recentpopularlog in

jerryking : data_marketplaces   12

Sponsor Generated Content: The State of the Data Economy
June 23, 2014

Where the Growth is
So for many companies right now, the core of the data economy is a small but growing segment—the information two billion-plus global Internet users create when they click "like" on a social media page or take action online. Digital customer tracking—the selling of “digital footprints” (the trail of information consumers leave behind each time they surf the Web)—is now a $3 billion segment, according to a May 2014 Outsell report. At the moment, that's tiny compared to the monetary value of traditional market research such as surveys, forecasting and trend analysis. But digital customer tracking "is where the excitement and growth is," says Giusto.

Real-time data that measures actions consumers are actually taking has more value than study results that rely on consumer opinions. Not surprising, businesses are willing to pay more for activity-based data.

Striking it Richer
Outsell Inc.'s analyst Chuck Richard notes that the specificity of data has a huge affect on its value. In days past, companies would sell names, phone numbers, and email addresses as sales leads. Now, data buyers have upped the ante. They want richer data—names of consumers whose current "buying intent" has been analyzed through behavioral analytics. Beyond the “who,” companies want the “what” and “when” of purchases, along with “how” best to engage with prospects.
"Some companies are getting a tenfold premium for data that is very focused and detailed," Richard says. "For example, if you had a list of all the heart specialists in one region, that’s worth a lot."

Tapping into New Veins
Moving forward, marketers will increasingly value datasets that they can identify, curate and exploit. New technology could increase the value of data by gleaning insights from unstructured data (video, email and other non-traditional data sources); crowdsourcing and social media could generate new types of shareable data; predictive modeling and machine learning could find new patterns in data, increasing the value of different types of data.

Given all this, the data economy is sure to keep growing, as companies tap into new veins of ever-richer and more-specific data.
data  data_driven  SAS  real-time  digital_footprints  OPMA  datasets  unstructured_data  data_marketplaces  value_creation  specificity  value_chains  intentionality  digital_economy  LBMA  behavioural_data  predictive_modeling  machine_learning  contextual  location_based_services  activity-based  consumer_behavior 
july 2014 by jerryking
Deloitte: Companies Engage in ‘Hidden Market for Data Monetization’ - The CIO Report - WSJ
January 23, 2014 | WSJ | By Michael Hickins.

Companies are engaging in “a hidden market for data monetization,” and are starting to “trade data among themselves for mutual benefit,” according to John Lucker, Deloitte LLP’s market leader for advanced analytics and modeling. The question they still haven’t wrestled to the ground is how much is too much data, and when does trading data cause consumers to revolt.
data  monetization  exhaust_data  privacy  data_marketplaces  CIOs  hidden  latent 
february 2014 by jerryking
Using 'remarkable' source of data, startup builds rich customer profiles - The Globe and Mail
Ivor Tossell

Special to The Globe and Mail

Published Monday, Jan. 06 2014

RetailGenius, a product from a Toronto startup called Viasense, promises to algorithmically generate customer profiles based on a remarkable source of data: Anonymous location data that’s collected by big mobile carriers, from the passive pings that every single cellphone sends out as it goes through the day.

The data that RetailGenius uses is anonymized – it doesn’t have any way of knowing whose cellphone belongs to who; it simply has a gigantic plot of where thousands of cellphones were at any given time.

“We create a unique identifier between those signals, and we can see those signals move throughout the city,” says Mossab Basir, RetailGenius’ founder. “We can see those changes in your location but we never really know who it is.”

What the product does next is intriguing: Based on some 50 million pieces of location data a day, RetailGenius crunches the numbers to make inferences from where each cellphone spends its time, and generates customer profiles by the thousands.

For instance, if a given cellphone spends the hours between 7 p.m. and 6 a.m. in a single area, it’s a good bet that its owner lives there. If that cellphone spends its working hours downtown five days a week, its owner is probably a daily commuter. And if it visits a given retail store once a week, a picture of its owner’s habits living and shopping habits starts to emerge.

By lumping these inferred profiles together, RetailGenius can give retailers a picture of who walks through their doors. For instance: What are the top 50 postal codes that are represented in their customers? What kind of volumes of customers are arriving at the store? How long do they stay?
data  start_ups  customer_insights  customer_profiling  RetailGenius  location_based_services  massive_data_sets  data_marketplaces  algorithms  Viasense  metadata  postal_codes  inferences  information_sources  anonymized  shopping_habits 
january 2014 by jerryking
An emerging market for market data -
June 29, 2008 | The New York Times | By Tim Arango.

the company has been testing a program called Reuters Market Light for several months in Maharashtra, an Indian state about the size of Italy. The state is one of India's prominent agricultural centers, with farmers growing onions, oranges, corn, soybeans, wheat and bananas. But the farmers' business suffers from the difficulty of comparing prices from one market to another.

"We kind of saw that there was a clear market inefficiency," said Mans Olof-Ors, a Reuters employee who had the idea for Market Light three years ago. "The farmer would decide which market to travel to, then would just sell to that market. So there was no competition between markets."

Reuters has dispatched about 60 market reporters to the region to report on the going price for, say, oranges or onions, and to package the data into a text message that is sent to subscribers.

The service is signing up about 220 subscribers a day at a price of 175 rupees, or about $4.10, for three months at post offices throughout Maharashtra. The average monthly income of a farm household is about $50, according to the Indian government. The service has about 40,000 customers so far - a tiny portion of India's farm population, which is in the hundreds of millions, but it proves that many farmers are hungry for more information.

Reuters has collected anecdotal evidence from farmers about how the service has influenced their decisions about crop sales. One farmer, according to Reuters, held back the sale of 30 quintals of soybeans - one quintal equals 100 kilograms, or 220 pounds - for 15 days after noticing that prices had been rising for several days. He was able to get 400 extra rupees a quintal.

Amit Mehra, managing director of Market Light, said early data showed that most subscribers were making more money from their crops.
food_crops  Thomson_Reuters  India  farmers'_markets  pricing  data  market_inefficiencies  inefficiencies  mobile_phones  text_messages  data_marketplaces  anecdotal 
october 2011 by jerryking
PeteSearch: How to turn data into money
October 20, 2010 by Pete Warden. The most important unsolved
question for Big Data startups is how to make money. Here's a hierarchy
showing the stages from raw data to cold, hard cash:
(1) Data. You have a bunch of files containing info. you've gathered,
way too much for any human to ever read. You know there's a lot of
useful stuff in there though, but you can talk until you're blue in the
face & the people with the checkbooks will keep them closed. The
data itself, no matter how unique, is low value, since it will take
somebody else a lot of effort to turn it into something they can use to
make $. (2) Charts. Take that massive deluge of data and turn it into
some summary tables & simple graphs. You want to give an unbiased
overview of the info., so the tables & graphs are quite detailed.
This makes a bit more sense to the potential end-users, they can at
least understand what it is you have, and start to imagine ways they
could use it. (3) Reports; (4) Recommendations.
analysis  commercialization  data  data_driven  data_marketplaces  data_scientists  entrepreneurship  hierarchies  ideas  InfoChimps  massive_data_sets  monetization  value_creation  visual_analysis  visualization 
july 2011 by jerryking
Data markets aren't coming. They're already here
26 January 2011 | O'Reilly Radar| by Julie Steele.

Jud Valeski is cofounder and CEO of Gnip, a social media data provider
that aggregates feeds from sites like Twitter, Facebook, Flickr,
delicious, and others into one API.

Jud will be speaking at Strata next week on a panel titled "What's Mine
is Yours: the Ethics of Big Data Ownership."
Find out more about growing business of data marketplaces at a "Data
Marketplaces" panel with Ian White of Urban Mapping, Peter Marney of
Thomson Reuters and Dennis Yang of Infochimps.

What do you wish more people understood about data markets and/or the
way large datasets can be used?

Jud Valeski: First, data is not free, and there's always someone out
there that wants to buy it. As an end-user, educate yourself with how
the content you create using someone else's service could ultimately be
used by the service-provider. Second, black markets are a real problem,
and just because "everyone else is doing it" doesn't mean it's okay.
markets  data  data_ownership  analytics  massive_data_sets  digital_economy  black_markets  Infochimps  Gnip  Thomson_Reuters  commercialization  data_scientists  data_marketplaces  social_data  financial_data  content_creators 
may 2011 by jerryking

Copy this bookmark:





to read