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How to Avoid Curses in the Era of Big Data

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and so it is and
you can I really that Republic life is going to end soon but I don't know about you guys I'm pretty excited to cut off my conference bandit kind smells like sweat and alcohol that so if I go on I wanna make sure that this is the beginning of a conversation so please tweet traditional long if you have any questions or notes for us to talk about afterward and about inquired fascinating to be at my 1st Republic I and my 1st time in Germany before I came here I have this perception of Republic less as an activist conference and now that i've hung out a few days I can also see that it's a place for industry and I find this confluence of communities of activists and industry particularly unique to Berlin and it's actually quite fascinating of politics technology finance innovation no 1 owns these things although everyone is willing to take responsibility for them and that to me seems to be the magic of what's going on here is just outside of Berlin sometimes you know
activism business circles don't usually talk to 1 another even though they have many similar interests and because of this they often miss their own overlaps and I'm sure that this has actually happened quite a lot you know here as well but at least you can feel you can really feel after the few days here that these 2 groups are mixing together so as as Geraldine has that I said most of my career doing work in China and Mexico in the US and I usually I talk about you know China or values and digital governance are how people negotiate density in social networks but today I wanna talk about a space where all of my research and work in those areas crossover because it's also a space where different communities are trying to solve the same problems but oftentimes miss their own overlaps and that's
space comes in the form of big data which is a very global topic and some call this Open Data others call it Business intelligence you know regardless of the language most people are using big data to uncover information that enables them to make better decisions I don't know if the conference produces did this on purpose but the message of what I have to
say today should underscore many of the things we've heard from various speakers you know Ethan Zuckerman in its opening keynote suggested that greater data transparency has actually led to you know more to mistrust in some areas James bridles projects a critique of this idea that collecting more data makes a society better and you know the wonderful and enduring Zygmunt Bauman is concerned very deeply concerned that the
accumulation of personal data is corrupting our relationship with privacy and we've also heard of a critical take on big data in different context you know in different sessions from their Precrime prediction from that same year to corpora surveillance from all the other crystal and ethics and development context in several people in the Global Innovation gathering tracks but the common thread that I've noticed amongst all the speakers is actually a very new wants questioning of the value of the data and in some cases you know asking what is all this type of big data about and so what I wanna try and do my talk today is to continue these conversations and started by asking
you know why do we have this belief that big data is more valuable than what did you know where exactly did this come from and as a sociologist I wanna ask how did organizations start touting this idea how creep into are institutional mind-set hearted creep into nonprofits you know activist circles you know pretty much everything we see this big Data kind of you know mind set in the blind enforcement of austerity measures to global indices of you know happiness to the internalization of self worth based on social media accounts so how did this number mind-set become the de-facto way of decision making and evaluation
so just make sure we're all on the same page with the data is like you know a very kind of a catch-all word for many things but is quickly define that term big data so the
Korea the word of the data of item a goalless said that he coined the term to capture this new reality their organizations now have the capacity to produce and store in analyze massive amounts of data at a scale there has never been possible before in history so what is
practically means that instead of analyzing you know excel he spreadsheets outcome you were now analyzing many databases across many warehouses so what we're really talking about is just a matter of scale out you know we have so much data now that all the data created before 2003 from the beginning of time is now created every 2 days so quite
simply the data is just quantitative data at a larger scale than now involves new little digital technologies around capturing storing and analyzing that's all it it's it's it's not a scary term and it's not magic so if
they did i is really just lots of quantidade data what is all this hype about all this hype revolves around the idea that the more data we have the more it can predict our futures and you see it everywhere you look like a you know when you look at how people write about implement data as if it's like that next big thing that's gonna solve our problem I talked organizations to a single we don't know why we need they didn't we've just been told that collective story and 1 day it's gonna like B that thing that's going out you know give us the next innovation so essentially they didn't appeal is that it can tell us what's going to happen next and went but I think a big data utopianism the notion that more big data leads to more predictability is actually 1 of the most dangerous things are happening in organizations today and I think this you know idea of entirely trusting data from computers is terribly wrong
I the I personally witnessed the beginning of the downfall of an entire company that privilege data capture by machines over data capture by humans so in 2009 I started of researching note here which is the largest cellphone company and emerging markets at a time and this is when you know cell look like this and I was like super excited because I didn't you know my 1st cell phone was a Nokia's and I've been using it since the late nineties and I was a total notice anger also this is like my dream job interview was on the 1st a higher social sciences so when I and by time I
started working there I years of ethnographic work of mercy myself and the lives of of Chinese migrants from you know working as a street vendor selling dumplings to living inside Internet cafes had already been immersing myself so while a new I you know was able to take all this in the past experience and I started seeing something new that led me to conclude that tech USA monks Chinese migrants was going to
radically change and observing captured many indicators that led me to conclude that low-income consumers were actually ready to move from new feature us you know very inexpensive features some of us cell phones to you know more expensive smartphones I thought you know and as as I saw this because I saw the aspirations for upward mobility and you know this is you know their their houses at a time and center city slums of you know the migrants I was living with they were building you know large apartment complexes that featured of Cartesian families in places called the flower house you know romping through you know the green fields I even though this is you
know they're bathroom and the temporary housing for migrants in cities are they also were solution by advertisements that look like this featuring very high in toilets and even though they were using a Nokia phones that were very cheap you shines I once they were being bombarded by iPhone advertisements that promise entry into high-tech world so it was clear to me after years of living and working with migrant workers you know around the world and especially you know in China that the the poorest people and you know what some would call the bottom of the pyramid 1 wants to participate in a slice the consumer like that was aggressively being marketed everywhere so they sort of my data I conclude that Nokia need to replace the current product development strategy from making extensive smartphones for lead users to affordable smartphones for low-income users and I finally reported my findings even though I knew at the time this is a big you know controversial because you to
keep in mind enjoyed had just launched in late 2008 so that you know the idea of affordable smartphones really wasn't in the market
yet but what I didn't expect was for Nokia to really not know what to do with my findings are apparently they said my sample size of 100 was very weak and comparible to the sample size of millions and millions of data points which they said initial any evidence of my inside the might of course it is a show evidence because you didn't know that users to be gathering for the data this is emotional stuff that has a showed up yet in
your and the measures that you're looking for but they didn't do anything so they planned ahead with the business strategy based off of the quantity that they just kind of left myself at the side I'm well we all know what happened to node yeah it's I'm not hard against which line represents them they were so close to the claim that they couldn't
see it coming and here's the thing it wasn't because they didn't have enough data you know that they had to tons of what data is just that they didn't know how to handle data that was an easily measurable and didn't show up in existing reports what could have been their competitive advantage and up a key contributor to the eventual downfall in as much as Nokia privileges quot measurable statistical data over qualitative ethnographic methods you know many companies and organizations now
proclaimed that the practice data-driven decision-making but this isn't always working on making decisions would data in the absence of a clear purpose such a very dangerous side because it's and so much time China's is having a lot now western companies who 1 enter into the Chinese market and capture a by you know a big part of it but they often fail to you know deal with the know how to do the complexity of a non non-Western market so this is my time at Nokia I've been obsessing over this 1 question which is how and why the quantitative data becomes more become more value than qualitative data in organizations so I started digging around so I kind of landed in 8th century Greece i in Ancient Greece for over 12 centuries consulting oracles a person who could predict the future was a part of everyday Hellenistic life you know people for a wealthy slaves and free
they went to Oracle's asking them very important like questions like should I get married should go on this voyage to we you know go into this territory you know will will I come back alive and the most the meanest and powerful Oracle of all was a picture of the goddess of the of Delphi at the temple of Apollo and developing being the god of prophecies now the interesting thing is that recent research from geologists and other experts has revealed that when getting prophecies the Pythia was in healing enormous amounts of ethylene gas it actually just so happens that the temple of Apollo was built over to massive earthquake faults which created the stages that allowed for the release of petrol chemical fumes from the Earth so this the bird's eye view of the temple of Apollo on your left and the 2 red lines mark that you earthquakes fault Delphi's all and the kernel so essentially when the pit deal with 1 into predictor mode she was stripping out and
that he had been passed down the the regular predictions derived from new the hallucinations of the gas to create 2 then interpreted Part chemically travel as official words were kings and signatories and philosophers this pretty crazy that for several centuries this is how small and big decisions were made but there was a methodology to this entire process that wasn't as the picture you know too that is surrounded as you can see by all these priests who then would ask the solicitor you know what's the context of your
question and this helped them you presented in the pit years predictions in a relevant context now the process that a very tedious because a pity of words are often undecipherable pursue was high so oftentimes people have to wait days before prediction was made with this was the form of research
no reliance on prophesy is not just a Grecian phenomenon you know from the oracle bones of ancient China to the mind calendars people have used various ways the answer to the question of what happens next node so why has humanity been so determined to answer
this very timeless and difficult question because the future is scary you know making a decision without any assurances of outcomes can be absolutely frightening and this is just as true today as it was for the ancient Greeks in all of us in this room go to bed and I think you know what's going to happen tomorrow is going up and my book that my family my kids you know to have my career would why right online in a bubble plot it is that well today we might dismiss the predictions of someone tripping out on films being released from cracks in the earth as crazy but we still believe that prophesy is possible we still have the question of what comes next and the Oracle of Delphi is still here you know you making that's crazy you know how dare I compare the way we answer what comes next the Greeks answered it was so much more advanced and you're right you know how we do is different ways to answer this question are modern capability to predict the future relies on the epistemology goal and
test technical breakthroughs in the scientific revolution that created the scientific method a rationalistic set of
approaches to investigate the world ends at 1 of the most fascinating notions to emerge out of the scientific method is the idea that processes as investigation have to be based on empirical and measurable evidence the measurability without a very important because a lot of other scientists iterate upon other scientists work and at that time the idea that the world could be operationalized into a set of precise quantitative measurements the light the stars you know that the body could be mediated as a set of numbers was a very
new and powerful idea I mean we're talking about a time when things were done on got and memory and intuition you know people were drinking workers to live longer and secure sexually transmitted diseases doctors at that time about women and you know women's uteruses were actually wondering you know Williams I could go anywhere in the body and the treatment for this was early marriage and having lots of kids so it kind of makes sense that back then that the pendulum swung the other way to peer measurement but when you look at the history of how measurement was created it wasn't necessarily so clear-cut because there wasn't just based on truth was based on the politics of the time
so my search to understand how what became like the de-facto you know status . let me to the age of measurements then in 19th century Europe this is the time when a series of discoveries from non-Euclidean geometry to the Doppler effect new forms of measurement possible and the person who best embodies the those of this age of measurement is this guy here Irish-born mathematical and and English engineer William Thomson 1st Baron Kelvin and in
1883 he made a statement that essentially said that if you can't measure something that it doesn't even qualify as knowledge and this is the exact quote now this notion is commonly referred to as the curse of Calvin because when scientists you know heard this thing pretty much felt like they were screwed because discovering something with wasn't simply enough now they not we had to discover something but they had to measure it and then when I finally came upon this part of history I was like 0 my god this is this is the answer I was searching for my question is where it all began this 1 guy here is like the measurement dictator and now he is that would create this person that's being imposed on the rest of the world societies at the time really did feel like they workers because then they were like was during the answer this message about how we measure you know the quantity the message being sent through a telegraphist minimum motor you know the length of a telephone call but just as science had to measure the things that made electricity possible they also had to measure electricity itself not that time scientists could demonstrate a lecture city but they hadn't yet figured out how
to you know harnesses for commercial use because they did not know how to measure it so with this person Calvin hanging over you know scientists had they started developing all kinds of electric you know measuring devices and the reason why they were just like so many of these devices as you can see is partially because this is also a new but partially because no 1 could agree for decades how to measure
electricity you know these about in standard stripped apart friendships and and played on nationalism you know is Nicholas Tesla's alternating current or Thomas Edison's better do we measure resistance with the British only unit or Germany's Siemens unit and by the way the British unknown you know what the old was named after German physicists so
how do we measure the way electricity of the light coming with the light coming out the balls B is where the questions that they were asking back that and this 1 thing that is seen so standard that we all rely on this known known was very unknown and mysterious the debates of electricity were just as he did as open or closed source web browser apps or native apps in a single purchaser subscriptions ofAmerica PC Android or at least m of our flash measurement was not always absolute and it was not given had to be worked out over time you know these late 19th and 20th century debates reflected period in Western history were science challenges religion as authoritative source to answer the age-old question what comes next so chanting for Alex and scientists were then and as long as sign system filled Calvin's you measurement agree they had the power to produce knowledge and to tell us what comes next and this is the beginning of the air and conflating measurement with knowledge the contemporary admit
contemporary meant that we're living in were measurement is absolute and were measurement is truth has become just as unquestioned and sacred in modern times as oracles were in ancient Greece this is now Ahmed look different and our
methods for answering them have changed
or even mythology is called Big Data and in this narrative prediction has replaced prophesy numbers have replaced muons and measurement is replaced oracles and I and many others actually don't think our society is necessarily any better off with all these big data for all innovations made in health you know gender quality and governance we have economic quality systematic atrocities and massively displaced populations and its scale and speed never before seen in history but yet we continue to tell ourselves more obtained data will make us better and the big twist in our current is at this idea isn't just being reinforced by individual scientists like during the scientific revolution but also organizations cutting across all sectors you know from activists to businesses in government so in my exploration to understand you know how this happened and when a jump from electricity in 19th century to the 20th century to look at a coalescence of 3 forces that led to the current narrative of Big Data which are the incidence of the information revolution the rise of popular mass culture and the founding of Management Science and the
1st source information revolution
started in 1949 when Claude Shannon a scientist at 18 t Bell Labs invented but that's a unit information that lived in the form of a binary signal of 1 or a 0 which is just really a constrained form of electricity not the top the notion that the information was measurable and storable in digital form on transistors was groundbreaking because up until then measuring instruments were all analyzed you know Morse code was stored on
paper photos of plastic excel musical records electricity was measured through moving well mean temperature through a mercury barometer in time through a diet so with this all of with the with all this you know what gets now all of these things could be rendered in the digital form of ones and zeros the biggest of fundamental building block for everything that computing in big data is built on there's probably no coincidence that 1961 51 the world's 1st commercially available general-purpose electronic computer the Ferranti Mark 1 came from 1 of the largest electricity companies in the UK frontier Limited which made electricity meters in the late 19th century is is there meter and and this is the computer so we can think of computers as direct descendants of atrocities but the birth of the bit mark the beginning of the birth of a dead market beginning of information revolution because it was a technological innovation and make computers like the font to 1 block 1 possible but it also created a new language for describing the
digital world for things like speed storage and most importantly predictability the and fascinatingly this new language for computers as predictability machines you know came way before the invention of any personal computer they stood up in the form of mass culture the 2nd forces shapes are current narrative of the data you know in the 19 fifties computers took up an entire room and wait around 7 thousand 300 in kilograms on which is 16 thousand pounds and only a few organizations in the world could afford that but the greater public knew about these computers even though no individual could own and because broadcast television plays a pivotal role in introducing the computer at the prediction and measurement device starting in the late 19 fifties Univac aired commercials on national broadcast television in the US to educate the public about the weather prediction and the role order right giant electronic brain developed by running the less you know that that operate with them
and all that you do you to do that later was that the and more accurate weather predictions wherever possible with the and
then a few months later the Univac computer had a starring role in a Bugs Bunny cartoon episode in in this clip Wiley Coyote builds the Univac electron brain to come up with an answer on how to best capture his nemesis Bugs Bunny wing lower c means right it's all in the
name of the whole of
the combination of
the use of a
polymer ones yeah
and then a few years later in the Superman comic a scientist presented Lois Lane with the perfect husband chosen by her matchmaker the Univac and then a 3rd theme also appeared in about 30 years later in the war in the country in China through a popular comic book in radio program called black cat detected so clearly this is a global thing you know in this story the black cat you a supercomputer to predict which 1st all males from a warehouse and the computer was able to help like catenary down to 3 birds so clearly this is not just you know a US western thing this ended up
around you know other places around the world and I love to find out if something similar appeared in Germany or other parts of the country so please let me know if you you know how find any other examples so that then
using a computer to capture a bunny or birds may seem amazing totally far fetched but when we look at you know sigh 5 mainstream films on a main Monge we have to start seeing the trope of the computer as a prediction machine over and over again an outside of
popular culture the 3rd forces shaping data starts to develop inside businesses the founding of Management science in
during World War Two scientist called operational researches they use new techniques to operate the British Army supply chain of military stuff like you know food and tanks and fuel so these military science if you use this algorithm developed by George Dantzig called linear programming to calculate multiple decision such as the most optimal and least risky outcome for any given word situation or how to you
know decrease expenditure losses during about or a and when the war ended the scientists realized that they could actually repurposes algorithm for civilian settings in organizations they needed to calculate and predict business-related decisions such as lot Logistics Shipping and infrastructure and this application of wartime operational analysis to organizational analysis created the field of management science as
we know of today you know this you know may not sound radically new to us but that then to manage an organization through numbers was very innovative at the time they completely changed everything and if we take a look at modern management sciences science tools such as Six Sigma Total Quality management capability maturity model integration we cannot see all the roots of these programs going back to world war 2 operations because of fundamental core in all of these frameworks that whether for war were civilians sightings is that management is about measurement modelling and prediction that the 3 driving forces the information revolution popular mass culture and the founding in management science formed the foundation of the data modern narrative as a tool of predictability and measurement and they're all heralded by the award Calvin from the 18 hundreds and the after effects of
countless measurement cursor humongous is 100 and you to 32 years later we can see a sudden curve immeasurability you know we went from not knowing how to measure basic things such as you know popular electricity to knowing how to measure complex things like organizations
and information so now we're at this point that were all this rich history of measurement science in a popular science and military technology are finally coming to place of organizational relevance and what is officially known to us as there are a big data which is an error characterized by the idea that more information leads to more knowledge no using big data has become so appealing as some organizations have started to rely primarily on quantitative data the organization's shifting funding from Caltech 1 all the time when I when I ask organizations why do you do this they come with the numbers a less biased and more accurate stories and happened at data from a large sample size is a much more reliable than a small sample this is terribly terribly
belong to have impact numbers need stories and vise versa when people rely on numbers the actually aren't taking the full advantage of Big Data you know
even sciences these stories primatologists you know finds the wall argues that a sense of fairness the groundwork for morality can be seen in our ancestors monkeys and his argument comes from very rigorous scientific work and he says that he shows the grass to you know science is all the time and you can see the
number of copulations sessions in the occurrence of swollen genitals and through transfers related to sex and how many times male bonobos exchange sex for food from females so you know in all of this data is captured in form of what looks like some regression analysis but he says that societies don't really understand what these graphs about morality mean until they see this video what what
happens to this Bunke here on the left when he you know we're she gets great as a reward instead of a cucumber and greater among the world are equivalent to crack searching interactor of that's the past and we have a piece of to prevent features delegates to give a rock to us and literature and she gets a great and he said that sees the smell guessing and cucumber yeah and it i and j tests or optimal set against the wall you need to get to and to get to the end the it and so so do also says that his use of stories is just as important in his as its use of you know quantitative data and explains that his fellow primatologist when he shows them you know the graph they consider patterns was seen the video allows them to feel and experience the texture data in a way that it is impossible to grasp the prove statistical significance now the
story of the wall and the monkey says if you don't experience something directly you may not discover that the meaning of the phenomenon this representation of a thing is not the same thing as experiencing I think
now this idea of direct experience of what I call dictator data produced through qualitative ethnographic research methods uncover emotions stories and meanings it's all that sticky stuff is difficult to quantify any comes to us in the form of a small sample size and then return we get an increase citable tab of meanings and stories the data is the opposite of big data which requires a humongous sample size to uncover patterns in a large scale but here's the thing the processes of analyzing you know even producing big data to make it and what it is which involves normalizing standardizing defining in clustering its flattens it is at the club context meaning and stories that there's is nothing inherently wrong with this as it
actually enables us to see patterns that scale but that this loss and context comes to us at a cost and that is why to form a more complete picture of the use of big data must be coupled with big data because they each produce different types of insights at varying scales and debts both calling quant come with its own deficiencies and opportunities that could be better addressed when integrated together the sun is greater than the parts so what is this integration look like you know and I tell you quick story were for Ali Baba digital commerce site based in China the idea would add 22 billion euros which is the highest IPO ever in history they built the Big Data platform for vendors but this figure
to this platform just called observatory is that it only works if the vendors know how to use it with dictator so what I that the officers in hunter was few weeks ago explained you know several examples on 1 of them involve birds and next in China people he bird nests for health reasons and an animal that will burn markers from 4 . 5 billion euros and that it might be the white that is around 2 thousand 200 euros then they can go as high a for you know very high quality nest for 4 thousand 500 euros now I know I'm a sound on the firmness but so is eating caviar which is the baby exist effects
so the traditional stereotype of a bird nests consumer are wealthy vain housewives with children who said home and the this partners to make the skin you know more more firmer more subtle but how about observatory surface data points are revealed that bird nests consumers were buying diapers or any children's products so they probably were not house wives and children so that vendor unit-holder told the vendors momentous and ask the consumer so why are you buying these burdens and that's what they found out that the consumers were actually pregnant women who wanted to temporarily booster health during pregnancy so that vendor then went to the marketing agency to create a new campaign called here into their own which means 1 person needs to people become healthy this new ad targeting pregnant women achieved a click-through rate of 61 per cent and 46 per cent or a lot of vendors success comes from the
integration of quantitative points with further data research now another
illustration of the importance of marrying Big Data and Big data can be seen in the work of defending human rights Nathaniel Raymond is a human rights investigator at the signal program at Harvard humanitarian initiatives his job and his team start its investigate work crimes around the world and oftentimes the places Nathaniel and his team works or just read too dangerous for them to go in person so they created an entirely
new approach to data analysis that uses satellite imagery to remotely assess
areas of conflict from their programs office in Cambridge
Massachusetts now this prevents new data complications and new data opportunities had during the summer of 2011 Nathaniel received reports of mass graves you filled with new but people created by the
Sudanese armed forces in the city of Purdue going that could do believe this city that the annual couldn't travel to the satellite could so Nathaniel steaming used satellite imagery of could you glee taken over the course of the summer to assess what was going on here you can see that this is a time lapse and this allowed them to see what appear to be images consistent with body bags near disturbed earth but any honest he knew that digital data was enough to make a rather serious claim they have to find a new way to verify the information collected from satellites with the human source in the ground and essentially they needed dictator non-digital idea some
material was found contact with the very few people left in could you please and he ask that person to physically walk to the site of a suspected mass grave and has the
individual right on the site Nathaniel's team has to start you know task satellite to the location of the individual then gave an account of what they were seeing and measure the exact distance others suspected grave to a nearby radio tower Everything measured of Nathaniel team had the corroboration and only then was seen as team comparable with making assessment that these were indeed mass graves In the process of doing this they actually had developed a whole entirely new type of methodology for remote sensing that combines satellite imagery with information collective remotely from sources on the ground this is there in a version of integrating big dictator data now I ask Nathaniel we know why was it so important to have this additional data stream from a human being located independently considering how did this it was and he explained that the presence of data is not the president's presence of proof or evidence In a satellite imagery is simply reflections of light from a of that then produces country that is consistent with the heuristics of an object they're not
the actual being itself it says that without multiple streams of evidence outside of the satellite imagery you could have mistaken up a bunch of holes for mass graves non material says in his line of work there is a problem with people over extrapolating digital data because they think that's more cooperative property because as captured by computer the problem of privileging digital data over other forms of less quantifiable data it is it is isolated to
the field of human rights it's also pervasive in academia you know businesses and organizations of all sizes the many is not a meal the map is not the land the data is not that the as we acquire store and process the Morava information digitally we need to ensure that we have data streams that allow us to or hunches and signals from the digital data with experiences on the ground we live in a time where were still enamored by things that look like new technology we have to make sure that we don't mistake novelty refer revolution this social change will not come in the form of a service that the magic isn't about you know 1 type of data being the most innovative because then we would be self inflicting data fascism on ourselves the magic is really in seeing how all the parts fit together for it really depends on how you slice the data and from my perspective you look at it a free able to combine both in the value of both methodologies come out if you know what questions to ask and error big data we need a new way to think about information we need a new kind of socio data science 1 that unifies qual quan so this to me is an important problem to be solved by nonprofits global corporations governments artist activist groups that anyone trying to figure out how to gain significant insight into the behavior of citizens customers and users teachers to create systems that allow us to live more balanced life whether it stands on the streets of or Baltimore which increased sales of a product or declining audience viewership these are all examples of people giving feedback in ways that the system can merely capture you know into some predefined blocks variables or surveys the index measures and this is why the creation of any technological system platform or act must involve storytellers to surface narratives gaps and meaning now modern storytellers come in various forms you know they could be ethnographers artists journalists community organizers social media managers will die photographers designers product managers and pretty much really could be anyone who draws on empathy as the interface with data points the center is what drives us to make lot to make sense of our society in ourselves to settle differences or help strangers and is the 1 row to asking why into what had in a world where where we're talking about increasing automation and tracking of practically everything some of which happens by choice and some not asking ourselves how can I preserve the
human element in everything I do How do we build empathetic impact of the systems it could actually be 1 of the most consistently revolutionary acts
that all of us can do in a time of liquid modernity and in the process of doing that we can lift the curse of Calvin so thank you so much for listening my new
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Ruhmasse
Kernel <Informatik>
Entscheidungstheorie
RFID
Mereologie
Grundsätze ordnungsmäßiger Datenverarbeitung
Gerade
Orakel <Informatik>
Bildschirmmaske
Prozess <Physik>
Prognoseverfahren
RFID
Mereologie
Vorlesung/Konferenz
Wort <Informatik>
Kontextbezogenes System
Entscheidungstheorie
Knotenmenge
Prognoseverfahren
Cracker <Computerkriminalität>
Familie <Mathematik>
Orakel <Informatik>
Plot <Graphische Darstellung>
Entscheidungstheorie
Orakel <Informatik>
Softwaretest
Menge
Rotationsfläche
Zahlenbereich
Einflussgröße
Bildschirmmaske
Sender
Festspeicher
Nichteuklidische Geometrie
Reihe
Vorlesung/Konferenz
Doppler-Effekt
Neunzehn
Einflussgröße
Computeranimation
Dicke
Befehl <Informatik>
Gerichtete Menge
Extrempunkt
Meter
Grundsätze ordnungsmäßiger Datenverarbeitung
Mereologie
Lesen <Datenverarbeitung>
Systemaufruf
Vorlesung/Konferenz
Message-Passing
Einflussgröße
App <Programm>
Physiker
Pay-TV
Browser
Einfache Genauigkeit
Humanoider Roboter
Physikalisches System
Quellcode
Frequenz
EINKAUF <Programm>
Flash-Speicher
Einheit <Mathematik>
Vorzeichen <Mathematik>
Einflussgröße
Zentrische Streckung
Subtraktion
Selbst organisierendes System
Besprechung/Interview
Ruhmasse
Zahlenbereich
Strömungsrichtung
Neunzehn
Inzidenzalgebra
Wechselsprung
Prognoseverfahren
Forcing
Geschlecht <Mathematik>
Rotationsfläche
Information
Einflussgröße
Orakel <Informatik>
Bit
Formale Sprache
Computer
Computerunterstütztes Verfahren
Computer
Information
Code
Computeranimation
Eins
Richtung
Bildschirmmaske
Digitalsignal
Einheit <Mathematik>
Bit
Font
Digitale Photographie
Rotationsfläche
Inverser Limes
Operations Research
Fundamentalsatz der Algebra
Gebäude <Mathematik>
Rotationsfläche
Telekommunikation
Quellcode
p-Block
Einheit <Mathematik>
Digitalisierer
Information
Shape <Informatik>
Bell and Howell
Informationssystem
Shape <Informatik>
Selbst organisierendes System
Formale Sprache
Prognostik
Personalcomputer
Ruhmasse
Computerunterstütztes Verfahren
Computer
Virtuelle Maschine
Bildschirmmaske
Prognoseverfahren
Forcing
Digitalisierer
Vorlesung/Konferenz
Ordnung <Mathematik>
Speicher <Informatik>
Motion Capturing
Telekommunikation
Computer
Programmfehler
Supercomputer
Vorlesung/Konferenz
Turbo-Code
Computer
Kettenlinie
Optimierung
Eins
Virtuelle Maschine
Prognoseverfahren
Forcing
Mereologie
Datenmanagement
Operations Research
Vorlesung/Konferenz
Computer
Computeranimation
Shape <Informatik>
Algorithmus
Einfügungsdämpfung
Selbst organisierendes System
Operations Research
Kartesische Koordinaten
Optimierung
Computeranimation
Entscheidungstheorie
Entscheidungstheorie
Prognoseverfahren
Algorithmus
Verkettung <Informatik>
Datenfeld
Menge
Lineare Optimierung
Vorlesung/Konferenz
Wort <Informatik>
Logistische Verteilung
Analysis
Fundamentalsatz der Algebra
Nichtlinearer Operator
Total <Mathematik>
Selbst organisierendes System
Zahlenbereich
Ruhmasse
Operations Research
Sigma-Algebra
Framework <Informatik>
Informationsmodellierung
Prognoseverfahren
Datenmanagement
Forcing
Rotationsfläche
Speicherabzug
Information
Wurzel <Mathematik>
Kurvenanpassung
Optimierung
Einflussgröße
Cursor
CMMI
Punkt
Selbst organisierendes System
Asymptotik
Stichprobenumfang
Zahlenbereich
Vorlesung/Konferenz
Information
Einflussgröße
Fehlermeldung
Arithmetisches Mittel
Parametersystem
Bildschirmmaske
Zahlenbereich
Wärmeübergang
GRASS <Programm>
Ungerichteter Graph
Computeranimation
Videokonferenz
Regressionsanalyse
Arithmetisches Mittel
Softwaretest
Textur-Mapping
Graph
Selbstrepräsentation
Mustersprache
Besprechung/Interview
Computeranimation
Videokonferenz
Zentrische Streckung
Web Site
Einfügungsdämpfung
Subtraktion
Prozess <Physik>
Physikalische Schicht
Kontextbezogenes System
Systemplattform
Richtung
Integral
Arithmetisches Mittel
Bildschirmmaske
Digitalisierer
Stichprobenumfang
Mustersprache
Mereologie
Datentyp
Vorlesung/Konferenz
Figurierte Zahl
Soundverarbeitung
Punkt
Flächentheorie
Bitrate
Biprodukt
Systemplattform
Office-Paket
Programm
Punkt
Rechter Winkel
Prozess <Informatik>
Besprechung/Interview
Optimierung
Integral
Satellitensystem
Datenanalyse
Besprechung/Interview
Versionsverwaltung
Ruhmasse
Quellcode
Fastring
Office-Paket
Digitalsignal
Forcing
Flächeninhalt
Vorlesung/Konferenz
Information
Optimierung
Verkehrsinformation
Widerspruchsfreiheit
Bildgebendes Verfahren
Satellitensystem
Addition
Web Site
Prozess <Physik>
Versionsverwaltung
Heuristik
Ruhmasse
Quellcode
Paarvergleich
Computeranimation
Task
Objekt <Kategorie>
Rechter Winkel
Datenstrom
Beweistheorie
Datentyp
URL
Abstand
Information
Beweistheorie
Satellitensystem
Rückkopplung
Subtraktion
Punkt
Prozess <Physik>
Selbst organisierendes System
Mathematisierung
Gruppenkeim
Computer
Sondierung
Systemplattform
Streaming <Kommunikationstechnik>
Variable
Datensatz
Multiplikation
Bildschirmmaske
Weg <Topologie>
Datenmanagement
Digitale Photographie
Perspektive
Flächentheorie
Datenstrom
Datentyp
Rotationsfläche
Speicher <Informatik>
Hilfesystem
Einflussgröße
Gerade
Auswahlaxiom
Schnittstelle
Videospiel
Ruhmasse
Physikalisches System
p-Block
Biprodukt
Mapping <Computergraphik>
Dienst <Informatik>
Datenfeld
Digitalsignal
Automatische Indexierung
Rechter Winkel
Mereologie
Hypermedia
Information
Fehlermeldung
Prozess <Physik>
Vorlesung/Konferenz
Physikalisches System
Element <Mathematik>
Flüssiger Zustand
Hypermedia
Besprechung/Interview
Computeranimation

Metadaten

Formale Metadaten

Titel How to Avoid Curses in the Era of Big Data
Serientitel re:publica 2015
Teil 79
Anzahl der Teile 177
Autor Wang, Tricia
Lizenz CC-Namensnennung - Weitergabe unter gleichen Bedingungen 3.0 Deutschland:
Sie dürfen das Werk bzw. den Inhalt zu jedem legalen Zweck nutzen, verändern und in unveränderter oder veränderter Form vervielfältigen, verbreiten und öffentlich zugänglich machen, sofern Sie den Namen des Autors/Rechteinhabers in der von ihm festgelegten Weise nennen und das Werk bzw. diesen Inhalt auch in veränderter Form nur unter den Bedingungen dieser Lizenz weitergeben.
DOI 10.5446/31969
Herausgeber re:publica
Erscheinungsjahr 2015
Sprache Englisch
Produktionsort Berlin

Inhaltliche Metadaten

Fachgebiet Informatik
Abstract How to Avoid Curses in the Era of Big Data: The Answer Through a Brief Historical Detour of Electricity, Computers, and Algorithms

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