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Silicon Valley Startups Providing the Cannabis Industry with Data-Driven Tools
November 18, 2015 | High Times | by ??

Startups are providing a myriad of service to the legal marijuana industry—from Eaze, which connects dispensaries with customers, and Flowhub, which helps optimize growing factors for cultivators, to PotBiotics, a resource for doctors looking for medical research, and Leafly, which gathers customer reviews of dispensaries.

This new technology is providing insights and statistics, which until very recently were impossible to find.

“You couldn’t collect this information [previously] because all the transactions and purchases were conducted in the shadows via an illicit market,” Brendan Kennedy, the CEO of a private-equity firm that owns three pot businesses, told WSJ.
data_driven  cannabis  illicit  Silicon_Valley  start_ups  tools 
9 weeks ago by jerryking
Big data: legal firms play ‘Moneyball’
February 6, 2019 | Financial Times | Barney Thompson.

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

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

Having made an impact in patent cases these legal analytics companies are now expanding into a broad range of areas of commercial law. This is not about replacing judges,” says Daniel Lewis, co-founder of Ravel Law, a San Francisco lawtech company that built the database of judicial behaviour. “It is about showing how they make decisions, what they find persuasive and the patterns of how they rule.” 
analytics  data_driven  judges  law  law_firms  lawtech  lawyers  Lex_Machina  massive_data_sets  Moneyball  predictive_modeling  quantitative  tools 
february 2019 by jerryking
Platform companies have to learn to share
August 19, 2018 | Financial Times | Rana Foroohar.

Algorithmic management places dramatically more power in the hands of platform companies. Not only can they monitor workers 24/7, they benefit from enormous information asymmetries that allow them to suddenly deactivate drivers with low user ratings, or take a higher profit margin from riders willing to pay more for speedier service, without giving drivers a cut. This is not a properly functioning market. It is a data-driven oligopoly that will further shift power from labour to capital at a scale we have never seen before......Rather than wait for more regulatory pushback, platform tech companies should take responsibility now for the changes they have wreaked — and not just the positive ones. That requires an attitude adjustment. Many tech titans have a libertarian bent that makes them dismissive of the public sector as a whole.......Yet the potential benefits of ride-hailing and sharing — from less traffic to less pollution — cannot actually be realised unless the tech companies work with the public sector. One can imagine companies like Uber co-operating with city officials to phase in vehicles slowly, rolling out in underserved areas first, rather than flooding the most congested markets and creating a race to the bottom......Airbnb...often touts its ability to open up new neighbourhoods to tourism, but research shows that in cities like New York, most of its business is done in a handful of high end areas — and the largest chunk by commercial operators with multiple listings, with the effect of raising rents and increasing the strains caused by gentrification. On the labour side, too, the platform companies must take responsibility for the human cost of disruption. NYU professor Arun Sundararajan, has proposed allowing companies to create a “safe harbour” training fund that provides benefits and insurance for drivers and other on-demand workers without triggering labour laws that would categorise such workers as full-time employees (which is what companies want to avoid).
Airbnb  algorithms  dark_side  data_driven  gig_economy  information_asymmetry  New_York_City  oligopolies  on-demand  platforms  public_sector  Rana_Foroohar  ride_sharing  sharing_economy  safe_harbour  training  Uber 
august 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.
Amazon  AWS  cloud_computing  coopetition  partnerships  private_labels  fear  data_centers  unfair_advantages  data  data_driven  delivery_services  21st._century  brands  new_businesses  strengths  platforms  small_business  tools  rivalries 
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 
may 2018 by jerryking
The Quarterback of the Kitchen? It’s Not Always the Chef - The New York Times
By Tejal Rao

April 17, 2018

You’re most likely to notice it in the abstract, if you notice it at all. The work of a good expediter is in the pacing of your dinner. It’s in the steadiness of the room. It’s in the sense that everyone in the restaurant is moving to a single, unbreakable rhythm.
cooking  management  restaurants  expediting  data_driven 
may 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
What Land Will Be Underwater in 20 Years? Figuring It Out Could Be Lucrative
Feb. 23, 2018 | The New York Times | By Brad Plumer

In Charleston, S.C., where the ports have been expanding to accommodate larger ships sailing through the newly widened Panama Canal, a real-estate developer named Xebec Realty recently went looking for land to build new warehouses and logistics centers.

But first, Xebec had a question: What were the odds that the sites it was considering might be underwater in 10 or 20 years?......Yet detailed information about the city’s climate risks proved surprisingly hard to find. Federal flood maps are based on historical data, and won’t tell you how sea-level rise could exacerbate flooding in the years ahead.....So Xebec turned to a Silicon Valley start-up called Jupiter, which offered to analyze local weather and hydrological data and combine it with climate model projections to assess the potential climate risks Xebec might face in Charleston over the next few decades from things like heavier rainfall, sea level rise or increased storm surge....the reliability of Jupiter's predictive analytics is uncertain....that said, “In economics, information has value if you would make a different decision based on that information,”...... Congress has generally underfunded initiatives such as those at the Federal Emergency Management Agency to incorporate climate change into its federal flood maps.......to get a full picture of flooding risk, you need expertise in weather, but also climate and hydrology and engineering and running complex models on the latest computer hardware,” ... “All of those specialized disciplines are usually heavily siloed within the public sector or the scientific community.”....Jupiter, which acknowledges the uncertainties in climate forecasting, will have to prove that a market exists....flooding and other disasters have led to record losses by insurers.....[Those] losses raised the stakes in terms of trying to get the best possible science on your side when you’re pricing risk,” said John Drzik, president of global risk at Marsh,
climate_change  weather  start_ups  data_driven  forecasting  predictive_analytics  tools  Charleston  South_Carolina  uncertainty  sea-level_rise  floods  commercial_real_estate  adaptability  specificity  catastrophes  catastrophic_risk  unpredictability  coastal  extreme_weather_events  insurance  FEMA  cartography  floodplains  flood-risk  flood-risk_maps  mapping 
february 2018 by jerryking
Daring rather than data will save advertising
John Hegarty JANUARY 2, 2017

Algorithms are killing creativity, writes John Hegarty

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

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

Persuasion and promotion.

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

If everything ends up looking the same and feeling the same, markets stagnate.
algorithms  advertising  brands  creativity  data  data_driven  daring  emotional_connections  ingenuity  massive_data_sets  persuasion  Steve_Jobs 
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
The Limits of Amazon
Jan. 1, 2018 | WSJ | By Christopher Mims.

Amazon’s core mission as a data-driven instant-gratification company. Its fanaticism for customer experience is enabled by every technology the company can get its hands on, from data centers to drones. Imagine the data-collecting power of Facebook wedded to the supply-chain empire of Wal-Mart—that’s Amazon.

There is one major problem with the idea that Amazon-will-eat-the-entire-universe, however. Amazon is good at identifying commodity products and making those as cheap and available as possible. “Your margin is my opportunity” is one of Chief Executive Jeff Bezos’s best-known bon mots. But this system isn’t very compatible with big-ticket, higher-margin items.....

How Amazon Does It
Amazon now increasingly makes its money by extracting a percentage from the sales of other sellers on its site. It has become a platform company like Facebook Inc. or Alphabet Inc.’s Google, which serve as marketplaces for businesses with less reach of their own.....Eventually, Amazon could become the ultimate platform for retail, the “retail cloud” upon which countless other online retail businesses are built....Think of Amazon as an umbrella company composed of disconnected and sometimes competing businesses, though critically they can access common infrastructure, including the retail platform and cloud services.

Ultimately, these smaller businesses must feed the core mission. Amazon’s video business isn’t just its own potential profit center; it’s also a way to keep people in Amazon’s world longer, where they spend more money,

What Amazon Can’t Do
Ultimately, the strategies that allow Amazon to continue growing will also be its limitation. “If the platform needs to be one-size-fits-all across many, many different product categories, it becomes difficult to create specific experiences for different kinds of products,”
contra-Amazon  Amazon  strengths  data_driven  instant_gratification  customer_experience  platforms  one-size-fits-all  limitations  Jeff_Bezos  weaknesses  commoditization  third-party  Christopher_Mims 
january 2018 by jerryking
Novartis’s new chief sets sights on ‘productivity revolution’
SEPTEMBER 25, 2017 | Financial Times | Sarah Neville and Ralph Atkins.

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

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

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

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

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

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

More broadly, he is realistic about the likely attrition rate. “We will fail at many of these experiments, but if we hit on a couple of big ones that are transformative, I think you can see a step change in productivity.”
algorithms  analytics  artificial_intelligence  attrition_rates  CEOs  data_driven  data_scientists  drug_development  failure  Indian-Americans  multiple_targets  Novartis  pharmaceutical_industry  predictive_analytics  productivity  productivity_payoffs  product_development  real-time  scaling  spreadsheets 
november 2017 by jerryking
How Peloton is Marketing a $2,000 Bike Beyond the Rich - WSJ
By Alexandra Bruell
Oct. 25, 2017

When Carolyn Tisch Blodgett joined fitness startup Peloton as its brand marketing lead a year-and-a-half ago, the company’s executives were focused on promoting the functionality of their product -- a $1,995 stationary bike with an attached tablet and a $39-a-month subscription service for access to live and on-demand classes.

What they were missing, however, was a compelling brand story about the bike’s convenience and its role in connecting riders around the country, largely through a leaderboard that displays rider data, said Ms. Blodgett.

“My challenge over the last year-and-a-half has been telling this brand story,” she said. “We wanted to bring the product to life but also the brand.”

Ms. Blodgett also conducted research showing that the company had been targeting a core, affluent audience, but overlooking a less affluent consumer who was willing to splurge on a convenient fitness habit.

Peloton is now shifting gears with a new financing program ($97 per month for 39 months for both the bike and subscription service), an ad campaign that’s more relatable to a diverse consumer base and an NBC Olympics sponsorship.
Peloton  fitness  storytelling  brand_identity  brands  data_driven  connected_devices  subscriptions  overlooked  overlooked_opportunities  functionality 
october 2017 by jerryking
Folks, We’re Home Alone
SEPT. 27, 2017 | The New York Times | Thomas L. Friedman.

we’re going through three climate changes at once:

We’re going through a change in the actual climate — disruptive, destructive weather events are steadily on the rise.

We’re going through a change in the “climate” of globalization — going from an interconnected world to an interdependent one, from a world of walls where you build your wealth by hoarding the most resources to a world of webs where you build your wealth by having the most connections to the flow of ideas, networks, innovators and entrepreneurs. In this interdependent world, connectivity leads to prosperity and isolation leads to poverty. We got rich by being “America Connected” not “America First.”

Finally, we’re going through a change in the “climate” of technology and work. We’re moving into a world where computers and algorithms can analyze (reveal previously hidden patterns); optimize (tell a plane which altitude to fly each mile to get the best fuel efficiency); prophesize (tell you when your elevator will break or what your customer is likely to buy); customize (tailor any product or service for you alone); and digitize and automatize more and more products and services. Any company that doesn’t deploy all six elements will struggle, and this is changing every job and industry.

What do you need when the climate changes? Adaptation — so your citizens can get the most out of these climate changes and cushion the worst. Adaptation has to happen at the individual, community and national levels.

At the individual level, the single most important adaptation is to become a lifelong learner, so you can constantly add value beyond what machines and algorithms can do.

“When work was predictable and the change rate was relatively constant, preparation for work merely required the codification and transfer of existing knowledge and predetermined skills to create a stable and deployable work force,” explains education consultant Heather McGowan. “Now that the velocity of change has accelerated, due to a combination of exponential growth in technology and globalization, learning can no longer be a set dose of education consumed in the first third of one’s life.” In this age of accelerations, “the new killer skill set is an agile mind-set that values learning over knowing.”
GOP  Democrats  Donald_Trump  Tom_Friedman  climate_change  adaptability  extreme_weather_events  Dean_Acheson  weather  interconnections  interdependence  data_driven  wealth_creation  life_long_learning  the_single_most_important 
september 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
We are still waiting for the robot revolution
2017 | Financial Times | Tim Harford.

“Our chief economic problem right now isn’t that the robots are taking our jobs, it’s that the robots are slacking off. “

Or at least — it should. Our chief economic problem right now isn’t that the robots are taking our jobs, it’s that the robots are slacking off. We suffer from slow productivity growth; the symptoms are not lay-offs but slow-growing economies and stagnant wages. In advanced economies, total factor productivity growth — a measure of how efficiently labour and capital are being used to produce goods and services — was around 2 per cent a year in the 1960s, when the ATM was introduced. Since then, it has averaged closer to 1 per cent a year; since the financial crisis it has been closer to zero. Labour productivity, too, has been low.

Plenty of jobs, but lousy productivity: imagine an economy that was the exact opposite of one where the robots took over, and it would look very much like ours. Why? Tempting as it may be to blame the banks, a recent working paper by John Fernald, Robert Hall and others argues that productivity growth stalled before the financial crisis, not afterwards: the promised benefits of the IT revolution petered out by around 2006. Perhaps the technology just isn’t good enough; perhaps we haven’t figured out how to use it. In any case, results have been disappointing.

There is always room for the view that the productivity boom is imminent. Michael Mandel and Bret Swanson, business economists, argue in their policy paper that we are starting to find digitally driven efficiencies in physical industries such as energy, construction, transport, and retail. If this happens, Silicon Valley-style innovation will ripple through the physical economy. If.
Tim_Harford  artificial_intelligence  productivity  automation  economists  efficiencies  energy  construction  transportation  retailers  robotics  physical_economy  data_driven 
august 2017 by jerryking
J.Crew’s Mickey Drexler Confesses: I Underestimated How Tech Would Upend Retail
By Khadeeja Safdar
Updated May 24, 2017

For decades, fashion was essentially a hit or miss business. Merchants like Mr. Drexler would make bets on what people would be wearing a year in advance, since that’s how long it took to design and produce items. Hits guaranteed handsome returns until the next season.

Now, competitors with high-tech, data-driven supply chains can copy styles faster and move them into stores in a matter of weeks. Online marketplaces drive down prices, and design details such as nicer buttons and richer colors are less apparent on the internet. Social media adds fuel to the style churn—consumers want a new outfit for every Instagram post. “The rules of the game have changed,” said Janet Kloppenburg, president of JJK Research, a retail-focused research firm. “It’s not just about product anymore. It’s also about speed and pricing.”

Mr. Drexler’s plan is to emphasize lower prices, pivot toward more digital marketing and adopt a more accessible image........Mr. Drexler didn’t appreciate how the quality of garments could easily get lost in a sea of options online, where prices drive decisions, or how social media would give rise to disposable fashion. Online, price has more impact than the sensory qualities of clothing. “You go into a store—I love this, I love this, I love this,” he said. “You go online and you just don’t get the same sense and feel of the goods because you’re looking at a picture.”.....Amazon.com and other algorithm-based websites can change prices by the hour based on demand, and the variety of options makes it easy to mix and match brands.

“The days of people wearing head-to-toe J.Crew are over,”......Today, with nearly two billion people using Facebook every month, he feels differently: “You cannot be successful without being obsessed with the product, obsessed with social media, and obsessed with digital,” he said. “Retail is now about all that.”

Mr. Drexler said he hasn’t given up on quality. Instead, he is now lowering prices on about 300 items and creating an analytics team dedicated to optimizing prices for each garment......TPG co-founder David Bonderman recently acknowledged J.Crew and its peers are struggling with declining mall traffic and the shift to online shopping. “The internet has proven much more resilient and much more important than most of us thought a decade ago,” he said at a conference earlier this month.
retailers  e-commerce  Mickey_Drexler  J.Crew  fashion  apparel  LBOs  private_equity  hits  copycats  social_media  Instagram  data_driven  supply_chains  Clayton_Christensen  disruption  brands  Old_Navy  Banana_Republic  Madewell  digital_influencers  TPG  fast-fashion  disposability 
may 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
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
Inside the brutal transformation of Tim Hortons - The Globe and Mail
MARINA STRAUSS
THE GLOBE AND MAIL
LAST UPDATED: WEDNESDAY, FEB. 22, 2017

Since taking over the iconic chain in 2014, its new Brazilian owner, 3G Capital, has purged head office, slashed costs and squeezed suppliers. Shareholders are happy, but is 3G tearing the heart out of Timmy’s?.....3G is regarded as ultra-disciplined owners who are sticking to the same playbook they have followed at companies including Burger King, Anheuser-Busch, Kraft Foods and Heinz: massive layoffs, replacing legacy managers with hungry youngsters and, above all, a fanatical devotion to financial benchmarks and cost-cutting. (It remains to be seen whether this will also be the approach for RBI’s latest acquisition, Popeyes Louisiana Kitchen.)....Will 3G's analytics-driven overhaul of Tim Hortons—using the same template the private equity firm’s founders have deployed at railroads, brewers and food makers—succeed in the long run, or is it in danger of cutting the heart out of a Canadian icon? ......Suppliers are also feeling the squeeze. From the get-go, RBI made it clear it would be reviewing vendor relationships. And the company pushed for better terms, including extensions on bill payments to as much as 120 days from 30 days or less. Maple Leaf Foods, a major partner that supplied meat to Tim Hortons, declined to accept the new terms, and walked away....
Former employees also say RBI has cut back on product research and development spending at Tim Hortons, offloading some of that work to suppliers. That’s not uncommon in the fast-food world, but it can be risky. “Suppliers can do a great job with innovating and R&D, but you’re limited to what the supplier is trying to develop,” ......3G has never encountered a brand quite like Tim Hortons. It isn’t just another coffee company. It is a Canadian destination, an integral part of many Canadians’ day and a brand that defines us, to some degree, around the world.......“The risk, in looking at Tim Hortons through the lens of efficiency alone, is to miss the greatest value of the asset, and that is the Tim’s brand and its deep connection to the fabric of the country,” says Joe Jackman, founder of strategic retail consultant Jackman Reinvents, whose clients have included Old Navy, Hertz, Rexall and FreshCo. “You can’t cost-cut your way to retail nirvana.”
3G_Capital  Tim_Hortons  Marina_Strauss  cost-cutting  head_offices  iconic  brands  organizational_culture  private_equity  layoffs  data_driven  franchising  transformational  fast-food  supply_chains  R&D  Canadiana  goodwill  JWT  community_support  downsizing  efficiencies  coffee  staying_hungry  cultural_touchpoints  restructurings  supply_chain_squeeze  RBI  playbooks 
february 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
Technology and markets are driving employment in the right direction - The Globe and Mail
RICK LASH
Special to The Globe and Mail
Published Monday, Oct. 17, 2016

The best way to achieve higher profits is ensuring maximum flexibility in the workforce so the organization can adapt to rapidly changing market needs. Having a more flexible employee pool that you can hire and furlough depending on business demands is one way to manage risk.

If technology and new finance-driven business models are fundamentally altering the future of jobs and work, what’s a new graduate (or an older worker) to do? All is not hopeless, and in fact there is indeed a silver lining, if one knows where to look.

Companies like Uber are figuring it out, at least for now. The same technology that is replacing workers with intelligent robots (on the shop floor or as an app on your smartphone) is also being used to create new models of generating wealth. Whether you are a bank driving growth through new on-line channels, a streaming music company designing creative new ways for consumers to subscribe, or an entrepreneur raising capital online for a new invention, key skills stand out as differentiators for success.
automation  technology  artificial_Intelligence  risk-management  data_driven  silver_linings  skills  new_graduates  job_search  business_models  rapid_change  workforce  flexibility  Uber  on-demand  streaming 
october 2016 by jerryking
Make Algorithms Accountable
AUG. 1, 2016 | The New York Times | By JULIA ANGWIN.

An algorithm is a procedure or set of instructions often used by a computer to solve a problem. Many algorithms are secret. ....Algorithms are ubiquitous in our lives. They map out the best route to our destination and help us find new music based on what we listen to now. But they are also being employed to inform fundamental decisions about our lives:
résumés sorting, credit scoring, prediction of a defendant’s future criminality.....as we rapidly enter the era of automated decision making, we should demand more than warning labels [about the algorithms that are being used].

A better goal would be to try to at least meet, if not exceed, the accountability standard set by a president not otherwise known for his commitment to transparency, Richard Nixon: the right to examine and challenge the data used to make algorithmic decisions about us.

Algorithms should come with warning labels. Obama White House called for automated decision-making tools to be tested for fairness, and for the development of “algorithmic auditing.”
tools  automation  decision_making  algorithms  data_driven  transparency  fairness  Richard_Nixon  proprietary  accountability  biases 
august 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
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
Emergency planning: Flood warning — new data help predict risk - FT.com
September 4, 2015 4:42 pm
Emergency planning: Flood warning — new data help predict risk
Clive Cookson

Historical information can be a practical tool for planning responses to future emergencies....KnowNow Information, a spinout from computing giant IBM, has produced a prototype “flood event model” for Hampshire. Working with the Science and Technology Facilities Council’s Hartree supercomputing centre in Daresbury, Cheshire, its team crunched a vast accumulation of data — about water falling from the sky and lying on the ground, geology and landforms, urban geography and infrastructure, as well as past emergencies.
floods  massive_data_sets  history  extreme_weather_events  natural_calamities  data  data_driven  warning_signs  emergencies  anticipating  preparation 
september 2015 by jerryking
The Analytics Mandate
May 12, 2014

by: DAVID KIRON, PAMELA KIRK PRENTICE AND RENEE BOUCHER FERGUSON
analytics  MIT  decision_making  data_driven  LBMA  location_based_services 
september 2015 by jerryking
The Value of Bad Data - The Experts - WSJ
Apr 22, 2015 | WSJ | by Alexandra Samuel--technology researcher and the author of “Work Smarter with Social Media.”
*** Can I apply the idea of negative space towards evolving a dataset?

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

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

None of these scenarios offers you the kind of big data that would make a data scientist happy. But you can still dig into your data scientist’s toolbox, and use a quasi-quantitative approach to get some of the benefits of statistical analysis… even in the absence of statistically valid data.
massive_data_sets  data  data_driven  small_business  data_scientists  books  hustle  statistics  quantitative  small_data  data_quality 
july 2015 by jerryking
Five Steps to Get Started with Data Journalism
May 6, 2015 | | ICFJ - International Center for Journalists | by Alexandra LudkaCommunications Officer.
data_journalism  data_driven  data  Communicating_&_Connecting 
may 2015 by jerryking
TV Networks Borrow Page From Digital Rivals to Attract Advertisers - NYTimes.com
MAY 11, 2015 | NYT | By SYDNEY EMBER.

Coca-Cola is just one of many brands now shifting advertising budgets to digital and social media, which offer the promise of better consumer data and the ability to reach targeted audiences....“Everyone is coming out with a data play, a data product, right now,” said Jeff Lucas, head of sales for Viacom Media Networks, whose channels include MTV and Nickelodeon.

Television networks, which rely on the upfront season for tens of billions of ad dollars, are facing declining ratings and heightened competition from digital outlets. And while television still dominates the ad market, with some $70 billion in ad spending last year in the United States, online ad spending is swelling. In particular, digital video, which attracted $5.8 billion in ad spending in the United States last year, is expected to grow to $7.8 billion this year and to $12.8 billion by 2018, according to the research firm eMarketer.....the line between TV and digital is blurring, and that advertisers care more about the effectiveness of their ads than where they run.
Coca-Cola  television  advertising  digital_media  online_advertising  web_video  Hulu  tools  brands  effectiveness  data_driven 
may 2015 by jerryking
Feeling uncertain, CEO? Better go on the attack - The Globe and Mail
HARVEY SCHACHTER
Special to The Globe and Mail
Published Tuesday, May. 05 2015

Taking control of uncertainty is the fundamental leadership challenge of our time … ” he writes in The Attacker’s Advantage. “The advantage now goes to those who create change, not just learn to live with it. Instead of waiting and reacting, such leaders immerse themselves in the ambiguities of the external environment, sort through them before things are settled and known, set a path, and steer the organization decisively onto it.”
Harvey_Schachter  Ram_Charan  uncertainty  algorithms  mathematics  data  management_consulting  anomalies  change  Jack_Welch  books  gurus  offense  data_driven  leadership  ambiguities  offensive_tactics 
may 2015 by jerryking
How Not to Drown in Numbers - NYTimes.com
MAY 2, 2015| NYT |By ALEX PEYSAKHOVICH and SETH STEPHENS-DAVIDOWITZ.

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

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

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

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

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

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

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

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

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

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

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

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

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

Politics is no longer about the art of persuasion or about having an honest debate about what’s best for your country, province or city. It’s about microtargeting individuals who’ve already demonstrated by their Facebook posts or responses to telephone surveys that they are suggestible. Voters are data points to be manipulated, not citizens to be cultivated....Campaign strategists euphemistically refer to this data collection and microtargeting as “grassroots engagement” or “having one-on-one conversations” with voters....The data analysts on the 2012 Obama campaign came up with “scores” for each voter in its database, or what author Sasha Issenberg called “a new political currency that predicted the behaviour of individual humans.
Konrad_Yakabuski  persuasion  middle_class  politicians  massive_data_sets  political_campaigns  data_scientists  data_driven  data_mining  microtargeting  behavioural_targeting  politics  data  analytics  Campaign_2012 
april 2015 by jerryking
The Evolving Automotive Ecosystem - The CIO Report - WSJ
April 6, 2015| WSJ | By IRVING WLADAWSKY-BERGER.

An issue in many other industries. Will the legacy industry leaders be able to embrace the new digital technologies, processes and culture, or will they inevitably fall behind their faster moving, more culturally adept digital-native competitors? [the great game]

(1) Find new partners and dance: “The structure of the automotive industry will likely change rapidly. Designing and producing new vehicles have become far too complex and expensive for any likely one company to manage all on its own.
(2) Become data masters: “Know your customers better than they know themselves. Use that data to curate every aspect of the customer experience from when they first learn about the car to the dealership experience and throughout the customer life cycle. Having data scientists on staff will likely be the rule, not the exception.
(3) Update your economic models: “Predicting demand was hard enough in the old days, when you did a major new product launch approximately every five years. Now, with the intensity of competition, the rapid cadence of new launches, and the mashup of consumer and automotive technology, you may need new economic models for predicting demand, capital expenditures, and vehicle profitability.
(4)Tame complexity: “It’s all about the center stack, the seamless connectivity with nomadic devices, the elegance of the Human Machine Interface.
(5) Create adaptable organizations: “It will take a combination of new hard and soft skills to build the cars and the companies of the future. For many older, established companies, that means culture change, bringing in new talent, and rethinking every aspect of process and people management.
Apple  automotive_industry  autonomous_vehicles  ecosystems  Google  know_your_customer  adaptability  CIOs  layer_mastery  competitive_landscape  competitive_strategy  connected_devices  telematics  data  data_driven  data_scientists  customer_experience  curation  structural_change  accelerated_lifecycles  UX  complexity  legacy_players  business_development  modelling  Irving_Wladawsky-Berger  SMAC_stack  cultural_change  digitalization  connected_cars  the_great_game 
april 2015 by jerryking
IBM to Invest $3 Billion in Sensor-Data Unit - WSJ
March 31, 2015 | WSJ | By DON CLARK. Can CBC get good at communicating the final product on behalf of clients of Pelmorex. So CBC considers supplying the communications platform?

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

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

The Weather Company is turning to IBM, he said, because of its software expertise and relationships with customers in many industries.
sensors  IBM  weather  massive_data_sets  data  data_driven  analytics  Industrial_Internet  smartphones  cloud_computing 
march 2015 by jerryking
Behind Martin Sorrell’s Data Binge - CMO Today - WSJ
Mar 12, 2015 | WSJ | By NATHALIE TADENA.

Sorrell, this is about putting his sprawling holding company in control of all the various data marketers are demanding nowadays to make sense of their ad campaigns. They want to know a lot about who is viewing. They want to know which TV shows or Web sites are ideal to reach their desired audience. And ultimately, they want to know how much an ad contributes to an actual sale of a product or service.

By becoming a global data powerhouse, WPP hopes to help clients draw connections across different data sources, better target audiences and ultimately improve the effectiveness of their advertising dollars.
data_sources  Martin_Sorrell  WPP  mergers_&_acquisitions  ROI  CMOs  M&A  data  metrics  measurements  advertising_agencies  advertising  marketing  data_driven  targeting  target_marketing 
march 2015 by jerryking
On the Case at Mount Sinai, It’s Dr. Data - NYTimes.com
MARCH 7, 2015 | NYT |By STEVE LOHR.

“Data-ism: The Revolution Transforming Decision Making, Consumer Behavior, and Almost Everything Else,” by Steve Lohr,
Steve_Lohr  data  data_driven  data_scientists  Wall_Street  Facebook  hospitals  medical  books  Cloudera  consumer_behavior 
march 2015 by jerryking
Meet the SEC’s Brainy New Crime Fighters - WSJ
By SCOTT PATTERSON
Updated Dec. 14, 2014

The SEC is mustering its mathematical firepower in its Center for Risk and Quantitative Analytics, which was created last year soon after Mary Jo White took charge of the agency to help it get better at catching Wall Street misconduct. The enforcement unit, led by 14-year SEC veteran Lori Walsh, is housed deep within the warrens of the SEC’s Washington headquarters, and staffed by about 10 employees trained in fields such as mathematical finance, economics, accounting and computer programming.

Ms. Walsh says access to new sources of data and new ways of processing the data have been key to finding evidence of wrongdoing. “When you look at data in different ways, you see new things,” she said in an interview
alternative_data  analysis  analytics  arms_race  data  data_driven  enforcement  fresh_eyes  hiring  information_sources  mathematics  misconduct  models  modelling  patterns  perspectives  quantitative  quants  SEC  stockmarkets  Wall_Street 
december 2014 by jerryking
The Data Companies Wish They Had About Customers - WSJ
March 23, 2014 | WSJ | by Max Taves.

We asked companies what data they wish they had—and how they would use it. Here's what they said....
(A) Dining----Graze.com has a huge appetite for data. Every hour, the mail-order snack business digests 15,000 user ratings about its foods, which it uses to better understand what its customers like or dislike and to predict what else they might like to try...more data could help him understand customers' tastes even better. Among the information he wants most is data about customers' dietary habits, such as what they buy at grocery stores, as well as better information about what they look at on Graze's own site. And because the dietary needs of children change rapidly, he'd like to know if his customers have children and, if so, their ages.
(B) Energy-----Energy consumption is among its customers' main concerns, says CEO William Lynch. For instance, the company offers a product giving homeowners the real-time ability to see things like how many kilowatts it takes to heat the hot tub in Jan. Because of privacy concerns, Savant doesn't collect homeowners' energy data. But if the company knew more about customers' energy use, it could help create customized plans to conserve energy. "We could make recommendations on how to set up your thermostat to save a lot of money,
(C) Banking-----the Bank of the West would like "predictive life-event data" about its customers—like graduation, vacation or retirement plans—to create products more relevant to their financial needs...At this point, collecting that breadth of data is a logistical and regulatory challenge, requiring very different sources both inside and outside the bank.
(D) Appliances-----Whirlpool Corp.has a vast reach in American households—but wants to know more about its customers and how they actually use its products. Real-time use data could not only help shape the future designs of Whirlpool products, but also help the company predict when they're likely to fail.
(E) Healthcare----Explorys creates software for health-care companies to store, access and make sense of their data. It holds a huge trove of clinical, financial and operational information—but would like access to data about patients at home, such as their current blood-sugar and oxygen levels, weight, heart rates and respiratory health. Having access to that information could help providers predict things like hospitalizations, missed appointments and readmissions and proactively reach out to patients,
(F) Healthcare----By analyzing patient data, Carolinas HealthCare System of Charlotte, N.C., can predict readmission rates with 80% accuracy,
(G) Law----law firms that specialize in defense work are typically reactive, however some are working towards becoming more proactive, coveting an ability to predict lawsuits—and prevent them.How? By analyzing reams of contracts and looking for common traits and language that often lead to problems.
(H) Defense---BAE Systems PLC invests heavily in protecting itself from cyberattacks. But it says better data from its suppliers could help improve its defenses...if its suppliers get cyberattacked, its own h/w and s/w could be compromised. But "those suppliers are smaller businesses with lesser investments in their security," ...A lack of trust among suppliers, even those that aren't direct competitors, means only a small percentage of them disclose the data showing the cyberattacks on their systems. Sharing that data, he says, would strengthen the security of every product BAE makes. [BAE is expressing recognition of its vulnerability to network risk].
data  data_driven  massive_data_sets  Graze  banking  cyber_security  BAE  law_firms  Whirlpool  genomics  social_data  appliances  sense-making  predictive_analytics  dark_data  insights  customer_insights  real-time  design  failure  cyberattacks  hiring-a-product-to-do-a-specific-job  network_risk  shifting_tastes  self-protection  distrust  supply_chains 
november 2014 by jerryking
The Single Worst Marketing Decision You Can Make
Oct 29 2014 | LinkedIn | Ryan Holiday, Founder, Partner at Brass Check

Make something people want.

—Paul Graham

Growth hackers believe that products—even whole businesses and business models—can and should be changed until they are primed to generate explosive reactions from the first people who see them. In other words, the best marketing decision you can make is to have a product or business that fulfills a real and compelling need for a real and defined group of people—no matter how much tweaking and refining this takes...Some companies like Airbnb and Instragram spend a long time trying new iterations until they achieve what growth hackers call Product Market Fit (PMF); others find it right away. The end goal is the same, however, and it’s to have the product and its customers in perfect sync with each other. Eric Ries, author of The Lean Startup, explains that the best way to get to Product Market Fit is by starting with a “minimum viable product” and improving it based on feedback—as opposed to what most of us do, which is to try to launch publicly with what we think is our final, perfected product...marketers need to contribute to this process. Isolating who your customers are, figuring out their needs, designing a product that will blow their minds—these are marketing decisions, not just development and design choices.

The imperative is clear: stop sitting on your hands and start getting them dirty.
delighting_customers  start_ups  coding  growth  hacks  growth_hacking  marketing  Paul_Graham  lean  data_driven  product_launches  minimum_viable_products  visceral  experimentation  iterations  business_models  product-market_fit  good_enough 
october 2014 by jerryking
Lou Eccleston: The data-driven emergence of TMX’s new CEO - The Globe and Mail
BOYD ERMAN
The Globe and Mail
Published Wednesday, Oct. 01 2014

And while he has not been CEO of a large public company, he has run big operations. At McGraw Hill, his business unit generated $1.7-billion (U.S.) in revenue, close to three times that of TMX.

McGraw Hill’s executive vice president of corporate affairs, Ted Smyth, worked with Mr. Eccleston on the company’s executive committee. He said Mr. Eccleston is “high energy, global, forward-looking, eloquent and a strong advocate for the power of technology and big data.”
Boyd_Erman  Bay_Street  CEOs  data_driven  forward-thinking  forward_looking  high-energy  Lou_Eccleston  massive_data_sets  TMX 
october 2014 by jerryking
Data-mining retailers prove it’s hip to use Square - The Globe and Mail
SHANE DINGMAN - TECHNOLOGY EDITOR
The Globe and Mail
Published Thursday, Aug. 21 2014

...Forrester Research has predicted the global mobile payments market could be worth $90-billion by 2017. But Square makes very little revenue per purchase, and has long acknowledged that customer data was the real value centre in its business. The convenience and “wow factor” of turning a smartphone into a cash register is appealing to sellers, but for them, too, Square’s data tools let them identify new or returning customers, average spend and those critical customer preferences.
data  data_driven  data_mining  retailers  Square  customer_insights  payments 
august 2014 by jerryking
What Cars Did for Today's World, Data May Do for Tomorrow's - NYTimes.com
August 10, 2014 | NYT | Quentin Hardy.

General Electric plans to announce Monday that it has created a “data lake” method of analyzing sensor information from industrial machinery in places like railroads, airlines, hospitals and utilities. G.E. has been putting sensors on everything it can for a couple of years, and now it is out to read all that information quickly.

The company, working with an outfit called Pivotal, said that in the last three months it has looked at information from 3.4 million miles of flights by 24 airlines using G.E. jet engines. G.E. said it figured out things like possible defects 2,000 times as fast as it could before.....Databricks, that uses new kinds of software for fast data analysis on a rental basis. Databricks plugs into the one million-plus computer servers inside the global system of Amazon Web Services, and will soon work inside similar-size megacomputing systems from Google and Microsoft....If this growing ecosystem of digital collection, shipment and processing is the new version of cars and highways, what are the unexpected things, the suburbs and fast-food joints that grew from cars and roads?

In these early days, businesses like Uber and Airbnb look like challengers to taxi fleets and hotels. They do it without assets like cars and rooms, instead coordinating data streams about the location of people, cars, and bedrooms. G.E. makes engines, but increasingly it coordinates data about the performance of engines and the location of ground crews. Facebook uses sensor data like location information from smartphones
Quentin_Hardy  data  data_driven  AWS  asset-light  massive_data_sets  resource_allocation  match-making  platforms  resource_management  orchestration  ecosystems  GE  sensors  unexpected  unforeseen  Databricks  Uber  Airbnb  data_coordination  instrumentation_monitoring  efficiencies 
august 2014 by jerryking
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