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jerryking : behavioural_data   8

Big Investors Don’t Want Wall Street Analysts Snooping on Them - WSJ
June 14, 2018 | WSJ | By Telis Demos

the research shops are finding ways to make up the lost revenue, turning to readership data. They do say that information is power, and in this case I guess the banks have the power again.
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I think the WSJ is conflating two very different issues. The privacy concerns apply on ethical (possibly criminal) grounds rather than moral ones, in the example given of hedge funds asking a broker to provide aggregated readership data. It's very hard to imagine a responsible research provider doing this. The other piece - the tracking of utilization of research product is exactly what brokers need to do to ensure they are being paid appropriately for the level of service a client is receiving. MiFID 2 has and will continue to put pressure on how much research clients consume, and to precisely account for how much they pay for it. Transparency is a two-way street. A 90-day embargo on the readership data is a simple solution, as quarterly/bi-annual reviews should suffice to true-up the bank/client ledger.

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behavioural_data  investment_research  institutional_investors  reading  research_analysts  snooping  traders  Wall_Street  buy_side  informational_advantages  privacy  transparency 
june 2018 by jerryking
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
Tame big data and you'll reap the rewards - The Globe and Mail
HARVEY SCHACHTER
Special to The Globe and Mail
Published Tuesday, Apr. 15 2014

“It’s a catchall term for data that doesn’t fit the usual containers. Big data refers to data that is too big to fit on a single server, too unstructured to fit into a row-and-column database, or too continuously flowing to fit into a static data warehouse. While its size receives all the attention, the most difficult aspect of big data really involves its lack of structure,”....He cites some industries that have big data but aren’t making proper use of it. Banks have massive amounts of information about their customers but have been underachievers in helping them make sense of it all and presenting targeted marketing offers. Retailers have purchase behaviour information from their point-of-sales systems but, with the exception of Wal-Mart and Britain’s Tesco, haven’t done a lot until recently.
Harvey_Schachter  Thomas_Davenport  banks  retailers  massive_data_sets  behavioural_data  books  book_reviews  unstructured_data  analytics  competingonanalytics  sense-making  point-of-sale  Wal-Mart  Tesco 
june 2014 by jerryking
Small Data: Why Tinder-like apps are the way of the future — Medium
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The card-based UI updates the classic way in which we’ve always interacted with physical cards. When you think about it, cards are nothing more than bite-size presentations of concrete information. They’re the natural evolution of the newsfeed, which is useful for reading stories but not for making decisions.
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Cards are kind of natural choice for mobile screens because of their size and shape. But lay your cards on the table or put them on a board and they will also help you in revealing connections, understanding the topic and making decisions.
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every single interaction with card-swiping apps can affect the outcome.

We can call it small data. Imagine if every time you made a yes or no decision on Tinder, the app learned what kind of profiles you tended to like, and it showed you profiles based on this information in the future.

“With swipes on Tinder, the act of navigating through content is merged with inputting an action on that content,” says Rad. That means that every time a user browses profiles, it generates personal behavioral data.
bite-sized  Tinder  small_data  ux  design  decision_making  information_overload  behavioural_data  metadata  gestures  Snapchat  personal_data 
march 2014 by jerryking
The value is in the details
November 30, 2012 | FT.com | By Ravi Mattu.

Troy Carter is the Founder and CEO, Atom Factory. He's also
Lady Gaga's manager used the web to help build her career and is turning his sights to big data.

One of those friends was Joe Lonsdale, co-founder of the Palo Alto-based data management company Palantir. “He said, ‘Send me all the data you have.’ So, we sent him everything and he said it was the worst data he had ever seen in his life.” The problem wasn’t the amount of data – they had lots of it, from Ticketmaster, Lady­gaga.com and merchandise sales – but the quality. Existing social media platforms weren’t much better. “When you deal with Facebook, the information you get is geographical – what city people are logging in from, what time of day – but you don’t get the behavioural information to help you build a better experience.”
massive_data_sets  music_industry  Lady_Gaga  data_driven  Facebook  African-Americans  behavioural_data  entrepreneur  data_quality  haystacks  data_management  customer_experience  detail_oriented  Palantir 
february 2013 by jerryking

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