Eric Siegel answers eight questions about predictive analytics
Summary
TLDRErich Segal's book 'Predictive Analytics' explores the power of predicting individual behaviors to drive organizational success. Organizations use predictive models to make decisions, enhancing sales, healthcare, crime-fighting, and even political campaigns. Segal discusses the importance of big data in creating accurate models and touches on the ethical considerations of this powerful tool. He also highlights advancements like uplift modeling and ensemble models, showcasing IBM's Watson as an example of predictive analytics in action.
Takeaways
- 📚 The book 'Predictive Analytics' by Erich Segal explores the power of predicting individual behaviors to drive organizational success.
- 🏆 Organizations across sectors like business, government, healthcare, and law enforcement use predictive analytics to make personalized decisions that reduce risk and increase efficiency.
- 👥 Two key groups benefit from predictive analytics: organizations that use it for strategic decision-making and individuals who are the subjects of these predictions.
- 🔎 Predictive analytics is not just about forecasting but also about influencing outcomes by making data-driven, individual-specific decisions.
- 📈 The more data an organization has, the better its predictive models become, as data represents the collective experience that fuels accurate predictions.
- 🌐 Big data is crucial for predictive analytics as it provides the extensive information needed to train and refine predictive models.
- 🤓 The Obama campaign's use of predictive analytics during the election is highlighted as a successful example of using individual predictions to influence voter behavior.
- 💡 Predictive analytics can reveal sensitive insights about individuals, raising important questions about privacy and the ethical use of such power.
- 🚀 Uplift modeling, a subset of predictive analytics, focuses on predicting how individuals will respond to specific treatments or interventions.
- 🤖 Ensemble models, which combine multiple predictive models, are a trending technique in predictive analytics for improving the accuracy and reliability of predictions.
- 💡 IBM's Watson, which used ensemble models and predictive analytics, successfully competed against human champions on the TV quiz show Jeopardy, demonstrating the potential of these technologies.
Q & A
What is the definition of predictive analytics according to Erich Segal?
-Predictive analytics is defined as the power to predict who will click, buy, lie, or die. It involves making per person predictions to drive decisions in various sectors such as business, government, healthcare, and law enforcement.
Who are the two kinds of people that care about predictive analytics?
-The two kinds of people who care about predictive analytics are organizations that win by making per person predictions to drive decisions, and everyone else who is being predicted upon by these organizations.
How do organizations benefit from using predictive analytics?
-Organizations benefit from predictive analytics by decreasing risk, making healthcare more robust, toughening crime-fighting, boosting sales, and gaining more votes in presidential campaigns.
What is the relationship between big data and predictive analytics?
-Big data provides the extensive experience or collective experience of an organization from which predictive models are created to make individual predictions more accurate or precise.
What is the difference between forecasting and predictive analytics?
-Forecasting predicts overall outcomes, whereas predictive analytics focuses on making per person decisions to influence individual behavior or outcomes.
How did the Obama campaign use predictive analytics differently from Nate Silver?
-While Nate Silver made forecasts for overall state outcomes, the Obama campaign used predictive analytics to make per voter decisions, focusing on persuading individual voters.
What is uplift modeling in the context of predictive analytics?
-Uplift modeling, also known as predict persuasion modeling, predicts how likely a particular treatment or campaign contact will make a positive difference for an individual, such as persuading a voter or influencing a medical treatment outcome.
What is an ensemble model in predictive analytics?
-An ensemble model is a method where multiple predictive models are grouped together to make decisions collectively, improving the accuracy and robustness of the predictions.
How did IBM's computer Watson use predictive analytics to compete on Jeopardy?
-Watson used predictive modeling and ensemble models to predict the correct answers to complex questions on Jeopardy, learning from historical data and improving its accuracy through the collective intelligence of multiple models.
What ethical considerations does predictive analytics raise?
-Predictive analytics raises ethical considerations such as privacy and civil liberties, as it can reveal sensitive insights about individuals and requires responsible use of this power.
What are some of the improvements in predictive analytics technology mentioned in the script?
-Some improvements include uplift modeling for predicting persuasion and driving decisions, and the use of ensemble models to enhance the accuracy and precision of predictive analytics.
Outlines
🔮 The Power of Predictive Analytics
Erich Segal introduces the concept of predictive analytics, emphasizing its ability to forecast individual behaviors with significant implications for organizations and individuals alike. He outlines two primary groups interested in predictive analytics: organizations that benefit from making personalized predictions to enhance operations and the general public who are subjects of these predictions. Segal discusses the importance of data in refining predictions and touches on the ethical considerations and responsibilities that come with the power of predictive analytics. He also highlights the role of big data in enhancing predictive models and distinguishes between forecasting and predictive analytics, using the Obama campaign's use of analytics as a case study to illustrate the latter's influence on individual decision-making.
🚀 Advancements in Predictive Analytics
This paragraph delves into recent developments in predictive analytics, focusing on uplift modeling, which predicts the impact of specific treatments or interventions on individuals, and ensemble models, which aggregate the predictions of multiple models to improve accuracy. The IBM computer Watson is cited as a prime example of predictive analytics in action, demonstrating how ensemble models can be used to answer complex questions and compete successfully in a human-centric domain like the TV quiz show Jeopardy. The summary underscores the potential of predictive analytics to revolutionize decision-making across various fields and the ongoing innovation within the field.
Mindmap
Keywords
💡Predictive Analytics
💡Organizations
💡Big Data
💡Per Person Predictions
💡Risk Decrease
💡Healthcare
💡Nate Silver
💡Uplift Modeling
💡Ensemble Models
💡IBM Watson
💡Responsibility
Highlights
Predictive analytics is the power to predict actions such as who will click, buy, lie, or die.
Organizations like companies, governments, and nonprofits use predictive analytics to make decisions on individuals, improving outcomes such as decreasing risk and boosting sales.
Predictive analytics is crucial for everyone because organizations make predictions about individuals daily, affecting areas like buying behavior, health, education, and law enforcement.
Predictive analytics doesn’t require perfect accuracy to be valuable; even a slight improvement over guessing can significantly optimize decisions.
Big data enhances predictive analytics by providing more data to learn from, improving the precision of per-person predictions.
Predictive analytics allows organizations to not just predict the future but also influence it by driving decisions based on individual predictions.
The Obama campaign used predictive analytics to predict voter persuasion, influencing campaign strategies and helping to win the election.
Uplift modeling, or predicting persuasion, is a key technology in predictive analytics, used in both political campaigns and marketing to influence individuals' decisions.
Ensemble models, which combine the outputs of multiple predictive models, improve the accuracy and robustness of predictions.
Predictive analytics can lead to sensitive insights, raising ethical concerns regarding privacy and civil liberties.
Watson, IBM’s AI, used predictive modeling and ensemble models to win against human champions in the game show Jeopardy, showcasing the power of predictive analytics.
Predictive analytics in healthcare can drive decisions on treatments by predicting the most effective approach for individual patients.
Predictive models can be used in law enforcement to predict recidivism, influencing decisions about parole and release.
Organizations are using predictive analytics in retail to predict customer behaviors, like whether a consumer is pregnant or if an employee might quit.
The ethical use of predictive analytics requires careful consideration of its power and potential societal impact, as it affects significant aspects of life.
Transcripts
my name is Erich Segal and my book is
called predictive analytics
predictive analytics is well the
shortest definition is the subtitle of
my book the power to predict who will
click buy lie or die
there's two kinds of people who care
about predictive analytics number one
all the organizations that win by making
per person predictions this is companies
governments hospitals universities law
enforcement nonprofit even presidential
campaigns these organizations win by
predicting for each person to drive
decisions as far as who to call who to
send mail to who lend money to who to
investigate for crime or fraud who to
treat in certain ways for healthcare by
doing this the organizations win they
decrease risk
Healthcare is made more robust they
toughen crime-fighting sales are boosted
for presidential campaigns more votes
are gained number two the other kind of
people who care about predictive
analytics is everyone else because these
organizations are making predictions on
you and I every day we're being
predicted as far as whether we're likely
to buy whether we're going to heal get
better with health care whether it will
drop out of school whether we're going
to commit theft we're going to steal
something or commit a crime whether
we're going to crash our car so all
these predictions millions of
predictions a day are being made about
us by these organizations larger to our
benefit definitely to their benefit
you don't need to predict accurately to
get great value organizations by
predicting better than guessing get a
little bit of you that between through
the fog you know that blocks between
today and tomorrow so by making these
predictions per person that are a good
bit better than guessing they actually
played the numbers game better all
organizations essential are playing
numbers games and the way to optimize
the vast operational scale of
organizations today is with prediction
three men alex is a big data thing well
you know big data is basically just a
grammatically incorrect way to say a lot
of data but yeah the more data the
better the more experience data
essentially is experience of an
organization's the aggregate or
collective experience and the more you
have the more there is from which to
learn to create predictive models that
make the per person individual
predictions which will in turn be more
accurate or more precise having been
trained or learned over a greater amount
of data so with today's excitement over
big data it's all big data this big data
that the question that begs is right
well what's the point what are you going
to do with it what's the most valuable
you can think you can do with this
greater and greater amount of data and
the answer is the most actionable thing
that organization can get out of data is
learning from it how to make predictions
per person because those per person
predictions drive all the individual
purpose and actions and decisions that
organizations make
no Nate silver didn't use predictive
analytics to forecast the election
however Obama did so silver made
forecasts for overall state overall how
with the prediction go whereas the Obama
campaign actually use predictive
analytics to make per voter decisions so
that's really the difference between
forecasting and predictive analytics
predictive analytics makes it possible
not just to predict the future but to
influence it by driving these per
individual person decisions by these
individual predictions in the case of
Obama's campaign the analytics group
made these predictions to drive campaign
decisions so while Nate Silver competed
to predict the outcome of the election
the Obama campaign competed to win the
election itself and the thing that's
interesting is instead of just
predicting you know how each individual
would vote are they more likely to vote
for Obama or Romney are they likely to
vote at all it's something completely
different the Obama campaign predicted
how likely is that this voter would be
persuaded can we change their mind can
we convince them will they be receptive
to campaign contact a phone call a knock
on the door and so by driving decisions
with that way what they call predict
persuasion modeling also known as uplift
modeling they were actually able to gain
more votes within individual swing
states towards winning the election
predictive analytics is powerful in that
it produces these insights these
predictions per each individual in some
cases yes the thing that's being
ascertained about the individual is very
sensitive you know in the case of retail
is that consumer pregnant in the case of
large corporations is this employee
likely to quit their job and the case of
law enforcement is this incarcerated
convict likely to commit a crime again
if they're released so I like to quote
spider-man's wise uncle who said with
great power comes great responsibility
because you know predictive analytics
nobody would care if it weren't potent
these things go together the fact it's
so powerful that it brings up sensitive
insights in some cases privacy in some
cases
to other civil liberties issues but
there are some tricky things there's a
lot that needs to be looked at and
considered as as a society in terms of
what to do with this newfound power and
how to harness it in a safe way
well as far as the underlying technology
there are a lot of improvements taking
place in predictive analytics and let me
go over a couple of them right now
number one uplift modeling that's
predicting persuasion that's driving
decisions as far as what's going to make
the biggest difference this is what the
Obama campaign uses I mentioned a moment
ago they make decisions per voter which
treatment which compact campaign contact
or lack thereof is the best choice for
each voter as far as swaying than in the
right direction and avoiding the adverse
effect of swaying them in the wrong
direction very much the same thing with
marketing I'm trying to sell something
how am I going to persuade that person
in health care which treatment medical
treatment or lack thereof leaves a
better chance of the positive outcome
for that individual same core technology
instead of predicting what a person is
going to do the action or the outcome or
behavior you're predicting will this
treatment towards that person make a
difference in the right direction that
would like things to go that's called
uplift modeling another hot trend in
predictive analytics is what's called
ensemble models so it turns out just
like the collective intelligence over a
crowd called the wisdom of the crowds
where a bunch of people might come
together and essentially vote or each
put in their opinion and then the
overall average opinion turns out to be
better than most individual people you
get the same effect with predictive
models so the mechanisms that make
predictions can in some case be pretty
mundane that can be pretty primitive but
when you group them together and there's
not a lot of science or math you
basically just pull them all together
and make them vote just like people the
models are voting and then the overall
prediction machine is suddenly much
better than most individual models so
it's a great way to sort of tweak the
robustness and correctness the precision
the accuracy of how well these
predictive models work simply by
grouping them together and have pooling
them so that they're basically what's
called an ensemble mall
well there's a lot of exciting really
inspirational things going on in
predictive analytics one of them is the
IBM computer Watson that was able to
successfully compete against the
all-time human champions on the TV quiz
show Jeopardy where those questions
could be about anything they're intended
for humans to answer the questions are
very complex and they're grammar the
written in English human language it
turns out the predictive modeling the
core analytical process of predictive
analytics is the way Watson succeeds in
choosing the answer it predicts is this
candidate answer the correct answer to
this question and in fact the way it
does it so precisely is using what I
mentioned a moment ago and ensemble
models with using lots of models all
together and it learns from historical
previously given questions on you know
from this TV shows history and is able
to compete get one answer after another
thing it questions could be about any
topic the answer could be anything that
picks out the one singular correct
answer it's just it's just incredible
you can see it on YouTube the actual
broadcasted episodes of that TV show
Jeopardy is just amazing it just rings
off one correct answer after another
you
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