that's family we didn't tell me where to
turn that I i will like nod and that's
how you'll notice to turn next it's true
all right well we're gonna get started
it is nine o'clock on a friday the bar
has been open since five so being a data
person the lady behind me is coming you
guys as you not off while i talk so
that's one if you fall out of your chair
you win the prize so okay so i'm going
to get started so I'm job with this I'm
going to talk about predictive analytics
and some of the tools that are readily
accessible to people who probably don't
use predictive analytics on a daily
basis I don't know apologize right now I
don't have the word hash anywhere in my
discussion okay there's no md5 no sha-1
so I'm sorry okay yeah when I first
talked to Mike if you haven't met Mike
gets men he's the guy that that runs
this and you definitely gotta make sure
that there is
yeah well if I have to give the shoutout
moment here if you have complaints you
talk to him but more importantly this is
a huge accomplishment that last year's
last year's was half this size and it
just gets better and better so make sure
you you know give them a nod before you
walk up door hey yeah the question and
the question Mike asked me when I said
you know yeah I'll come and talk as long
as there's free beer well why on earth
would I want to date a guy at a hacker
con because I already told them Mike
that the word hash is not anywhere in my
discussion so you think about the grand
theory of hacking in general you know
because the average public kind of have
this tremendous misconception of hackers
you know that there's this malicious
basement dweller in terms of 9j and
streams you know hacking people's
facebook accounts and getting ed when
they do it mm-hmm I got another one ok
this is this general impression ok but
that's not what it is we know that you
know cuz they're really at its core the
term hacking has to do with you know
somehow altering and manipulating what
we already have to get to something we
don't or taking something that's already
been created and tweaking it in such a
way that it's new and different right
see the way to the bottom it says right
ok so i am going to then argue that as a
data miner data is like the ultimate
hack
and it really is let's be realistic how
many of you and your jobs have ever had
somebody high up who shouldn't even know
your name come ask you for data I mean
your hands I won't ask you to do that
often but yeah and you know what the
view that didn't raise your hands you're
lying okay because they do we live in a
data-driven universe so data mining
becomes the ultimate hack and and here's
the Testament out on the bottom and
that's the only thing I'll read off the
screen but it's the truth with it we can
gain knowledge that can benefit mankind
absolutely we human perceptions you show
people data you present the data in a
way that promotes an argument and it
becomes gospel you can change the course
of human history you can alter the
trajectory of a billion dollar company
with data you can take what you have
manipulated in a new and interesting way
to discover something you didn't hacking
right there it's awesome but I'm sorry
there's no hash involved okay so with
that said and this is my disclaimer the
reality is is you can do all that I mean
you really can I'm not joking I do it
millions of dollars in John the data
that I produce and the way I produce it
people's lives change based on the data
I produce and the way I produce so the
reality is is that somewhere within that
comes a certain ethical responsibility
to ensure that you're presenting data in
a logical in a reasonable in an
efficient manner that portrays the
greatest similarity to the truth
possible so just to give everybody a
little bit of background on me and again
she's counting the nods so I'll know who
does it there has always been a question
and again I'll kind of get to the notion
of where this fits with information
security in a moment but you know
there's always been this notion of okay
in order to be somebody who's into data
you have to have a certain disposition
again there's that basement dweller
surrounded by calculus books and you
know that's who gets into data well the
reality is that that's not all that
typical statistically the average
millennial will have 17 jobs or 17
different careers throughout the course
of their life okay so depending on where
you are I think I'm on number six you
will undoubtedly have been exposed to
and use data on a regular basis to draw
conclusions and share information with
others oftentimes in an attempt to
persuade them to your train of thought
so this is me in a nutshell long time
ago I started as a retail manager where
I was responsible for implementing Sims
strategic inventory management system
running on the ever popular as400 they
don't die it just as amazing I've walked
into a billion-dollar corporations and
they still have them because they don't
die okay so you learn a little bit but
the truth is that wasn't my primary job
my primary job was catching
sixteen-year-old shoplifters as they
tried to walk out the door
four dollars of bubble gum in their
pocket okay but despite all that data
played a very important role in my life
as I track and maintain market
researched and promote sales within my
organization again taking what I know
hacking that to produce something that I
don't know which then promote sales
within the organization okay so on my
trip to data land start there and then I
become a history teacher so again as you
think to your own life because I'm not
just walking down memory lane do you
think your own life and what you've done
in order to get to where you are we all
have very different backgrounds what I'm
trying to convey and I won't go through
my entire life is the fact that data
plays a critical part in your day-to-day
affairs and as an IT security specialist
you more and more have to become
responsible for the data that you use in
order to make intelligent decisions
about your organization because let's be
realistic statistically the odds of the
basement dweller hacking your
organization are pretty slim okay it's
true statistically I mean you I was in a
presentation earlier today where they
were talking about different methods in
order to ensure that passwords are
properly hashed and continuously
rehashed to ensure they can't be hacked
someone will figure it out but
realistically it's not going to impact
you guys at all so ultimately as you
make decisions for your organization
you're going to have to decide what's
important is what's not and the only way
to do that is by effectively looking at
the data you have available and being
able to make intelligent decisions from
it
so where does this fit in information
security if I can get that to play
because I refuse to use the wireless
here can you see if you can get that
pair goes email about diamond we need
some volume I'll go your contact for my
South African health officer and gone I
know you don't know how to do it I need
to move a thousand carrots and we may
not keep the great joy that is that
video does everybody know that when Bill
Gates introduced windows XP hit blue
screen during its reveal that's the blue
screen at all right well I know we won't
spend a lot of time troubleshooting now
if you have the opportunity just go
ahead and go to ghal toaster scam a lot
this particular individual you know how
you get spam mail to stuff that we all
spend a tremendous amount of time trying
to block the
station well this is the guy who
responds to it and so he's made an
interesting living being the person who
responds to scam email until the point
where the scammer asks him to stop
emailing that's his goal okay so I'm not
going to tell you this story because the
truth is it's quite fun and I I think
you should watch it because he'll
definitely do a better job than I would
explaining it but he has multiple
situations where he gets scanned mail
about you know two million dollars in
diamonds and all you need to do is set
up an account in the bank and and he
responds and and you know works with
these people until finally they can't
take it anymore and this camera tells
them to stop calling them or stop
emailing the point of this is the fact
that people do strange things so coming
back into that security mode people just
do weird things and when people do weird
things it has a tendency to impact our
organization I'm not talking about the
basement dweller anymore and please I'm
not trying to offend anyone I felis just
trying to be funny I'm talking about the
average person on floor six who's
machine continuously becomes infected
regardless of how many times you clean
it and tell them please if you didn't
order anything from UPS don't click on
the attachment in the UPS email but it
could be something cool no it's not okay
so what we need to do is the problem and
again I'll ask you to raise your hand
how many of you are overstaffed you
didn't hear me how many of you are
overstaffed
yeah I know oh it doesn't matter pick
one okay the reality is as none of us
are overstaffed I'll give you an example
from my own life although I won't go
into what I do or my company does I will
tell you my responsibility is to make
sure black friday works I am literally
on speed dial for banks around the globe
for black friday and cyber monday and
then tax tuesday those are those are the
biggies and and if any of them have
problems my phone goes off okay so
obviously that's sighs so in order to
ensure that it isn't a disaster I have
to use predictive analytics to make sure
that I predict every possible outcome
and ensure that I've addressed the
reasonable ones in IT security that's
your problem too okay the truth is you
can't stop the nut okay but they're an
anomaly your bigger problems usually
have to do with your average everyday
user that makes bad choices at the wrong
time and you don't have the staff or the
supplies or the software or the money or
the executive endorsement to ensure that
all of that is fixed so you have to pick
your battles and that's what we're going
to talk about how you can use something
that you can download the minute you
walk home unless you're a huge
programming geek and if you are ours
spectacular okay but you know you also
have to be glutton for punishment to
learn it okay so next one here's why I'm
talking at a hacker con and I've kind of
alluded to it the fact is is that we
have issues you don't have enough staff
you don't have the resources you need
the job gets
here by the day and you need a map to
pick your battles so that's why you need
to become data scientists because you
needed something else on your plate yes
thank you but you do because it will
make your life and long run a little bit
easier and here's why because without
data opinions only exist because lack
data I love that okay as you pick your
battles you need to know what and what
statistically the number one demographic
group for shoplifting in the United
States is women weight women between the
age of 40 and 55 is that who mall
security watches them pick their battle
right ok give me data or give me no I
just love that phrase and then without
data you're just another person with an
opinion we need data you need data to
pick your battle or you need more staff
and we all know how well that argument
goes over ok so in order to become data
scientists and particularly good data
scientists you need to keep in mind that
you're dealing with a general public
that doesn't have the data you have it
you own it and you get to decide how
we're why and when they get it so some
of the things you can walk through
because I am going to get at a point I
don't just like talking some of the
things we're going to talk through are
going to be those pieces that are going
to help you do that and I will leave you
with a very famous quote on this slide
from art pain and it comes back to the
idea that as you are the guardians of
your data and you really can decide what
you show the other people you need to
keep that ethical factor in the back of
your head
so Mark Twain once said there are three
kinds of life there's life there's damn
lies and their statistics so people are
already somewhat skeptical of data need
to make sure that when you're challenged
with using data to manipulate people's
opinions thank you do so in an ethical
fashion okay so what we're going to talk
about this is like predictive analytics
machine learning 101 I'm this isn't a 12
hour session I'm not going to give you
an extensive demo that you're going to
forget as you count down the second step
are close right but I am going to make
sure that you leave here knowing
tomorrow when you wake up what's
saturday it's a workday for IT people
you need to know that tomorrow here's
what you can do to pick your battles 2
things we're going to look at supervised
learning and unsupervised learning okay
these two terms get thrown around a lot
I'm going to try and put them in
layman's terms not because I concerned
that you can understand the technical
term I'm concerned I don't understand
the technical term so I'm going to put
it in layman's terms supervised learning
is you're predicting something you're
taking your data and you are discovering
something you don't already have okay
unsupervised learning is you're taking
your data or you're not predicting
keeping your just grouping it in ways to
ensure that you have an answer to a
question you actually get to hire new
people but you know what your last three
people stole from the company fired your
judgment is in question here so how do
you pick the right people and you've got
three files and applications I can do it
in less than three minutes without
looking at a single resume that's
unsupervised learning and I will show
you the tool and the particular piece in
order to do ok let's go on to the next
one
all right what you see up here and again
thinking in terms of data mining 101 is
kind of the process and what I don't
know about you guys I hate process I
just want to get to the fun part okay
the flag do this it's like when you have
to fill out change tickets at work even
though you could do the change in eight
seconds I don't know about you guys but
I can build server in minutes why does
it take two and a half months anyhow
unfortunately both my argument for why
we don't need process where I work it
and I'm going to tell you you can't skip
the process when it comes to data mining
here's why give the wrong data to the
right people at the right time and
you're screwed because they will never
ever let you forget it remember that
time mm at that time you know you gave
me the wrong data whoo yeah you don't to
do that so you have to go through the
process and again I'm going to put the
process in layman's terms not for your
benefit for mine first one is business
understanding do not skip this part nit
insecurity in business in whatever you
do that revolves around data
particularly tons of it because we live
in a new world now storage is cheap you
can keep tons of data and companies do
some of us support it truckloads of data
about random junk that nobody will ever
need but we keep it because it's cheap
and then the CIL calls you at 443 on
Friday and says I need to know this
okay here's the problem and I'm going to
tell you you got to do it because he's
gonna get real angry with you on the
phone I just need to know this hang on
see I got it tell me specifically what
you want what you're trying to show and
who the audience is going to be I'm
gonna say that again what you want who
you're going to show or excuse me what
you're trying to show and who the
audience is going to be 0 remember that
I'm saving you heartache because when
you give him the wrong stuff and now 648
on Friday night and he calls you back up
so that's not what I asked for yeah how
to make sure he spells it out so in the
very first phase you have to understand
business understanding okay you need to
know you need to stop and ask the
critical questions because here's the
reality he don't know what to ask he
knows what he has to show and and he
wants you to just blend now I don't have
time to talk about this just do it my
CIO is not like that by the way so I'm
exaggerating because i know this is
being filmed anyhow you have to make
sure that you understand what's going on
you have to ask the critical questions
you need to talk about how frequently
you want this data because it may be
something you could produce on the fly
but not something you collect on a
regular basis or your data collection
methods may change over time and in
doing so change the deliverable you need
to understand the audience is this is
going to sea level management or if it's
going to be presented to the board okay
a little bit different if it's going to
be distributed in a newsletter to the
shop a little bit different okay you
need to know the anticipated outcome and
sometimes they don't know you have to
read between the lines I'll give you
some use cases where that's kind of
obvious don't skip this
that do not get off the phone with the
guy or the lady or whoever's asking
until you know what is being asked for
okay how do you do that number one you
interview people particularly the
business leaders the SMEs I've done
focus groups because you know again
people don't always know what they want
okay surveys all that good stuff you
know get creative you need to know
what's being asked so here's some
examples of the types of things that we
can be asked that you know aren't so
easy to come up with I love the top one
because it's probably the most typical
one that I hear your employee wants to
under your employer wants to understand
the rate of change in trajectory of
identified malware threats across the
enterprise it's a logical question how
are we doing in security is it getting
worse to getting better are you doing
your job is really the question under
the covers he wants to see of what
you're doing is actually improving
maintaining our sins efficient before
deciding if he's going to approve those
two new wrecks and the renewal of that
software you like you have to read
between the lines you need to hire three
new security and it excuse me I didn't
write that right security analysts but
they have to meet strict criteria again
I bring it to back the last three quit
and that one dude stole that guy's lunch
okay we need to make sure you're picking
the right people one of your office's
has more malware threats detected than
the other what's going on there and why
haven't you caught it your company has
just acquired another you're not getting
any more people
but you doubled in size so your CIO has
asked for a recommendation for hardware
software and personnel to ensure
security is not compromised between the
lines I need you to show that you can do
it with no additional cost to the
company I love it I see all these mods
gone yeah I've heard that one okay so
point of all that there's a
million-in-one use cases you have to
read between the lines you have to
understand the purpose so once you can
do that here's the second part you
probably don't know how to answer the
question yet and you know what I've
become accustomed to not being the
smartest guy in the room and I've
learned to accept it so maybe you do
know how to answer two questions but i
can tell you seventy percent of the
questions i get asked when i'm asked it
I don't know how to answer that I don't
know where I'm going to get the
information and it's 443 on Friday again
dang it so we need to understand the
data that it's going to take to answer
the question what's available where do
you get it from the next part of that
question in what format oh I love that
because that lady on the third floor has
that data on a spreadsheet on her laptop
why is it there what does it tell you at
first glance you know people say you
should trust your gut as a data person I
cringe when I say that but the truth is
if you know how to look at it the first
time you can save yourself a lot of wild
goose chase okay will it meet the
business objectives so as you think
about these things some of the things
that come up is well alright well what
do I do about that you'd be shocked what
you can do in Excel I don't know about
you guys company but for some reason
mine lives in Excel Excel and PowerPoint
I mean that that's how we communicate so
okay the truth is excels pretty awesome
if you can use the behind-the-scenes
stuff that's alright but this is the
21st century my quote on his I learned
sequel I love sequel I love our and
sequel so I'm glutton for punishment
that's just me but if you really get
stuck manipulating a truckload of data
learn sequel learn are you really or get
stuck with this a lot okay what's that
well I'm sorry was that it it was that a
joke it's fast enough I want to come
work by you I've never you know it be
fast enough okay so your ability to see
your data and again I'm coming back to
the step one or this a step two excuse
me your understanding your data its 443
the CIL wants as questions and here on
the up left is the trend of malware
detection across the enterprise over the
last oh what did I get it I think I gave
it known several months you can't show
on that well you could I'm just trying
to think of the questions that would
come out of that discussion GE boss how
am i doing this is the number of threats
that the trend in our organization we're
doing fine the next question is going to
come out all how do we get there so then
you can look at on the bottom here it is
here's the number of threats distributed
across the different departments well
apparently computers electronics and the
jewelry department or they're just the
worst
[Music]
you know you get all them in your
computer departments well we're all in
computer departments but I don't know
about you get out confess I've installed
random stuff just to check it out and
you know it happens usually I was the
only one that knew it happened but you
know in large organizations particularly
ones with a strong data strategy other
people will know and of course then
there's also and again this is all part
of understanding your data this is the
relationship between men and women and
the number of malware detection in their
environment the red that's that's men
and then the other one is women I'm just
gonna make it clear this is bogus data
by the way so I didn't do that on
purpose even though my wife is is my
number one most bothersome technical
support require okay but I didn't do
that on purpose by the way ok next one
so once you've understood it you
visualized it you've drawn those initial
conclusions now you have to clean it up
eighty percent of your time is spent
doing this case because most data is
dirty and it's full of bad stuff the
bank no no I'm scary American Express as
of last year they have spent four years
building a centralized data warehouse of
both structured and unstructured data
using Hadoop for years when I talked to
their Jeep data architect I asked him
for years man I mean what taken four
years he says no it's not it's not
compiling the data that's real quick you
know it gets really impressive you know
when you're at four and a half billion
rows like ahahaha here's but two-thirds
of its garbage
and we have to go through it because if
I share that with my CFO I'm gonna get
fired for years so you need to make sure
is the data there is there data you
shouldn't use my last slide who cares
what genders involved in the most what
are you going to do ban that gender from
using the computer that's just not going
to work I mean there's like lawsuits and
all sorts of stuff just waiting to
happen so you have to make sure that you
remove data that doesn't contribute to
your costs are all the Antony entries
logical and usable be complete most
analytics don't work with missing data
and again when you think about the tools
I mean it depends on what you have at
your disposal you'll see that there's
two things that have a tendency to pop
up once called rapidminer the other ones
called are in our studio if you haven't
checked these out free download it use
it rapidminer was Gartner's 2016 big
data analytics tool of the year and they
have been in the top ranking for the
last few years I'm not trying to sell it
it's free it's awesome and if you look
at the more commercial alternatives like
SPSS modeler and SPSS ok I will preface
so this is my own personal opinion I'm
not meant to sway or or discredit any
organization but you know those things
are old they're antiquated they're hard
to use it look like they're built in the
90s okay so great tool by the way dude
with the camera and yeah so they're all
perfectly good acceptable huge parts of
the everyday data mining community
however if you want something to start
with that you can use in your own
organization to answer the 443 question
from the CIO use rapidminer it's awesome
all right next one cleaning your data ok
on the right hand side you see a screen
of what rapidminer looks like because
right now there's a lot of people in the
arts going I don't have time to do this
I don't have the time I don't have the
interest to three people walking out the
back door we see you okay so I don't
have time to do this and I'm certainly
not going through that spreadsheet on
the left okay I can't do 77,000 rows of
that stuff ok this is what rapidminer
looks like it's also what SPSS modeler
it looks like in other tools that you
can use for these purposes all of them
are certainly exceptional again rapid
minus free well because their next time
that's how things work but what you'll
see is that it's a series of operational
modules that you can include in order to
manipulate your data over time so coming
back no what's being asked get the data
that you need however you need to get it
clean that data so that it is functional
and then the next step is the actual fun
part so by this point you're exhausted
and you can't take it anymore and you
know now it's time to do the fun part
this is where we get to the modeling
piece in case everybody's wondering what
that means and again I keep trying to
tie
back to security but the reality is is
universal for anybody who arcs with data
particularly a large amount of okay when
you talk about modeling you're talking
about taking your data to draw a new
conclusion that perhaps isn't obvious in
the first place and you're going to do
that by grouping it or you're going to
do that by using what you know to
predict what you don't the mathematical
equations behind it I've looked at them
I kind of get them and then this is my
own confession I do some of them I don't
even know how to describe that equation
that's that some calculus guy at you
know at Harvard you know threw up one
night and that's what it looks like so
but I understand what I need to get and
I understand which of those calculations
is going to get me there and I
understand the tools that I'm going to
use to do it so in this point we're
getting to the modeling part we've done
all the dirty work using whatever tool
suits us including you know the more
commercial ones which I prefer other
ones good the modeling pieces is the
part you got to get to that's the
question or the answer to the question
you've been given so in this case here's
your malware threats over time the
question is well how are we doing well
it's not our fault you know people just
keep introducing new malware into the
world those darn basement bowlers are
you keeping track of how my
that's always for all I can beat that
anyhow now where's going up but the
question is where will we be in the
future i know where i am now it's too
late can't fix that it crashed 10
minutes ago where are we going to be in
the future that's all we want to know
we'll black friday of 2017 survive
that's what i need to know so i need to
use predictive analytics and modeling to
do it in this case I've stretched out
the reality out another 120 days and you
are adding new malware to your
environment at a rate of one every 10
days so if we continue to go down the
rate we're going we're going to have
bigger problems we can model this we can
model it with a certain degree of
accuracy and I can even tell you how to
get to the accuracy so we've done the
hard work we've done the model excellent
there's other modeling tool kits that
you can use if you're going to take any
notes and you haven't fallen asleep yet
this is the gold right here okay this is
the stuff that when you get back
tomorrow and you've you've rethought of
all the things you learned over the last
two days and you think what is the crazy
data guy talk about I don't know there's
Friday night and the borrower's going to
close this is what you need to remember
because because this is the stuff that
it takes people years to figure out and
I'm just going to tell you there's two
things we've talked about it there's
supervised and unsupervised learning
let's let's forget those terms you want
to classify stuff these are the ones you
do it your three thousand applicants and
you have to find the top five and i need
it by Thursday at noon this is how you
do it k means predict k k like in most
things is in a relevant number
what it's basically saying is I'm going
to take all your stuff and I'm going to
group it into logical groupings and you
can pick the group you like best if you
look right here on the right hand side i
did it i took 1,000 applicants all with
different characteristics and I picked
the 100 that makes sense and the rest
get I'm sorry no thank you letters how
did I do that well I only want the ones
with the most years of training I want
the ones with the most sophisticated
certifications I want the ones with the
longest work history I want the ones
that have passed our background check
and scored the highest on our screening
surveys I can't go through all that
stuff so in less than three minutes and
I'm exaggerating and the truth is it
just takes seconds for a thousand I
already know this is the group I want
and I know there's a thousand of them
I'm not reading more than a hundred so I
broke it into ten groups and I'm picking
the best group now I can go on and solve
all those other problems go back not yet
a couple things with k-means it is
simple okay on the left-hand side you
can see it this is the process i'm going
to read my data i'm going to select the
relevant data i am going to choose the
role in other words when I choose the
role I'm deciding what's the one that I
want you to decide for me and then I'm
going to run the clustering module and
like magic it spits out what I want
all right now you can go on yes basement
hey yeah as you add to that classify our
group another one's called k-nearest
neighbor in k-nearest neighbor you take
all your data you let the computer group
it you find the ones that you want
answered like what will this new one go
to Group a group B and the calculation
will determine that based on distance
from the team number of variables you
choose to compare it to so in other
words if I have a thousand records 500
or over here 497 over here I've got
three left and I want you to divide the
three into whichever group is closest by
three individuals it'll do it it'll tell
you who will get picked last for
kickball next door on decision tree
these rock I mean these are awesome if
you and I know if you saw the first
slide I used to be a history teacher so
I run this against the Titanic data to
see who would have survived and not and
why they died it's really more of it I'm
really ashamed of myself but the truth
is statistically I'd love to get there
in nineteen 414 so much for the history
teacher I'd love to be there before they
get on and run around and go dude don't
get on the boat it's not going to end
well because you can determine quite
precisely who is going to die and why
it's cool and moral bit yes I'm ashamed
anyhow in addition the decision trees
nit this is totally awesome i love it
you can take that statistical analytics
and you can build rules and we do this
it
totally awesome because you know what a
high amount of CPU memory or disk
utilization on a system is not
necessarily bad can you give me what you
paid for that thing was expensive you
should be working at heart but when a
certain combination of events occurs it
always results in something bad nobody
knows to watch for this this this and
this statistically I can tell you what
to watch for create rules for it in the
mood at those flags pop up you get a
phone call how cool is that call me when
a real problem is going to happen that's
a unique concept naive Bayes I don't
know our babies came from but I'll tell
you why it's called naive it's called
naive because it makes the assumption
that all variables in your equation are
independent okay the truth is they're
not always the nice thing about naive
Bayes it always works even if your data
is flawed it just seems to work so this
is your tool kit for clustering next one
oh man nobody saw out of their chair yet
so when you use your tool kit you can
also use predictors so forget the
clustering we just talked about we're
taking all our data and grouping it to
draw some conclusion well not a question
is well I don't need to draw a
conclusion I have one variable I just
need to know when bad things are going
to happen and that's where we use our
predictors particularly linear and
logistic regression here's my plug for
our this to do all this is one line of
code in arms okay totally awesome but if
you like the boxes you can use SPSS you
can use SPSS modeler you can use SAS you
can use rapidminer all of them have
their
built-in for logistic and and linear
regression if you look at the bottom
right that's what it does it takes the
data you have it determines a reasonable
statistical trend and forecasted out as
far as you want to go the difference
between linear and logistic linear works
with numbers okay logistic works with
yes or no will you died on the Titanic
logistic how many days do you have left
to live on the Titanic well yeah sorry
not many all right next one all right
we're almost done next one we've done
the work right we got the request we
determine what was asked we identified
what I needed to collect we cleaned it
up and we even did the fun part the
model okay but it has to pass the smell
test you can't just send that model off
to somebody I mean they never turn out
right the first time okay it has to pass
the smell test have you ever done p2v
migration analysis okay it's a disaster
right your model eight year old hardware
on two brand new next-gen blades they go
from a hundred and twenty-eight CPU down
to eight and you hand them at me look at
you like your girl horn out your
forehead that's not possible well
statistically it is ok but it has to
pass the smell test you have to look at
it you got to make sure it's right and
the way you do that if you look at the
next slide my accuracy look at at my
Titanic people 45.6 seven percent
oh yeah we might as well guess a 50-50
shot anyway never comes out right the
first time sorry white is going to come
out right because the first time you do
it you include everything you have to
figure out which variables are important
at which art keep in mind the best way
in the best but the simplest way to
determine accuracy is using a confusion
matrix I love that term yeah it just
doesn't make any sense how do you know
if you're right well you check the
confusion matrix oh my sounds like a bad
joke and basically what it does is it
says well look i'm going to take the
number of predicted true and the number
of real true and see how close you are
and the number of predicted false and
the number of real falls and see how
close you are most tools do this for you
rapidminer it has a module and I keep
coming back to it because you can go
home and download it when we're done has
a module called performance it'll tell
you if it's less than ninety five
percent accurate no yeah cool for that
and you hand that off to your boss okay
if if fifty percent accurate that that's
yeah no don't use that so make sure it's
accurate before you send it on all right
next last part once you've done it
you've done all of that stuff he hit to
the point it's time to deploy it put it
in production that's when you hand it
off to your boss I'm gonna recap too
quickly you started out with the
critical question you get them all the
time more and more if you don't get data
related questions now can I come work by
you because it's just it's all I get but
that's kind of my job but it's all data
you want more money show me the data you
want more people show me the data you
don't want to get fired because the bank
of Singapore went down show me the data
you need to use data to help people draw
accurate conclusions and insecurity you
have to do it too this is the process if
you follow this process and you use one
of the tools that you have available
when you walk in there and they say hey
how's it doing how's it going are we
still having security threats on the
East Coast yeah the please ghost what do
you do about it that's why we all live
in the Midwest all right I have anything
after that no that's pretty much it so I
come back to the very last thing I want
to wrap this all up in a bow for you
because you know there was no hash
algorithms I love group policy but a
group policy rocks but I'm sorry there's
no group policy in my discussion it all
comes down to once the hackers are done
once you have manipulated change or
gained access to something in order to
gain new insights well then what do you
do then you have the data and oftentimes
the data is something that your boss
your boss's boss are worse yet your
boss's boss's boss so she really
shouldn't even know who you are will
come right to you and ask for the data
and you have to know how to deliver it
in a way that is both accurate and
provides direction for the next steps
this is how you do it and if you follow
these steps you're going to be
successful even if the answer is not
what they want to hear so there you go
any questions yes hi wells great next
[Music]
[Applause]
I figure if i don't talk long enough
you'll forget you asked the question the
truth is I don't know the answer to that
it's a very legitimate point and he's
part of the problem statistical analysis
is not is ancient so everything that we
just talked about is the computerized
version of intelligence that in some
cases date back to the ancient Greeks
okay so this is not new having a machine
do it and do it with some level of
accuracy that makes human interaction
less and less needed that's new so the
only thing I can say to that question is
that it's part of an expected learning
curve as those types of situations arise
the algorithms and the people who
produce the tools to use those
algorithms have to mature with it I know
that's a real cop-out answer because I
you know but that's what I got okay
other questions alright I appreciate
everyone's time I hope that a little bit
of data talk didn't bore you too much
everybody have a great evening and
thanks for coming in if you see Mike
downstairs you know give him a high five
because this really was an awesome
conference so stop there is you can't
miss the pink shirt alright guys thanks
[Applause]