A Primer on Big Data taken by the book: "Big Data" by Schoenberger and Cukier

A Primer on Big DataMAURO MEANTI

HAWASSA UNIVERSITY, MARCH 11-13 2014

Based on the work of V.M. Schonberger and K. Cukier: «Big Data»

2Agenda Today:

What is Big Data about

More Data Messy Data Correlation

Thursday: Data: their essence,

their value Implications Risk Remedies

3

WHAT IS BIG DATA ABOUT

4What is Big Data about:

The 2009 US Flu Epidemic H1N1: big scare, no vaccine. Need for a map to contain the spread

Center for Diseas Control and Prevention method: good but 2 weeks late

Google came with a predictive algorithm based on what people searched for. 50m terms and 450m models brought down to 45 “marker” terms. No assumptions were taken

When the flu stroke, those 45 terms painted the same map as CDC, but in real time

http://www.nature.com/nature/journal/v457/n7232/full/nature07634.html


A Definition

THE ABILITY OF SOCIETY TO

HARNESS INFORMATION IN NOVEL

WAYS TO PRODUCE USEFUL INSIGHT

OR GOODS OR SERVICES OF

SIGNIFICANT VALUE


Buying Airplane Tickets Airplane fares do not behave linearly with time. A Computer Scientist (Oren

Etzioli) got mad at it

He collected all historical data on a number of routes. The data, not the rules behind them. 200Billion flight-price records

Can predict if a price will go up or down with a 75% hit rate, saving 50$ in average

Sold to Microsoft for 110M$


What is New Here? Ability to process huge quantities of data, and not necessary tidy

Hadoop vs relational DataBases

A Mindset shift: DATA are no longer static – they have more value than their original use

DATA can be reused

DATA can reveal secrets

Look for correlation versus causality

Quantity shift leads to a Quality shift


How Big? Non Linear growth of data– New telescopes collect today 50 times the info they collected 5

years ago

Google process 24 petabytes per day = US Library of Congress time 1000

Facebook uploads 10M photos per hour and 3Billion “like” per day

YouTube adds one hour of video every second

….

In 2000 – 25% of data were digital

In 2007 – 300 exabytes of data were stored. As in 300 Billions compressed digital films. And it represented 93% of data

In 2012 - 1200 exabytes – representing 98% of all data. Like 5 piles of CD reaching the moon

Every person on Earth now has 320 times the information that were (estimate) stored in the Library of Alexandria

In Gutenberg time, it took 50 years to double the amount of info, now it takes 3 years


3 main shifts MORE Data

We can now process almost ALL data we want

Using ALL data les us see details we could not see when we were limited

MESSY Data Having ALL data available we can forgive some imperfections in them

Removing the sampling error allows for some measurement error

The loss in accuracy at the micro level is compensated by the insight at the macro level

From causality to CORRELATION Big Data tells us the “WHAT”, not the “WHY”

From validation of our hypotheses to observing connections we never thought about


Datafication Taking informations on everything and making it analyzable opens the door to new

usage for the data

Like a gold hunt, there is lot of value to be discovered

Data is the OIL of the “Information Economy” and will soon move to the Balance Sheets of companies

Subject Matter Expert will become less relevant, Statisticians will become more (! )

There will be value for Data, for people being able to manage them and for people with ideas on HOW to use them


Risks Moving from human-driven decision (based on small dataset) to machine-based

decision (based on huge dataset containing OUR data) have implications

Who regulates the algorithms

How we preserve individual volition “sanctity”?

Examples: Data predict you will have a hearth attack soon. Insurance asks you to pay more

Data predict you will default on a mortgage. Mortgage is denied

Data predict you will commit a crime. Should you be arrested?

12

MORE DATA

13More Data:

We were all biased by scarcity Statistic in the past: confirm the richest finding using the smallest amount of data

The CENSUS history (by the way, CENSUS comes from “to estimate”) Caesar Augustus (1 BC)

Domesday Book (1086). King William I did not live enough to see its end

London during the plague (1390) – First attempt to make inference.

US, 19th century. Constitution mandates one every 10 years, in 1890 the estimation was for 13 years

So Herman Hollerith invented punch cards and tabulation machines – Data Processing (and IBM!) is born

Still too complex and expensive to be run more frequently than each decade

Sampling gets invented First, it looked like building a “representative sample” was the best approach

1934: Jerry Neyzman proves that random sampling provides a better result

14More Data:

23andMe For 100$ they (used to) analyze your

DNA to reveal traits making you more likely to get some heart and cancer problems

But they only sequence a small portion of your DNA – relative to the markers they know

So, if a new marker is discovered – they would need to sequence you again

So, working with a subset only answers the questions you considered in advance

15More Data:

Steve Jobs He got his entire DNA sequenced (3B

pairs)

In choosing medications, doctors normally hope for similarities between what they know of their patient DNA and the one of who participated to the drug’s trial

In Job’s case, they could precisely select drugs according to their efficacy given his genetic make-up

They kept changing treatment, as the cancer mutated

This did not save Steve’s life, but extended it by many years

16More Data:

Sampling make no more sense In many cases today, we can get close to N=ALL

Google Flu Trends used billion of search queries

Farecast used all US routes price data for an entire year

In many cases, the interesting data points are the “outliers” – and you only see them when you get N=ALL Detection of credit cards fraud - based on anomalies, need to be real time

International money transfer: Xoom. Discovered a large scam when they observed a pattern where there should not have been a pattern

17More Data:

“Big” does not to be BIG The real power is not from the sheer size of the data

, is from N=ALL

The SUMO example Steve Levitt from the Chicago University proved (after

many unsuccessful attempts) what everybody knew: there was corruption in Sumo!

Analyzed 11 years of matches, all of them (64K)

Crossed the results with the ranking

Corruption was not in the matches for the top position but in the matches with mid-ranking players (you need to win 8 of 15 matches to retain your salary and ranking)

When a 8-6 player met a 7-7 player at the end of the season, he lost 25% more often than normal

And in their first match of the next season, the former 8-6 won much more frequently than normal….as a gift back

18More Data:

Summing up Big Data (or N=ALL) allows us to reuse information and not to

resample

It allows us to look a details and test new hypothesis at each level of granularity

The Albert-Laszlo Barabasi example The chart on the right comes from ALL calls done over one mobile

operator network in a 4 months period

The study (Barabasi et al) is the first network analysis at a societal level

It shows that people with many links are less important that people with links outside their immediate community. It indicates a premium on diversity within societies

Using random sample in the era of big data is like using dial phones in the era of cell phones. Go for ALL, whenever you can!

19

MESSY DATA

20Messy Data:

We were all obsessed with precision Focused on sampling, we were trying to get exactitude – since errors got hugely

amplified

And with few data, the quest for exactitude was reasonable and aligned with our inner belief since the 19th century

Quantum mechanics in 1920s should have changed that mindset, but did not

But if we relax the precision standard, we can get many more data, and “more trumps better” Messiness (likelihood of errors) grows linearly with more data

Messines grows when combining different types of data: think an anagrafic where the company IBM can be represented as IBM, I.B.M., International Business machine, T.J.W Labs…….

Messiness kicks in when we transform data as when we use twitter messages to predict the success of a movie

21Messy Data:

The vineyard example If we have one temperature sensor for a whole

vineyard, we must and can ensure it works perfectly, having a high-cost sensor and high-cost maintenance

If we have one per vine, we can use cheaper sensors since the aggregate data will provide a better picture even with few imprecise measurements

If each sensor sends a reading every minute, we have no sync issues, but if each sends every millisecond, we can have data “out-of-sequence” but we still collect a much better representation

Maintaining exactitude in the word of Big Data can be done (look at Wall Street 30,000 trades per second) but is expensive

Very often less precision is “good enough” and allow to scale data

22Messy Data:

More Trumps Less As Moore Law says processor speed keeps improving, also

performance of algorithms has kept increasing But most of the gains do not come from faster chips or

better algorithms but from more data. In Chess, the system has been fed with ALL data for a match with <=

6 pieces left an now the computer always win

In Natural Language, given 4 existing algorithms for grammar-checking, Microsoft discovered that feeding them more words changed the performance dramatically, and also altered the ranking of the algorithms

So Microsoft invested in developing a corpus of words versus developing new algorithms

23Messy Data:

The case of machine-translation Started with very small data: 250 word pairs were used to translate 60 Russian

phrases into English in the cold war. It worked, but it was useless

But it did not improve fast: the issues were fuzzy words: is “bonjour” good morning, or good day, or hello, or hi ?

IBM in 1990 launched Candide: ten years of Canadian parliament transcripts in French and English. 3 millions sentence pairs, very well translated. It worked better, but not good enough to become commercial. And could not improve further

Enters Google: Takes every translation it can find on the web. A trillion of words, 95 billions English

sentences, unevenly translated.

It works way better than anything else before

Not because a better algorithm, not because better quality of the dataset. Just because its size

And it got the size because it accepted messiness

24Messy Data:

The Billion Prices Project Calculating the Consumer Price Index (or inflation rate) is a complex project that

costs 250M$ a year. And gives the output after few weeks, often too late to predict crises

The MIT launched a project to get the prices of products over the Web. 500,000 prices a day. Messy and not neatly comparable

The project produces an accurate prediction of CPI in real time

It spawn off a commercial venture, PriceStats, that sells analysis real-time to banks and Governments over the world . At a much cheaper price

25Messy Data:

Tags –imprecise but powerful Traditional hierarchical taxonomies were painful, but good-enough in small data

But how to categorize the 6 Billions photo Flickr has, from 75M users?

Use TAGS. Created by people in ad-hoc way, simply typed in

They may be misspelled, so they introduce inaccuracy, but they give us natural access to our universe of photos, thoughts, expressions….

26Messy Data:

How to handle them? Traditional databases “Structured Query Language” requires structured and

precise data. If a field is defined as numeric, it must be a number. And so on

They are designed for a world when data are few, and hence are curated carefully and precise

Also indexes are predefined, so you need to know in advance what you will be searching for

Now we have large amounts of data with different types and different qualities, and we need to mix them. This required a new database design, “noSQL”.

Hadoop is an example of this. It accepts data of different type and size, it accept messy data, and it allows to

search for everything

But it requires more processing and storage, typically distributed across physical locations.

It has redundancy built in, and it perform processing in place.

Its output is less precise than a SLQ output. So don’t use it for your bank account

Segmenting a list of customer for a marketing campaign: Visa reduced processing time from one month to 13 minutes

27Messy Data:

Past the tradeoffs Only 5% of data are structured – we need to accept that and the

inevitable messiness it brings if we want to tap into the universe of web pages, pictures, videos,….

We were used to be limited to small sets and focused on exactitude

We can now embrace the reality: data sets ARE large and they ARE messy. We have the tools to handle those characteristics and better understand the world

28

CORRELATION

29Correlation:

The Amazon story In 1997, the top selling tool for Amazon were critics’

reviews. They had 12 full time book critics

Then they realized they had huge quantities of data: every purchase, every book looked at but not bought, the time spent on each book…

First attempt to use those data: taking a sample to find similarities across customers. Outcome: dumb.

Second attempt: use all data and just look at correlation between products (“item-to-item” collaborative filtering) It worked, and it was book-independent

They marked-tested the 2 approach: books suggested by the algorithm beat books suggested by the critics 100:1

The 12 critics got fired, and Amazon sales soared

30Correlation:

Machine-gen recommendations work Nobody knows WHY a customer who bough book A also want to but book B

But one third of Amazon’s sales result from this system

75% of orders for Netflix come from this system

It is like the merchandise placed close to the cashiers – but it analyses your cart real time and real time it puts the right merchandise in the basket

Professional skills, subject-matter expertise, have no impact on those sales processes

Knowing what, not why, is good enough

Correlation cannot foretell the future, but through identifying a really good proxy for a phenomenon, it can predict it with a certain likelihood

31Correlation:

Don’t make hypothesis, be data-driven Walmart – the largest retailer in the world, crossed its historical sales

data with the weather reports. Discovered that before every hurricane, people rushed to buy….Pop-Tart, a sugary snack. Now they know and they stock it next to the hurricane supplies

Nobody could have made that hypothesis

The traditional approach was to make hypothesis and validate them through test. Slow and cumbersome and influenced by our bias

Let sophisticated computational analysis identify the optimal proxy

No need to know which are the search items correlated to flu

No need to know the rules the airlines use to compute prices

No need to know the taste of Walmart buyers

32Correlation:

More examples of the use of correlation FICO Medication Adherence Score: To know if somebody will take his medicines, FICO analyzed apparently irrelevant

variables as how often they changed job, if they were married, if they had a car

Historical data gave them correlations helping creating an index that helps health providers to better target the money they spend reminding the patients to take their medicines

Experia estimates people’s income based on their credit history. It cost 1$ to get itm while it would cost 10$ to get the tax return form

Aviva uses credit reports and lifestyle data as proxies for the blood and urine tests. The data driven prediction costs 5$ while the tests would cost 125$

Target used its shopping history to predict if a woman was pregnant. Found 20 products that were good predictors and used them to target those women. Even targeting the different phases of pregnancy

33Correlation:

Predictive Analysis Place sensors on motors, equipment or infrastructure like bridges

to monitor the data patterns around temperature, vibration, sound, etc

Failures typically observe a pattern in those data so once the pattern is spotted, predicting it becomes easy

UPS use it for its 60,000 cars. Before it, it replaced each part every 2 years, to be on the safe side. Now it has saved millions of dollars

University of Ontario used it to help making better diagnostic decision while caring for premature babies Data showed that very constant vital signs are a precursor of a serious

infection – against any apparent logic

This stability is likely the calm before the storm, but the causality is not important, the correlation is

Big data saves lives

34Correlation:

Not only linear We already said that in small data every analysis started with an hypothesis

Today with big data , the hypothesis is no longer important

Also, in small data the analysis was limited to linear correlation. Today, no longer

Are happiness and income directly correlated? They are linearly correlated for low income, than it plateau

How measles immunity depends on healthcare spend? Again, it is linear at the beginning but then it drops (likely since more affluent people

shy away from vaccines)

35Correlation:

A philosophical problem Those analysis help us understand the world by primarily asking WHAT and

not WHY

As humans, we desire to make sense of the world through causal explanations

Causality normally is a very superficial (quick, illusory) mechanism. When two events happen one after the other, we are urged to see a causal relation.

Got a flu. It happened since I did not wear a hat yesterday

Got stomach sick. It happened since I ate at the restaurant yesterday

Big data correlation will routinely disprove our causal intuitions

Sometimes causality is a deep scientific experimental process In this case, correlation is a fast and cheap way to accelerate it, providing proxies

instead of hypothesis

Be careful with correlation: In a “quality of used cars” study, it was proven that cars painted in orange were

50% less prone to have defects.

But painting you car orange will not make the trick!

36Correlation:

The Manhattan Manhole In Manhattan there are 51,000 manholes, each weighs 150K

They tend to explode in the air and crash on the ground

A typical Big Data problem for MIT : identify the ones at risk so to be able to service them preventively 94,000 miles of cables, some laid before 1930

Records kept since 1880, formats immensely different. Same object ( a “service box”) is identified with 38 different names

After a huge work to format the data to make them machine readable, the MIT team identified 106 predictors and mapped them against the historical data up to 2008, then used the result to predict 2009

It turned out that there were 2 important ones: age of cables and having had previous problems. The top 10% of the manholes in the list prioritized by those two factors contained 44% of the manholes that had incidents

Using those predictors in the future allows to reduce the number of incidents dramatically

37Correlation:

Is it the end of theory? Chris Anderson in 2008 Wired asked his readers if

correlation and statistical analysis mark the end of theory

Likely NO, Big Data is founded on theories itself and requires them through its process

But it marks a shift in the way we make sense of the world, and this change will require time to get us used to

And this change is, in the end, due to the fact that we have far more data than ever

38

Data: their essence, their value

39Data: their essence, their value

Navigating the Oceans In 1840, ocean navigation was still a mystery. Captains were afraid of the

uncertain, they always repeated their own preferred routes, with no rationale

Enters M. Maury, head of the “Depot of Charts and Instruments” bureau of the US Navy

In his office, he discovers hundreds of thousands of “logs” of previous trips. They contain info on winds, tides, streams, weather….

He hires 10 “computers” to transform those logs in data to be able to tabulate them, he divides the oceans in 5x5 degrees squares … and those data indicates amazingly clearly the most efficient routes. On average, it saved one third of the navigation time

To improve further and get more data, he then created standards for logging (to save $ on the “computers”), he gave his charts only to whom agreed to return the data, he gave flags for the ships supporting the initiative to show

In the end he tabulated 1.2M data points, and changed the world. His maps are still in use


Datafication Commander Maury was one of the first to understand the special value of huge

corpus of data. He took data nobody cared about and transformed them into objects of value, This is called: Datafication

Similarly, Farecast had taken old price points for airplane tickets, and Google had taken old search queries and they had transformed those in something of value

Another example: a research in Japan Institute of Industrial Technology They took data nobody thought to use: the way people sit in the car. 360 sensors on the

car seat

They obtain a digital map that can be used as antitheft signature, for insurance purpose, as a safety tool

This is another example of taking some data with apparently little use and transform them in useful data. Datafication


Datafication ≠ Digitalization To datafy a phenomenon is to put it in a quantified format so it can be

tabulated and analyzed

It requires us to know how to measure and how to record what we measure

This idea pre-date the “IT Revolution” age by far. Roman numeration was extremely hard to use for calculating large (or very small)

amounts. Counting board helped with calculating but were np use for recording

Arabic numerals were introduced in Europe in 1200 but they only took off at in the 1500 thanks to Luca Pacioli and the double-entry bookkeeping: a clear tool for datafication

Double-entry bookkeeping standardized the recording of information, allowed quick queries to the data set and provided and audit trail to allow data to be retraced (a build-in “error-correction” mechanism)

Computers made datafying much more efficient. And improved immensely the ability to analyze data. But the act of digitalization, by itself does not datafy


Google vs Amazon Both Google and Amazon has datafied a huge number of books

Google as part of his huge project “Google Book Search” First digitized the text, then, using custom-build OCR, datafied it

Now 20M titles are fully searchable. Look at http://books.google.com/ngrams for a quick idea

15% of all published books

Google uses is for his machine translation service

Amazon, with Kindle, has datafied books too, for millions of new books but it has decided not to use that for any relevant project/analyses

(with the exception of the service of statistically relevant word)

Possibly since books are its core business

http://books.google.com/ngrams


Location is also Data Introduction of GPS in 1978 it allowed the simple datafication of location data

Price going down from >100$ to <1$ makes it possible to get location data for many different things

Insurances now price (also) based on location logs

UPS used geo-location to build, similarly to Maury’s navigation map of the oceans, an optimized navigation map for its 60,000 vehicles, saving 30 million miles

AirSage buys cellphone data to create real-time traffic reports

Jana uses cellphone data to understand consumer behaviors…. A powerful tool

The important point is that those data are used for different purposes versus what they were created for


Interaction is also Data Facebook social graph (in 2012) covered >10% of the world

population, all datafied and available to a single company This could be used for credit scores: bad payers tend to stick with

their similes: Facebook could be the next credit scoring agency Twitter (who sells access to its data) is already used to read the

“sentiment” about politics, movies, songs…. Now sentiment analysis starts being used also to drive investments

in the stock market. MarketPsich sells reports on that, covering 18,864 indices across 119 countries

Social Media networks sit on a immense treasury of data, the exploitation of which has just started


Everything is also Data The “Internet of things” is about sensors on everything, incessantly transmitting

data in a format suitable for datafication

It is is starting with fitness, medical, manufacturing

Zeo has created a database of sleep activity uncovering differences between men and women

Heapsylon has created a sock that tells you phone if you are running well or not

Georgia Tech has created an app that allows a phone to monitor a person body tremor to diagnose and control Parkinson disease. It is just less effective than the expensive tools used in the hospitals

GreenGoose sells tiny sensors that everyone can put on objects to measure how much they are used. Allows anyone to create his own data environment


Datafication is a fundamental project It is an infrastructure project rivaling the ones in the past, the Roman aqueducts or the Encyclopediè of the Enlightenment age

We may not notice, because we are in the middle of it In time, datafication will give us the means to map the

world in quantifiable, analyzable way Today, it is mostly used in business to create new forms of

value


The Value of reusing You all (annoyingly) digit the captcha Luis von Ahn

invented in 2000 When von Ahn realized he was wasting 10 seconds of

your time 100M times a day, he thought harder He invented ReCaptcha. The second word is a

digitized word a computer cannot read 5 consistent user inputs disambiguate that word Data has a primary use (to prove you are human) and

a secondary use (to decipher unclear words) And it saves 750M$/yr in digitalization manual work

Captcha = Compeletely Automated Public Turing test to tell Computers and Humans Apart


A new Value for Data Data has always been used and traded

Prices, Contents, Financial informations, Personal data…

But they used to be either ancillary to the business, or narrowly used like in contents or personal informations

Now, all data can become valuable Fuel levels from a delivery vehicle

Readings from heat sensors

Billions of old search queries

Old price records for airline tickets

….

And the cost of gathering and keeping them keeps falling. In 50 years storage density has increased by a 50-million fold factor….


Data can be reused and multiused The primary used for data is typically evident to who collects them:

Stores for proper accountingFactories for quality controlWebsites for content optimizationSocial sites for ads optimization

But data do not get consumed by usage and can be reused for multiple purposes.

So data full value is greater than the one extracted from their first use

This is called the “option value” of data. They have a “potential energy”


Reuse Search terms are a classic for reuse

Hitwise use search terms to learn about consumer preferences. Will “pink” or “black” be next season fashion color?

Bank of England use search terms to get a sense on the housing market

Logistic companies use their records to create business forecast they sell (under a different company name)

SWIFT offers GDP forecast based on the money transfers it handles Mobile operators start reselling their infos (enriched with geo-loc info) for

local advertisement and promotions They can also sell the signal strength information (with geo-loc) to handset

manufacturers to improve the reception quality Large companies start spinning off dedicated companies to take $

advantage of their data option value


Data combinationAt times the dormant value can only be unleashed thru

combining different datasets – often vey differentCancer and Cell Phones

A question that has always been hanging aroundThe Danes took a N=ALL approach, combining all consumer

mobile operator data from 1987 to 1995, all cancer patient registers from 1990 to 2007 and all income and education information for each inhabitant

The result was that there was NO correlation

With Big data, the sum is more valuable than the parts


Data Extensibility To enable reuse – design extensibility from the ground up Google Street view was originally used to allow the “street

view” in Google maps. But data had been collected with extensibility in mind, so they

will be reused to allow functioning of Google self-driving car

In-shops camera (and software) are designed to prevent shop-lifting but they can be extended to provide marketing-relevant data on customer behaviors and preference

The extra cost of collecting multiple data streams is low, and can drive massive benefit when a dataset can be used for multiple instances


Data Exhaust Bad, Incorrect or Defective Data can bring

a value Google spell-checker is built using the end-user input when correcting

misspelled queries Data exhaust, in general, means data the users leave behind them Also voice recognition, spam filters system improves in a similar way Social networks are obviously looking at this But other sectors are starting:

E-Book readers – gather an amazing amount of information that could help authors and publishers make better books

Online education programs can predict student behavior

This will constitute a huge barrier to entry for new-entrants


What is the value of dataData are an intangible asset, as brand, talent and

strategyBut it can explain some strange things that

happened recently like WhatsApp evaluation (or Facebook IPO itself)

There are emerging marketplaces for Data, like Import.io, or Factual

But there is no clear answer yet, also since most of the value of data is in their (re)use, not in the data possession

55

Implications

56Implications

Decide.com Decide.com had an ambition: to be a price-prediction engine for

almost every consumer product

They scrapped the web to obtain 25Billion price observations. Lot of data, and lot of text to be transformed in data

Identified un-natural behaviors, like prices increasing for old model at the introduction of a new one

Spotted any un-natural price spike

Provided 77% of accuracy, and saved on average 100$ per purchase

If the prediction was wrong, they reimbursed the difference

They got bought by eBay…

What makes them special? Data were available on the Internet, they did not use any special algorithm….

57Implications

Ideas matter Decide.com had an IDEA. And that idea came from a big data

mindset: they saw the opportunity and realized it could have been realized with existing data and tools

Moving from the data itself to the companies who use data, how does the value-chain work?

There are three types of big-data companies, differentiated by the value they offer: The Data

The Skills

The Ideas

(and of course some companies have a mix….)

58Implications

Who has Data Some companies have lot of data, but data is not what

they are in business for Twitter – as an example – turned to two independent

companies to license its data to other users Telecom companies could do the same – and in some

cases they start doing it ITA provided data to Farecast – they did not do the job

themselves since they would have been in competition with the airlines

Master Card created a division (MC Advisors) to extract value from its data and resell

59Implications

Who has Skills Consultants, technology vendors analytics providers who

have competencies to do the work but do not have access to data and do not have a “big-data” mindset

Accenture is a good example Microsoft (Consulting) is another:

Worked with an Hospital in Seattle to analyze years of anonymized medical record to find a way to minimize readmissions

Found that the mental state of the patient is a key predictor

Addressed that and reduced the overall healthcare spend

60Implications

Who has a big-data mindset (1) They see opportunities before the others, and they see

what is possible without thinking too early to its feasibility

FlightCaster.com – predicts if a flight will be delayed Analyze every flight over ten years, matches against

weather data, and apply the correlation to current flights and current weather

Data where all available openly (government owned) but the government had no interest in using them

Airlines had no interest (they want to hide the delays)

It worked perfectly… even airlines’ pilots used them...

They were a first mover – it was not difficult to copy them

61Implications

Who has a big-data mindset (2) Very often it takes an outsider to get a brilliant idea The incumbent are often too “encumbered” by their present to

think well to the future Amazon was not funded by a bookstore but by an hedge fund…

Ebay was not launched by an auction company but by a software developer….

Entrepreneurs with big-data mindset do not normally have the data but they also miss the vested interest/fear preventing to use the data

62Implications

Data Intermediaries Today, both skills and ideas seem to dominate the value-chain, but long term most of

the value will be in the data themselves Data intermediaries will emerge Inrix – a traffic-analysis firm

They get geo-loc data from car manufacturers, taxis, delivery vans

They aggregate, combine with historical data, weather data and local events information and predict traffic

They collect data from rival companies, who could do nothing with their data alone and who have no competencies in predictive methods

What Inrix does benefits their customers so they have a return themselves (even if not a competitive advantage)

This “collaboration” is not new (banks need to send their data to central bank etc) but now it is about a secondary use of data. And maybe tertiary.. Inrix stated using traffic data to provide informations about health of commercial centers and health of the economy in general….

64Implications

What are the experts for? In the movie Moneyball the old “scouts” confront the geek

statistician and offer their arguments against him “He’s got a baseball body… a good face”

“He has an ugly girlfriend, it means no confidence”

This shows the shortcoming of human judgment

Data driven decisions are poised to augment and overrule the human judgment

The subject matter expert loses appeal versus the data analyst

The online training company Coursera uses machine-recorded data to advise teachers on what to improve in their lessons

Skills in the workplace are changing. Experience is a bit like exactitude. Very useful in a small data word where you need to make many inferences, less useful in a big data world where data talk

66Implications

Who will be the winners Large companies will continue to soar. Their advantage will rest on data

scale and not on physical scale. And ownership of large set of data will be a competitive barrier.

But large companies need to get the big-data mindset. Rolls-Royce is a good example – using sensors and big data they transformed from a manufacturer to a services companies (charging on usage time and support)

Small companies will also do well since they can have “scale without mass” and big-data does not require large initial investments, they can license data vs owning them, they can rely on cheap cloud computing and storage

Mid-sized companies will be squeezed in between Individuals will likely be able to take advantage of this revolution. Personal

data ownership may empower individual consumers. But it will need new technologies, albeit companies as Mydex are already working on it

67

Risks

68Risks

3 categories of risk Internet already threatened PRIVACY, with big data the change of

scale created a change of state. Google knows what we search, Amazon knows what we buy (or would like to buy), Twitter and Facebook know how we feel and who we like

PROPENSITY now can become something affecting our life. We can see insurance and mortgages denied, even if we have never been sick or never been a bad payer

We can fall victim of a DATA DICTATORSHIP where we fetish our analysis and end-up misusing them

69Risks

Privacy Big Data is not all about personal informations (think to UPS or the

manhole examples) but much of the data being generated now contain personal informations (or can be traced back to them “Smart meters” collect info on electric usage very 6 minutes. It can tell

whichever appliance you use, and of course when

The traditional approach to privacy is “notice and consent” that limits to the primary usage

How to use it in a big data world where secondary usages have not being imagined yet?

Opt-out leaves a trace Anonymization does not work either since big data creates too many

references to ensure we can not be identified

71Risks

Probability and free will

Parole boards in the US use data analysis – based predictions to decide whether release somebody from prison

US Homeland has a project to identify terrorists by monitoring body language and other physiological patterns

In Los Angeles police use big data to select streets, groups, individuals need to be subject to more surveillance

It at looks like a great idea (preventing crime) but it is dangerous. We may want to punish the probable criminal

And while “small data” techniques were based on profiling based on a model of the issue at hand (causal), “big data” only look at correlations – that makes things even more dangerous

72Risks

A potential bad outcome

Going back to the Google Flu example What if the government decides to impose a quarantine on people in

the more risky areas The Google algorithm allows to identify them individually So they can be quarantined only since they made the queries… But remember: Correlation is NOT Causation….

73

Remedies

74Remedies

Every revolution bring new rules Gutenberg invention brought censorship, licensing,

copyright, freedom of speech, defamation rules First the focus was on limiting the information flow , than

it edged in the opposite direction With the Big Data transformation, we will also need a new

set of rules. Simply adapting the existing ones will not be sufficient. But we need to move fast

75Remedies

Few suggestions Privacy should move from end-user consent to data-user

accountability Big data users should provide use-assessements on the dangers of the

intended use

They should also provide a time-frame for the usage (and retention) of data to avoid a “permanent memory” scenario (as we have today)

Decisions based on big data predictions must be documented and the algorithm certified, and they need to be disprovable

Decisions mast be framed in a language of risks and avoidance not in a language of “personal responsibility”

Judgment must stick to personal responsibility and actual behavior

76Remedies

A new profession As the complexity of Finance paved the way for the creation of

auditing firms, we will need a new set of experts: the “Algorithmists”

Companies will have internal algorithmists , as they have controllers now, and external ones, as they have auditors

Those people will be the expert ensuring that big data system do not remain “black-boxes” offering no accountability, traceability or confidence

77Remedies

Data Antitrust As for any other raw material or key service, access to data must

be regulated Competition must be ensured and data transactions enabled

through licensing and interoperability Government (and others willing to do so) should publicly release

its own data (this is already happening under the name of “Open Data”)

79

Closing

80Big Data today

The effects are large on a practical level, finding solutions to real problems

Big Data is when the “Information Society” becomes true. Data (information) takes the center stage, and it speaks

Data will keep increasing Messines will be acceptable in return for capturing far more data Correlation is faster and cheaper than causality so it is often preferable Much of the value will come from secondary use of data We will need to establish new principles to govern the change Big Data is a resource and a tool. It informs, it does not explain. It points

us towards understanding, but is it not the truth

81Big Data tomorrow

What’s past is prologue(William Shakespeare)

Education

A Primer on Big Data taken by the book: "Big Data" by Schoenberger and Cukier