Tracy Alloway at Bloomberg: “Two of the largest Wall Street banks are trying to measure the market impact of Donald Trump’s tweets.
Analysts at JPMorgan Chase & Co. have created an index to quantify what they say are the growing effects on U.S. bond yields. Citigroup Inc.’s foreign exchange team, meanwhile, report that these micro-blogging missives are also becoming “increasingly relevant” to foreign-exchange moves.
JPMorgan’s “Volfefe Index,” named after Trump’s mysterious covfefetweet from May 2017, suggests that the president’s electronic musings are having a statistically significant impact on Treasury yields. The number of market-moving Trump tweets has ballooned in the past month, with those including words such as “China,” “billion,” “products,” “Democrats” and “great” most likely to affect prices, the analysts found….
JPMorgan’s analysis looked at Treasury yields in the five minutes after a Trump tweet, and the index shows the rolling one-month probability that each missive is market-moving.
They found that the Volfefe Index can account for a “measurable fraction” of moves in implied volatility, seen in interest rate derivatives known as swaptions. That’s particularly apparent at the shorter end of the curve, with two- and five-year rates more impacted than 10-year securities.
Meanwhile, Citi’s work shows that the president’s tweets are generally followed by a stretch of higher volatility across global currency markets. And there’s little sign traders are growing numb to these messages….(More)”
Richard Harris at NPR: “More than a million Americans have donated genetic information and medical data for research projects. But how that information gets used varies a lot, depending on the philosophy of the organizations that have gathered the data.
Some hold the data close, while others are working to make the data as widely available to as many researchers as possible — figuring science will progress faster that way. But scientific openness can be constrained b y both practical and commercial considerations.
Three major projects in the United States illustrate these differing philosophies.
VA scientists spearhead research on veterans database
The first project involves three-quarters of a million veterans, mostly men over age 60. Every day, 400 to 500 blood samples show up in a modern lab in the basement of the Veterans Affairs hospital in Boston. Luis Selva, the center’s associate director, explains that robots extract DNA from the samples and then the genetic material is sent out for analysis….
Intermountain Healthcare teams with deCODE genetics
Our second example involves what is largely an extended family: descendants of settlers in Utah, primarily from the Church of Jesus Christ of Latter-day Saints. This year, Intermountain Healthcare in Utah announced that it was going to sequence the complete DNA of half a million of its patients, resulting in what the health system says will be the world’s largest collection of complete genomes….
NIH’s All of Us aims to diversify and democratize research
Our third and final example is an effort by the National Institutes of Health to recruit a million Americans for a long-term study of health, behavior and genetics. Its philosophy sharply contrasts with that of Intermountain Health.
“We do have a very strong goal around diversity, in making sure that the participants in the All of Us research program reflect the vast diversity of the United States,” says Stephanie Devaney, the program’s deputy director….(More)”.
Stefaan G. Verhulst in apolitical: “If I had only one hour to save the world, I would spend fifty-five minutes defining the questions, and only five minutes finding the answers,” is a famous aphorism attributed to Albert Einstein.
Behind this quote is an important insight about human nature: Too often, we leap to answers without first pausing to examine our questions. We tout solutions without considering whether we are addressing real or relevant challenges or priorities. We advocate fixes for problems, or for aspects of society, that may not be broken at all.
This misordering of priorities is especially acute — and represents a missed opportunity — in our era of big data. Today’s data has enormous potential to solve important public challenges.
However, policymakers often fail to invest in defining the questions that matter, focusing mainly on the supply side of the data equation (“What data do we have or must have access to?”) rather than the demand side (“What is the core question and what data do we really need to answer it?” or “What data can or should we actually use to solve those problems that matter?”).
As such, data initiatives often provide marginal insights while at the same time generating unnecessary privacy risks by accessing and exploring data that may not in fact be needed at all in order to address the root of our most important societal problems.
A new science of questions
So what are the truly vexing questions that deserve attention and investment today? Toward what end should we strategically seek to leverage data and AI?
The truth is that policymakers and other stakeholders currently don’t have a good way of defining questions or identifying priorities, nor a clear framework to help us leverage the potential of data and data science toward the public good.
This is a situation we seek to remedy at The GovLab, an action research center based at New York University.
Our most recent project, the 100 Questions Initiative, seeks to begin developing a new science and practice of questions — one that identifies the most urgent questions in a participatory manner. Launched last month, the goal of this project is to develop a process that takes advantage of distributed and diverse expertise on a range of given topics or domains so as to identify and prioritize those questions that are high impact, novel and feasible.
Because we live in an age of data and much of our work focuses on the promises and perils of data, we seek to identify the 100 most pressing problems confronting the world that could be addressed by greater use of existing, often inaccessible, datasets through data collaboratives – new forms of cross-disciplinary collaboration beyond public-private partnerships focused on leveraging data for good….(More)”.
Mary Hui at Quartz: “The “Be Water” nature of Hong Kong’s protests means that crowds move quickly and spread across the city. They might stage a protest in the central business district one weekend, then industrial neighborhoods and far-flung suburban towns the next. And a lot is happening at any one time at each protest. One of the key difficulties for protesters is to figure out what’s happening in the crowded, fast-changing, and often chaotic circumstances.
Citizen-led efforts to map protests in real-time are an attempt to address those challenges and answer some pressing questions for protesters and bystanders alike: Where should they go? Where have tear gas and water cannons been deployed? Where are police advancing, and are there armed thugs attacking civilians?
One of the most widely used real-time maps of the protests is HKMap.live, a volunteer-run and crowdsourced effort that officially launched in early August. It’s a dynamic map of Hong Kong that users can zoom in and out of, much like Google Maps. But in addition to detailed street and building names, this one features various emoji to communicate information at a glance: a dog for police, a worker in a yellow hardhat for protesters, a dinosaur for the police’s black-clad special tactical squad, a white speech-bubble for tear gas, two exclamation marks for danger.
HKMap during a protest on August 31, 2019.
Founded by a finance professional in his 20s and who only wished to be identified as Kuma, HKMap is an attempt to level the playing field between protesters and officers, he said in an interview over chat app Telegram. While earlier on in the protest movement people relied on text-based, on-the-ground live updates through public Telegram channels, Kuma found these to be too scattered to be effective, and hard to visualize unless someone knew the particular neighborhood inside out.
“The huge asymmetric information between protesters and officers led to multiple occasions of surround and capture,” said Kuma. Passersby and non-frontline protesters could also make use of the map, he said, to avoid tense conflict zones. After some of his friends were arrested in late July, he decided to build HKMap….(More)”.
Swissinfo: “Swiss researchers have found that algorithms that mine large swaths of data can eliminate anonymity in federal court rulings. This could have major ramifications for transparency and privacy protection.
This is the result of a study by the University of Zurich’s Institute of Law, published in the legal journal “Jusletter” and shared by Swiss public television SRF on Monday.
The study relied on a “web scraping technique” or mining of large swaths of data. The researchers created a database of all decisions of the Supreme Court available online from 2000 to 2018 – a total of 122,218 decisions. Additional decisions from the Federal Administrative Court and the Federal Office of Public Health were also added.
Using an algorithm and manual searches for connections between data, the researchers were able to de-anonymise, in other words reveal identities, in 84% of the judgments in less than an hour.
In this specific study, the researchers were able to identify the pharma companies and medicines hidden in the documents of the complaints filed in court.
Study authors say that this could have far-reaching consequences for transparency and privacy. One of the study’s co-authors Kerstin Noëlle Vokinger, professor of law at the University of Zurich explains that, “With today’s technological possibilities, anonymisation is no longer guaranteed in certain areas”. The researchers say the technique could be applied to any publicly available database.
Vokinger added there is a need to balance necessary transparency while safeguarding the personal rights of individuals.
Adrian Lobsiger, the Swiss Federal Data Protection Commissioner, told SRF that this confirms his view that facts may need to be treated as personal data in the age of technology….(More)”.
Article by Priceonomics Data Studio: “For all the talk of how data is the new oil and the most valuable resource of any enterprise, there is a deep dark secret companies are reluctant to share — most of the data collected by businesses simply goes unused.
This unknown and unused data, known as dark data comprises more than half the data collected by companies. Given that some estimates indicate that 7.5 septillion (7,700,000,000,000,000,000,000) gigabytes of data are generated every single day, not using most of it is a considerable issue.
In this article, we’ll look at this dark data. Just how much of it is created by companies, what are the reasons this data isn’t being analyzed, and what are the costs and implications of companies not using the majority of the data they collect.
Before diving into the analysis, it’s worth spending a moment clarifying what we mean by the term “dark data.” Gartner defines dark data as:
“The information assets organizations collect, process and store during regular business activities, but generally fail to use for other purposes (for example, analytics, business relationships and direct monetizing).
To learn more about this phenomenon, Splunk commissioned a global survey of 1,300+ business leaders to better understand how much data they collect, and how much is dark. Respondents were from IT and business roles, and were located in Australia, China, France, Germany, Japan, the United States, and the United Kingdom. across various industries. For the report, Splunk defines dark data as: “all the unknown and untapped data across an organization, generated by systems, devices and interactions.”
While the costs of storing data has decreased overtime, the cost of saving septillions of gigabytes of wasted data is still significant. What’s more, during this time the strategic importance of data has increased as companies have found more and more uses for it. Given the cost of storage and the value of data, why does so much of it go unused?
The following chart shows the reasons why dark data isn’t currently being harnessed:
By a large margin, the number one reason given for not using dark data is that companies lack a tool to capture or analyze the data. Companies accumulate data from server logs, GPS networks, security tools, call records, web traffic and more. Companies track everything from digital transactions to the temperature of their server rooms to the contents of retail shelves. Most of this data lies in separate systems, is unstructured, and cannot be connected or analyzed.
Second, the data captured just isn’t good enough. You might have important customer information about a transaction, but it’s missing location or other important metadata because that information sits somewhere else or was never captured in useable format.
Additionally, dark data exists because there is simply too much data out there and a lot of is unstructured. The larger the dataset (or the less structured it is), the more sophisticated the tool required for analysis. Additionally, these kinds of datasets often time require analysis by individuals with significant data science expertise who are often is short supply.
The implications of the prevalence are vast. As a result of the data deluge, companies often don’t know where all the sensitive data is stored and can’t be confident they are complying with consumer data protection measures like GDPR. …(More)”.
Blog post by Geoff Mulgan: “Governance sinkholes appear when shifts in technology, society and the economy throw up the need for new arrangements. Each industrial revolution has created many governance sinkholes – and prompted furious innovation to fill them. The fourth industrial revolution will be no different. But most governments are too distracted to think about what to do to fill these holes, let alone to act. This blog sets out my diagnosis – and where I think the most work is needed to design new institutions….
It’s not too hard to get a map of the fissures and gaps – and to see where governance is needed but is missing. There are all too many of these now.
Here are a few examples. One is long-term care, currently missing adequate financing, regulation, information and navigation tools, despite its huge and growing significance. The obvious contrast is with acute healthcare, which, for all its problems, is rich in institutions and governance.
A second example is lifelong learning and training. Again, there is a striking absence of effective institutions to provide funding, navigation, policy and problem solving, and again, the contrast with the institution-rich fields of primary, secondary and tertiary education is striking. The position on welfare is not so different, as is the absence of institutions fit for purpose in supporting people in precarious work.
I’m particularly interested in another kind of sinkhole: the absence of the right institutions to handle data and knowledge – at global, national and local levels – now that these dominate the economy, and much of daily life. In field after field, there are huge potential benefits to linking data sets and connecting artificial and human intelligence to spot patterns or prevent problems. But we lack any institutions with either the skills or the authority to do this well, and in particular to think through the trade-offs between the potential benefits and the potential risks….(More)”.
Karolina Mackiewicz at ICT & Health: “…Better innovation opportunities, quicker access to comprehensive ready-combined data, smoother permit procedures needed for research – those are some of the benefits for society, academia or business announced by the Ministry of Social Affairs and Health of Finland when the Act on the Secondary Use of Health and Social Data was introduced.
It came into force on 1st of May 2019. According to the Finnish Innovation Fund SITRA, which was involved in the development of the legislation and carried out the pilot projects, it’s a ‘groundbreaking’ piece of legislation. It’ not only effectively introduces a one-stop-shop for data but it’s also one of the first, if not the first, implementations of the GDPR (the EU’s General Data Protection Regulation) for the secondary use of data in Europe.
The aim of the Act is “to facilitate the effective and safe processing and access to the personal social and health data for steering, supervision, research, statistics and development in the health and social sector”. A second objective is to guarantee an individual’s legitimate expectations as well as their rights and freedoms when processing personal data. In other words, the Ministry of Health promises that the Act will help eliminate the administrative burden in access to the data by the researchers and innovative businesses while respecting the privacy of individuals and providing conditions for the ethically sustainable way of using data….(More)”.
Blog post by Cassie Kozyrkov: “…Decision intelligence is a new academic discipline concerned with all aspects of selecting between options. It brings together the best of applied data science, social science, and managerial science into a unified field that helps people use data to improve their lives, their businesses, and the world around them. It’s a vital science for the AI era, covering the skills needed to lead AI projects responsibly and design objectives, metrics, and safety-nets for automation at scale.
Let’s take a tour of its basic terminology and concepts. The sections are designed to be friendly to skim-reading (and skip-reading too, that’s where you skip the boring bits… and sometimes skip the act of reading entirely).
What’s a decision?
Data are beautiful, but it’s decisions that are important. It’s through our decisions — our actions — that we affect the world around us.
We define the word “decision” to mean any selection between options by any entity, so the conversation is broader than MBA-style dilemmas (like whether to open a branch of your business in London).
In our parlance, a “decision-maker” is not that stakeholder or investor who swoops in to veto the machinations of the project team, but rather the person who is responsible for decision architecture and context framing. In other words, a creator of meticulously-phrased objectives as opposed to their destroyer.
What’s decision-making?
Decision-making is a word that is used differently by different disciplines, so it can refer to:
taking an action when there were alternative options (in this sense it’s possible to talk about decision-making by a computer or a lizard).
performing the function of a (human) decision-maker, part of which is taking responsibility for decisions. Even though a computer system can execute a decision, it will not be called a decision-maker because it does not bear responsibility for its outputs — that responsibility rests squarely on the shoulders of the humans who created it.
Decision intelligence taxonomy
One way to approach learning about decision intelligence is to break it along traditional lines into its quantitative aspects (largely overlapping with applieddata science) and qualitative aspects (developed primarily by researchers in the social and managerial sciences)….(More)”.
Matt High at CSO:”…The sector also faces considerable pressure in terms of its transparency, largely driven by shifting consumer preferences for responsibly sourced and environmentally-friendly goods. The UK, for example, has seen shoppers transition away from typical agricultural commodities towards ‘free-from’ or alternative options that combine health, sustainability and quality.
It means that farmers worldwide must work harder and smarter in embedding corporate social responsibility (CSR) practices into their operations. Davis, who through Anthesis delivers financially driven sustainability strategies, strongly believes that sustainability is no longer a choice. “The agricultural sector is intrinsic to a wide range of global systems, societies and economies,” he says, adding that those organisations that do not embed sustainability best practice into their supply chains will face “increasing risk of price volatility, security of supply, commodity shortages, fraud and uncertainty.” To counter this, he urges businesses to develop CSR founded on a core set of principles that enable sustainable practices to be successfully adopted at a pace and scale that mitigates those risks discussed.
Data is proving a particularly useful tool in this regard. Take the Cool Farm Tool, for example, which is a global, free-to-access online greenhouse gas (GHG), water and biodiversity footprint calculator used by farmers in more than 115 countries worldwide to enable effective management of critical on-farm sustainability challenges. Member organisations such as Pepsi, Tesco and Danone aggregate their supply chain data to report total agricultural footprint against key sustainability metrics – outputs from which are used to share knowledge and best practice on carbon and water reductions strategies….(More)”.
