jerryking + massive_data_sets   406

The Man Who Solved the Market — how Jim Simons built a moneymaking machine
November 1, 2019 | | Financial Times | Robin Wigglesworth

The Man Who Solved the Market: How Jim Simons Launched the Quant Revolution, by Gregory Zuckerman, Portfolio, RRP$30/£20, 384 pages

Jim Simons looked to math and computers as ways to eliminate the emotional ups and downs of investing. “I don’t want to have to worry about the market every minute. I want models that will make money while I sleep.”
algorithms  books  finance  hedge_funds  James_Simons  massive_data_sets  mathematics  moguls  quantitative  Renaissance_Technologies  talent_spotting  winner-take-all 
5 weeks ago by jerryking
Investment managers need to become coders, says former CPPIB CEO - The Globe and Mail
CLARE O’HARAWEALTH MANAGEMENT REPORTER
DAVID MILSTEADINSTITUTIONAL INVESTMENT REPORTER

Mark Wiseman is learning Python, one of the world’s top computer programming languages.

The former chief executive officer of the Canada Pension Plan Investment Board is not trying to become a master coder, but instead believes investment managers must become proficient in manipulating large data sets to beat the market.

“If you are waiting to get a company’s quarterly or annual report and you think that is how you’re going to make an investment, you are dead meat,”........

“Sources of information are completely different than they were even 10 years ago for investors,” he says.

Today, BlackRock has already begun using “alternative data sources” to gain more in-depth information on companies such as sales predictions, customer traffic and inventory......“As we look at data in industry and how fast it’s moving, there is going to be an increasing bifurcation between proprietary and non-proprietary data."

Non-proprietary data is information that is readily available on the internet and can easily be used by competitors. Now, money managers are increasingly looking for proprietary data to win a competitive advantage.

For BlackRock’s equities business alone, Mr. Wiseman says the firm has tripled the budget for data over the past two years and holds between 400 and 500 proprietary data sets at a time.......learning Python is a more important skill for a young investment manager than learning foreign languages, or even some of the curriculum taught to chartered financial analysts.

“But this is what investing is about today,” he said. “So those of you who are spending your time on your CFA Level III, that is really nice to have the letters after your name on the business card. But you probably would have been better off spending your time learning how to code Python.”
alternative_data  BlackRock  coding  commoditization_of_information  CPPIB  information_sources  investment_management  Mark_Wiseman  massive_data_sets  proprietary  software_developers  software_development 
6 weeks ago by jerryking
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 
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
The Art of Statistics by David Spiegelhalter
May 6, 2019 | Financial Times | Review by Alan Smith.

The Art of Statistics, by Sir David Spiegelhalter, former president of the UK’s Royal Statistical Society and current Winton professor of the public understanding of risk at the University of Cambridge.

The comparison with Rosling is easy to make, not least because Spiegelhalter is humorously critical of his own field which, by his reckoning, has spent too much time arguing with itself over “the mechanical application of a bag of statistical tools, many named after eccentric and argumentative statisticians”.

His latest book, its title,
books  book_reviews  charts  Communicating_&_Connecting  data  data_journalism  data_scientists  Hans_Rosling  listening  massive_data_sets  mathematics  statistics  visualization 
may 2019 by jerryking
Spy tactics can spot consumer trends
MARCH 22, 2016 | Financial Times | John Reed.
Israel’s military spies are skilled at sifting through large amounts of information — emails, phone calls, location data — to find the proverbial needle in a haystack: a suspicious event or anomalous pattern that could be the warning of a security threat.....So it is no surprise that many companies ask Israeli start-ups for help in data analysis. The start-ups, often founded by former military intelligence officers, are using the methods of crunching data deployed in spycraft to help commercial clients. These might range from businesses tracking customer behaviour to financial institutions trying to root out online fraud......Mamram is the Israel Defense Forces’ elite computing unit.
analytics  consumer_behavior  cyber_security  data  e-mail  haystacks  hedge_funds  IDF  insights  intelligence_analysts  Israel  Israeli  Mamram  maritime  massive_data_sets  security_&_intelligence  shipping  spycraft  start_ups  tracking  traffic_analysis  trends 
april 2019 by jerryking
Every Company Wants to Become a Tech Company–Even if It Kills Them
March 8, 2019 | WSJ | By John D. Stoll.

Wall Street loves a good reinvention story. The tough part is finding a happy ending.

All the plots seem to go something like this: Every company wants to convince us it’s becoming a tech company–even if it kills them..... an increasing number of companies are at least dabbling in new tech ventures to improve operations......The boom in vendors offering affordable ways to crunch data or utilize cloud computing, for instance, unlocks new strategies for companies across a wide variety of industries........Planet Fitness Inc. is one of the interested companies. The gym boasts 12 million members but CEO Chris Rondeau admits the company knows relatively little about them.

“Besides checking in the front door, we don’t know what members do,”.....The company is spending millions to retool certain treadmills and cardio equipment to better collect data as people exercise, commissioning a new smartphone app, and wants to tie into its customers’ wearable technology....many other CEOs aren’t convinced they have the luxury (of time to take things slowly). Even if it is hard to figure out what to do with all the data gathered and tools employed in the course of regular business, paralysis is not an option. Like a shark, they feel they need to keep swimming or die....... Nokia Corp., the Finnish company, started as a pulp mill in the 19th century and then branched off into various industries, including a successful venture into rubber boot making, ditched its failed mobile handset unit in 2013 to focus on a networks business that was thriving under the radar. Today, it’s locked in a high-stakes race to deploy 5G technology......In 2000, Major League Baseball owners committed $120 million to fund MLB Advanced Media. It aimed to infuse technology into the game and resulted in initiatives like online ticket sales and expanded radio coverage. The gem of that initiative, however, was a streaming television network launched in 2002...... it has attracted outside clients, such as ESPN, the WWE Network, Playstation Vue and HBO. The Walt Disney Co. acquired control recently for nearly $3 billion.... Dunnhumby Ltd., the data and analytics consultancy owned by European grocery chain Tesco PLC. Tesco bought Dunnhumby after it created the chain’s loyalty-card program. Dunnhumby ballooned into a storehouse of information and amassed clients and partners...Searching for the next BAMTech or Dunnhumby is now a religion at many companies......Walmart Inc., which has already heavily invested in e-commerce, wants to take its technology, marry it with everything the world’s largest retailer knows about us and use it to get into the advertising business......“Everyone’s thinking ‘we’ve got a ton of this stuff (data), how do we use it,’” Executives are trying to answer that question by hiring outside firms to analyze trends or setting up in-house units for experimentation.

Walmart is dumping digital-marketing agency Triad, a unit of WPP PLC, and will try its hand at selling advertising space. Armed with a trove of shopper data and connected to a chain of suppliers wanting to place ads in stores and on websites, Walmart hopes to challenge Amazon.com Inc. on this new front......At Ford Motor Co. , CEOJim Hackett envisions a day when automobiles roam streets collecting data from the occupants and the cars’ behavior like rolling smartphones. This is part of that “mobility as a service” vision car makers peddle.......“Corporations tend to reward action over thinking,”“But the truth is…you’ll find the companies that didn’t do the deep thinking and acted quickly have to redo things.
BAMTech  digital_savvy  Dunnhumby  experimentation  Ford  in-house  Jim_Hackett  massive_data_sets  MLB  Planet_Fitness  reinvention  Wal-Mart  mobility_as_a_service  technology  under_the_radar 
march 2019 by jerryking
Big data: legal firms play ‘Moneyball’
February 6, 2019 | Financial Times | Barney Thompson.

Is the hunt for data-driven justice a gimmick or a powerful tool to give lawyers an advantage and predict court outcomes?

In Philip K Dick’s short story The Minority Report, a trio of “precogs” plugged into a machine are used to foretell all crimes so potential felons could be arrested before they were able to strike. In real life, a growing number of legal experts and computer scientists are developing tools they believe will give lawyers an edge in lawsuits and trials. 

Having made an impact in patent cases these legal analytics companies are now expanding into a broad range of areas of commercial law. This is not about replacing judges,” says Daniel Lewis, co-founder of Ravel Law, a San Francisco lawtech company that built the database of judicial behaviour. “It is about showing how they make decisions, what they find persuasive and the patterns of how they rule.” 
analytics  data_driven  judges  law  law_firms  lawtech  lawyers  Lex_Machina  massive_data_sets  Moneyball  predictive_modeling  quantitative  tools 
february 2019 by jerryking
Opinion | Warning! Everything Is Going Deep: ‘The Age of Surveillance Capitalism’
Jan. 29, 2019 | The New York Times | By Thomas L. Friedman, Opinion Columnist.

Recent advances in the speed and scope of digitization, connectivity, big data and artificial intelligence are now taking us “deep” into places and into powers that we’ve never experienced before — and that governments have never had to regulate before. I’m talking about deep learning, deep insights, deep surveillance, deep facial recognition, deep voice recognition, deep automation and deep artificial minds.

Some of these technologies offer unprecedented promise and some unprecedented peril — but they’re all now part of our lives. Everything is going deep........how did we get so deep down where the sharks live?

The short answer: Technology moves up in steps, and each step, each new platform, is usually biased toward a new set of capabilities. Around the year 2000 we took a huge step up that was biased toward connectivity, because of the explosion of fiber-optic cable, wireless and satellites.

Suddenly connectivity became so fast, cheap, easy for you and ubiquitous that it felt like you could touch someone whom you could never touch before and that you could be touched by someone who could never touch you before.

Around 2007, we took another big step up. The iPhone, sensors, digitization, big data, the internet of things, artificial intelligence and cloud computing melded together and created a new platform that was biased toward abstracting complexity at a speed, scope and scale we’d never experienced before.....as big data got really big, as broadband got really fast, as algorithms got really smart, as 5G got actually deployed, artificial intelligence got really intelligent. So now, with no touch — but just a voice command or machines acting autonomously — we can go so much deeper in so many areas....DeepMind, the artificial intelligence arm of Google’s parent, developed an A.I. program, AlphaGo, that has now defeated the world’s top human players of the ancient strategy game Go — which is much more complex than chess — by learning from human play......Today “virtual agents” — using conversational interfaces powered by artificial intelligence — can increasingly understand your intent... just by hearing your voice.....The percentage of calls a chatbot, or virtual agent, is able to handle without turning the caller over to a person is called its “containment rate,” and these rates are steadily soaring. ....But bad guys, who are always early adopters, also see the same potential to go deep in wholly new ways.....On Jan. 20, The London Observer looked at Harvard Business School professor Shoshana Zuboff’s new book, the title of which perfectly describes the deep dark waters we’ve entered: “The Age of Surveillance Capital.”....“Surveillance capitalism,” Zuboff wrote, “unilaterally claims human experience as free raw material for translation into behavioral data. Although some of these data are applied to service improvement, the rest are declared as a proprietary behavioral surplus, fed into advanced manufacturing processes known as ‘machine intelligence,’ and fabricated into prediction products that anticipate what you will do now, soon and later. Finally, these prediction products are traded in a new kind of marketplace that I call behavioral futures markets. Surveillance capitalists have grown immensely wealthy from these trading operations, for many companies are willing to lay bets on our future behavior.”
5G  algorithms  AlphaGo  artificial_intelligence  automation  books  complexity  connectivity  dark_side  DeepMind  digitalization  gaming_the_system  human_experience  massive_data_sets  patterns  rogue_actors  Tom_Friedman  trustworthiness  virtual_agents 
january 2019 by jerryking
Piecing Together Narratives From the 0′s and 1′s: Storytelling in the Age of Big Data - CIO Journal. - WSJ
Feb 16, 2018 | WSJ | By Irving Wladawsky-Berger.

Probabilities are inherently hard to grasp, especially for an individual event like a war or an election, ......Why is it so hard for people to deal with probabilities in everyday life? “I think part of the answer lies with Kahneman’s insight: Human beings need a story,”....Mr. Kahneman explained their research in his 2011 bestseller Thinking, Fast and Slow. Its central thesis is that our mind is composed of two very different systems of thinking. System 1 is the intuitive, fast and emotional part of our mind. Thoughts come automatically and very quickly to System 1, without us doing anything to make them happen. System 2, on the other hand, is the slower, logical, more deliberate part of the mind. It’s where we evaluate and choose between multiple options, because only System 2 can think of multiple things at once and shift its attention between them.

System 1 typically works by developing a coherent story based on the observations and facts at its disposal. Research has shown that the intuitive System 1 is actually more influential in our decisions, choices and judgements than we generally realize. But, while enabling us to act quickly, System 1 is prone to mistakes. It tends to be overconfident, creating the impression that we live in a world that’s more coherent and simpler than the actual real world. It suppresses complexity and information that might contradict its coherent story.

Making sense of probabilities, numbers and graphs requires us to engage System 2, which, for most everyone, takes quite a bit of focus, time and energy. Thus, most people will try to evaluate the information using a System 1 simple story: who will win the election? who will win the football game?.....Storytelling has played a central role in human communications since times immemorial. Over the centuries, the nature of storytelling has significantly evolved with the advent of writing and the emergence of new technologies that enabled stories to be embodied in a variety of media, including books, films, and TV. Everything else being equal, stories are our preferred way of absorbing information.

“It’s not enough to say an event has a 10 percent probability,” wrote Mr. Leonhardt. “People need a story that forces them to visualize the unlikely event – so they don’t round 10 to zero.”.....
in_the_real_world  storytelling  massive_data_sets  probabilities  Irving_Wladawsky-Berger  Communicating_&_Connecting  Daniel_Kahneman  complexity  uncertainty  decision_making  metacognition  data_journalism  sense-making  thinking_deliberatively 
february 2018 by jerryking
Daring rather than data will save advertising
John Hegarty JANUARY 2, 2017

Algorithms are killing creativity, writes John Hegarty

Ultimately, brands are built by talking to a broad audience. Even if part of that audience never buys your product. Remember, a brand is made not just by the people who buy it, but also by the people who know about it. Fame adds value to a brand, but to build it involves saying something that captures the public’s imagination. It needs to broadcast.

Now, data are fundamentally important in the building of a market. “Big data” can provide intelligence, gather information, identify buying patterns and determine certain outcomes. But what it cannot do is create an emotional bond with the consumer. Data do not make magic. That is the job of persuasion. And it is what makes brands valuable...... Steve Jobs or James Dyson did not build brilliant companies by waiting for a set of algorithms to tell them what to do.

Persuasion and promotion.

In today’s advertising world, creativity has taken a back seat. Creativity creates value and with it difference. And difference is vital for giving a brand a competitive edge. But the growing belief in “data-only solutions” means we drive it out of the marketplace.

If everything ends up looking the same and feeling the same, markets stagnate.
advertising  Steve_Jobs  creativity  human_ingenuity  data  massive_data_sets  data_driven  brands  emotional_connections  persuasion  ingenuity  daring  algorithms 
february 2018 by jerryking
Can big data revolutionise policymaking by governments?
January 30, 2018 | FT | Robin Wigglesworth in New York.

Alberto Cavallo is today a professor of applied economics at MIT, where he runs the Billion Prices Project with Roberto Rigobon, another MIT professor. The project started in 2006, during a period when the then-Argentine government was accused of manipulating its inflation data. Professors Cavallo and Rigobon realised that by compiling the online prices listed by Argentine retailers they could build a more accurate and contemporaneous measure of the true inflation rate....The project’s commercial arm, PriceStats, now collects enough data to provide daily inflation updates for 22 economies. “It was kind of an accident. But we quickly realised that it had applications elsewhere,” Prof Cavallo says....Quandl, an alternative data provider

The project is just one example of a broader trend of trawling the swelling sea of big data for clues on how companies, industries or entire economies are performing.
alternative_data_provider  informational_advantages  massive_data_sets  MIT  Quandl  policymaking 
february 2018 by jerryking
When biased data holds a potentially deadly flaw
SEPTEMBER 27, 2017 | FT | Madhumita Murgia.

Researchers at scientific journal Nature said findings from its own investigation on the diversity of these data sets “prompted warnings that a much broader range of populations should be investigated to avoid genomic medicine being of benefit merely to ‘a privileged few’ ”.

This insidious data prejudice made me curious about other unintended biases in the tech world. Several new consumer technologies — often conceived by, built by and tested overwhelmingly on Caucasian males — are flawed due to biases in their design.
massive_data_sets  biases  data  data_driven  unintended_consequences  racial_disparities  algorithms  value_judgements 
january 2018 by jerryking
Algos know more about us than we do about ourselves
NOVEMBER 24, 2017 | Financial Time | John Dizard.

When intelligence collectors and analysts take an interest in you, they usually start not by monitoring the content of your calls or messages, but by looking at the patterns of your communications. Who are you calling, how often and in what sequence? What topics do you comment on in social media?

This is called traffic analysis, and it can give a pretty good notion of what you and the people you know are thinking and what you are preparing to do. Traffic analysis started as a military intelligence methodology, and became systematic around the first world war. Without even knowing the content of encrypted messages, traffic analysts could map out an enemy “order of battle” or disposition of forces, and make inferences about commanders’ intentions.

Traffic analysis techniques can also cut through the petabytes of redundant babble and chatter in the financial and political worlds. Even with state secrecy and the forests of non-disclosure agreements around “proprietary” investment or trading algorithms, crowds can be remarkably revealing in their open-source posts on social media.

Predata, a three-year-old New York and Washington-based predictive data analytics provider, has a Princeton-intensive crew of engineers and international affairs graduates working on early “signals” of market and political events. Predata trawls the open metadata for users of Twitter, Wikipedia, YouTube, Reddit and other social media, and analyses it to find indicators of future price moves or official actions.

I have been following their signals for a while and find them to be useful indicators. Predata started by creating political risk indicators, such as Iran-Saudi antagonism, Italian or Chilean labour unrest, or the relative enthusiasm for French political parties. Since the beginning of this year, they have been developing signals for financial and commodities markets.

The 1-9-90 rule
1 per cent of internet users initiate discussions or content, 9 per cent transmit content or participate occasionally and 90 per cent are consumers or ‘lurkers’

Using the example of the company’s BoJ signal. For this, Predata collects the metadata from 300 sources, such as Twitter users, contested Wikipedia edits or YouTube items created by Japanese monetary policy geeks. Of those, at any time perhaps 100 are important, and 8 to 10 turn out to be predictive....This is where you need some domain knowledge [domain expertise = industry expertise]. It turns out that Twitter is pretty important for monetary policy, along with the Japanese-language Wiki page for the Bank of Japan, or, say, a YouTube video of [BoJ governor] Haruhiko Kuroda’s cross-examination before a Diet parliamentary committee.

“Then you build a network of candidate discussions [JK: training beds] and look for the pattern those took before historical moves. The machine-learning algorithm goes back and picks the leads and lags between traffic and monetary policy events.” [Jk: Large data sets with known correct answers serve as a training bed and then new data serves as a test bed]

Typically, Predata’s algos seem to be able to signal changes in policy or big price moves [jk: inflection points] somewhere between 2 days and 2 weeks in advance. Unlike some academic Twitter scholars, Predata does not do systematic sentiment analysis of tweets or Wikipedia edits. “We only look for how many people there are in the conversation and comments, and how many people disagreed with each other. We call the latter the coefficient of contestation,” Mr Shinn says.

The lead time for Twitter, Wiki or other social media signals varies from one market to another. Foreign exchange markets typically move within days, bond yields within a few days to a week, and commodities prices within a week to two weeks. “If nothing happens within 30 days,” says Mr Lee, “then we say we are wrong.”
algorithms  alternative_data  Bank_of_Japan  commodities  economics  economic_data  financial_markets  industry_expertise  inflection_points  intelligence_analysts  lead_time  machine_learning  massive_data_sets  metadata  non-traditional  Predata  predictive_analytics  political_risk  signals  social_media  spycraft  traffic_analysis  training_beds  Twitter  unconventional 
november 2017 by jerryking
Novartis’s new chief sets sights on ‘productivity revolution’
SEPTEMBER 25, 2017 | Financial Times | Sarah Neville and Ralph Atkins.

The incoming chief executive of Novartis, Vas Narasimhan, has vowed to slash drug development costs, eyeing savings of up to 25 per cent on multibillion-dollar clinical trials as part of a “productivity revolution” at the Swiss drugmaker.

The time and cost of taking a medicine from discovery to market has long been seen as the biggest drag on the pharmaceutical industry’s performance, with the process typically taking up to 14 years and costing at least $2.5bn.

In his first interview as CEO-designate, Dr Narasimhan says analysts have estimated between 10 and 25 per cent could be cut from the cost of trials if digital technology were used to carry them out more efficiently. The company has 200 drug development projects under way and is running 500 trials, so “that will have a big effect if we can do it at scale”.......Dr Narasimhan plans to partner with, or acquire, artificial intelligence and data analytics companies, to supplement Novartis’s strong but “scattered” data science capability.....“I really think of our future as a medicines and data science company, centred on innovation and access.”

He must now decide where Novartis has the capability “to really create unique value . . . and where is the adjacency too far?”.....Does he need the cash pile that would be generated by selling off these parts of the business to realise his big data vision? He says: “Right now, on data science, I feel like it’s much more about building a culture and a talent base . . . ...Novartis has “a huge database of prior clinical trials and we know exactly where we have been successful in terms of centres around the world recruiting certain types of patients, and we’re able to now use advanced analytics to help us better predict where to go . . . to find specific types of patients.

“We’re finding that we’re able to significantly reduce the amount of time that it takes to execute a clinical trial and that’s huge . . . You could take huge cost out.”...Dr Narasimhan cites one inspiration as a visit to Disney World with his young children where he saw how efficiently people were moved around the park, constantly monitored by “an army of [Massachusetts Institute of Technology-]trained data scientists”.
He has now harnessed similar technology to overhaul the way Novartis conducts its global drug trials. His clinical operations teams no longer rely on Excel spreadsheets and PowerPoint slides, but instead “bring up a screen that has a predictive algorithm that in real time is recalculating what is the likelihood our trials enrol, what is the quality of our clinical trials”.

“For our industry I think this is pretty far ahead,” he adds.

More broadly, he is realistic about the likely attrition rate. “We will fail at many of these experiments, but if we hit on a couple of big ones that are transformative, I think you can see a step change in productivity.”
adjacencies  algorithms  analytics  artificial_intelligence  attrition_rates  CEOs  data_driven  data_scientists  drug_development  failure  Indian-Americans  kill_rates  massive_data_sets  multiple_targets  Novartis  pharmaceutical_industry  predictive_analytics  productivity  productivity_payoffs  product_development  real-time  scaling  spreadsheets  Vas_Narasimhan 
november 2017 by jerryking
A Tale of Two Metrics
August 7, 2017 | | RetailNext | Ray Hartjen, Director, Content Marketing & Public Relations.

Traffic can’t alone measure the effectiveness of demand creation efforts, but some well-placed math can show retailers strong correlations over a myriad of relevant variables. More over, as my colleague Shelley E. Kohan pointed out in her post earlier this summer, “Expanding the Scope of Metrics,” Traffic is foundational for meaningful metrics like Conversion and Sales Yield (Sales per Shopper), key measurements that help managers make daily decisions on the floor from tailoring merchandising displays to allocating staffing and refining associate training.
With metrics, it’s important to remember there’re different strokes for different folks, with different measurements critical for different functions, much like financial accounting and managerial accounting serve different masters. Today’s “big data” age allows retailers to inexpensively collect, synthesize, analyze and report almost unbelievable amounts of data from an equally almost unbelievable number of data streams. Paramount is to get the right information in front of the right people at the right time.
Sometimes, the right data is Sales per Square Foot, and it certainly makes for a nice headline. But, not to be outshined, other instances call for Traffic. As Chitra Balasubramanian, RetailNext’s Head of Business Analytics, points out in the same Sourcing Journal Online article, “Traffic equals opportunity. Retailers should take advantage of store visits with loyalty programs, heightened customer service, and a great in-store experience to create a long-lasting relationship with that customer to ensure repeat visits.”
metrics  sales  foot_traffic  retailers  inexpensive  massive_data_sets  data  creating_demand  correlations  experiential_marketing  in-store  mathematics  loyalty_management  the_right_people  sales_per_square_foot 
august 2017 by jerryking
We Survived Spreadsheets, and We’ll Survive AI - WSJ
By Greg Ip
Updated Aug. 2, 2017

History and economics show that when an input such as energy, communication or calculation becomes cheaper, we find many more uses for it. Some jobs become superfluous, but others more valuable, and brand new ones spring into existence. Why should AI be different?

Back in the 1860s, the British economist William Stanley Jevons noticed that when more-efficient steam engines reduced the coal needed to generate power, steam power became more widespread and coal consumption rose. More recently, a Massachusetts Institute of Technology-led study found that as semiconductor manufacturers squeezed more computing power out of each unit of silicon, the demand for computing power shot up, and silicon consumption rose.

The “Jevons paradox” is true of information-based inputs, not just materials like coal and silicon......Just as spreadsheets drove costs down and demand up for calculations, machine learning—the application of AI to large data sets—will do the same for predictions, argue Ajay Agrawal, Joshua Gans and Avi Goldfarb, who teach at the University of Toronto’s Rotman School of Management. “Prediction about uncertain states of the world is an input into decision making,” they wrote in a recent paper. .....Unlike spreadsheets, machine learning doesn’t yield exact answers. But it reduces the uncertainty around different risks. For example, AI makes mammograms more accurate, the authors note, so doctors can better judge when to conduct invasive biopsies. That makes the doctor’s judgment more valuable......Machine learning is statistics on steroids: It uses powerful algorithms and computers to analyze far more inputs, such as the millions of pixels in a digital picture, and not just numbers but images and sounds. It turns combinations of variables into yet more variables, until it maximizes its success on questions such as “is this a picture of a dog” or at tasks such as “persuade the viewer to click on this link.”.....Yet as AI gets cheaper, so its potential applications will grow. Just as better weather forecasting makes us more willing to go out without an umbrella, Mr. Manzi says, AI emboldens companies to test more products, strategies and hunches: “Theories become lightweight and disposable.” They need people who know how to use it, and how to act on the results.
artificial_intelligence  Greg_Ip  spreadsheets  machine_learning  predictions  paradoxes  Jim_Manzi  experimentation  testing  massive_data_sets  judgment  uncertainty  economists  algorithms  MIT  Gilder's_Law  speed  operational_tempo  Jevons_paradox  decision_making  steam_engine  William_Jevons 
august 2017 by jerryking
How Nature Scales Up
June 23, 2017 | WSJ | By Charles C. Mann

Review of SCALE By Geoffrey West; Penguin Press, 479 pages, $30
books  book_reviews  physicists  scaling  growth  innovation  sustainability  cities  economics  business  linearity  efficiencies  economies_of_scale  sublinearity  massive_data_sets  natural_selection 
june 2017 by jerryking
Review: How Laws of Physics Govern Growth in Business and in Cities
MAY 26, 2017 | The New York Times | By JONATHAN A. KNEE

Book review of “Scale: The Universal Laws of Growth, Innovation, Sustainability and the Pace of Life in Organisms, Cities, Economies, and Companies” (Penguin), by Geoffrey West, a theoretical physicist.....Mr. West’s core argument is that the basic mathematical laws of physics governing growth in the physical world apply equally to biological, political and corporate organisms.....The central observation of “Scale” is that a wide variety of complex systems respond similarly to increases in size. Mr. West demonstrates that these similarities reflect the structural nature of the networks that undergird these systems. The book identifies three core common characteristics of the hierarchal networks that deliver energy to these organisms — whether the diverse circulatory systems that power all forms of animal life or the water and electrical networks that power cities. First, the networks are “space filling” — that is, they service the entire organism. Second, the terminal units are largely identical, whether they are the capillaries in our bodies or the faucets and electrical outlets in our homes. Third, a kind of natural selection process operates within these networks so that they are optimized......These shared network qualities explain why when an organism doubles in size, an astonishing range of characteristics, from food consumption to general metabolic rate, grow something less than twice as fast — they scale “sublinearly.” What’s more, “Scale” shows why the precise mathematical factor by which these efficiencies manifest themselves almost always relate to “the magic No. 4.”

Mr. West also provides an elegant explanation of why living organisms have a natural limit to growth and life span following a predictable curve, as an increasing proportion of energy consumed is required for maintenance and less is available to fuel further expansion.

....Despite his reliance on the analysis of huge troves of data to develop and support his theories, in the concluding chapters, Mr. West makes a compelling argument against the “arrogance and narcissism” reflected in the growing fetishization of “big data” in itself. “Data for data’s sake,” he argues, “or the mindless gathering of big data, without any conceptual framework for organizing and understanding it, may actually be bad or even dangerous.”
books  book_reviews  physicists  scaling  growth  Jonathan_Knee  innovation  sustainability  cities  economics  business  linearity  efficiencies  economies_of_scale  sublinearity  massive_data_sets  natural_selection  physical_world  selection_processes 
may 2017 by jerryking
Building an Empire on Event Data – The Event Log
Michelle WetzlerFollow
Chief Data Scientist @keen_io
Mar 31

Facebook, Google, Amazon, and Netflix have built their businesses on event data. They’ve invested hundreds of millions behind data scientists and engineers, all to help them get to a deep understanding and analysis of the actions their users or customers take, to inform decisions all across their businesses.
Other companies hoping to compete in a space where event data is crucial to their success must find a way to mirror the capabilities of the market leaders with far fewer resources. They’re starting to do that with event data platforms like Keen IO.
What does “Event Data” mean?
Event data isn’t like its older counterpart, entity data, which describes objects and is stored in tables. Event data describes actions, and its structure allows many rich attributes to be recorded about the state of something at a particular point in time.
Every time someone loads a webpage, clicks an ad, pauses a song, updates a profile, or even takes a step into a retail location, their actions can be tracked and analyzed. These events span so many channels and so many types of interactions that they paint an extremely detailed picture of what captivates customers.
data  data_driven  massive_data_sets  data_scientists  event-driven  events  strategy  engineering  Facebook  Google  Amazon  Netflix 
april 2017 by jerryking
With 125 Ph.D.s in 15 Countries, a Quant ‘Alpha Factory’ Hunts for Investing Edge - WSJ
By BRADLEY HOPE
Updated April 6, 2017

The firm is part of the forefront of a new quantitative renaissance in investing, where the ability to make sense of billions of bits of data in real time is more sought after than old-school financial analysis.

“Brilliance is very equally distributed across the world, but opportunity is not,” said Mr. Tulchinsky, a 50-year-old Belarusian. “We provide the opportunity.”

To do this, WorldQuant developed a model where it employs hundreds of scientists, including 125 Ph.D.s, around the world and hundreds more part-time workers to scour the noise of the economy and markets for hidden patterns. This is the heart of the firm. Mr. Tulchinsky calls it the “Alpha Factory.”....Quantitative hedge funds have been around for decades but they are becoming dominant players in the markets for their ability to parse massive data sets and trade rapidly. Amid huge outflows, traditional hedge funds are bringing aboard chief data scientists and trying to mimic quant techniques to keep up, fund executives say.

Some critics of quants believe their strategies are overhyped and are highly susceptible to finding false patterns in the noise of data. David Leinweber, a data scientist, famously found that the data set with the highest correlation with the S&P 500 over a 10-year period in the 1990s was butter production in Bangladesh.
quantitative  Wall_Street  PhDs  alpha  investors  slight_edge  massive_data_sets  signals  noise  data_scientists  real-time  algorithms  patterns  sense-making  quants  unevenly_distributed  WorldQuant 
april 2017 by jerryking
Artificial intelligence is too important to leave unmanaged
September 26, 2016 | FT | John Thornhill.

Investors are scrambling to understand how technology will enable wealth to be created and destroyed

In the 60-year history of AI, the technology has experienced periodic “winters” when heightened expectations of rapid progress were dashed and research funding was cut. “It’s not impossible that we’re setting ourselves up for another AI winter,” says the co-founder of one San Francisco AI-enabled start-up. “There is a lot of over-promising and a real risk of under-delivering.”
One of the more balanced assessments of the state of AI has come from Stanford University as part of a 100-year study of the technology. The report, which brought together many of AI’s leading researchers, attempted to forecast the technology’s impact on a typical US city by 2030......Apart from the social impact, investors are scrambling to understand how such applications of AI will enable wealth to be created — and destroyed.
Suranga Chandratillake, a partner at Balderton Capital, a London-based venture capital firm, says “AI is the big question of the now” for many investors. The clue, he suggests, is to identify those companies capable of amassing vast pools of domain specific data to run through their AI systems that can disrupt traditional business models. [Large data sets with known correct answers serve as a training bed and then new data serves as a test bed]
artificial_intelligence  boom-to-bust  investors  disruption  data  training_beds  test_beds  massive_data_sets  wealth_creation  wealth_destruction  social_impact  venture_capital 
march 2017 by jerryking
We’re All Cord Cutters Now - WSJ
By FRANK ROSE
Sept. 6, 2016

Streaming, Sharing, Stealing By Michael D. Smith and Rahul Telang
MIT Press, 207 pages, $29.95

The authors’ point is not that the long tail is where the money is, though that can be the case. It’s that “long-tail business models,” being inherently digital, can succeed where others do not. Mass-media businesses have always depended on the economics of scarcity: experts picking a handful of likely winners to be produced with a professional sheen, released through a tightly controlled series of channels and supported by blowout ad campaigns. This, the authors make clear, is a strategy for the previous century.
book_reviews  books  digital_media  entertainment_industry  massive_data_sets  Amazon  Netflix  data  granularity  cord-cutting  clarity  Anita_Elberse  The_Long_Tail  business_models  blockbusters  Apple  mass_media 
january 2017 by jerryking
Winton Capital’s David Harding on making millions through maths
NOVEMBER 25, 2016 | Financial Times | by Clive Cookson.

Harding’s career is founded on the relentless pursuit of mathematical and scientific methods to predict movements in markets. This is a never-ending process because predictive tools lose their power as markets change; new ones are always needed. “We have 450 people in the company, of whom 250 are involved in research, data collection or technology,” he says. That is equivalent to a medium-sized university physics department....Harding's approach to making money is to exploit failures in the efficient market theory...the problem with the EMT is that “It treats economics like a physical science when, in fact, it is a human or social science. Humans are prone to unpredictable behaviour, to overreaction or slumbering inaction, to mania and panic.”...The Winton investment system is based instead on “the belief that scientific methods provide a good means of extracting meaning from noisy market data. We don’t make assumptions about how markets should work, rather we use advanced statistical techniques to seek patterns in huge data sets and base all our investment strategies on the analysis of empirical evidence...Harding emphasises the breadth and volume of investments involved, covering bonds, currencies, commodities, market indices and individual equities. The aim is to exploit a large number of weak predictive signals, he says: “We don’t expect to find any strong relationships between data and the price of the market. That may sound counter-intuitive but if there are strong relationships, someone else is going to be exploiting those. Weak relationships are where we have a competitive advantage.” Weather strategies are one feature of Winton research, including analysis of cloud cover and soil moisture levels to predict the prices of agricultural commodities. Other important indicators, for which maths can uncover value not fully reflected in market prices, include seasonal factors and inventory levels across supply chains....When I ask Harding about the use of machine learning and artificial intelligence to guide investment decisions, he bristles slightly. “There is a sudden upsurge of excitement about AI,” he says, “but we have used techniques that would be described as machine learning for at least 30 years.”

Essentially, he says, quantitative investing, self-driving cars and speech recognition are all applications of “information engineering”....he heads off to a lecture by German psychologist Gerd Gigerenzer, who runs the Harding Centre for Risk Literacy in Berlin
communicating_risks  mathematics  hedge_funds  investment_research  financiers  Winton_Capital  physics  Renaissance_Technologies  James_Simons  moguls  quantitative  panics  overreaction  massive_data_sets  philanthropy  machine_learning  signals  human_factor  weak_links  JumpMath 
november 2016 by jerryking
Goodbye, Ivory Tower. Hello, Silicon Valley Candy Store. - The New York Times
By STEVE LOHR SEPT. 3, 2016

A number of tech companies are luring Ivy League economists out of academia with the promise of big sets of data and big salaries.

Silicon Valley is turning to the dismal science in its never-ending quest to squeeze more money out of old markets and build new ones. In turn, the economists say they are eager to explore the digital world for fresh insights into timeless economic questions of pricing, incentives and behavior....Businesses have been hiring economists for years. Usually, they are asked to study macroeconomic trends — topics like recessions and currency exchange rates — and help their employers deal with them.

But what the tech economists are doing is different: Instead of thinking about national or global trends, they are studying the data trails of consumer behavior to help digital companies make smart decisions that strengthen their online marketplaces in areas like advertising, movies, music, travel and lodging.

Tech outfits including giants like Amazon, Facebook, Google and Microsoft and up-and-comers like Airbnb and Uber hope that sort of improved efficiency means more profit....“They are microeconomic experts, heavy on data and computing tools like machine learning and writing algorithms,”
Silicon_Valley  massive_data_sets  economists  Steve_Lohr  Airbnb  Hal_Varian  digital_economy  academia  microeconomics  Ivy_League  insights  consumer_behavior  war_for_talent  talent 
september 2016 by jerryking
Burn the talent churn using Big Data - The Globe and Mail
JEAN-PAUL ISSON
Special to The Globe and Mail
Published Monday, Aug. 08, 2016
massive_data_sets  talent  churn 
august 2016 by jerryking
Little metrics can make a big difference (and here’s how to use them) - The Globe and Mail
BRIAN SCUDAMORE
Special to The Globe and Mail
Published Thursday, Jun. 09, 2016

small businesses can concentrate on collecting different metrics that have an impressive impact on the bottom line. I call it little data. It’s easier to collect and it’s a great way to take the pulse of your company on a day-to-day basis.

Here’s how to find the little data that matters, so you can make impactful changes to your business without spending a fortune.

Sweat the small stuff

Looking at traditional metrics – sales revenue, cost of customer acquisition and overhead – is important, but it’s also worth tracking intangible elements that don’t make it onto a spreadsheet.

I like to look around the office and focus on the energy – is there a buzz or are people bored? – or I’ll look at notes from exit interviews to see who is leaving the company and why. Keeping this little data in mind has enabled us to make important changes to our culture when we need to.

External feedback is powerful, too. Whenever I’m in a new city, the first thing I ask my taxi driver is, “Who would you call if you needed your junk removed?” I’m not just making conversation or trying to name-drop one of our brands – I’m doing my own survey to see if our marketing efforts are sticking....you can’t run your business on anecdotes, focus on key numbers that provide meaningful insight and measure them consistently.... communicating these benchmarks, everyone in the company can understand and can react quickly to fluctuations.

Our key metrics are call volume, website traffic, and jobs completed. We also work on our “customer wow factor” by looking at our Net Promoter Score (NPS), asking every customer how likely they are to recommend our services to a friend.[aka delighting customers]
anecdotal  Brian_Scudamore  consistency  delighting_customers  feedback  Got_Junk?  Haier  insights  massive_data_sets  measurements  metrics  NPS  small_business  small_data  Wal-Mart  UPS 
june 2016 by jerryking
Your next Netflix show is brought to you by big data
A look at what skills future business leaders need to have to tackle the challenges of an ever-shifting marketplace.A bridge that tells people if it has been damaged, a revenue-sharing model for mall
massive_data_sets 
april 2016 by jerryking
JetBlue Venture Capital Unit Taking Cautious Approach to Growth - The CIO Report - WSJ
Mar 3, 2016 ROLE OF THE CIO
JetBlue Venture Capital Unit Taking Cautious Approach to Growth
ARTICLE
COMMENTS
EASH SUNDARAM
JETBLUE
1
By STEVEN NORTON
JetBlue  Silicon_Valley  data  data_driven  venture_capital  CIOs  airline_industry  travel  hospitality  massive_data_sets  innovation  corporate_investors 
march 2016 by jerryking
JetBlue Airways Forms Technology Unit - WSJ
By SUSAN CAREY
Updated Feb. 11, 2016

JetBlue Airways Corp. is launching a subsidiary, JetBlue Technology Ventures, in Silicon Valley to find and help develop new technology ventures in the travel and hospitality sectors.... the initiative is focused on using technology in three areas: making passenger and employee experiences smoother, better using the massive amounts of data airlines acquire, and improving airline operations and logistics. She said potential technologies include geolocation, virtual reality, big data, connectivity and artificial intelligence....Robin Hayes, JetBlue’s chief executive, and Eash Sundaram, chief information officer, have been talking for a year about forming a subsidiary to look at innovation, while the airline’s board “has been thinking about how to expand the JetBlue brand.”
JetBlue  Silicon_Valley  airline_industry  travel  hospitality  massive_data_sets  innovation  corporate_investors  customer_experience  geolocation  virtual_reality  artificial_intelligence 
february 2016 by jerryking
How Stanford Took On the Giants of Economics - The New York Times
SEPT. 10, 2015 | NYT | By NEIL IRWIN.

Stanford’s success with economists is part of a larger campaign to stake a claim as the country’s top university. Its draw combines a status as the nation’s “it” university — now with the lowest undergraduate acceptance rate and a narrow No. 2 behind Harvard for the biggest fund-raising haul — with its proximity to many of the world’s most dynamic companies. Its battle with Eastern universities echoes fights in other industries in which established companies, whether hotels or automobile makers, are being challenged by Silicon Valley money and entrepreneurship....reflection of a broader shift in the study of economics, in which the most cutting-edge work increasingly relies less on a big-brained individual scholar developing mathematical theories, and more on the ability to crunch extensive sets of data to glean insights about topics as varied as how incomes differ across society and how industries organize themselves....The specialties of the new recruits vary, but they are all examples of how the momentum in economics has shifted away from theoretical modeling and toward “empirical microeconomics,” the analysis of how things work in the real world, often arranging complex experiments or exploiting large sets of data. That kind of work requires lots of research assistants, work across disciplines including fields like sociology and computer science, and the use of advanced computational techniques unavailable a generation ago....Less clear is whether the agglomeration of economic stars at Stanford will ever amount to the kind of coherent school of thought that has been achieved at some other great universities (e.g. Milton Friedman's The Chicago School neoclassical focus on efficiency of markets and the risks of government intervention and M.I.T.’s economics' Keynesian tradition)
economics  economists  empiricism  in_the_real_world  Stanford  MIT  Harvard  Colleges_&_Universities  recruiting  poaching  movingonup  rankings  machine_learning  cross-disciplinary  massive_data_sets  data  uChicago  microeconomics  Keynesian  Chicago_School 
september 2015 by jerryking
Emergency planning: Flood warning — new data help predict risk - FT.com
September 4, 2015 4:42 pm
Emergency planning: Flood warning — new data help predict risk
Clive Cookson

Historical information can be a practical tool for planning responses to future emergencies....KnowNow Information, a spinout from computing giant IBM, has produced a prototype “flood event model” for Hampshire. Working with the Science and Technology Facilities Council’s Hartree supercomputing centre in Daresbury, Cheshire, its team crunched a vast accumulation of data — about water falling from the sky and lying on the ground, geology and landforms, urban geography and infrastructure, as well as past emergencies.
floods  massive_data_sets  history  extreme_weather_events  natural_calamities  data  data_driven  warning_signs  emergencies  anticipating  preparation 
september 2015 by jerryking
Everything We Wish We'd Known About Building Data Products - First Round Review
Quote: "Where to Start Building: A lot of people choose to start building by modeling the product in question. Some start with feature discovery or feature engineering. Others start with building the infrastructure to serve results at scale. But for Belkin, there's only one right answer and starting point for a data product: Understanding how will you evaluate performance and building evaluation tools.
“Every single company I've worked at and talked to has the same problem without a single exception so far — poor data quality, especially tracking data,” he says.“Either there's incomplete data, missing tracking data, duplicative tracking data.” To solve this problem, you must invest a ton of time and energy monitoring data quality. You need to monitor and alert as carefully as you monitor site SLAs. You need to treat data quality bugs as more than a first priority. Don’t be afraid to fail a deploy if you detect data quality issues."
assessments_&_evaluations  control_systems  dashboards  data_quality  economies_of_scale  instrumentation_monitoring  testing  tracking  information  infrastructure  via:ajohnson1200  massive_data_sets 
september 2015 by jerryking
U.S. Fears Data Stolen by Chinese Hacker Could Identify Spies - The New York Times
By MARK MAZZETTI and DAVID E. SANGER JULY 24, 2015

the hackers — who government officials are now reluctant to say publicly were working for the Chinese government — could still use the vast trove of information to identify American spies by a process of elimination. By combining the stolen data with information they have gathered over time, they said, the hackers can use “big data analytics” to draw conclusions about the identities of operatives....The C.I.A. and other agencies typically post their spies in American embassies, where the officers pose as diplomats working on political affairs, agricultural policy or other issues. The American Embassy in Beijing has long housed one of the largest C.I.A. stations in the world, with intelligence officers gathering information on China’s political maneuvering, economic development and military modernization.

Several current and former officials said that even if the identities of the agency officers were not in the personnel office’s database, Chinese intelligence operatives could run searches through the database on everyone granted visas to work at American diplomatic outposts in China. If any of the names are not found in the stolen files, those individuals could be suspected as spies by a process of elimination.
Chinese  data_breaches  China  hacks  CIA  espionage  security_&_intelligence  cyber_warfare  cyber_security  massive_data_sets  David_Sanger 
july 2015 by jerryking
High-tech Singapore rides into the future
6 June 2015 | Financial Times|Louise Lucas in Singapore

Singapore has seen the future - and is busily putting it into practice.

From crowdsourced buses, designed to do for public transport what ...
Singapore  sharing_economy  massive_data_sets  disruption  from notes
july 2015 by jerryking
The Value of Bad Data - The Experts - WSJ
Apr 22, 2015 | WSJ | by Alexandra Samuel--technology researcher and the author of “Work Smarter with Social Media.”
*** Can I apply the idea of negative space towards evolving a dataset?

What do you do when you don’t have access to a large data set?...even without access to big data, you can still use some of the tools of data-driven decision-making to make all the other choices that arise in your day-to-day work.

Adopting and adapting the tools of quantitative analysis is crucial, because we often face decisions that can’t be guided by a large data set. Maybe you’re the founder of a small company, and you don’t yet have enough customers or transactions to provide a statistically significant sample size. Perhaps you’re working on a challenge for which you have no common data set, like evaluating the performance of different employees whose work has been tracked in different ways. Or maybe you’re facing a problem that feels like it can’t be quantified, like assessing the fit between your services and the needs of different potential clients.

None of these scenarios offers you the kind of big data that would make a data scientist happy. But you can still dig into your data scientist’s toolbox, and use a quasi-quantitative approach to get some of the benefits of statistical analysis… even in the absence of statistically valid data.
massive_data_sets  data  data_driven  small_business  data_scientists  books  hustle  statistics  quantitative  small_data  data_quality 
july 2015 by jerryking
ASAE Technology Conference & Expo Speakers, Attendees Discussed Big Data, New Ways to Connect With Members - About Us - ASAE
...the notion of big data and what that means for associations...Mobile is the key data point and global data will be a huge challenge for organizations. Dion Hinchcliffe advised attendees if they choose to be inactive in this space, then a new intermediary will form a presence and it will be the primary place where your members will engage, and not with your organization
associations  LBMA  massive_data_sets  data 
may 2015 by jerryking
How Not to Drown in Numbers - NYTimes.com
MAY 2, 2015| NYT |By ALEX PEYSAKHOVICH and SETH STEPHENS-DAVIDOWITZ.

If you’re trying to build a self-driving car or detect whether a picture has a cat in it, big data is amazing. But here’s a secret: If you’re trying to make important decisions about your health, wealth or happiness, big data is not enough.

The problem is this: The things we can measure are never exactly what we care about. Just trying to get a single, easy-to-measure number higher and higher (or lower and lower) doesn’t actually help us make the right choice. For this reason, the key question isn’t “What did I measure?” but “What did I miss?”...So what can big data do to help us make big decisions? One of us, Alex, is a data scientist at Facebook. The other, Seth, is a former data scientist at Google. There is a special sauce necessary to making big data work: surveys and the judgment of humans — two seemingly old-fashioned approaches that we will call small data....For one thing, many teams ended up going overboard on data. It was easy to measure offense and pitching, so some organizations ended up underestimating the importance of defense, which is harder to measure. In fact, in his book “The Signal and the Noise,” Nate Silver of fivethirtyeight.com estimates that the Oakland A’s were giving up 8 to 10 wins per year in the mid-1990s because of their lousy defense.

And data-driven teams found out the hard way that scouts were actually important...We are optimists about the potential of data to improve human lives. But the world is incredibly complicated. No one data set, no matter how big, is going to tell us exactly what we need. The new mountains of blunt data sets make human creativity, judgment, intuition and expertise more valuable, not less.

==============================================
From Market Research: Safety Not Always in Numbers | Qualtrics ☑
Author: Qualtrics|July 28, 2010

Albert Einstein once said, “Not everything that can be counted counts, and not everything that counts can be counted.” [Warning of the danger of overquantification) Although many market research experts would say that quantitative research is the safest bet when one has limited resources, it can be dangerous to assume that it is always the best option.
human_ingenuity  data  analytics  small_data  massive_data_sets  data_driven  information_overload  dark_data  measurements  creativity  judgment  intuition  Nate_Silver  expertise  datasets  information_gaps  unknowns  underestimation  infoliteracy  overlooked_opportunities  sense-making  easy-to-measure  Albert_Einstein  special_sauce  metrics  overlooked  defensive_tactics  emotional_intelligence  EQ  soft_skills  overquantification  false_confidence 
may 2015 by jerryking
The Sensor-Rich, Data-Scooping Future - NYTimes.com
APRIL 26, 2015 | NYT | By QUENTIN HARDY.

Sensor-rich lights, to be found eventually in offices and homes, are for a company that will sell knowledge of behavior as much as physical objects....The Internet will be almost fused with the physical world. The way Google now looks at online clicks to figure out what ad to next put in front of you will become the way companies gain once-hidden insights into the patterns of nature and society.

G.E., Google and others expect that knowing and manipulating these patterns is the heart of a new era of global efficiency, centered on machines that learn and predict what is likely to happen next.

“The core thing Google is doing is machine learning,” Eric Schmidt....The great data science companies of our sensor-packed world will have experts in arcane reaches of statistics, computer science, networking, visualization and database systems, among other fields. Graduates in those areas are already in high demand.

Nor is data analysis just a question of computing skills; data access is also critically important. As a general rule, the larger and richer a data set a company has, the better its predictions become. ....an emerging area of computer analysis known as “deep learning” will blow away older fields.

While both Facebook and Google have snapped up deep-learning specialists, Mr. Howard said, “they have far too much invested in traditional computing paradigms. They are the equivalent of Kodak in photography.” Echoing Mr. Chui’s point about specialization, he said he thought the new methods demanded understanding of specific fields to work well.

It is of course possible that both things are true: Big companies like Google and Amazon will have lots of commodity data analysis, and specialists will find niches. That means for most of us, the answer to the future will be in knowing how to ask the right kinds of questions.
sensors  GE  GE_Capital  Quentin_Hardy  data  data_driven  data_scientists  massive_data_sets  machine_learning  automated_reasoning  predictions  predictive_analytics  predictive_modeling  layer_mastery  core_competencies  Enlitic  deep_learning  niches  patterns  analog  insights  latent  hidden  questions  Google  Amazon  aftermath  physical_world  specialization  consumer_behavior  cyberphysical  arcane_knowledge  artificial_intelligence  test_beds 
april 2015 by jerryking
The lost art of political persuasion - The Globe and Mail
KONRAD YAKABUSKI
The Globe and Mail
Published Saturday, Apr. 25 2015

Talking points are hardly a 21st century political innovation. But they have so crowded out every other form of discourse that politics is now utterly devoid of honesty, unless it’s the result of human error. The candidates are still human, we think, though the techies now running campaigns are no doubt working on ways to remove that bug from their programs.

Intuition, ideas and passion used to matter in politics. Now, data analytics aims to turn all politicians into robots, programmed to deliver a script that has been scientifically tested...The data analysts have algorithms that tell them just what words resonate with just what voters and will coax them to donate, volunteer and vote.

Politics is no longer about the art of persuasion or about having an honest debate about what’s best for your country, province or city. It’s about microtargeting individuals who’ve already demonstrated by their Facebook posts or responses to telephone surveys that they are suggestible. Voters are data points to be manipulated, not citizens to be cultivated....Campaign strategists euphemistically refer to this data collection and microtargeting as “grassroots engagement” or “having one-on-one conversations” with voters....The data analysts on the 2012 Obama campaign came up with “scores” for each voter in its database, or what author Sasha Issenberg called “a new political currency that predicted the behaviour of individual humans.
Konrad_Yakabuski  persuasion  middle_class  politicians  massive_data_sets  political_campaigns  data_scientists  data_driven  data_mining  microtargeting  behavioural_targeting  politics  data  analytics  Campaign_2012 
april 2015 by jerryking
Toronto to use big data to help reduce traffic congestion - The Globe and Mail
Apr. 07 2015 | The Globe and Mail | OLIVER MOORE - URBAN TRANSPORTATION REPORTER

Toronto is creating a “big data” traffic team as the city tries to manage congestion better by learning what is actually happening on its streets.....The push is a start toward filling that vacuum of information. The city has released a job posting for someone to lead the data unit and will spend the rest of the year deciding what they want to learn. A “hackathon” in September will let people come in, look at the available data and see what they can do with it.

Big data has become a buzz phrase in traffic circles as smartphones and GPS units make it easier to track people’s movements. But in most places, the promise looms larger than the reality. Many cities are still trying to figure out how to turn the flood of data into useful information.
massive_data_sets  traffic_congestion  Toronto  John_Tory  transportation  analytics  data  information_vacuum 
april 2015 by jerryking
IBM to Invest $3 Billion in Sensor-Data Unit - WSJ
March 31, 2015 | WSJ | By DON CLARK. Can CBC get good at communicating the final product on behalf of clients of Pelmorex. So CBC considers supplying the communications platform?

IBM plans to invest $3 billion over four years on a new business helping customers gather and analyze the flood of data from sensor-equipped devices and smartphones.... IBM announced that it is forming an alliance with the Weather Company, which owns the Weather Channel and other information providers. The two companies plan jointly to exploit data about weather conditions to help businesses make better decisions....the centerpiece of IBM's new business unit is a collection of online software called IoT Foundation that runs on IBM’s existing cloud services and allows customers and partners to create new applications and enhance existing ones with real-time data and analysis....IBM is betting that correlating dissimilar kinds of data will yield the highest value. “It’s essential to federate information from multiple sources,” said Bob Picciano, IBM’s senior VP of analytics.... the Weather Channel serves up 700,000 weather forecasts a second. It already sells data to a range of customers in agriculture, transportation and other industries that rely on weather.

But the opportunities have expanded, Mr. Kenny said, as weather sensors installed in many more places have contributed to more timely, localized forecasts. The added detail helps farmers predict more precisely, for example, where hail could impact their fields, Mr. Kenny said.

The Weather Company is turning to IBM, he said, because of its software expertise and relationships with customers in many industries.
sensors  IBM  weather  massive_data_sets  data  data_driven  analytics  Industrial_Internet  smartphones  cloud_computing 
march 2015 by jerryking
‘Who Owns the Future?’ by Jaron Lanier - NYTimes.com
MAY 5, 2013
Continue reading the main story
Books of The Times
By JANET MASLIN
books  massive_data_sets  book_reviews  futurists  future 
march 2015 by jerryking
Mirtle: Sloan conference leading Big Data revolution in sports - The Globe and Mail
JAMES MIRTLE
BOSTON — Globe and Mail Update (includes correction)
Published Thursday, Feb. 26 2015
sports  MIT  massive_data_sets  analytics  Moneyball  Octothorpe_Software 
february 2015 by jerryking
Spinning raw government datasets into gold - The Globe and Mail
IVOR TOSSELL
Special to The Globe and Mail
Published Monday, Feb. 02 2015
massive_data_sets  open_data  data  datasets 
february 2015 by jerryking
Sandy Pentland on the Social Data That Business Should Use - WSJ
Feb. 10, 2014 | Journal Report - CIO Netowrk| WSJ's Steve Rosenbush speaking with MIT's Sandy Pentland.

MR. ROSENBUSH: For most of us, social data is Twitter, it's Facebook. What do you mean by it?

MR. PENTLAND: Those sorts of things are people's public face. On the other hand, for instance, there's badge data. Every corporation has name badges. Many of these record where people come and go, door swipes and things like that. That's a different type of social media. Or if I look at cellphone data, I can tell when people get together, what they search for, who they talk to. You can look at connections between people in ways you never could before. The way most people approach this is incorrect, because they're asking questions about individuals. A better way to approach is asking questions about interactions between people.
social_data  interpretation  Twitter  Facebook  social_physics  Communicating_&_Connecting  informed_consent  location_based_services  data  massive_data_sets  contextual  LBMA  interactivity  traffic_analysis  mobile_phones 
february 2015 by jerryking
It’s a Whole New Data Game for Business - WSJ
Feb. 9, 2015 | WSJ |

opportunistic data collection is leading to entirely new kinds of data that aren’t well suited to the existing statistical and data-mining methodologies. So point number one is that you need to think hard about the questions that you have and about the way that the data were collected and build new statistical tools to answer those questions. Don’t expect the existing software package that you have is going to give you the tools you need....Point number two is having to deal with distributed data....What do you do when the data that you want to analyze are actually in different places?

There’s lots of clever solutions for doing that. But at some point, the volume of data’s going to outstrip the ability to do that. You’re forced to think about how you might, for example, reduce those data sets, so that they’re easier to move.
data  data_collection  datasets  data_mining  massive_data_sets  distributed_data  haystacks  questions  tools  unstructured_data 
february 2015 by jerryking
The trick to making sense of Big Data - The Globe and Mail
TED WRIGHT
Special to The Globe and Mail
Published Monday, Jan. 26 2015,
massive_data_sets  books  howto  sense-making 
january 2015 by jerryking
Let me see
Posted by Seth Godin on July 08, 2008.

Passive contributions of public behaviour information to traditionally-sorted data
data  ideas  information  inspiration  Seth_Godin  social_data  datasets  open_data  social_physics  massive_data_sets  wisdom_of_crowds  thick_data  public_behavior  sorting  value_creation 
january 2015 by jerryking
Banking Start-Ups Adopt New Tools for Lending
JAN. 18, 2015 | - NYTimes.com | By STEVE LOHR.

When bankers of the future decide whether to make a loan, they may look to see if potential customers use only capital letters when filling out forms, or at the amount of time they spend online reading terms and conditions — and not so much at credit history.

These signals about behavior — picked up by sophisticated software that can scan thousands of pieces of data about online and offline lives — are the focus of a handful of start-ups that are creating new models of lending....Earnest uses the new tools to make personal loans. Affirm, another start-up, offers alternatives to credit cards for online purchases. And another, ZestFinance, has focused on the relative niche market of payday loans.
Steve_Lohr  tools  banking  banks  massive_data_sets  start_ups  data_scientists  Earnest  Affirm  ZestFinance  Max_Levchin  consumer_finance  credit_scoring  fin-tech  financial_services  consumer_behavior  signals 
january 2015 by jerryking
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