jerryking + organizing_data   9

The Mystery of the Miserable Employees: How to Win in the Winner-Take-All Economy -
June 15, 2019 | The New York Times | By Neil Irwin.
Neil Irwin is a senior economics correspondent for The Upshot. He is the author of “How to Win in a Winner-Take-All-World,” a guide to navigating a career in the modern economy.......
What Mr. Ostrum and the analytics team did wasn’t a one-time dive into the numbers. It was part of a continuing process, a way of thinking that enabled them to change and adapt along with the business environment. The key is to listen to what data has to say — and develop the openness and interpretive skills to understand what it is telling us.......Neil Irwin was at Microsoft’s headquarters researching a book that aims to answer one simple question: How can a person design a thriving career today? The old advice (show up early, work hard) is no longer enough....In nearly every sector of the economy, people who seek well-paying, professional-track success face the same set of challenges: the rise of a handful of dominant “superstar” firms; a digital reinvention of business models; and a rapidly changing understanding about loyalty in the employer-employee relationship. It’s true in manufacturing and retail, in banking and law, in health care and education — and certainly in tech......superstar companies — and the smaller firms seeking to upend them — are where pragmatic capitalists can best develop their abilities and be well compensated for them over a long and durable career.....the obvious disadvantages of bureaucracy have been outweighed by some not-so-obvious advantages of scale......the ability to collect and analyze vast amounts of data about how people work, and what makes a manager effective (jk: organizing data) .... is essential for even those who aren’t managers of huge organizations, but are just trying to make themselves more valuable players on their own corporate team.......inside Microsoft’s human resources division, a former actuary named Dawn Klinghoffer ....was trying to figure out if the company could use data about its employees — which ones thrived, which ones quit, and the differences between those groups — to operate better......Klinghoffer was frustrated that ....insights came mostly from looking through survey results. She was convinced she could take the analytical approach further. After all, Microsoft was one of the biggest makers of email and calendar software — programs that produce a “digital exhaust” of metadata about how employees use their time. In September 2015, she advised Microsoft on the acquisition of a Seattle start-up, VoloMetrix, that could help it identify and act on the patterns in that vapor......One of VoloMetrix's foundational data sets, for example, was private emails sent by top Enron executives before the company’s 2001 collapse — a rich look at how an organization’s elite behave when they don’t think anyone is watching.
analytics  books  data  datasets  data_driven  exhaust_data  Fitbit  gut_feelings  human_resources  interpretative  Managing_Your_Career  massive_data_sets  meetings  metadata  Microsoft  Moneyball  organizational_analytics  organizing_data  people_analytics  quantitative  quantified_self  superstars  unhappiness  VoloMetrix  winner-take-all  work_life_balance 
12 weeks ago by jerryking
Comments to How 5 Data Dynamos Do Their Jobs
I’d like someone to go through the tax data and find out what happened to all the accountants before and after Wang Spreadsheet, Lotus123, and Excel were released. What happened to their earnings, ...
data_scientists  letters_to_the_editor  organizing_data  storytelling  from notes
june 2019 by jerryking
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?....it 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
Affirmations for Getting Organized
1. I am organized in all areas of my life.
2. I am organized and productive.
3. I enjoy getting my life in order.
4. I am working on getting my life organized every day.
5. I love finding new way...
affirmations  GTD  organizing_data  self-organization  systematic_approaches  from notes
march 2019 by jerryking
A Former CIA Executive’s Advice On How To Make Hard Decisions | The future of business
05.28.15 | Fast Company | BY STEPHANIE VOZZA.
A Former CIA Executive’s Advice On How To Make Hard Decisions
A five-step decision-making process from a man who spent 25 years making life-and-death decisions.
(1) Question
(2) Drivers
(3) Metrics
(4) Data
(5) What's Missing/Blind Spots

1. FIND THE REAL QUESTION
Questions are NOT self-evident, says Mudd. Focusing on better questions up front yields better answers later.
“Good questions are hard to come up with,” he says. Delay data gathering and the conclusions.... think about exactly what it is we want to know..... Start with what you’re trying to accomplish and work your way back, instead of moving forward and making conclusions. The right question provides a decision advantage to the person at the head of the table.

2. IDENTIFY YOUR “DRIVERS”
Break down complex questions into characteristics or “drivers.” This approach gives you a way to manage data.
For example, sort data on Al Qaeda into information baskets that included money, recruits, leadership, communications, training, and access to weapons. When information flows in, rather than adding it to one unmanageable pile, sorting through it periodically, and offering a recitation of what appears to be relevant from the most recent stuff you’ve seen, file each bit into one of your baskets. Limit your drivers to 10.

3. DECIDE ON YOUR METRICS
Identify the metrics you’ll use to measure how the problem and solution are evolving over time.
What are the right metrics?
What are the new information sources and metrics?
Compare your thought process to the training process of an Olympic sprinter who measures success in hundredths of a second. “If we don’t, the analysis we provide will suffer the same fate as a sprinter who thinks he’s great but has never owned a stopwatch: he enters an elite competition, and reality intervenes,” Metrics provide a “mind mirror”–a system for judging your decisions. It provides a foundation for coming back to the table and assessing the process for success.

4. COLLECT THE DATA
Once you’ve built the framework that will help you make the hard decision, it’s time to gather the data. Overcome data overload by plugging data into their driver categories and excising anything that doesn’t fit. “Too much data might provide a false sense of security, and it doesn’t necessarily lead to clearer analytic decision making,”

Avoud intuition. It’s dangerous. Aggressively question the validity of your data. Once you have your data sorted, give yourself a grade that represents your confidence in assessing your question.

5. LOOK FOR WHAT’S MISSING
Complex analysis isn’t easy. Assume that the process is flawed and check for gaps and errors. Three common stumbling blocks are:

Availability bias: The instinct to rely on what you know or what has been most recently in the news.
Halo effect: When you write off the negative characteristics because you’re mesmerized by the positive attributes.
Intuitive versus analytic methodologies: when you go with your gut. Relying on intuition is dangerous.

Mudd says making complex decisions is hard work. “It’s a lot of fun to be an expert who bases their ideas on history and not a lot of fun to be an analyst who must always be assessing potential scenarios,” he says. “Every time you go into a problem, and before you rip into data, ask yourself, ‘Am I sure where I’m heading?’”
asking_the_right_questions  availability_bias  biases  decision_making  gut_feelings  halo_effects  hard_choices  intuition  life-and-death  metrics  Philip_Mudd  problem_definition  organizing_data  problem_framing  sorting  thinking_backwards 
october 2017 by jerryking
Your brain has limited capacity: Here's how to maximize it
Aug. 24 2014 | - The Globe and Mail | WENCY LEUNG.

Daniel Levitin explains in his new book, The Organized Mind: Thinking Straight in the Age of Information Overload, the evolution of the human brain hasn’t caught up with the demands of today’s world....The brain has a limited capacity to process information and juggle multiple tasks. But Levitin, a professor of psychology and behavioural neuroscience at McGill University, says we can help the brain do its job more efficiently by organizing our lives around how it functions. By using so-called brain extenders, methods that offload some of the brain’s functions, we can help declutter our thoughts and sharpen memories....Lessons learned:
(1) Evaluate the probabilities. To better systematize your approach to decision-making, use Bayesian inferencing which involves updating one’s estimates of probabilities, based on increasingly refining the information available.
(2) Take the time to write it down. Writing stuff down, improves the chances of it getting imprinted on your brain. Writing things down also conserves mental energy that you would otherwise expend fretting about forgetting them. Don’t settle for organizing your thoughts with notebooks and to-do lists. Levitin suggests writing them on index cards--which can be re-sorted.
(3) Your friendships could use a reminder. Actively organizing data about your social world to allow you to have more meaningful interactions. This means taking notes when you meet new people that help you contextualize your link to them, such as who made the introduction and whether you share any hobbies, and using memory “ticklers,” such as setting a reminder on your electronic calendar every few months to check in with friends if you haven’t heard from them in a while.
(4) When in doubt, toss it in a junk drawer. There is an important purpose for the junk drawer. It allows you to cut down on time and mental energy spent making trivial decisions.
cognitive_skills  thinking  information_overload  decision_making  books  friendships  decluttering  contextual  probabilities  journaling  Daniel_Levitin  sorting  pruning  note_taking  Bayesian  memorization  systematic_approaches  organizing_data 
august 2014 by jerryking
A Modern Approach to Open Data | Make government better, together.
October 01, 2013 by Ben Balter, GitHub.

Traditionally, consuming open government data required building and curating many custom tools and wrappers to convert the data from the form it’s exposed in to something more immediately consumable by civic hackers, watchdog groups, and the general public. Developers haphazardly wrote small scripts as one-off efforts and threw them away, or left their solutions buried inside larger infrastructure, reinventing the wheel with each new transparency initiative.

Developers from the Sunlight Foundation, GovTrack, and the New York Times, however, decided to join forces and break from tradition when they reached out to other civic-minded developers and “decided to stop each building the same basic tools over and over, and start building a foundation [they] could share.”
open_data  open_source  tools  self-organization  sharing_economy  reinventing_the_wheel  organizing_data 
december 2013 by jerryking
Be Data Literate -- Know What to Know - WSJ.com
November 15, 2005 | WSJ |By PETER F. DRUCKER. (This article originally appeared in The Wall Street Journal on Dec. 3, 1992).

Few executives yet know how to ask: What information do I need to do my job? When do I need it? In what form? And from whom should I be getting it? Fewer still ask: What new tasks can I tackle now that I get all these data? Which old tasks should I abandon? Which tasks should I do differently? Practically no one asks: What information do I owe? To whom? When? In what form?...A "database," no matter how copious, is not information. It is information's ore. For raw material to become information, it must be organized for a task, directed toward specific performance, applied to a decision. Raw material cannot do that itself. Nor can information specialists. They can cajole their customers, the data users. They can advise, demonstrate, teach. But they can no more manage data for users than a personnel department can take over the management of the people who work with an executive.

Information specialists are toolmakers. The data users, whether executive or professional, have to decide what information to use, what to use it for and how to use it. They have to make themselves information-literate. This is the first challenge facing information users now that executives have become computer-literate.

But the organization also has to become information-literate. It also needs to learn to ask: What information do we need in this company? When do we need it? In what form? And where do we get it?
CFOs  CIOs  critical_thinking  data  databases  data_driven  decision_making  digital_savvy  incisiveness  information-literate  information-savvy  insights  interpretative  managerial_preferences  metacognition  organizing_data  Peter_Drucker  questions 
may 2012 by jerryking
American Dream is Changing | Nye - Gateway to Nevada's Rurals
Oct. 31, 2010 | Nye Gateway | by Fareed Zakaria. What can
you do to make yourself thrive in this new global economy? (1) Be
unique. Try to do something that is a specialized craft or art,
something that is as much art as craft, something that feels more like
artisanship than routine work, things that are custom & custom-made
still survive. (2) Go local. Do something that can’t be outsourced,
jobs involving personal face-to-face contact will never go to India. (3)
Be indispensable. Can everyone become indispensable? Well, no, but if
you learn a difficult craft and are good at it, if you can collaborate
well, synthesize well, put things together, work with others and work
well across countries and cultures, you will have a leg-up. (4) Learn a
foreign language (e.g. Spanish or Mandarin or Hindi). (5) Excel at
mathematics, able to manipulate data, algorithms, symbols, graphs,
balance sheets and all of these skills are the essential skills for a
knowledge-based economy.
Fareed_Zakaria  21st._century  ksfs  indispensable  specialization  local  languages  mathematics  organizing_data  advice  new_graduates  artisan_hobbies_&_crafts  bespoke  quantitative  global_economy  digital_economy  knowledge_economy  the_American_dream  in-person  face2face  uniqueness 
october 2010 by jerryking

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