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London Stock Exchange lays $27bn bet that data are the future
July 28, 2019 | | Financial Times | by Arash Massoudi, Richard Henderson and Richard Blackden.

The London Stock Exchange Group more than 300 years old, is trying to get back on the front foot with a plan for its most ambitious acquisition, one that will shape the direction of the group for years to come. It is the most striking demonstration yet of the charge among exchange operators into the business of supplying the data that is at the heart of markets....The LSE on Friday confirmed a Financial Times report that it was in talks to buy data and trading venue group Refinitiv for $27bn including debt, from a consortium led by private equity group Blackstone. If an agreement is reached for a company best-known for its Eikon desktop terminals, it would transform the LSE into a provider of financial market infrastructure and data with the scale to take on US exchange industry heavyweights Intercontinental Exchange and CME Group as well as Michael Bloomberg’s financial information empire.

“This would be a bold move in the shift among exchanges away from the matching of buyers and sellers and into the business of selling information,” said Kevin McPartland, head of market structure research at consultancy Greenwich Associates. “Data are so valuable and so is having the network of traders and investors to access that data — that’s all at play here.”......The deal would also be a defining moment for the LSE’s chief executive, David Schwimmer, just a year after the relatively unknown former Goldman Sachs banker was parachuted in to steady the ship. Its scale will bring considerable risk in execution alongside the need to convince LSE shareholders that taking on Refinitiv’s $12bn of debt will prove worth it.

Industry analysts see the strategic logic of the deal for the LSE, best known for its UK stock exchange and derivatives clearing house LCH. While revenue from initial public offerings can be more volatile, spending by everyone from asset managers to hedge funds on financial data and the analytical tools to make use of it has been going in one direction. It hit a record $30.5bn last year
.......“What’s happened is exchanges have found it more difficult to find ways of generating revenue in their traditional businesses,” “You can deliver data so easily now, there is voracious appetite from anyone making investment decisions so they can get an edge.”.....As well as winning over LSE shareholders, any deal is likely to face a lengthy period of antitrust approvals.

“There is a wider market concern about exchanges and data vendors combining,” said Niki Beattie, founder of Market Structure Partners. “The global world of data distribution is presided over by a small number of players who have a lot of power.”
asset_management  Blackstone  Bloomberg  bourses  data  financial_data  hedge_funds  inflection_points  IntercontinentalExchange  investors  LSE  mergers_&_acquisitions  M&A  Refinitiv  stockmarkets  Thomson_Reuters  tools  trading_platforms  turning_points  defining_moments 
7 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
Opinion | The Surprising Benefits of Relentlessly Auditing Your Life
May 25, 2019 | The New York Times | By Amy Westervelt, a journalist and podcaster.

"The unexamined life is not worth living" is a famous dictum apparently uttered by Socrates at his trial for impiety and corrupting youth, for which he was subsequently sentenced to death, as described in Plato's Apology (38a5–6).
analytics  data  evidence_based  happiness  housework  marriage  note_taking  patterns  quality_of_life  quantitative  quantified_self  record-keeping  relationships  relentlessness  self-assessment  self-examination  self-improvement  spreadsheets 
may 2019 by jerryking
Past mistakes carry warnings for the future of work
May 21, 2019 | Financial Times | by SARAH O'CONNOR.

* Data can mislead unless combined with grittier insights on the power structures that underpin it.
* William Kempster, a master mason who worked on St Paul's Cathedral in the 18th century, left wage records that helped expose a flaw in our understanding of the past.

It is often said that we should learn from the mistakes of the past. But we can also learn from the mistakes we make about the past. Seemingly smooth data can mislead unless it is combined with a grittier insight into the structures, contracts and power relationships that underpin the numbers. On that score, economists and politicians who want to make sense of today’s labour market have an advantage over historians: it is happening right now, just outside their offices, in all its complexity and messiness. All they have to do is open the door
17th_century  18th_century  builders  contextual  data  datasets  developing_countries  economic_history  economists  freelancing  gig_economy  handwritten  historians  human_cloud_platforms  insights  labour_markets  London  messiness  mistakes  politicians  power_relations  power_structures  record-keeping  United_Kingdom  unstructured_data  wages  white-collar 
may 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
‘Math men’ not mad men rule advertising’s data age, says Lévy
May 5, 2019 | Financial Times | by Anna Nicolaou.

Maurice Levy: 'The future [of advertising] is based on data. It is not based on any mass media.' We know that mass media is [declining] every day,” “And if an advertising agency wants to have a future, data is absolutely indispensable.”

the advertising industry was undergoing a “metamorphosis” that required big bets.......As consumers shift attention away from pricey television commercials and towards the internet, where Facebook and Google dominate, the industry is more “math men” than mad men......In light of digital disruption Publicis, the world’s third-largest advertising agency by revenues, has made a big bet on data. In April the company made its largest acquisition with the purchase of Epsilon, a digital marketing company owned by Alliance Data Systems......Like its rivals WPP and Omnicom, Publicis is under pressure as Facebook and Google have disintermediated the traditional agency model. The two tech groups account for two-thirds of digital advertising sales in the US.....The industry has been consolidating as traditional agencies look to position themselves as data analytics gurus who can help brands target shoppers online. Last year Interpublic bought data business Acxiom for $2bn, while just last month buzzy agency Droga5 sold itself to Accenture......Despite lingering fears that an economic slowdown is looming, “the situation is much better now,”.... making the Epsilon decision easier. “The fastest-growing segment in our industry is data, technology, internet. Period. All the rest is suffering.”
advertising  advertising_agencies  analytics  big_bets  data  decline  disruption  disintermediation  Epsilon  Facebook  Google  Interpublic  Mad_Men  marketing  mass_media  mathematics  Maurice_Lévy  Omnicom  Publicis  WPP 
may 2019 by jerryking
How Spotify’s algorithms are ruining music
May 2, 2019 | Financial Times | Michael Hann.

(1) FINAL DAYS OF EMI, By Eamonn Forde, Omnibus, RRP£20, 320 pages
(2) SPOTIFY TEARDOWN, By Maria Eriksson, Rasmus Fleischer, Anna Johansson, Pelle Snickars and Patrick Vonderau, The MIT Press, RRP£14.99, 288 pages
(3) WAYS OF HEARING, By Damon Krukowski, The MIT Press, RRP£14.99, 136 pages

In April, the IFPI — the global body of the recording industry — released its latest annual Global Music Report. For the fourth consecutive year, revenues were up, to a total of $19.1bn, from a low of $14.3bn in 2014. Nearly half those revenues came from music streaming, driven by a 33 per cent rise in paid subscriptions to services such as Spotify, Apple Music and Tidal...... It is worth remembering that 20 years ago, the IFPI reported global music revenues of $38.6bn. Today’s “booming” recording industry is less than half the size it was at the turn of the century.....The nadir for the recording industry coincided with the first shoots of its regrowth. ....In August 2007, the British record company EMI — the fourth of the majors, alongside Universal, Sony and Warner — was bought by private equity firm Terra Firma (Guy Hands, the fund’s founder and chairman) for $4.7bn; a year later, a Swedish company called Spotify took its music streaming service public. The former was, perhaps, the last gasp of the old way of doing things — less than four years after buying EMI, Terra Firma was unable to meet its debts, and ceded control of the company to its main lender, Citigroup. Before 2011 was out, the process of breaking up EMI had begun...EMI’s demise was foreshadowed before Hands arrived, with a blaze of hubris in the early 2000s. Forde, a longtime observer and chronicler of the music business recounts the “disastrous and expensive” signings of that era......Handspreached the need to use data when signing artists, not just the “golden ears” of talent scouts; data are now a key part of the talent-spotting process.

* to qualify as having been listened to on Spotify, a song has to have been played for 30 seconds.
* hit songs have become increasingly predictable, offering up all their pleasures in the opening half-minute. Their makers dare not risk scaring off listeners.
* for all the money that the streaming services have generated for the music industry, very little of it flows back to any musicians except the select few who dominate the streaming statistics,

.......On Spotify, music consumption has been reorganised around “behaviours, feelings and moods” channelled through curated playlists and motivational messages......The data Spotify collects enable the industry to work out who its market is, where it lives, what else they like, how often they listen to music — almost anything, really. It’s the greatest assemblage of information about music listeners in history, and it has profoundly altered the industry: it has made Spotify music’s kingmaker......when an artist travels abroad to promote a new album, the meeting with the local Spotify office is more important than the TV appearances or the newspaper interviews. ...Spotify enables artists to plan their band’s set lists so they can play the most popular song in any given city.............So what? What does it matter if one model of music distribution has been replaced by another.....It matters because Spotify has profoundly changed the listener’s relationship with music....Older musicians often wax about how, when you had to buy your own music as a kid, you listened to it until you liked it, because you wouldn’t be able to afford a new album for another month. Now you simply skip to the next one, and probably don’t give it your full attention. Without ownership, there’s no incentive to study...........Faced with the impossibly wide choice of Spotify, it becomes easier to return to old favourites — easier than when flicking through your vinyl or CDs, because the act of looking through your own music makes things you had not thought of in years leap out at you. Spotify actually makes people into more conservative listeners, a process aided by its algorithms, which steer you towards music similar to your most frequent listening.....The theme of Krukowski’s book is that the changes in the way the music industry works have been about controlling and eliminating excess noise. That’s in a literal sense and in a metaphorical one, too. Streaming has stripped music of context, pared it back to being just about the song and the moment....but noise is the context of life. Without noise, the signal becomes meaningless......The world of the old EMI was one of both signal and noise; where myths and legends could be created: The Beatles! Queen! The Beach Boys! Pink Floyd! It was never all about the signal. The world of Spotify is one of signal only, and if you don’t appreciate that signal within the first 30 seconds of the song...all may be lost
abundance  algorithms  Apple_Music  books  book_reviews  business_models  curation  cultural_transmission  data  decontextualization  EMI  gatekeepers  Guy_Hands  hits  indoctrination  iTunes  legacy_artists  music  music_catalogues  music_labels  music_industry  music_publishing  noise  piracy  platforms  playlists  royalties  ruination  securitization  signals  songs  Spotify  streaming  subscriptions  talent  talent_scouting  talent_spotting  Terra_Firma  Tidal  transformational 
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
Supercharging retail sales through geospatial analytics
March 2019 | | McKinsey | By Rob Hearne, Alana Podreciks, Nathan Uhlenbrock, and Kelly Ungerman.

A retailer can now use geospatial analytics to understand the interactions between its online and offline channels. With these insights, it can create a higher-performing retail network.
====================================
Is our outlet store in San Francisco hurting foot traffic and sales at our full-price store two miles away? Or is it doing the opposite—attracting new customers and making them more likely to visit both stores? How are our five Manhattan stores affecting our e-commerce revenue? Are they making consumers more likely to shop on our website or to search for our products on Amazon? If we open a new mall store in the Dallas metro area, what impact will it have on sales at our existing stores, at our department-store partners, and online?

The answers to these kinds of questions are increasingly crucial to a retailer’s success, as more and more consumers become omnichannel shoppers......most retailers don’t give adequate thought to the cross-channel impact of their stores. They rely on gut feel or on high-level analysis of aggregated sales data to gauge how their offline and online channels interact.....there’s a way for retailers (and other omnichannel businesses) to quantify cross-channel effects, thus taking the guesswork out of network optimization. Through advanced geospatial analytics and machine learning, a retailer can now generate a detailed quantitative picture of how each of its customer touchpoints—including owned stores and websites, wholesale doors, and partner e-commerce sites—affects sales at all its other touchpoints within a micromarket......US retail sales are on an upward trajectory.....despite the growth of e-commerce, the vast majority of these purchases still happened in brick-and-mortar stores. .....So why have US retailers closed thousands of stores in the past year, with thousands more closures to come?....Because the consumer journey is changing!!......Consumers are transacting in different channels....engaging across multiple channels, often simultaneously rather than sequentially. It’s critical for omnichannel retailers to have a detailed understanding of the interplay between online and offline touchpoints, and between owned and partner networks.

Quantifying cross-channel effects

the starting point is data......from a wide range of internal and external sources. Inputs into a geospatial model would ideally include not just transaction and customer data but also store-specific details such as store size and product mix; site-specific information such as foot traffic and retail intensity; environmental data, including local-area demographics; and anonymized mobile-phone location data.......A simulation model can then quantify the sales effect of each of the retailer’s customer touchpoints on its other channels within a local market. The model must be sophisticated enough to simulate the upward or downward revenue impact of adding or removing a particular touchpoint.

Geospatial analysis reveals that the consistency and magnitude of cross-channel effects vary significantly across channel types and markets.
analytics  bricks-and-mortar  cross-channel  customer_journey  customer_touchpoints  data  e-commerce  foot_traffic  geospatial  gut_feelings  location_based_services  McKinsey  moments_of_truth  omnichannel  privacy  retailers  store_closings  security_consciousness  site_selection 
march 2019 by jerryking
Meet Amanda Cox, Who Brings Life to Data on Our Pages
Feb. 28, 2019 | The New York Times | By Jake Lucas

Ms. Cox was stepping into a new role: data editor. She will help coordinate data work across departments, in interactive news, computer-assisted reporting, graphics and The Upshot, and pave the way for journalism using data to play a bigger role throughout the newsroom. She will also act as an adviser when big questions arise about how to think about and use data thoughtfully, without overstating what it supports.
charts  Communicating_&_Connecting  data  data_journalism  infographics  NYT  quantitative  visualization 
march 2019 by jerryking
Everything still to play for with AI in its infancy
February 14, 2019 | Financial Times | by Richard Waters.

the future of AI in business up for grabs--this is a clearly a time for big bets.

Ginni Rometty,IBM CEO, describes Big Blue’s customers applications of powerful new tools, such as AI: “Random acts of digital”. They are taking a hit-and-miss approach to projects to extract business value out of their data. Customers tend to start with an isolated data set or use case — like streamlining interactions with a particular group of customers. They are not tied into a company’s deeper systems, data or workflow, limiting their impact. Andrew Moore, the new head of AI for Google’s cloud business, has a different way of describing it: “Artisanal AI”. It takes a lot of work to build AI systems that work well in particular situations. Expertise and experience to prepare a data set and “tune” the systems is vital, making the availability of specialised human brain power a key limiting factor.

The state of the art in how businesses are using artificial intelligence is just that: an art. The tools and techniques needed to build robust “production” systems for the new AI economy are still in development. To have a real effect at scale, a deeper level of standardisation and automation is needed. AI technology is at a rudimentary stage. Coming from completely different ends of the enterprise technology spectrum, the trajectories of Google and IBM highlight what is at stake — and the extent to which this field is still wide open.

Google comes from a world of “if you build it, they will come”. The rise of software as a service have brought a similar approach to business technology. However, beyond this “consumerisation” of IT, which has put easy-to-use tools into more workers’ hands, overhauling a company’s internal systems and processes takes a lot of heavy lifting. True enterprise software companies start from a different position. They try to develop a deep understanding of their customers’ problems and needs, then adapt their technology to make it useful.

IBM, by contrast, already knows a lot about its customers’ businesses, and has a huge services operation to handle complex IT implementations. It has also been working on this for a while. Its most notable attempt to push AI into the business mainstream is IBM Watson. Watson, however, turned out to be a great demonstration of a set of AI capabilities, rather than a coherent strategy for making AI usable.

IBM has been working hard recently to make up for lost time. Its latest adaptation of the technology, announced this week, is Watson Anywhere — a way to run its AI on the computing clouds of different companies such as Amazon, Microsoft and Google, meaning customers can apply it to their data wherever they are stored. 
IBM’s campaign to make itself more relevant to its customers in the cloud-first world that is emerging. Rather than compete head-on with the new super-clouds, IBM is hoping to become the digital Switzerland. 

This is a message that should resonate deeply. Big users of IT have always been wary of being locked into buying from dominant suppliers. Also, for many companies, Amazon and Google have come to look like potential competitors as they push out from the worlds of online shopping and advertising.....IBM faces searching questions about its ability to execute — as the hit-and-miss implementation of Watson demonstrates. Operating seamlessly in the new world of multi-clouds presents a deep engineering challenge.
artificial_intelligence  artisan_hobbies_&_crafts  automation  big_bets  cloud_computing  contra-Amazon  cultural_change  data  digital_strategies  early-stage  economies_of_scale  Google  hit-and-miss  IBM  IBM_Watson  internal_systems  randomness  SaaS  standardization  Richard_Waters 
february 2019 by jerryking
Cause or effect? The link between gentrification and violent crime
July 12, 2018 | | Financial Times | by Nathan Brooker YESTERDAY.

London, which is experiencing a sustained increase in violent offences as crime rates in other global cities such as New York, Sydney and Hong Kong continue to fall......The escalation of violence has been linked to provocation on social media, increased competition in the drugs trade, a reduction in police measures such as stop and search and an overall drop in police funding— the Met has seen its annual budget cut by about 20 per cent since 2010-11, and it has lost 10 per cent of its police officers in that time......However, one factor that is often overlooked and, according to professional and academic observers, has played a key role in exacerbating London’s recent crime wave, is its gentrifying property market.

Areas of London that have higher levels of deprivation also tend to have higher crime rates.........The level of violence you see is getting much more extreme......Gentrification has had a significant impact on the area....“One of the issues young people have in Hackney Wick is the lack of aspiration, the lack of hope,” says Allen. “They’re all living in a rich, diverse city, but it still feels very separate to them. It’s not their development; it’s somebody else’s. They think they won’t be able to live in the area they were brought up in because they’re not going to be able to spend £600,000 on an apartment.”.........gentrification has not only affected gang recruitment..... it has fundamentally altered how some gangs operate.........“It changed their idea of territory, since some senior members were forced out of the area [by the redevelopment] and had to commute in, for want of a better term,” he says. “Ten years ago there was a very strong connection to territory. There was an emotional connection. But the redevelopment changed that. The only territory that was left was the market place — the drugs market place — and that needs to be protected.”

It’s the protection of that market — one both lucrative and highly nebulous — that is behind some of the increase in violent crime. Without the clear boundaries an estate or a postcode might provide, he says, and with the high value of the drugs trade upping the stakes, transgressions are met with more intense violence.....The reasons behind the dramatic decline in New York’s murder count are much argued over: the growing economy, the end of the crack epidemic have all been put up as possible causes. Yet improvements to policing brought in under former New York police commissioner Bill Bratton cannot be overlooked.

Bratton’s policies, which included clampdowns on various low-level offences, and an increase in stop-question-and-frisk, are often mischaracterised as a zero-tolerance approach to policing, he says.

“What he really did was a management innovation.” Bratton, who was in the office 1994-96 and returned in 2014-16, introduced CompStat, measures that used computer programs to map where and when crimes were taking place, and how police resources were being shared. “When [Bratton] took over, the largest number of cops were on the day shift, but the largest number of crimes took place on the evening shift and the night shift,” he says. Bratton reallocated officers accordingly. They had a slogan: “Put cops on the dots”.......the most important thing Bratton did, Kleiman says, was make management more accountable, hauling in three precinct captains each week to grill them on their CompStat data. During his first year as commissioner, Bratton replaced something like two-thirds of the city’s 76 precinct commanders......The problem with fear is that it’s an unhelpful response. Fear raises money for private security firms, not community programmes; it improves funding to free schools, not failing academies; it promotes only the most brutal, careless forms of policing. In communities that are undergoing gentrification, fear further divides the haves and the have-nots: decreasing the kinds of relationships that might aid social mobility and better connect disadvantaged youth with the city they live in.

And what gets forgotten, says Allen, is that fear goes both ways. “A lot of the young people that get caught up in youth violence are caught up because they’re vulnerable and they’re frightened,”
accountability  Bill_Bratton  budget_cuts  carding  causality  CompStat  criminality  criminal_justice_system  data  deprivations  disaffection  fear  gentrification  homicides  killings  London  New_York_City  NYPD  organized_crime  policing  property_markets  redevelopments  United_Kingdom  violent_crime  youth 
july 2018 by jerryking
Music’s ‘Moneyball’ moment: why data is the new talent scout | Financial Times
JULY 5, 2018 | FT | Michael Hann.

The music industry loves to self-mythologise. It especially loves to mythologise about taking young scrappers from the streets and turning them into stars. It celebrates the men and women — but usually the men — with “golden ears” almost as much as the people making the music....A&R, or “artists and repertoire”, are the people who look for new talent, convince that talent to sign to the record label and then nurture it: advising on songs, on producers, on how to go about the job of being a pop star. It’s the R&D arm of the music industry......What the music business doesn’t like to shout about is how inefficient its R&D process is. The annual global spend on A&R is $2.8bn....and all that buys is the probability of failure: “Some labels estimate the ratio of commercial success to failure as 1 in 4; others consider the chances to be much lower — less than 1 in 10,” observes its 2017 report. Or as Mixmag magazine’s columnist The Secret DJ put it: “Major labels call themselves a business but are insanely unprofitable, utterly uncertain, totally rudderless and completely ignorant.”......The rise of digital music brought with it a huge amount of data which, industry executives realized, could be turned to their advantage. ....“All our business units must now leverage data and analytics in innovative ways to dig deeper than ever for new talent. The modern day talent-spotter must have both an artistic ear and analytical eyes.”

Earlier this year, in the same week as Warner announced its acquisition of Sodatone, a company that has developed a tool for talent-spotting via data, another data company, Instrumental, secured $4.2m of funding. The industry appeared to have reached a tipping point — what the website Music Ally called “A&R’s data moment”. Which is why, wherever the music industry’s great and good gather, the word “moneyball” has become increasingly prevalent.
........YouTube, Spotify, Instagram were born and changed the way talent begins its journey. All the barriers came down. Suddenly you’ve got tens of thousands of pieces of music content being uploaded.......Home computing’s democratization of recording removed the barriers to making high-quality music. No longer did you need access to a studio and an experienced producer, plus the money to pay for them. But the music industry had no way to keep abreast of these new creators. “....The way A&R people have discovered talent has barely changed since the music industry began, and it’s fundamentally the same for indie labels, who put artistry above sales, as it is for major labels who have to answer to shareholders. It’s always been about information.....“We find them by listening to new music constantly, by people giving us tips, by going out and seeing things that sound interesting,”.....“The most useful people to talk to are concert promoters and booking agents. They are least inclined to bullshit; they’ll tell you how many people an act is drawing,”...like labels, publishers also have an A&R function, signing up songwriters, many of whom will also be in bands)....“Journalists and radio producers are [also] very useful people to give you information. If you know you’ve got particular DJs or particular writers who are going to pick up something, that’s really good.”
.......Instrumental’s selling point is a dashboard called Talent AI, which scrapes data from Spotify playlists with more than 10,000 followers.....“We took a view that to build momentum on Spotify, you need to be on playlists,”....“If no one knows who you are, no one’s going to suddenly start streaming a track you’ve just put up. It happens when you start getting included on playlists.”......To make it workable, the Talent AI dashboard enables users to apply a series of filters to either tracks or artists: to sort by nationality, by genre, by number of playlists they appear on, by the number of playlist subscribers, by their industry standing — are they signed to a major? To an independent label? Are they unsigned?
.......What A&R people are looking for, though, is not totals, it’s evidence of momentum. No one wants to sign the artist who has reached maximum popularity. They want the artist on the way up....“It’s the direction. Is it going in the right direction?”....when it comes to assessing what an artist can offer, the data isn’t even always about the numbers. “The one I look at the most is Instagram, because that’s the easiest way for an artist to express themselves in a way other than the music — how they look, what they’re into,” she says. “That gives a real snapshot into [them] and whether they really have formulated a world for themselves or not.”......not everyone is delighted with the drive to data. “[the advent of] Spotify...became the driving force for signings...“A&Rs were using their eyes rather than their ears — watching numbers change rather than listening to music, and then jumping on acts....they saw something happening and got it out quickly without having to invest in the traditional A&R process.”... online heat tends to be generated by transient teenage audiences who are likely to move on rather than stick around for a decade: online presence is a big thing in electronic dance music, or some branches of urban music, in which an artist might only be good for a single song. In short, data does not measure quality; it does not tell you whether an artist has 20 good songs that can be turned into their first two albums; it does not tell you whether they can command a crowd in live performance..........The music industry, of course, has always had an issue with short-termism/short-sightedness: [tension] between the people who sign the cheques and those who go to bat for the artists is built into the way it works..........The problem is that without career artists, the music industry just becomes even more of a lottery. It is being made harder, not just by short-termism, but by the fact that music has become less culturally central. “It’s so much harder to connect with an audience or grow an audience, because there’s so much noise,”
.......Today the A&R...agree that the new data has its uses, but insist it still takes second place to the evidence of their own eyes and ears.......As for Withey, he is not about to tell the old-school scouts their days are done....Instrumental can tell A&R people which artists are hot, but not which are good. Also, there will be amazing acts who simply don’t get the traction on the internet to register on the Talent AI dashboard.....All of which will come as a relief to the people running those A&R departments. .....when asked if data will become the single most important factor in scouting talent: “I hope not. Otherwise we may as well have robots.” For now, at least, the golden ears are safe.
A&R  algorithms  analytics  data  dashboards  tips  discoveries  filters  hits  Instagram  inefficiencies  momentum  music  music_industry  music_labels  music_publishing  Moneyball  myths  playlists  self-mythologize  songwriters  Spotify  SXSW  success_rates  talent  talent_spotting  tipping_points  tracking  YouTube  talent_scouting  high-quality  the_single_most_important 
july 2018 by jerryking
‘You’re Stupid If You Don’t Get Scared’: When Amazon Goes From Partner to Rival - WSJ
By Jay Greene and Laura Stevens
June 1, 2018

The data weapon
One Amazon weapon is data. In retail, Amazon gathered consumer data to learn what sold well, which helped it create its own branded goods while making tailored sales pitches with its familiar “you may also like” offer. Data helped Amazon know where to start its own delivery services to cut costs, an alternative to using United Parcel Service Inc. and FedEx Corp.

“In many ways, Amazon is nothing except a data company,” said James Thomson, a former Amazon manager who advises brands that work with the company. “And they use that data to inform all the decisions they make.”

In web services, data across the broader platform, along with customer requests, inform the company’s decisions to move into new businesses, said former Amazon executives.

That gives Amazon a valuable window into changes in how corporations in the 21st century are using cloud computing to replace their own data centers. Today’s corporations frequently want a one-stop shop for services rather than trying to stitch them together. A food-services firm, say, might want to better track data it collects from its restaurants, so it would rent computing space from Amazon and use a data service offered by a software company on Amazon’s platform to better analyze what customers order. A small business might use an Amazon partner’s online services for password and sign-on functions, along with other business-management programs.
21st._century  Amazon  AWS  brands  cloud_computing  contra-Amazon  coopetition  data  data_centers  data_collection  data_driven  delivery_services  fear  new_businesses  one-stop_shop  partnerships  platforms  private_labels  rivalries  small_business  strengths  tools  unfair_advantages 
june 2018 by jerryking
12 CRUCIAL QUESTIONS TO BETTER DECISION-MAKING:
May 31, 2018 | The Globe and Mail | HARVEY SCHACHTER.

Here are 12 crucial factors that consultant Nathan Magnuson says you should consider in decision-making:

* Are you the right person to make the decision?
* What outcomes are you directly respons...
benefits  clarity  core_values  costs  data  data_driven  decision_making  delighting_customers  long-term  managing_up  Occam's_Razor  personal_control  priorities  questions  the_right_people  what_really_matters 
may 2018 by jerryking
The digital economy is disrupting our old models
Diane Coyle 14 HOURS AGO

To put it in economic jargon, we are in the territory of externalities and public goods. Information once shared cannot be unshared.

The digital economy is one of externalities and public goods to a far greater degree than in the past. We have not begun to get to grips with how to analyse it, still less to develop policies for the common good. There are two questions at the heart of the challenge: what norms and laws about property rights over intangibles such as data or ideas or algorithms are going to be needed? And what will the best balance between collective and individual actions be or, to put it another way, between government and market?
mydata  personal_data  digital_economy  Facebook  externalities  knowledge_economy  public_goods  algorithms  data  ideas  intangibles  property_rights  protocols 
april 2018 by jerryking
Dump the PowerPoints and do data properly — or lose money
APRIL 15, 2018 | FT| Alan Smith.

So what can data analysts in organisations do to get their messages heard?

Board members and senior managers certainly need to consider new ways of thinking that give primacy to data. But reasoning with data requires what psychologist Daniel Kahneman describes as “System 2 thinking” — the rational, reasoning self — and a move away from the “gut intuition” of System 1. That’s not an easy culture change to achieve overnight.

Freelance consultant, author and data visualisation expert Andy Kirk believes there is a duty of care on both analysts and their audiences to develop skills, particularly in relation to how data is communicated through an organisation.......many senior managers “neither have the visual literacy nor the confidence to be exposed to [data presentations] they don't understand — and they just don't like change”. Mr Kirk describes it as a kind of “Stockholm syndrome” in data form — “I’ve always had my report designed like this, I don't want anything different”.......data analysts need to nurture their communication skills, taking a responsibility for encouraging change and critical thinking, not just being “the data people”. Acting as agents of change, they need to be effective marketers of their skills and sensitive educators that show a nuanced appreciation of the needs of the business. Organisations that bind data to the business model — and data literacy to the board — will inevitably stand a better chance of achieving long-term change.....The truth is that data in the boardroom enjoys a patchy reputation, typified by dull, overlong PowerPoint presentations. A cynic might suggest that even the most recent addition to boardroom structures — the chief data officer — is used by many boards simply as a device to prevent other members needing to worry about the numbers.

Here are 3 techniques that can be used to encourage progressive change in the boardroom.
(1) Use KPIs that are meaningful and appropriate for answering the central questions about the business and the market it operates in. Try to eliminate “inertia metrics” — i.e. “we report this because we always do”.

(2) Rework boardroom materials so that they encourage board members to read data, preferably in advance of meetings, rather than glance at it during one. This might mean transforming the dreaded PowerPoint deck into something a little more journalistic, a move that will help engage “System 2” thinking.

(3) Above all, be aware of unconscious bias in the boardroom and focus on debunking it. Most of us are poor intuitive statisticians with biases that lurk deep in our “System 1” view of the world. There is insight, value and memorability in the surprise that comes with highlighting our own ignorance — so use data to shine a light on surprising trends, not to simply reinforce that which is already known.
boards_&_directors_&_governance  change  data  data_driven  psychologists  absenteeism  storytelling  Communicating_&_Connecting  PowerPoint  change_agents  KPIs  Daniel_Kahneman  insights  surprises  gut_feelings 
april 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
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
To Survive in Tough Times, Restaurants Turn to Data-Mining
AUG. 25, 2017 | The New York Times | By KAREN STABINER.

“Silicon Valley looks at inefficiencies in the world, and they aim to disrupt the food space,” said Erik Oberholtzer, a founder and the chief executive of Tender Greens, a quick-service chain based in Los Angeles that is using data to guide its East Coast expansion.
data  data_mining  hard_times  inefficiencies  restaurants 
august 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
How Data Is Revolutionizing The Sports Business
March 10, 2017 | Forbes | By Robert Tuchman , CONTRIBUTOR who writes about live events, deals, and brand marketing.

A top-notch record might be chalked-up to the right players and exceptional coaching, but a team’s increased brand awareness can be credited to its effective use of newly sourced data. The Panthers have been able to grow its business in a multitude of ways since it started acquiring and using key fan data....[there is] an array of data companies who are looking to assist organizations in this area.

Many of these emerging companies access information through individual data systems, third-party vendors, and social media sites. Beyond educating teams about the buyer of their tickets, these companies are helping teams better understand the individuals entering their building. This insight is a game-changer for teams as it can help to better service existing fans and develop new ones. To better service its fans, the Panthers created unique events that catered to their interests, which they learned from their data. For example, in a game against the Colorado Avalanche, Florida hosted an evening honoring the Grateful Dead. The Panthers organization secured a well-known and beloved Florida cover band, Unlimited Devotion, to play the hits of the legendary musical icons. Incentivizing “Dead Heads” to purchase tickets via the Internet, limited edition memorabilia was made available only for online ticket purchasers, with a portion of the profits going to the Grateful Dead's non-profit organization. These types of cross promotions work best when you understand the specific interests of your fans.

And the results are in. The Miami Herald reported that during the 2015-2016 season, attendance went up 33.5 % from the previous season. In addition, season ticket renewals are reportedly increasing at four or five times last year’s rate......In today’s fragmented world, it is more important than ever for teams to generate loyalty and create a personalized customer experience. As in the case of the Florida Panthers, the greater involvement a fan may have in a team’s activities, the greater the possibility they migrate from their living rooms to the venue. More fans equal more sponsors, which leads to greater revenue for teams.

Data companies can help teams better understand its fans. Innovative sports franchises are figuring out how to use this data to create stronger engagements with their actual fans.
sports  data  data_driven  Moneyball  event-driven  events  event_marketing  fans  fan_engagement  musical_performances  cross-promotion  customer_loyalty  personalization  customer_experience 
august 2017 by jerryking
Your Roomba May Be Mapping Your Home, Collecting Data That Could Be Sold
JULY 25, 2017 | The New York Times | By MAGGIE ASTOR.

High-end models of Roomba, iRobot’s robotic vacuum, collect data as they clean, identifying the locations of your walls and furniture. This helps them avoid crashing into your couch, but it also creates a map of your home that iRobot is considering selling to Amazon, Apple or Google.

Colin Angle, chief executive of iRobot, told Reuters that a deal could come in the next two years, though iRobot said in a statement on Tuesday: “We have not formed any plans to sell data.”

In the hands of a company like Amazon, Apple or Google, that data could fuel new “smart” home products.

“When we think about ‘what is supposed to happen’ when I enter a room, everything depends on the room at a foundational level knowing what is in it,” an iRobot spokesman said in a written response to questions. “In order to ‘do the right thing’ when you say ‘turn on the lights,’ the room must know what lights it has to turn on. Same thing for music, TV, heat, blinds, the stove, coffee machines, fans, gaming consoles, smart picture frames or robot pets.”

But the data, if sold, could also be a windfall for marketers, and the implications are easy to imagine. No armchair in your living room? You might see ads for armchairs next time you open Facebook. Did your Roomba detect signs of a baby? Advertisers might target you accordingly.... iRobot said that it was “committed to the absolute privacy of our customer-related data.” Consumers can use a Roomba without connecting it to the internet, or “opt out of sending map data to the cloud through a switch in the mobile app.”

“No data is sold to third parties,” the statement added. “No data will be shared with third parties without the informed consent of our customers.”
data  mapping  privacy  location_based_services  LBMA  advertising  smart_homes  iRobot  homes  home_appliances  home_automation  home_based  informed_consent 
july 2017 by jerryking
The Pop-Up Employer: Build a Team, Do the Job, Say Goodbye -
JULY 12, 2017 | The New York Times | By NOAM SCHEIBER.

Two Stanford biz profs, Melissa Valentine and Michael Bernstein, have introduced the idea of “flash organizations” — ephemeral setups to execute a single, complex project in ways traditionally associated with corporations, nonprofit groups or governments.....information technology has made the flash organization a suddenly viable form across a number of industries.....intermediaries are already springing up across industries like software and pharmaceuticals to assemble such organizations. They rely heavily on data and algorithms to determine which workers are best suited to one another, and also on decidedly lower-tech innovations, like middle management......Temporary organizations capable of taking on complicated projects have existed for decades, e.g. Hollywood, where producers assemble teams of directors, writers, actors, costume and set designers and a variety of other craftsmen and technicians to execute projects with budgets in the tens if not hundreds of millions.....Jody Miller, a former media executive and venture capitalist, a co-founder of the Business Talent Group, sets up temporary teams of freelancers for corporations. “We’re the producers,” Ms. Miller said. “We understand how to evaluate talent, pick the team.”.....
Three lessons stand out across the flash-type models. First is that the platforms tend to be highly dependent on data and computing power....Second is the importance of well-established roles. ...Third, there is perhaps the least likely of innovations: middle management. The typical freelancer performs worker-bee tasks. Flash-like organizations tend to combine both workers and managers...........Flash organizations have obvious limits....they tend to work best for projects with well-defined life spans, not continuing engagements....“The bottleneck now is project managers,” ... “It’s a really tough position to fill.”.....even while fostering flexibility, the model could easily compound insecurity. Temporary firms are not likely to provide health or retirement benefits. ..... the anxiety is legitimate, but these platforms could eventually dampen insecurity by playing a role that companies have historically played: providing benefits, topping off earnings if workers’ freelance income is too low or too spotty, even allowing workers to organize.
pop-ups  freelancing  on-demand  ephemerality  producers  execution  Hollywood  project_management  teams  data  algo  lessons_learned  Business_Talent_Group  Gigster  Artella  Foundry  Slack  pharmaceutical_industry  Outsourcing  contractors  job_insecurity  middle_management  gig_economy  ad_hoc  dissolutions  short-term  short-lived 
july 2017 by jerryking
Big Prize in Amazon-Whole Foods Deal: Data - WSJ
By Laura Stevens and Heather Haddon
June 20, 2017

The deal for Whole Foods Market Inc., which people familiar with the matter said came together quickly, presents Amazon with several potential gains. It could use the stores as distribution hubs to build out its online grocery-delivery business. Amazon also could stock gadgets such as its Kindle e-readers and Echo speakers, as well as goods from its burgeoning private label.

The bigger opportunity, though, is data.

Amazon for years has been looking for more ways to gather information about how consumers shop. It has long been rumored to be on the prowl for a breakthrough deal, even as it set up its own much smaller Amazon Go and AmazonFresh Pickup stores as experiments.

If the deal goes through, the combination likely will be powerful. Amazon and Whole Foods can join their online and in-store knowledge to better predict what goods to carry in each store, said James Thomson, a former senior manager in business development at Amazon and now partner at the brand consultancy Buy Box Experts.....One enticing aspect of a deal between Amazon and Whole Foods is the significant overlap, analysts say, between the companies’ traditionally loyal customer bases.

A Morgan Stanley survey shows about 62% of Whole Foods shoppers are members of Amazon’s Prime service, opening the door for cross-sell promotions to entice customers who shop at both to spend more.

Amazon, though, doesn’t know how those customers shop in stores—a gaping hole in data about its more than 300 million shoppers.....Amazon has had a more difficult experiment with Amazon Go, its convenience-style store in which customers scan their phones as they walk in, pick up items to purchase and exit without a traditional checkout. The public opening has been delayed, in part because of technological hurdles and Amazon’s limited experience in managing the flow of customers and products in a physical space....

.......The data Amazon collects will likely help it decide which of its growing roster of private-label brands to expand and which new ones to launch, especially when it comes to consumables and food. Whole Foods already has a large private-label business...Bringing together online and offline data can help Amazon learn how to entice customers to make more impulse purchases online, according to analysts and retail consultants.
data  omnichannel  Amazon  Whole_Foods  physical_space  private_labels  impulse_purchasing  Amazon_Go  AmazonFresh  experimentation  cashierless  Amazon_Prime  cross-selling  in-store 
june 2017 by jerryking
The Data Behind Dining
FEB 7, 2017 | The Atlantic | BOURREE LAM.

Damian Mogavero, a dining-industry consultant, has analyzed the data behind thousands of restaurants—which dishes get ordered, which servers bring in the highest bills, and even what the weather’s like—and found that these metrics can help inform the decisions and practices of restaurateurs.....Mogavero recently wrote a book about analytics called The Underground Culinary Tour—which is also the name of an annual insider retreat he runs, in which he leads restaurateurs from around the nation to what he considers the most innovative restaurants in New York City, with 15 stops in 24 hours.....they really understood the business problem that I understood, as a frustrated restaurateur. There was not accessible information to make really important business decisions.

Lam: Why is it that the restaurant business tends to be more instinct-driven than data-driven?

Mogavero: It is so creative, and it really attracts innovative and creative people who really enjoy the art and the design of the guest experience. When I was a frustrated restaurateur, I would ask my chefs and managers simple questions, such as: Who are your top and bottom servers? Why did your food costs go up? Why did your labor costs go up? And they would give me blank stares, wrong answers, or make up stuff. The thing that really killed me is why so much time gets spent in administrative B.S.

They were frustrated artists in their own way, because all those questions I was posing were buried in a bunch of Excel spreadsheets. What I like to say is, nothing good ever happens at the back office. You can't make customers happy and you can’t cook great food there. That was the business problem that I saw. I assembled a chef, a sommelier, a restaurant manager, and three techies as the founding team of the company. The message was: We’re going to create software, so you can get back to what you love to do with a more profitable operation.......Mogavero: Because information is flowing so quickly, you’re likely to see trends from a big city go to a secondary city more often. But you’ll see regional trends come to the big city as well. It’s all part of this information flow that’s more transparent and faster. The secondary-market awakening is coupled with the fact that it’s really expensive for chefs to live in big cities, and we’re seeing many chefs leaving the big cities.
bullshitake  dining  data  books  restaurants  data_driven  New_York_City  innovation  restauranteurs  analytics  back-office  information_flows  secondary_markets 
may 2017 by jerryking
Three Hard Lessons the Internet Is Teaching Traditional Stores
April 23, 2017 | WSJ | By Christopher Mims.
Legacy retailers have to put their mountains of purchasing data to work to create the kind of personalization and automation shoppers are getting online
(1) Data Is King
When I asked Target, Walgreens and grocery chain Giant Food about loyalty programs and the fate of customers’ purchasing data—which is the in-store equivalent of your web browsing history—they all declined to comment. ...Data has been a vital part of Amazon’s retail revolution, just as it was with Netflix ’s media revolution and Google and Facebook ’s advertising revolution. For brick-and-mortar retailers, purchasing data doesn’t just help them compete with online adversaries; it has also become an alternate revenue source when profit margins are razor-thin. ....Physical retailers must catch up to online retailers in collecting rich data without making it feel so intrusive. Why, exactly, does my grocery store need my phone number?

(2) Personalization + Automation = Profits
Personalization and Automation = Profits
There’s a debate in the auto industry: Can Tesla get good at making cars faster than Ford, General Motors and Toyota can get good at making self-driving electric vehicles? The same applies to retail: Can physical retailers build intimate digital relationships with their customers—and use that data to update their stores—faster than online-first retailers can learn how to lease property, handle inventory and manage retail workers? [the great game ]

Online retailers know what’s popular, and how customers who like one item tend to like certain others. So Amazon’s physical bookstores can put out fewer books with more prominently displayed covers. Bonobos doesn’t even sell clothes in its stores, which it calls “guideshops.” Instead, customers go there to try clothes on, and their selections are delivered through the company’s existing e-commerce system.

Amazon’s upcoming Go convenience stores, selling groceries and meal kits, don’t require cashiers. That’s the sort of automation that could position Amazon to reap margins—or slash prices—to a degree unprecedented for retailers in traditionally low-margin categories like food and packaged goods.

While online retailers are accustomed to updating inventory and prices by the hour, physical retailers simply don’t have the data or the systems to keep up, and tend to buy and stock on cycles as long as a year, says George Faigen, a retail consultant at Oliver Wyman. Some legacy retailers are getting around this by teaming up with online players.

Target stocks men’s shaving supplies from not one but two online upstarts, Harry’s and Bevel. Target has said that, as a result, more customers are coming in to buy razors, increasing the sales of every brand on that aisle—even good old Gillette. Retailers have long relied on manufacturers to drive customers to stores by marketing their goods and even managing in-store displays. The difference is this: In the past, new brands had to persuade store buyers to dole out precious shelf space; now the brands can prove themselves online first.

(3) Legacy Tech Won’t Cut It

Perhaps the biggest challenge for existing retailers, says Euromonitor’s Ms. Grant, is finding the money to transition to this hybrid online-offline model. While Target has announced it will spend $7 billion over the next three years to revamp its stores, investors fled the stock in February after Target reported 2017 profits might be 25% less than expected.

When Warby Parker, the online eyeglasses retailer, set out to launch stores across the U.S., the company looked for in-store sales software that could integrate with its existing e-commerce systems. It couldn’t find a system up to the task, so it built one from scratch.

These kinds of systems allow salespeople to know what customers have bought both online and off, and what they might be nudged toward on that day. “We call it the ‘point of everything’ system,” says David Gilboa, co-founder and co-chief executive.

Having this much customer knowledge available instantly is critical, but it’s precisely what existing retailers struggle with, Mr. Faigen says.

Even Amazon is experiencing brick-and-mortar difficulties. In March, The Wall Street Journal reported that the Go stores would be delayed because of kinks in the point-of-sale software system.

Andy Katz-Mayfield, co-founder and co-chief executive of Harry’s, is skeptical that traditional retailers like Wal-Mart can make the leap, even if they invest heavily in technology.

The problem, he says, is that selling online isn’t just about taking orders through a website. Companies that succeed are good at selling direct to consumers—building technology from the ground up, integrating teams skilled at navigating online marketing’s ever-shifting terrain and managing the experience through fulfillment and delivery, Mr. Katz-Mayfield says.

That e-commerce startups are so confident about their own future doesn’t mean they are right about the fate of traditional retailers, however.

A report from Merrill Lynch argues Wal-Mart is embarking on a period of 20% to 30% growth for its e-commerce business. A spokesman for the company said that in addition to acquisitions, the company is focused on growing its e-commerce business organically.

It isn’t hard to picture today’s e-commerce companies becoming brick-and-mortar retailers. It’s harder to bet on traditional retailers becoming as tech savvy as their e-competition.[the great game]
lessons_learned  bricks-and-mortar  retailers  curation  personalization  e-commerce  shopping_malls  automation  privacy  Warby_Parker  Amazon_Go  data  data_driven  think_threes  Bonobos  Amazon  legacy_tech  omnichannel  Harry’s  Bevel  loyalty_management  low-margin  legacy_players  digital_first  Tesla  Ford  GM  Toyota  automobile  electric_cars  point-of-sale  physical_world  contra-Amazon  brands  shelf_space  the_great_game  cyberphysical  cashierless  Christopher_Mims  in-store  digital_savvy 
april 2017 by jerryking
Steve Ballmer Serves Up a Fascinating Data Trove - The New York Times
Andrew Ross Sorkin
DEALBOOK APRIL 17, 2017
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Steve_Ballmer  government  Andrew_Sorkin  databases  data  measurements  economics  indicators  real-time  forecasting  economic_data 
april 2017 by jerryking
Explosion in data ushers in new high-tech era.pdf
December 5, 2016 | Financial Times | Ian Whylie.

However, one of the consequences of the introduction of AI into consulting will be greater clarification of consulting methodologies, predicts Harvey Lewis, Deloitte's UK artificial intelligence lead in technology consulting. There will be repeatable, common approaches that are supported by machines, and then a class of essentially human approaches for dealing with more varied, wide-ranging and uncertain problems........However, AI could also have a significant impact on the way strategy consultants do their job.

“Consulting firms have a lot of intellectual property locked up inside their consultants’ heads, which, if codified and converted into algorithms, can be used by computers instead,” he says. “This will allow computers to work on the repeatable consulting tasks by following prescribed methodologies, while the human consultants are freed to work on those projects where inputs, outputs and outcomes are more uncertain or which require greater creativity, subjectivity, social interaction and perceptiveness or human judgment.”

Clients want us to arrive, ready to load in their data, and provide insights on the first day of the project
Paul Daugherty. If consulting can be codified then the cost of performing certain types of consulting work is likely to fall, says Mr Lewis. This means that consulting can be offered to more organisations, such as start-ups, small and medium enterprises and charities that might not previously have been able to afford consulting services.

“The days of old-style consulting, where the work was centred around a bunch of people mulling over a PowerPoint presentation and analysis for the client, are either dead or dying fast,” says Mr Daugherty. “Increasingly, strategy consulting is moving to fast-paced database analysis, supported by machine learning. Clients will want us to arrive, ready to load in their data, understand the situation and particular dynamics of their business and provide insights on the first day of the project.”
Accenture  artificial_intelligence  automation  data  fast-paced  insights  machine_learning  management_consulting  PowerPoint  situational_awareness  virtual_agents 
april 2017 by jerryking
How Goldman Sachs Made More Than $1 Billion With Your Credit Score - WSJ
By LIZ HOFFMAN and ANNAMARIA ANDRIOTIS
April 9, 2017

Goldman bought TransUnion , TRU -0.21% the smallest of the three main credit-reporting firms, in 2012. By the time it went public three years later, TransUnion had become a data-mining machine, gathering billions of seemingly insignificant tidbits about ordinary Americans that it analyzed and sold to lenders, insurers and others.....As Goldman and Advent dug into TransUnion’s business, they found the fastest-growing revenue was coming from the company’s dealings with online lending startups, people familiar with the investment said.

These companies, such as LendingClub Corp. and Prosper Marketplace Inc., were using information from credit bureaus to find and vet potential borrowers. They were increasingly hungry for data that could pinpoint borrowers who traditional lenders might overlook or overcharge.....TransUnion’s new owners doubled down on these clients. They recruited Jim Peck, a big-data enthusiast who had run LexisNexis Risk Solutions, as CEO. He spent his first day in the company’s data center.

TransUnion began appearing at fintech conferences. It rebranded itself with a techy, purposeful vibe, wrapping its initials, a lowercase “tu,” in an @ sign. “We’re not just a credit bureau; we’re a force for good,” chirped a 2015 video.

The company spent heavily on technology and acquisitions. It replaced its old mainframe, a relic from the 1970s, with nimbler systems that allow it to splice information in new ways. It built a new data center and started scooping up small companies with niche data sets.....One acquisition tracks public records to help with fraud enforcement related to online shopping, among other things. Another uses utility payments, cellphone billing records and other data points to identify creditworthy borrowers who lenders might have overlooked, either because they have little or no debt history or potential red flags on their traditional credit reports. ​ ​​ ​​....By the time of its IPO in 2015, TransUnion had 30 million gigabytes of data, growing at 25% a year and ranging from voter registration in India to drivers’ accident records in the U.S. The company’s IPO documents boasted that it had anticipated the arrival of online lenders and “created solutions that catered to these emerging providers.”

Goldman itself is a customer. In 2016, the Wall Street firm launched Marcus to make online personal loans of a few thousand dollars. Its main pitch to borrowers: refinance expensive credit-card debt at lower rates.

Goldman buys the names and credit information of potential borrowers from TransUnion and sends direct-mail and other advertising to them.
Goldman_Sachs  TransUnion  Advent  private_equity  credit_reporting  credit_scoring  Equifax  Experian  data  data_driven  Marcus  subprime  solution-finders 
april 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
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
Thomas Friedman’s Guide to Hanging On in the ‘Age of Accelerations’ - Bloomberg
by Paul Barrett
November 11, 2016,

Thank You for Being Late: An Optimist’s Guide to Thriving in the Age of Accelerations (Farrar, Straus & Giroux, $28)....the wisdom of pausing.... take time “to just sit and think”— a good reminder for the overcommitted.....Friedman's “core argument,” is his description of our disruptive times. By “accelerations,” he means the increases in computing power, which are enabling breakthroughs from 3D printing to self-driving cars. Meanwhile, globalization is creating vast wealth for those who capitalize on innovation and impoverishment for populations who don’t. All of this sped-up economic activity contributes to rising carbon levels, feeding the climate change that threatens civilization.....Friedman relishes catchphrases like “the Big Shift,” borrowed in this case from the HBR. He deploys B-school jargon to explain it, but the definition boils down to companies making the move from relying exclusively on in-house brainpower, patents, and data to exploiting “flows” of knowledge from anywhere in the world.... Friedman makes the case for changed policies to respond to the accelerations he chronicles.
accelerated_lifecycles  sustained_inquiry  Tom_Friedman  books  slack_time  reflections  3-D  globalization  impoverishment  climate_change  in-house  talent_flows  information_flows  GE  prizes  bounties  innovation  contests  contemplation  patents  data  brainpower  jargon  thinking  timeouts  power_of_the_pause 
january 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
Uber Extends an Olive Branch to Local Governments: Its Data
JAN. 8, 2017 | - The New York Times | By MIKE ISAAC.

unveiled Movement, a stand-alone website it hopes will persuade city planners to consider Uber as part of urban development and transit systems in the future.

The site, which Uber will invite planning agencies and researchers to visit in the coming weeks, will allow outsiders to study traffic patterns and speeds across cities using data collected by tens of thousands of Uber vehicles. Users can use Movement to compare average trip times across certain points in cities and see what effect something like a baseball game might have on traffic patterns. Eventually, the company plans to make Movement available to the general public.
municipalities  urban  urban_planning  cities  Boston  partnerships  traffic_patterns  Uber  Movement  data  data_driven 
january 2017 by jerryking
Edward Tufte: Courses
"Edward Tufte's one-day course on "Presenting Data and Information" is the best value-for-money that you can spend if you are involved in any way in presentation of information to users. When I receive this new schedule of these courses each year I get to thinking whom do I know whose career might change for the better if they take this course. I've taken it twice (the content is always up to date with the latest examples of both good and bad information design). Every attendee gets copies of Tufte's four major works on visual display of information. Tufte offers a group discount so your company can send a whole department or product team. And there's a steep discount for full-time students, faculty members, and postdocs.
training  design  classes  Edward_Tufte  presentations  data  infographics  visualization 
january 2017 by jerryking
Abe Ankumah of Nyansa: Are You a ‘First Principle’ Thinker? - The New York Times
Corner Office
By ADAM BRYANT DEC. 2, 2016

We tend to be very “first principle” thinkers. What I mean by that is when you’re trying to solve a problem, you start by trying to understand the essence of the problem, rather than starting with what the answer should be and then working your way to justifying it.

So it’s all about making sure that everyone understands the problem we’re trying to solve. And to do that, you have to maintain a broader perspective and listen very carefully to people.

I have one-on-ones with every single person on the team and then connect the dots. So I ask a lot of questions and build a mental model of the outline of what we need to do.
data  African-Americans  HBS  engineering  Caltech  Ghanian  connecting_the_dots  problem_solving  first_principle  mental_models 
december 2016 by jerryking
Auction houses embracing digital technology to sell to the new global rich
SEPTEMBER 18, 2014 by: John Dizard.

....The auction houses have been under pressure to adapt to this changing universe. While the most visible aspect of the houses’ digital revolution may be their online auctions, the most essential is in the systematising and networking of their customer, market and lot information. Without that, the auctioneers would lose control of their ability to charge gross margins in the mid-teens as intermediaries of the $30bn global art auction market....Within the quasi-duopoly of Christie’s and Sotheby’s at the top of the auction world, Christie’s has now moved to implement what it calls its “digital strategy”....Christie’s now has James Map (as in founder James Christie), a sort of private internal social network that allows specialists, client service staff, support staff and executives to see what is known about a client and his tastes. Past auction records, relatives’ purchases and sales, statistical inferences on how likely clients are to move from buying an expensive watch online to participating in a high-end evening sale – it all can be in the mix.

The idea, Murphy explains, was “to create an internal app that spiders into our database of information and brings up on our internal [screen] environment lots of connectivity. This is faster and better than the email chains [that it replaced].”....This summer, Sotheby’s announced a partnership with eBay, the online auction giant. While the details of the partnership are still being developed, it is understood eBay will distribute live Sotheby’s auctions to its global audience of 150m buyers.

Ken Citron, Christie’s head of IT

The digital strategy is also making it easier to take part in auctions. Even with all the unseen know-your-customer checks now required by financial supervisory agencies, it has become much faster and easier to register as an auction house client. About half now do so online.

But while the online revolution may have left some auction houses behind, for others it is generating new business. Auction houses used to regard the sale of smaller, cheaper objects from, for example, estate liquidations as an annoying loss-leader business that just wasted their specialists’ time. Now, however, many are making money selling objects for $2,000-$3,000; it’s just a matter of cutting transaction costs. “We have a new app with which you can take a picture, push a button, and it goes to a specialist, with a description. Then the specialist can decide if it might fit into an auction,” says Citron.
auctions  Sotheby's  Christie's  data  art  collectors  high_net_worth  partnerships  eBay  duopolies  digital_strategies  CRM  IT  margins  intermediaries  internal_systems  loss-leaders  transaction_costs  cost-cutting  know_your_customer  Bottom_of_the_Pyramid  estate_planning  liquidity_events  online_auctions  digital_revolution 
november 2016 by jerryking
VC Pioneer Vinod Khosla Says AI Is Key to Long-Term Business Competitiveness - CIO Journal. - WSJ
By STEVE ROSENBUSH
Nov 15, 2016

“Improbables, which people don’t pay attention to, are not unimportant, we just don’t know which improbable is important,” Mr. Khosla said. “So what do you do? You don’t plan for the highest likelihood scenario. You plan for agility. And that is a fundamental choice we make as a nation, in national defense, as the CEO of a company, as the CIO of an infrastructure, of an organization, and in the way we live.”....So change, and predictions for the future, that are important, almost never come from anybody who knows the area. Almost anyone you talk to about the future of the auto industry will be wrong on the auto industry. So, no large change in a space has come from an incumbent. Retail came from Amazon. SpaceX came from a startup. Genentech did biotechnology. Youtube, Facebook, Twitter did media … because there is too much conventional wisdom in industry. ....Extrapolating the past is the wrong way to predict the future, and improbables are not unimportant. People plan around high probability. Improbables, which people don’t pay attention to, are not unimportant, we just don’t know which improbable is important.
Vinod_Khosla  artificial_intelligence  autonomous_vehicles  outsiders  gazelles  unknowns  automotive_industry  change  automation  diversity  agility  future  predictions  adaptability  probabilities  Uber  point-to-point  public_transit  data  infrastructure  information_overload  unthinkable  improbables  low_probability  extrapolations  pay_attention 
november 2016 by jerryking
Wall Street’s Insatiable Lust: Data, Data, Data
By BRADLEY HOPE
Updated Sept. 12, 2016

One of his best strategies is to attend the most seemingly mundane gatherings, such as the Association for Healthcare Resource & Materials Management conference in San Diego last year, and the National Industrial Transportation League event in New Orleans.

“I walk the floor, try to talk to companies and get a sense within an industry of who collects data that could provide a unique insight into that industry,” he said.....Data hunters scour the business world for companies that have data useful for predicting the stock prices of other companies. For instance, a company that processes transactions at stores could have market-moving information on how certain products or brands are selling or a company that provides software to hospitals could give insights into how specific medical devices are being used......A host of startups also are trying to make it easier for funds without high-powered data-science staffers to get the same insights. One, called Quandl Inc., based in Toronto, offers a platform that includes traditional market data alongside several “alternative” data....
alternative_data  conferences  data  data_hunting  hedge_funds  insights  investors  exhaust_data  market_moving  medical_devices  mundane  private_equity  Quandl  quants  sentiment_analysis  unconventional  unglamorous  Wall_Street 
september 2016 by jerryking
Advice for Data Scientists on Where to Work | Stitch Fix Technology – Multithreaded
It's a good time to be a data scientist. If you have the skills, experience, curiosity and passion, there is a vast and receptive market of companies to choose from. Yet there is much to consider when evaluating a prospective firm as a place to apply your talents. Even veterans may not have had the opportunity to experience different organizations, stages of maturity, cultures, technologies, or domains. We are amalgamating our combined experience here to offer some advice - three things to look for in a company that could make it a great place to work.

Work for a Company that Leverages Data Science for its Strategic Differentiation

Companies employ various means of differentiation in order to gain a competitive advantage in the market. Some differentiate themselves using price, striving to be the low-price leader. Others differentiate by product, providing an offering that is superior in some way. Still others differentiate by their processes - for example providing faster shipping.

A Data Scientist should look for a company that actually uses data science to set themselves apart from the competition. Note that data science may be supportive of lower prices, better products, and faster shipping, however, it is not typically the direct enabler of these differentiators. More commonly, the enablers are other things - economies of scale in the case of lower prices, patents or branding in the case of product, and automation technology in the case of faster shipping. Data science can directly enable a strategic differentiator if the company's core competency depends on its data and analytic capabilities. When this happens, the company becomes supportive to data science instead of the other way around. It's willing to invest in acquiring the top talent, building the necessary infrastructure, pioneering the latest algorithmic and computational techniques, and building incredible engineering products to manifest the data science.

"Good enough" is not a phrase that is uttered in the context of a strategic differentiator. Rather, the company and the data scientist have every incentive to push the envelope, to innovate further, and to take more risks. The company's aspirations are squarely in-line with that of the data scientist's. It's an amazing intersection to be at – a place that gets you excited to wake up to every morning, a place that stretches you, a place that inspires you (and supports you) to be the best in the world at what you do.

Work for a Company with Great Data

In determining what will be a great company to work for, data-science-as-a-strategic-differentiator is a necessary criteria, but it is not sufficient. The company must also have world-class data to work with.

This starts with finding a company that really has data. Spotting the difference between data and aspirations of data can be especially important in evaluating early-stage companies. Ideally you'll find a company that already has enough data to do interesting things. Almost all companies will generate more data as they grow, but if you join a company that already has data your potential for impact and fulfillment will be much higher.

Next look for data that is both interesting and that has explanatory power. One of the most important aspects of your daily life will be the extent to which you find the data you work with compelling. Interesting data should require your creativity to frame problems, test your intuition and push you to develop new algorithms and applications. Explanatory power is just as important - great data enables great applications. There should be enough signal to support data science as a differentiating strength.

Finally, don't fixate on big data. The rising prominence of the data scientist has coincided with the rise of Big Data, but they are not the same thing. Sheer scale does not necessarily make data interesting, nor is it necessarily required. Look for data with high information density rather than high volume, and that supports applications you find interesting or surprising. This enables you to spend most of your mental energy on analysis and framing rather than on efficient data processing.

Work for a Company with Greenfield Opportunities

When evaluating opportunities, find a company that doesn't have it all figured out yet. Nearly all companies that fit the criteria in the sections above will already have some applications in place where the work of data scientists is essential. Look for those companies that have a strong direction and strongly established data science teams, but have an array of problems they are solving for the first time.

Often the most exciting and impactful opportunities for data scientists at a company are not being actively pursued. They probably have not even been conceived of yet. Work somewhere that encourages you to take risks, challenge basic assumptions, and imagine new possibilities.

Observing the relationship between engineering and data science teams is a quick way to determine if an organization adopts this mindset. Is engineering enthusiastic to partner with data science teams to experiment and integrate ideas back into the business? Is there an architecture in place that supports agile integration of new ideas and technologies? In fact, in companies that embody this mindset most effectively, it is likely difficult to locate the boundary between data science and engineering teams.

A greenfield can be intimidating in its lack of structure, but the amount of creativity and freedom available to you as a data scientist is never greater than when you're starting from scratch. The impact of putting something in place where nothing existed previously can be immeasurable. Look for chances to be involved in designing not just the math and science, but also the pipeline, the API, and the tech stack. Not only is creating something new often more challenging and rewarding, but there is no better opportunity for learning and growth than designing something from the ground up.

Incremental improvements have incremental impacts, but embrace the chance to operate on a greenfield. While it is extremely important to constantly iterate and improve on systems that already exist, the Version 1 of something new can fundamentally change the business.

Summary

Of course, there are other considerations: domain, the company's brand, the specific technology in use, the culture, the people, and so forth. All of those are equally important. We call out the three above since they are less frequently talked about, yet fundamental to a data scientist's growth, impact, and happiness. They are also less obvious. We learned these things from experience. At first glance, you would not expect to find these things in a women's apparel company. However, our very different business model places a huge emphasis on data science, enables some of the richest data in the world, and creates space for a whole new suite of innovative software.
career  strategy  via:enochko  economies_of_scale  data_scientists  job_search  Managing_Your_Career  greenfields  data  differentiation  good_enough  information_density  product_pipelines  think_threes 
september 2016 by jerryking
A Danish Wind Turbine Maker Harnesses Data in a Push to Stay Ahead - The New York Times
By STANLEY REEDAUG. 18, 2016
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alternative_energy  green  renewable  wind_power  Danish  Denmark  data 
august 2016 by jerryking
U.S. Cyber Command Chief on What Threats to Fear the Most - WSJ
June 19, 2016 | WSJ |

But the types of threats that we worry most about today that are new are adversaries taking full control of our networks, losing control of our networks, having a hacker appear to be a trusted user......MS. BLUMENSTEIN: Extraordinary investments are required now for cybersecurity. But looked at another way, there’s an extraordinary cost to getting it wrong.

I was talking to one of the CFOs out there who said, “Can you ask, what is the estimated loss?” Is there a total number? Or do you just know specific incidences?

On the military side, you can imagine the difficulty that would cause a commander, if he didn’t trust his own network or his data.
adversaries  cyber_security  cyber_warfare  threats  North_Korea  ISIS  network_risk  capabilities  Russia  China  Sony  data  Pentagon  U.S._Cyber_Command  cyberattacks 
june 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
Gearing Up for the Cloud, AT&T Tells Its Workers: Adapt, or Else - The New York Times
FEB. 13, 2016| NYT | By QUENTIN HARDY.

For the company to survive in this environment, Mr. Stephenson needs to retrain its 280,000 employees so they can improve their coding skills, or learn them, and make quick business decisions based on a fire hose of data coming into the company.....Learn new skills or find your career choices are very limited.

“There is a need to retool yourself, and you should not expect to stop,”....People who do not spend five to 10 hours a week in online learning, he added, “will obsolete themselves with the technology.” .......By 2020, Mr. Stephenson hopes AT&T will be well into its transformation into a computing company that manages all sorts of digital things: phones, satellite television and huge volumes of data, all sorted through software managed in the cloud.

That can’t happen unless at least some of his work force is retrained to deal with the technology. It’s not a young group: The average tenure at AT&T is 12 years, or 22 years if you don’t count the people working in call centers. And many employees don’t have experience writing open-source software or casually analyzing terabytes of customer data. .......By 2020, Mr. Stephenson hopes AT&T will be well into its transformation into a computing company that manages all sorts of digital things: phones, satellite television and huge volumes of data, all sorted through software managed in the cloud.

.......“Everybody is going to go face to face with a Google, an Amazon, a Netflix,” he said. “You compete based on data, and based on customer insights you get with their permission. If we’re wrong, it won’t play well for anyone here.
Quentin_Hardy  AT&T  cloud_computing  data  retraining  reinvention  skills  self-education  virtualization  data_scientists  new_products  online_training  e-learning  customer_insights  Google  Amazon  Netflix  data_driven 
february 2016 by jerryking
Looking Beyond the Internet of Things
JAN. 1, 2016 | NYT | By QUENTIN HARDY.

Adam Bosworth is building what some call a “data singularity.” In the Internet of Things, billions of devices and sensors would wirelessly connect to far-off data centers, where millions of computer servers manage and learn from all that information.

Those servers would then send back commands to help whatever the sensors are connected to operate more effectively: A home automatically turns up the heat ahead of cold weather moving in, or streetlights behave differently when traffic gets bad. Or imagine an insurance company instantly resolving who has to pay for what an instant after a fender-bender because it has been automatically fed information about the accident.

Think of it as one, enormous process in which machines gather information, learn and change based on what they learn. All in seconds.... building an automated system that can react to all that data like a thoughtful person is fiendishly hard — and that may be Mr. Bosworth’s last great challenge to solve....this new era in computing will have effects far beyond a little more efficiency. Consumers could see a vast increase in the number of services, ads and product upgrades that are sold alongside most goods. And products that respond to their owner’s tastes — something already seen in smartphone upgrades, connected cars from BMW or Tesla, or entertainment devices like the Amazon Echo — could change product design.
Quentin_Hardy  Industrial_Internet  data  data_centers  data_driven  machine_learning  Google  Amazon  cloud_computing  connected_devices  BMW  Tesla  Amazon_Echo  product_design  Michael_McDerment  personalization  connected_cars 
january 2016 by jerryking
More foreign buyers snapping up Canadian condos: CMHC - The Globe and Mail
TAMSIN MCMAHON - REAL ESTATE REPORTER
The Globe and Mail
Published Thursday, Dec. 03, 2015

The federal housing agency said it has struggled to get a full picture of foreign purchaser activity in the housing market and that its numbers are far lower than those in other studies. ....

The only data that CMHC has on foreign buyers are from surveys of property managers and condo boards, who are asked to identify non-resident condo owners. The survey doesn’t measure the number of overall home sales in a given year to foreign investors, nor whether foreign owners are buying units for themselves and family members or purely as speculative investments. CMHC also includes Canadians who now live abroad but who still own property in the country as part of its definition of foreign owner.....“We’re trying to work with other people and make an effort to get these data gaps solved so we can have more information about what some are saying is an important part of the market,” Mr. Dugan said. “You can see from our data that the rate of foreign ownership seems relatively low, certainly not the kind of levels that some other studies might suggests.
real_estate  CMHC  offshore  data  condominiums  information_gaps  housing  dark_data 
december 2015 by jerryking
Toronto aims to use data for traffic insight - The Globe and Mail
OLIVER MOORE - URBAN TRANSPORTATION REPORTER
The Globe and Mail
Published Friday, Oct. 02, 2015
Toronto  data  transportation  hackathons  analytics  traffic_congestion  John_Tory  GPS  location_based_services  LBMA 
october 2015 by jerryking
When Big Data Isn’t an Option
May 19, 2014 / Summer 2014 / Strategy + Business | by David Meer
When Big Data Isn’t an Option
Companies that only have access to “little data” can still use that information to improve their business.

Many companies—probably most—work in relatively sparse data environments, without access to the abundant information needed for advanced analytics and data mining. For instance, point-of-sale register data is not standard in emerging markets. In most B2B industries, companies have access to their own sales and shipment data but have little visibility into overall market volumes or what their competitors are selling. Highly specialized or concentrated markets, such as parts suppliers to automakers, have only a handful of potential customers. These companies have to be content with what might be called little data—readily available information that companies can use to generate insights, even if it is sparse or of uneven quality....the beverage manufacturer developed an algorithm based on observable characteristics, then asked its sales professionals to classify all the bars and restaurants in their territories based on the algorithm. (This is a classic little data technique: filling in the data gaps internally.)

. Little data techniques, therefore, can include just about any method that gives a company more insight into its customers without breaking the bank. As the examples above illustrate, mining little data doesn’t mean investing in expensive data acquisition, hardware, software, or technology infrastructure. Rather, companies need three things:

• The commitment to become more fact-based in their decision making.

• The willingness to learn by doing.

• A bit of creativity. ...

The bottom line: Companies have to put in the extra effort required to capture and interpret data that is already being generated.
small_data  data  analytics  data_driven  market_segmentation  observations  call_centres  insights  data_quality  data_capture  interpretation  point-of-sale  mindsets  creativity 
september 2015 by jerryking
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