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How 5 Data Dynamos Do Their Jobs
June 12, 2019 | The New York Times | By Lindsey Rogers Cook.
[Times Insider explains who we are and what we do, and delivers behind-the-scenes insights into how our journalism comes together.]
Reporters from across the newsroom describe the many ways in which they increasingly rely on datasets and spreadsheets to create groundbreaking work.

Data journalism is not new. It predates our biggest investigations of the last few decades. It predates computers. Indeed, reporters have used data to hold power to account for centuries, as a data-driven investigation that uncovered overspending by politicians, including then-congressman Abraham Lincoln, attests.

But the vast amount of data available now is new. The federal government’s data repository contains nearly 250,000 public datasets. New York City’s data portal contains more than 2,500. Millions more are collected by companies, tracked by think tanks and academics, and obtained by reporters through Freedom of Information Act requests (though not always without a battle). No matter where they come from, these datasets are largely more organized than ever before and more easily analyzed by our reporters.

(1) Karen Zraick, Express reporter.
NYC's Buildings Department said it was merely responding to a sudden spike in 311 complaints about store signs. But who complains about store signs? was hard to get a sense of the scale of the problem just by collecting anecdotes. So I turned to NYC Open Data, a vast trove of information that includes records about 311 complaints. By sorting and calculating the data, we learned that many of the calls were targeting stores in just a few Brooklyn neighborhoods.
(2) John Ismay, At War reporter
He has multiple spreadsheets for almost every article he works on......Spreadsheets helped him organize all the characters involved and the timeline of what happened as the situation went out of control 50 years ago......saves all the relevant location data he later used in Google Earth to analyze the terrain, which allowed him to ask more informed questions.
(3) Eliza Shapiro, education reporter for Metro
After she found out in March that only seven black students won seats at Stuyvesant, New York City’s most elite public high school, she kept coming back to one big question: How did this happen? I had a vague sense that the city’s so-called specialized schools once looked more like the rest of the city school system, which is mostly black and Hispanic.

With my colleague K.K. Rebecca Lai from The Times’s graphics department, I started to dig into a huge spreadsheet that listed the racial breakdown of each of the specialized schools dating to the mid-1970s.
analyzed changes in the city’s immigration patterns to better understand why some immigrant groups were overrepresented at the schools and others were underrepresented. We mapped out where the city’s accelerated academic programs are, and found that mostly black and Hispanic neighborhoods have lost them. And we tracked the rise of the local test preparation industry, which has exploded in part to meet the demand of parents eager to prepare their children for the specialized schools’ entrance exam.

To put a human face to the data points we gathered, I collected yearbooks from black and Hispanic alumni and spent hours on the phone with them, listening to their recollections of the schools in the 1970s through the 1990s. The final result was a data-driven article that combined Rebecca’s remarkable graphics, yearbook photos, and alumni reflections.

(4) Reed Abelson, Health and Science reporter
the most compelling stories take powerful anecdotes about patients and pair them with eye-opening data.....Being comfortable with data and spreadsheets allows me to ask better questions about researchers’ studies. Spreadsheets also provide a way of organizing sources, articles and research, as well as creating a timeline of events. By putting information in a spreadsheet, you can quickly access it, and share it with other reporters.

(5) Maggie Astor, Politics reporter
a political reporter dealing with more than 20 presidential candidates, she uses spreadsheets to track polling, fund-raising, policy positions and so much more. Without them, there’s just no way she could stay on top of such a huge field......The climate reporter Lisa Friedman and she used another spreadsheet to track the candidates’ positions on several climate policies.
311  5_W’s  behind-the-scenes  Communicating_&_Connecting  data  datasets  data_journalism  data_scientists  FOIA  groundbreaking  hidden  information_overload  information_sources  journalism  mapping  massive_data_sets  New_York_City  NYT  open_data  organizing_data  reporters  self-organization  systematic_approaches  spreadsheets  storytelling  timelines  tools 
june 2019 by jerryking
Six rules for managing our era’s oversupply of non-stop news, high-decibel outrage
May 11, 2019 | The Globe and Mail | editorials.

Rule No. 1: You don’t need to have an opinion about everything. Shocking but true. ....It’s perfectly fair to say, “I don’t know enough to have an opinion on that," or, “I will leave that to others to debate,” or even, “Both sides have some good points.” You might not please everyone, but see Rule No. 2.

* Rule No. 2: You can’t please everyone. Get over it.

* Rule No. 3: Embrace ambivalence....often misinterpreted as indifference, or derided as indecision. In fact, the ability to entertain contradictory but animating ideas goes to the heart of what it means to be a mature and civilized human being. It’s also central to preserving political freedom. The most dangerous person in a democracy is the blind partisan who outsources her opinions to politicians or an ideology, and who sees those who don’t agree as enemies to be righteously chased from town by a torch-wielding mob. The biggest threat to such black-and-white partisanship is the person who keeps her mind open, is not blindly loyal to any one team and sees people with different opinions not as monsters to be slain but as human beings to be understood, especially when you disagree with them, and they disagree with you.

* Rule No. 4: When you take a stand, be forceful. While the process of reaching a conclusion should involve a lot of “on the one hand” and “on the other,” at some point you have to make a choice.

In a criminal trial, the decision to convict an accused person can only be taken if the evidence is persuasive beyond a reasonable doubt – in other words, if the evidence is irrefutable and the conclusion is certain. But in politics, business and life, most decisions must be taken under conditions that cannot meet that exacting standard. Reasonable doubts are reasonable. Only the extreme partisan is without them.

* Rule No. 5: Set your bottom line. How far are you willing to let another person go before you feel obliged to offer a counter-opinion? Not every take you hear deserves the energy required to argue against it. Sometimes, you have to just let people say things you don’t agree with. You might learn something.

And remember, just as there is no obligation to have an opinion on every subject, there is also no rule that says you must express your opinion every time the chance presents itself. But when someone or something does cross a line, sometimes you can’t hold back. It may be as lofty as a matter of justice, or a simple as a question of common sense, but there comes a moment when your opinion will matter.

* Rule No. 6: Opinions are not the same thing as empathy. Empathy is what makes it possible for people who disagree to live together in peace and harmony – to agreeably disagree. And in a multicultural, multireligious, multiracial, multiparty democracy, people are going to disagree about all sorts of things, all the time.

The world has enough opinions. What it really needs is more empathy. Without it, life isn’t possible.
21st._century  agreeably_disagree  ambivalence  commoditization_of_information  disagreements  disinformation  dual-consciousness  empathy  hard_choices  incivility  incompatibilities  indecision  information_overload  news  opinions  open_mind  outrage  partial_truths  partisanship  partisan_loyalty  political_spin  propaganda  rules_of_the_game 
may 2019 by jerryking
Getting smarter, knowing less
March 16, 2018 | FT | by Robert Armstrong.

The point is that for me, and perhaps most people, the main barrier to being smart is not what we do not know. It is the masses of things we know and mistakenly believe to be relevant.

My wife and I have been thinking about the next stage of our kids’ education. Being central-casting middle-class professional types, we hired an educational consultant to talk us through a range of state schools. She provided briefings about each school, crammed with facts about test scores, teacher turnover, class sizes, and so on.

Feeling slightly dizzy, I asked which bits I should pay attention to. She responded — with glorious honesty for someone being paid by the hour — that there was only one piece of information that really mattered: how many students are late or absent on a regular basis. If a school is the kind of place where almost everybody shows up and shows up on time, then it is the kind of place where kids and teachers can achieve a lot together. The rest is noise.

That comment made me smarter, not because it was a surprising revelation but because it allowed me to clear a lot of junk out of my head — and avoid putting a lot more junk into it. What we all need is the cognitive equivalent of decluttering guru Marie Kondo, who can help us to go into our own heads and throw out all the beliefs that have outlived their usefulness.
decluttering  problem_framing  signals  noise  information_overload  questions  smart_people  incisiveness  education  schools  pretense_of_knowledge  pay_attention  what_really_matters  work_smarter 
march 2018 by jerryking
GE’s flow of financial information has become fantastically muddled - Too little information
Jan 27th 2018

Jan 27th 2018

The curse of rotten information can strike companies, too. That seems to be the case with General Electric (GE), which has had a vertiginous fall. Its shares, cashflow and forecast profits have dropped by about 50% since 2015. .....GE’s boss, John Flannery, an insider who took office in August, must clear up the mess made by his predecessor, Jeff Immelt.....Is the conglomerate formerly known as the world’s best-run firm a victim of weak demand for gas turbines, a low oil price, lavish digital initiatives, timing lags in client payments, morbidity rates, bad deals, cost overruns or a 20-year squeeze in industrial-equipment margins because of Chinese competition? You can imagine GE’s 12-man board blinking at this list, like Pentagon generals huddled around maps of the Gulf of Tonkin which they are too embarrassed to admit they do not understand......Schumpeter’s theory is that GE’s flow of financial information has become fantastically muddled. There is lots of it about.....[does great granularity necessarily lead to greater insight].... it offers volume and ambiguity instead of brevity and clarity. It is impossible—certainly for outsiders, probably for the board, and possibly for Mr Flannery—to answer central questions. How much cashflow does GE sustainably make and where? How much capital does it employ and where? What liabilities must be serviced before shareholders get their profits?....GE's public accounting system reveals eight problems.
(1) No consistent measure of performance.....18 definitions of group profits and cashflow....there is a large gap between most measures of profits and free cashflow.
(2) GE’s seven operating divisions (power, for example, or aviation) are allowed to use a flattering definition of profit that excludes billions of dollars of supposedly one-off costs. Their total profits are almost twice as big as the firm’s.
(3) GE does not assess itself on a geographical basis. Does China yield solid returns on capital? Has Saudi Arabia been a good bet? No one seems to know.
(4) GE pays little attention to the total capital it employs, which has ballooned by about 50% over the past decade (excluding its financial arm). Its managers rarely talk about it and have set no targets. It is unclear which parts of the firm soak up disproportionate resources relative to profits, diluting returns.
(5), it is hard to know if GE’s leverage is sustainable. Its net debts are 2.6 times its gross operating profits, again excluding its financial arm. That is high relative to its peers—for Siemens and Honeywell the ratio is about one.
(6) the strength of GE’s financial arm is unclear. The new insurance loss will lower its tangible equity to 8% of assets. This is well below the comfort level.
(7) it is hard to calibrate the risk this poses to GE shareholders. GE likes to hint that its industrial and financial arms are run separately. But they are umbilically connected by a mesh of cross-guarantees, factoring arrangements and other transactions.
(8) is GE sure that its industrial balance-sheet accurately measures its capital employed and its liabilities? Some 46% of assets are intangible, which are hard to pin down financially: for example, goodwill and “contract” assets where GE has booked profits but not been paid yet.

Time for some command and control

GE’s situation is like that of the global bank conglomerates post-financial crisis. Citigroup, JPMorgan Chase and HSBC did not entirely trust their own numbers and lacked a framework for assessing which bits of their sprawl created value for shareholders. Today, after much toil, the people running these firms know whether, say, loans in California or trading in India make sense.

This does not happen naturally. If neglected, financial reporting becomes a hostage to internal politics, with different constituencies claiming they bring in sales, while arguing that costs and capital are someone else’s problem. Flannery is a numbers guy who seeks to slim GE to its profitable essence. But he is trapped in a financial construct that makes it hard to pursue that mission intelligently. Until he re-engineers how GE measures itself, he will be stumbling about in the murk.
measurements  metrics  GE  financial_metrics  financial_performance  level_of_comfort  John_Flannery  Jeffrey_Immelt  cash_flows  ROCE  information_overload  financial_reporting  calibration 
february 2018 by jerryking
Impact of Social Sciences – Big data problems we face today can be traced to the social ordering practices of the 19th century.
This is not the first ‘big data’ era but the second. The first was the explosion in data collection that occurred from the early 19th century – Hacking’s ‘avalanche of numbers’, precisely situated between 1820 and 1840. This was an analogue big data era, different to our current digital one but characterized by some very similar problems and concerns. Contemporary problems of data analysis and control include a variety of accepted factors that make them ‘big’ and these generally include size, complexity and technology issues. We also suggest that digitisation is a central process in this second big data era, one that seems obvious but which has also appears to have reached a new threshold. Until a decade or so ago ‘big data’ looked just like a digital version of conventional analogue records and systems. Ones whose management had become normalised through statistical and mathematical analysis. Now however we see a level of concern and anxiety, similar to the concerns that were faced in the first big data era....

there is general acknowledgement that the early 19th century was when the collection, analysis and production of various forms of information accelerated at a rate not previously seen in human history. More specifically, Richards called it the first information age. Linnaeus’ botanical taxonomic approach proved so powerful a heuristic and practical device that it was swiftly applied to human social phenomena including the production of racial taxonomies. The sciences as we know them were assuming their modern shape (Whewell coined the term ‘scientist’ in 1833), the social sciences were emerging from what were known as ‘political arithmetic’, ‘social physics’ and latterly the ‘moral sciences’, while science became an undertaking distinct from natural philosophy....

The 19th century was a pre-digital era in which the ‘computer’ was an individual at a desk doing the counting and calculations manually rather than an electro-mechanical or electronic device, but even this early infrastructure clearly set the scene for our current situation. The 18th century had already seen rapid developments in dictionaries of various kinds, including Diderot’s 1751 Encyclopédie (based on Chamber’s Cyclopedia) and Johnson’s 1755 Dictionary of the English Language (not the first of its kind) illustrating a growing need to not just to collect but classify, categorise and order information to make it both meaningful and useful. The idea of and search for innate rules and regularity across a wide spectrum of phenomena emerged, with the search for laws of nature came in the following century....

These information devices were supported by a growing number and variety of formalised knowledge production processes and products – the library, the museum, the census office, the printers and publishers with their books, newspapers, periodicals, magazines, journals, forms and envelopes . Cataloguing systems had existed for centuries but this period saw their emergence as formalized systems ranging from Brunet’s Paris Bookseller’s classification (1842) to the Dewey Decimal System (1876). Storage and retrieval also became an issue, leading to the development of library science, archival management strategies and mechanical handling systems.

In the context of colonial administration and scientific research fieldwork became a central concept, one which continues to be relevant to contemporary knowledge production in several disciplines and fields of practice (e.g. botany, geology, anthropology). The development of societies and associations also gained momentum as forums for identifying, exploring and formalizing new and expanding fields of knowledge.

In the United Kingdom parliamentary Blue Books were being produced on an unprecedented scale as government increasingly concerned itself with the collection and analysis of data about this expanding information environment. They became such a phenomenon that many people despaired of their potential to overload bureaucratic knowledge systems that lacked the capacity to analyse the volumes of information being produced. Data visualisation and social mapping developed rapidly in response to this situation including the innovations of William Playfair (the line graph, bar and pie charts) and Florence Nightingale (polar diagrams) which provided new techniques for visualising these large and complex quantities of data.

...shifts in the production, processing and analysis of that information. Many of these methods are still with us including information taxonomies and knowledge trees to name but two. Hacking observed that while social categories are epistemic products their application can have marked ontological effects. Knowledge of the natural world was rapidly applied to the social world and the politicking of social identifies began in earnest, supported by a rising tide of data and analytical methods. Conservatives and social critics alike relied on the production and dissemination of data, both large and small, to support repression and reform. The public inquiry emerged as another 19th century mechanism that persists in the present, with the same general focus – poverty, crime, health and systemic failures....

Our social ordering practices have influenced our social epistemology. We run the risk in the social sciences of perpetuating the ideological victories of the first data revolution as we progress through the second. The need for critical analysis grows apace not just with the production of each new technique or technology but with the uncritical acceptance of the concepts, categories and assumptions that emerged from that first data revolution. That first data revolution proved to be a successful anti-revolutionary response to the numerous threats to social order posed by the incredible changes of the nineteenth century, rather than the Enlightenment emancipation that was promised.
big_data  statistics  logistics  information_overload  classification  disciplinarity 
september 2017 by shannon_mattern
Abstracts | The Conquest of Ubiquity
In 1947, a group of seasoned photojournalists including Robert Capa and Henri Cartier-Bresson founded the international photo cooperative Magnum. At first, Magnum’s challenge was to cover the world with its limited network of photographers, and to get their pictures to as many magazine clients as possible before the novelty of those pictures expired. A decade later, Magnum’s problems had to do with filing cabinets, log books, storage space, and “dead” material. In 1958, Magnum’s New York-based executive editor John Morris begged photographers to “STOP shooting for a period of one month” so that staff could figure out a better system for editing, captioning, and selling their stories
filing  archives  photography  information_overload 
april 2017 by shannon_mattern
VC Pioneer Vinod Khosla Says AI Is Key to Long-Term Business Competitiveness - CIO Journal. - WSJ
Nov 15, 2016

“Improbables, which people don’t pay attention to, are not unimportant, we just don’t know which improbable is important,” Mr. Khosla said. “So what do you do? You don’t plan for the highest likelihood scenario. You plan for agility. And that is a fundamental choice we make as a nation, in national defense, as the CEO of a company, as the CIO of an infrastructure, of an organization, and in the way we live.”....So change, and predictions for the future, that are important, almost never come from anybody who knows the area. Almost anyone you talk to about the future of the auto industry will be wrong on the auto industry. So, no large change in a space has come from an incumbent. Retail came from Amazon. SpaceX came from a startup. Genentech did biotechnology. Youtube, Facebook, Twitter did media … because there is too much conventional wisdom in industry. ....Extrapolating the past is the wrong way to predict the future, and improbables are not unimportant. People plan around high probability. Improbables, which people don’t pay attention to, are not unimportant, we just don’t know which improbable is important.
Vinod_Khosla  artificial_intelligence  autonomous_vehicles  outsiders  gazelles  unknowns  automotive_industry  change  automation  diversity  agility  future  predictions  adaptability  probabilities  Uber  point-to-point  public_transit  data  infrastructure  information_overload  unthinkable  improbables  low_probability  extrapolations  pay_attention 
november 2016 by jerryking
The Economist launches on Snapchat Discover
Oct 7, 2016 | Medium | Lucy Rohr.

The Economist is a weekly, [and] we’d like to think that our weekend editions on Snapchat Discover will offer people an antidote to the information overload of today’s noisy news environment. I really want our readers to finish an edition feeling that they’ve learned something—and have been entertained at the same time.
Snapchat  information_overload  magazines  digital_media  platforms  visualization 
october 2016 by jerryking
A field guide to lies : critical thinking in the information age : Levitin, Daniel J., author. : Book, Regular Print Book : Toronto Public Library
Year/Format: 2016, Book , 304 pages

It's becoming harder to separate the wheat from the digital chaff. How do we distinguish misinformation, pseudo-facts, distortions and outright lies from reliable information? In A Field Guide to Lies, neuroscientist Daniel Levitin outlines the many pitfalls of the information age and provides the means to spot and avoid them. Levitin groups his field guide into two categories--statistical infomation and faulty arguments--ultimately showing how science is the bedrock of critical thinking. It is easy to lie with stats and graphs as few people "take the time to look under the hood and see how they work." And, just because there's a number on something, doesn't mean that the number was arrived at properly. Logic can help to evaluate whether or not a chain of reasoning is valid. And "infoliteracy" teaches us that not all sources of information are equal, and that biases can distort data. Faced with a world too eager to flood us with information, the best response is to be prepared. A Field Guide to Lies helps us avoid learning a lot of things that aren't true.
books  nonfiction  critical_thinking  infoliteracy  biases  lying  information_overload  TPL  Daniel_Levitin  engaged_citizenry  signals  noise  information_sources 
september 2016 by jerryking

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