

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Power to the new people analytics | McKinsey & Company

Power to the new people analytics | McKinsey & Company | Data & Informatics | Scoop.it

The application of new techniques and new thinking to talent management is becoming more mainstream. The implications are dramatic because talent management in many businesses has traditionally revolved around personal relationships or decision making based on experience—not to mention risk avoidance and legal compliance—rather than deep analysis. Advanced analytics provides a unique opportunity for human-capital and human-resources professionals to position themselves as fact-based strategic partners of the executive board, using state-of-the-art techniques to recruit and retain staff.

Stephen Dale's insight:

Interesting application of data analytics to HR datasets, for the purposes of improving staff loyalty and retention rates.

 

Reading time: 6 mins

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Data storytelling skills take on key role in analytics process

Data storytelling skills take on key role in analytics process | Data & Informatics | Scoop.it
Data storytelling has become a vital part of the analytics process in some organizations, which are tapping workers with good communications skills to explain analytics results to execs.
Stephen Dale's insight:

The article advocates an extension to traditional (science-based) data analytics teams to include journalistic skills in order to support more effective communication of findings and insights through data storytelling techniques. It is argued that this will help communicate the team's analytical findings in a way that corporate executives and business managers can easily understand. 


Reading time: 5mins

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Jean-Pierre Blanger's curator insight, July 30, 4:42 PM

The article advocates an extension to traditional (science-based) data analytics teams to include journalistic skills in order to support more effective communication of findings and insights through data storytelling techniques. It is argued that this will help communicate the team's analytical findings in a way that corporate executives and business managers can easily understand. 

 

Reading time: 5mins

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How people engage with data visualisations and why it matters | CILIP

How people engage with data visualisations and why it matters | CILIP | Data & Informatics | Scoop.it

The skills that we and our participants identified as integral to ‘visualisation literacy’ include:

Language skills, to be able to read the text within visualisations (not always easy for people for whom English is not their first language).A combination of mathematical or statistical skills (knowing how to read particular chart types or what the scales mean) and visual literacy skills (understanding meanings attached to the visual elements of datavis) – sometimes called ‘graphicacy’ skills.Computer skills, to know how to interact with a visualisation on screen, where to input text, and so on.Critical thinking skills, to be able to ask ourselves what has been left out of a visualisation, or what point of view is being prioritised.
Stephen Dale's insight:

The skills identified as integral to ‘visualisation literacy’ include:

Language skills, to be able to read the text within visualisations (not always easy for people for whom English is not their first language).A combination of mathematical or statistical skills (knowing how to read particular chart types or what the scales mean) and visual literacy skills (understanding meanings attached to the visual elements of datavis) – sometimes called ‘graphicacy’ skills.Computer skills, to know how to interact with a visualisation on screen, where to input text, and so on.Critical thinking skills, to be able to ask ourselves what has been left out of a visualisation, or what point of view is being prioritised.

 

Reading time: 7mins

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Big data: are we making a big mistake? - FT.com

Big data: are we making a big mistake? - FT.com | Data & Informatics | Scoop.it
Five years ago, a team of researchers from Google announced a remarkable achievement in one of the world’s top scientific journals, Nature. Without needing the results of a single medical check-up, they were nevertheless able to track the spread of
Stephen Dale's insight:

From the article:


"Cheerleaders for big data have made four exciting claims... that data analysis produces uncannily accurate results; that every single data point can be captured, making old statistical sampling techniques obsolete; that it is passé to fret about what causes what, because statistical correlation tells us what we need to know; and that scientific or statistical models aren’t needed because, to quote “The End of Theory”, a provocative essay published in Wired in 2008, “with enough data, the numbers speak for themselves”.


Unfortunately, these four articles of faith are at best optimistic oversimplifications. At worst, according to David Spiegelhalter, Winton Professor of the Public Understanding of Risk at Cambridge university, they can be “complete bollocks. Absolute nonsense.”

 

A well-rounded and informative piece of journalism from the FT.

 

Reading time: 12mins

 
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Proportional Pie Chart of the World's Most Spoken Languages

Proportional Pie Chart of the World's Most Spoken Languages | Data & Informatics | Scoop.it

There are at least 7,102 known languages alive in the world today. 

The 23 languages make up the native tongue of 4.1 billion people.


Via Ivo Nový
Stephen Dale's insight:

Each language is within black borders with the numbers of native speakers (in millions) by country. The colour of these countries shows how languages have taken root in many different regions.

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SageRave of Get Custom Content's curator insight, June 30, 6:25 PM

Ladies and Gentlemen, find your Chinese tutors!

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Internet of Things video series: the tipping point has come

Internet of Things video series: the tipping point has come | Data & Informatics | Scoop.it
Predictions about the amount of devices that will be connected to each other via Internet of Things systems vary, but the numbers are always impressive: 50 billion to 212 billon devices. And although Van Der Bel says that the landscape can seem "big, noisy and confusing”, he offers consolation.
Stephen Dale's insight:

Rolls Royce and Formula 1 have been connecting monitoring technology for a long time, so there are lessons to be learned from the past. Now, a “tipping point has come”,  thanks to cheaper components and connectivity; the power of cloud computing; and the advancement of tools that enable you to exploit the data.


Video: 9mins

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The era of "big data analytics" - WSI

The era of "big data analytics" - WSI | Data & Informatics | Scoop.it
While it may sound like a buzzphrase, there’s no denying that we are well and truly in the era of big data analytics. An ever-increasing portion of our daily lives, business and social relations are mediated by digital platforms and computational processes. The implications of these changes are both dramatic and subtle, and we are still only beginning to understand …
Stephen Dale's insight:

Many people fear that ubiquitous data collection and profiling will only undermine their autonomy, individuality and social solidarity. This is beginning to change, however, with a slew of new applications and initiatives designed to empower people to control their own data, and use it to serve their own purposes. 

 

Reading time: 3mins

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Machine Learning Goes Mainstream II: Guesswork Automates CRM With Digital Division Of Labor

Machine Learning Goes Mainstream II: Guesswork Automates CRM With Digital Division Of Labor | Data & Informatics | Scoop.it
Guesswork's strategy has been to build vertical applications on top of Google's Prediction API. Doraisamy contends that the lead qualification in his machine learning-powered product is 10x better than conventional CRM practices.
Stephen Dale's insight:

Google Search provides one of the most familiar examples of predictive intelligence. When you enter keywords in the search box, Google predicts what you are interested in and then presents you with results that match that intent. Since it released the first version of its Prediction API in 2010, Google has made some of these methods available to developers. Adoption among developers has not been high because machine learning requires a lot of infrastructure and validation to produce accurate results. But can we trust the decisions made by products using black box APIs and hidden algorithms?


Reading time: 8mins

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10 Big Data Case Studies

10 Big Data Case Studies | Data & Informatics | Scoop.it
These 10 insurance companies developed cross-enterprise big data strategies, hired the right data scientists and staff members, and delivered impressive results.
Stephen Dale's insight:

Insurance Networking News, our sister brand, identified 10 insurance companies, across lines of business, that demonstrate true leadership in big data and analytics excellence by developing cross-enterprise strategy, delivering results from the corporate investment, and perhaps most importantly, identifying and recruiting key staff members with the right expertise. The strategies may vary, but the commitment is real.


Reading time: 30mins

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Disinformation Visualization: How to lie with datavis

Disinformation Visualization: How to lie with datavis | Data & Informatics | Scoop.it
Mushon Zer-Aviv dissects the beautiful lies inherent to many infographics, showing how these visuals can be as manipulative as a devious argument.
Stephen Dale's insight:

We don’t spread visual lies by presenting false data. That would be lying. We lie by misrepresenting the data to tell the very specific story we’re interested in telling. If this is making you slightly uncomfortable, that’s a good thing, it should. If you’re concerned about adopting this new and scary habit, well, don’t worry, it’s not new. Just open your CV to be reminded you’ve lied with truthful data before. This time however, it will be explicit and visual.

 

Reading time: 10mins

 
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How open data is transforming the business landscape | Information Age

The explosion of data has created the case for the free exchange of information – particularly in regard to public services. But a large opportunity also lies in creating solid business value
Stephen Dale's insight:

The article identifies three key benefits to businesses.


Firstly, it allows them to spot patterns and market opportunities faster than their competition. However, this is only the case if businesses have the right tools to analyse the data.


Secondly, it allows businesses to run more efficiently and productively by creating a feedback loop that is based on large data sets from the market and internal sources – often in real time.

 

Thirdly, the rapid growth of open data will spur innovation in ways we probably can’t imagine – leading to new products and new businesses.

 

Reading time 5 mins

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Are you ready to decide? | McKinsey & Company

Are you ready to decide? | McKinsey & Company | Data & Informatics | Scoop.it
Before doing so, executives should ask themselves two sets of questions. A McKinsey Quarterly article.
Stephen Dale's insight:

Two particular types of bias weigh heavily on the decisions of large corporations—confirmation bias and overconfidence bias. The former describes our unconscious tendency to attach more weight than we should to information that is consistent with our beliefs, hypotheses, and recent experiences and to discount information that contradicts them. Overconfidence bias frequently makes executives misjudge their own abilities, as well as the competencies of the business. It leads them to take risks they should not take, in the mistaken belief that they will be able to control outcomes.

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You Can Be a Trusted Guide To The Most Relevant Information Online: Not Google

You Can Be a Trusted Guide To The Most Relevant Information Online: Not Google | Data & Informatics | Scoop.it




Via Robin Good
Stephen Dale's insight:

Put simply - Google (and for that matter any commercial search engine) may skew search results to promote their own commercial interests. The question to ask yourself is "are the (search) results good enough?" - I'd say in Google's defence "yes they are".

 

Reading time: 5mins

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rwestby's curator insight, March 29, 8:07 PM

 A bit of a lengthy read but certainly worth a look and the thoughts it provokes.

WSI Digital Wave's curator insight, April 2, 7:22 AM

https://plus.google.com/+PaulMathewsWSI/posts

Nedko Aldev's curator insight, April 5, 12:21 PM

 

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A Sucker Is Optimized Every Minute

A Sucker Is Optimized Every Minute | Data & Informatics | Scoop.it

Now that we have hard data on everything, we no longer make decisions from our hearts, guts or principles.


Via Kenneth Mikkelsen
Stephen Dale's insight:

Data optimisation - the antidote to common sense?

 

Reading time: 8mins

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Kenneth Mikkelsen's curator insight, March 18, 8:05 AM

Not long ago, our blockbuster business books spoke in unison: Trust your gut. The secret to decision-making lay outside our intellects, across the aisle in our loopy right brains, with their emo melodramas and surges of intuition. Linear thinking was suddenly the royal road to ruin. Dan Ariely’s “Predictably Irrational” tracked the extravagant illogic of our best judgment calls. The “Freakonomics” authors urged us to think like nut jobs. In “Blink,” Malcolm Gladwell counseled abandoning scientific method in favor of snap judgments. Tedious hours of research, conducted by artless cubicle drones, became the province of companies courting Chapter 11. To the artsy dropouts who could barely grasp a polynomial would go the spoils of the serial bull markets.



No more. The gut is dead. Long live the data, turned out day and night by our myriad computers and smart devices. Not that we trust the data, as we once trusted our guts. Instead, we “optimize” it. We optimize for it. We optimize with it.