Yeah, okay, so I'm gonna uh take just
a second or two and uh kind of do a
little bit of an introduction to me. Uh probably
I'm gonna know me better than anybody, so I'm probably
disqualified. I'm I'm an animal scientist by training.
So some pedigrees are pretty important to me, so
I'll kind of give you a little bit of a
pedigree, so you kind of have an idea of
where I'm coming from and whether I'm really qualified to
talk to you about something going to talk to you
about. Uh So, So I actually started my
career in extension in 1980, which I know for
most of you as some point in history for which
you're not aware. Um But uh went to Virginia
Tech, got a degree in animal science, uh
started as a as a county extinction agent in northern
Virginia 1980 1981. Uh when it really kind of
started my uh I guess what I would consider my
professional career. Oh, and that sort of moved
on to becoming the director of something at the time
that we called Extension Information Systems. So I came
back to Blacksburg, I was here for a few
years. Um I left here went to Purdue University
and I was the director of uh for the but
at that time was the School of Agriculture in West
Lafayette and it was produced for a couple of years
left there, you can't hold a job, went
to Penn State, where I was the director of
I.T. For the college of Ag for about
12.5 years and March of 2014. I was fortunate
enough to get the opportunity to come back to Virginia
Tech. And uh I think I can pretty definitively
say, or at least my wife will definitively say
that we're done moving, we're gonna roost here,
this is gonna be it. Um So you know
, kind of the neat thing about that is You
know that sort of time period I guess, you
know like 1982 now in my mind really sort of
encompasses I guess what you would sort of call the
modern age of what we now call information technology.
Um you know, we've seen the advent of the
web and you know now people can actually spell internet
, whereas before the 1980 they probably couldn't. Um
and so from a library perspective, what I've seen
is kind of like you recalling that uh he used
to go out on friday afternoons and have a beer
with some guy named Gutenberg. And after you get
a couple of points in and he starts sketching out
this printing press thing on a bar now. So
uh so that's kind of the things that that sort
of thing over my career. So I'll fly back
and forth with this thing a lot of times people
ask is an I. T. Director. You
know what are the things that sort of keep you
up at night? Um fortunately I'm a pretty sound
sleeper so it doesn't do it a lot. But
the one thing that really does keep me awake at
night this data. Um and I guess over over
the period of my career um you know we've seen
data kind of move from of course the traditional uh
print kinds of things into sort of our digital formats
when uh when I first started working with computers as
they say what I had was something that we called
a dumb terminal. So it was kind of imagine
sort of like a little typewriter that had an acoustic
coupling in the back of it. So you would
dial a number that connected to Honeywell Computer and Minnesota
. And you plug the phone coupling into the back
of it. And that's how you establish your connection
. And the data was actually printed on thermal paper
. So the biggest risk to data compromise was leaving
that thermal paper in the seat of the pickup truck
when the sun hit on it. Because when you
came back all your data was gone. So that's
kind of where we've, you know, sort of
come from. Okay. So so what are the
things that generally kind of give me anxiety when it
comes to data? Well collecting it, okay,
storing it, securing it, recovering it, using
it, sharing it. And actually we're moving.
So pretty much anything you can think about as it
relates to data are the things that really kind of
give me uh pause or it gives me concern or
it's the things that I'm really concerned about and you
know what I think I think just the notion that
we focus on data as I. T. Professionals
is actually a little bit of a change. I
mean you would think it sort of makes sense but
you know historically as I. T. Professionals we
focused on things like um you're making sure that computer
was functional and operation making sure that the network was
secure and had enough bandwidth. Uh making sure that
our software was patched and everything was up to date
. And I think what happened is we we kind
of did a lot of that stuff. Uh not
necessarily thinking about what the real prize was and the
real prize was the data because all these things are
sort of superfluous to having a really good solid data
that we can you know that we can use and
that we can secure we can keep in all these
kinds of. So what I'm gonna do is um
just kind of take you through each one of these
things and and just sort of kind of give it
to you a little bit from the From the perspective
of an IT. professional and particularly 90 director and
I'll just kind of throw out my wife will will
argue against this and probably most of my staff but
I'm a lot better at having a conversation that I
am preaching. So you know you know if you
have questions or you want to kind of prove stuff
out let's have a dialogue about this because yeah the
way that I approach it it's gonna be a lot
different maybe than the way that you view it.
And so I would much rather you kind of throw
some questions and we have some conversation about it and
you know, we just kind of standing up here
and lecturing to you because I'm not really particularly good
at that. So when it comes to data gathering
, um you know, we think about all the
technologies that are involved around the processes for getting this
data to some point. So there's kind of two
cats, two ways. That's where to look at
the data collection. One is just the data gathering
part. So one of the things, of course
that that's important for us, particularly in our college
in terms of data gathering is sensor collection. So
we have sensors that collect uh stuff on plants.
We have sensors that collect things are livestock. Uh
We've just done, you may know that we just
moved our dairy from, you know, out here
, opening to the universe, moved it down to
Kendall and Farm, which is just kind of across
the river from the, from the Radford arsenal.
Um, we have, we have tags on our
livestock, on our dairy cows. That really kind
of tell us what the cow is doing all during
the day. So it tells us when she's moving
, tells us when she's standing, still, tells
us when she's laying down. It even tells us
the angle that she's laying down and all that data
is really kind of important because it has an impact
on her health and it has an impact on milk
production. And so it allows us to kind of
study sort of the habits that are dairy cows have
and how that's going to impact. You know,
there will be. So the other thing about sensors
is how many of you have heard of the concept
of the internet of things. I don't know what
that is. So the notion behind that is basically
everything at some point is probably going to be connected
to the internet. It's gonna be a sensor in
your refrigerator, it's gonna be a sensor in your
tennis shoes, it's gonna be something on your watch
. You know, anything that can collect data is
going to ultimately somehow be connected to something and we're
going to have to collect the data that's associated with
all of those particular sensors. Um one of the
things in our college is that's kind of unique as
opposed to some other colleges is in addition to having
academic departments and research and all that kind of stuff
that goes on here on campus. We also have
offices in every county in state Virginia. So we
have 107 county offices. I have uh 64 H
Educational centers for district offices. 13 Ag Research and
extension center is a partridge in a pear tree.
And we have to provide support for all of those
locations and all of the faculty and staff that are
at those locations. So with our, so they're
ag research and extension centers. Um those are sort
of effectively, they're just like farms that the university
ends and they're scattered all over the state of Virginia
. And so one of the big things that I
get from all of our a wreck directors is they
want a connected a rate, they want wireless coverage
over the whole farm. They want to be able
to put soil most moisture probes out in the field
so they can, you know, not only tell
how much rainfall we got what the soil moisture is
because of course that all impacts are, you know
, the research is associated with, you know,
with their crop production. So all of that kind
of sensor data and suffer things that we're really always
looking at and thinking about how we're gonna manage what
we're gonna do. Um So you ate these unmanned
aerial vehicles, drones, the latest craze. One
of my absolute passions. I don't even have one
, but I want one desperately. Um but we
have a lot more of our faculty now are interested
in uh in drone research and the types of data
that we can collect with drones, uh things like
aerial photography. Uh folks in our plant pathology department
use them to collect airborne particulate matter so that we
can kind of study how uh insects and how uh
disease and spores and all these kinds of things move
around. And so you can imagine with digital fouls
that are generated from U. A. B.
S. We're looking at gigabytes and gigabytes and gigabytes
of data. But then we also have to combine
all that data because we're taking snapshots of you know
like 100 acre or 500 acre field. We have
to put all that stuff together so that we can
see where guests infiltrations are happening, all the sort
of thing. So you a bs are generating a
lot of data for us and things that we're trying
to sort of understand how we're gonna deal. Um
And then the third category on data gathering is what
I just call recording. So it's just the more
or less the manual hectoring of data. So it
could be something you're putting on a laptop, could
be something you're putting into a tablet, uh you
know any kind of thing that you know sort more
or less involved, some type of keystroke or pens
or whatever else. And so again, when we
think about like with our without research facilities, you
know, we're doing a lot of data gathering out
field, so we're kind of out in the middle
of some cases nowhere, at least you can see
no word from there. Um And so we're thinking
about how do we get those devices connected? What
kinds of things do we do in real time,
all that sort of stuff, It's all related to
the data report and then the second category of,
I guess the day together and I talk about it's
just what I would call the creation. Yeah,
so for lack of a better term, I kind
of call it creative work. So this is where
I sort of generate stuff online that could be in
my case when I put on my administrator had that
might be a budget. Um If I'm in the
classroom, that might be how do I, how
do I articulate a particular concept to my students so
that they can kind of grasp the notion or grasp
the idea. And then the second part of that
one is uh more or less what I've turned the
data regressions. So we start off with a Davis
and then I want to I want to see what
the introduction of a particular variable does to that particular
dataset. So now I started out with a one
terabyte data set, I introduced a variable. Now
I have to one terabyte data sets and then that
goes 23 and four and 10. And then I
get to a point where I'm like okay enough of
that and blow it all away, go back to
my original dataset and start all over again with a
different set of areas. So what this does for
us is it means that we have to be cognizant
of the flex if you will in terms of data
body. So I have to kind of think about
what I'm looking at my data storage or what I'm
recommending from my faculty to have a data storage.
I've kind of got to be able to think about
right sizing that storage. I don't want to have
too little because they are going to run out of
space. But if I try to throw everything but
the kitchen sink at it, then I end up
with a lot more storage and a lot more expensive
I would ordinarily have. So that's a consideration that
comes, comes in for us when we kind of
think about what we're doing with data and data sets
and the impact of all the research and things that
goes on with that. So when we think about
data storage, um that's probably um probably the thing
that creates the most headache if you will for me
versus all these other things. And one of the
real reason behind that is because data storage is so
unpredictable. It's really hard for us to get a
grasp on data storage because what we're doing is we're
trying to predict what data story is going to be
, not just what it is. And the things
we were just talking about with the day of regression
and how that flex all kind of moves back and
forth. Um You know, our data growth in
way back in history uh was somewhat predictable and we
kind of saw typical trends of how the data was
growing and we could sort of plan for that.
But with all the other vectors that are coming into
play with data collection, our data storage lawyer is
growing exponentially. And so we're trying to sort of
try to get our arms around and how do we
how do we continue to grow our data storage capacity
and an affordable and sustainable manner? And then what
are all the options and everything that they go around
? So of course one of the big things that
these stories, how much I've got to know,
you know, are we dealing with gigabytes? Are
we dealing with terabytes, petabytes? Exabytes? Um
When when I was in ST I was involved with
a uh with a working group or a university committee
that was tasked with designing the next generation data.
And so I can tell you that this was absolutely
the most one of the most wonderful and one of
the most humbling experiences I've ever had because I was
by far the dumbest person we had, meteorologist and
geologists and people that we're dealing with exabytes and petabytes
and exabytes of data. I mean, we were
starting to run out of, you know, little
numbers in front of the white thing. And so
it was just interesting from their perspective to kind of
here, How they were using data and the kind
of data needs that they thought they were going to
have in the next 5, 10, 15 years
, which was kind of a cycle that we were
using around the around the data. So how much
is really important? How um for for us on
the I teach side, um we're always kind of
thinking about the frequency with which data is accessed.
So that kind of thing has an impact for us
on the type of storage media that we have,
the location of the storage media. Uh you know
, we have to think about the networks that you
have to think about, the frequency of this.
If we think about things like uh you know,
is a is a particular set of data gonna be
accessed relatively frequently in a short period of time.
And then beyond that we're probably not gonna do anything
with it that has an impact on the count of
storage that I purchased and it has an impact on
where I put that storage but that how fast um
obviously certain kinds of data require faster access than others
. So if I'm doing a lot of video editing
that requires a lot of fast access. If I'm
just doing tables or things like that, you know
, not so much. So I have to you
have to kind of work with the faculty, understand
you know what type of data that we're talking about
, How often they're gonna be doing that if we're
doing a bunch of more and more, I think
of our work is actually coming in the video.
I'm a, I'm a visual learner that really speaks
to me. So I understand all that, but
you know, that has an impact on the types
of storage and the types of media where it's stored
. Um, is there anybody that hasn't heard about
the cloud? Whatever. That little nebulous thing is
. Um, you know, a lot of,
uh, when, when, when the whole concept
of cloud storage begin begin coming around, one of
the things that a lot of administrators and even some
of our faculty hopped on board were like, we'll
just put it in the cloud because it's unlimited,
right? But it's not. And there's certain things
that we can put in the cloud and there's certain
things that we can't put in the club and we'll
talk a little bit later about about some of the
some of the regulatory pieces. But you know,
there's a there's a classification of data. Uh I
thought probably guys are probably familiar with that international trade
and arms agreement. It's a lot of that is
um you know, things that are protected in terms
of where they can be stored and who can actually
be involved in the management of that storage. So
without our data it's got to be stored within,
you know, it has to be stored within the
United States and then it has people who administer or
work on the machines where the data store had to
be U. S. Citizens. So okay,
we can do that maybe. But that presents problems
with people like google and people like amazon people like
Microsoft who have stuff spread everywhere. So they can
they can sort of give you a guarantee that is
going to be out are compatible. What do you
think that comes with price tag? It's infinitely more
expensive, sore that stuff in the cloud over those
kind of restrictions. So we have to think um
longevity, you know, any discussion of data uh
comes with the understanding of data retention rules, data
retention policies. Uh You know, we would all
like to think that uh once we put a particular
piece of data into storage that it's pretty much gonna
be there forever um since we just moved a couple
of years ago, I can definitively say that there's
a lot of stuff in my basement that has been
stored forever. Um but we we need to think
when we're when we're looking at that storage piece,
uh we need to think about what the retention rules
are around the data that's going in there. And
we have to think about the retention policies that are
associated with it. And I'll tell you a few
little horror stories here in a little yeah um data
security is probably the thing that gets the most headlines
when it comes, when it comes to take it
. Um you know, certainly anytime there's uh there's
a credit card breach from a company. Mhm.
How many have you ever gotten one of those little
letters? It says uh hey by the way we
screwed up and your Social security number has been compromised
. Got those, how many have you got that
call from your from your credit card company that says
um were you in Outer Mongolia last week? And
did you buy a leader of petrol for you know
for whatever their uh whatever their currency um You know
it's like somebody got your credit card numbers, you
got to go through that, you know, that
whole process. So the data security is one of
those things that uh, that everybody kind of goes
through and kind of points is uh, oh,
this is the one big thing that, you know
, that we really have to be worried about.
And it is, but in the context of a
lot of other things. So I think the posture
that and that we as it professionals, are sort
of beginning to adopt if you will, is that
is no longer a question of this. But it's
a question of when um, you know, we
will probably always be in a ketchup game when it
comes to securing systems and the degrees to which people
will go in order to penetrate those systems. So
, you know, it's it's no longer the uh
, you know, the, The reclusive 14 year
old sitting in their bedroom, you know, trying
to break in what's the scorpion, you know,
where he broke into massachusetts to get a poster first
one. Um, it's, uh, you know
, it's, it's organized crime. It's state sponsored
. And, you know, in a lot of
times our tendency is to think, well, you
know, what do they want with my system?
Well, it may not be they want anything that
you actually don't, they just want your system as
a vehicle to get to somewhere else. But in
the process of doing that, we entertain exposure to
data, the risk of data is going to be
compromised and risk of data is going to be stolen
or the risk of data is gonna move. So
I think our, our posture now, it's kind
of shifting more to the, uh, okay,
let's just assume that our systems are going to get
compromised at some point. What do we do to
either be able to recover from that or to make
sure that our data is not put at risk?
So it's really an important I think an important change
in our philosophy. Um So we have it's with
the security peace. Um There are internal things that
we deal with as well as the external things.
So you know, we can sort of think about
, okay, somebody is out there, you know
, banging away on the keyboard. They're trying to
you know trying to access our systems. Uh give
you just a little just a little bit of trivia
yet. I don't know the numbers here attack but
when I was a pencil work replaced with security office
on a couple of things Um in 2010 at Penn
State We got on average 120 million probes of our
network a month. That's how often people are trying
to access or to compromise data on a university system
. Um and you kind of read papers will find
out that, you know, last year they were
actually successful with College of Engineering. So, so
you know, there is just this activity is going
on. They're just not much way we're going to
prevent it. But some of the internal challenges we
have are some of the regulatory pieces that we've got
to be available that we have to think about.
So PC. I compliance payment card industry standards.
So anything that we're using where we take credit card
information falls under PC I guidelines so that credit card
data, even if we're not particularly holding the numbers
, just the transmission of those kinds of things are
some pretty stringent standards around how that stuff is handled
and how it's me. And if there is credit
card compromise um and numbers are compromised or something problems
, the way that we handle our transactions, the
penalties for that stuff are not insignificant, large breaches
and things, not even counting what you have to
pay to get, you know to do the the
credit card protection afterwards. But you know you're talking
millions of dollars and you end up in situations where
right now for most of the university we have kind
of what we call voluntary compliance if you will,
which means we kind of self manage ourselves within certain
guidelines. But once you go through a significant data
breach then you have to pay a a consultant to
come in and do that kind of manager. And
those consultants obviously don't come cheap. Um We talked
about, we talked about our tar uh ferpa so
all the protection of our student information and for pa
is a big driver in terms of uh just generally
how we manage systems here at the university, not
just think about the production of uh of the data
. Uh Of course hip uh most of you are
familiar with. Um there's a new one that we
actually I'm on one of the university committees, you
know, kind of looking at how we're gonna deal
with this one. But there's a new category out
called controlled unclassified information. And effectively that is that's
that's data from from the federal government that's um that's
not classified as you know within the regular classification ranges
. You know like you would expect from the Department
of Defense. But see you I data comes with
178 different data protection requirements. So if I get
one of my faculty uh lands of federal grant and
the data associated with that grant is classified by that
particular agency is being C. U. I.
Then I've got 178 things but I have to in
order to protect that data so that it meets the
federal government standards. And some of those are not
insignificant one of them. But you know, we
thought a couple years ago this would be kind of
a challenge. It's not so much today. But
you know your all seeing the publicity about two factor
authentication. Well that's one of the big restrictions under
see you out. There's things about who can access
that. There's things about uh huh. About wireless
communications of the data. So if we think about
when we think about making that stuff available to the
public or what we're gonna do with you, I
was going to be a really really important, a
really important category for us moving forward and thinking about
how we deal with, let me stop. I've
been riding alone for a while. What kind of
questions do you have about what I'm talking about so
far makes sense. Told you anything you didn't already
know. So when you're dealing with the data that
means to comply with the U. I. How
often do they like is there even any like a
random mind during to see there you guys are compliant
? Quite also, we really don't know. I
haven't been out there long enough. We're assuming that
it probably would. Um One of our challenges will
see you out right now is that the the rules
keep changing a little bit. One of the,
one of the cuz requirements. Kind of this kind
of a funny thing, it's just sort of how
, you know the government is really working for you
. Um What is the U. I. Requirements
is that is that they require you that uh if
you have a machine that's gonna be accessing see you
idea. That machine can only be used to access
. See you idea. Right? So if you're
processing that day you can't do your email on.
You can surf the web can do that kind of
stuff. Okay. The other thing is really strange
is that the computer that you used to process the
requisition to purchase the computer is also considered cva nothing
weird thing in the world. I mean so anyway
so we're that kind of gives you a sense of
what were you know kind of what we're dealing with
this stuff. So uh it didn't make any difference
how hard we try what all we do data is
going to get lost. It's going to get deleted
or it's going to get stolen. That's just the
way it is. So we have to be very
very uh purposeful and our efforts to back up and
recover. Okay. And you know, the real
the real deal here is that if if the only
thing was having the technology to do it, it
wouldn't be a problem. I mean the technologies to
back up data have been around for years. Every
time you've got a disk, you put the date
on one disk, you put another copy on another
disk and you put the other distance, you can
do that kind of stuff. The problem is that's
a manual process and people forget to do it.
That's where the struggle. So when I when I
came back to campus uh two years ago, um
one of the first things I did is I really
just started wandering, if you will through the college
, um, meeting with department is meeting with faculty
, meeting with our extension agents, being with administrators
. Um, you know, really just trying to
get a sense of a land where are some of
the things that we were doing well for some of
the things we weren't doing very well, where some
of the things that people really clamoring for. And
, you know, one of the things that I
found is we really did a crappy job 10 of
backing up our data. We really didn't have an
organized method or an organized system, if you will
, of data backup. So well, the first
initiatives that we did is, uh, put together
a team of folks and we looked at a number
of different applications and kind of put them through their
paces and so on. So, and I'm actually
, you know, with pretty good degree of certainty
. Tell you, David about 95 of the faculty
and staff extension agents ministries home. So in our
college have their data reliably backed up and in a
fashion that allows us to easily restore it and keep
it secure. We we went through the process,
we ended up with a we ended up with a
product called called Crash Plan. It's from a company
called Code 42. Uh, we felt like it
gave us a lot of flexibility in terms of the
diversity of our storage needs because again, we've got
storage here on campus with folks, we've got our
all the off campus locations that we deal with all
kinds of different bandwidth restrictions and all that sort of
stuff. So, so this is kind of an
unabashed advertisement and uh and plea that as you,
you know, you kind of work with folks at
the university and you encounter people that are kind of
looking for reliable data backup and recovery solutions point of
our way because what we did is we we actually
negotiated the contract so that we could implement crash plan
on a university wide scale. So in addition to
our college today we have folks in business, we
have folks in liberal large, we have folks in
engineering, they're all effectively just using the same system
. It provides us with a mix of uh private
cloud, in other words, sources that we have
here on campus as well as public cloud stuff that's
secure and encrypted and available to uh through co 42
storage. And and it's also really really cheap.
Uh When I was kind of talking about this with
some faculty, uh you know, one of the
, one of the questions that they asked how much
the cost? Well at the time I think we
had the time I was going to cost us like
$75 per person per year. No thought for your
life or do I wipe my check? Because it's
not, you know, it's not just it's not
just data in general from for so many of our
folks, this is their career, this is I
mean everything that they've ever done professionally falls into some
type of data category. So it was just just
a nice way for us to kind of go through
and do the whole data system. So first when
we lose data um it's a loss of productivity,
you gotta go back and re create it. Mhm
. Its relationship damage. So you've got somebody that's
expecting you to have their data and to keep it
and you don't do it and you don't do it
effectively and that damages your relationship. And of course
, you've got contractual obligations, branding agencies, you've
been giving you money to generate data and they expect
you to keep it. And I know today you've
got to think on the agenda, you've got something
update, you're doing your data management plans and you
know how that how that if you hold that kind
of stuff works. So it's essential I think for
us to you really think about our ability to,
you know, kind of back this stuff up and
be able to get it back. So of course
the data don't do so good. You can't use
it, right? So, so we think a
lot about um you know how the data is going
to be used and by whom. So one of
the one of the conversations that we have when uh
when folks come to us about, you know,
it could be anything, could be somebody comes and
says, well, I need a database to do
X. Um we really try to get into a
conversation about how are you going to use the data
? And uh, you know, I'm trying to
get sort of a better feel of, you know
, is this being generated by one system and you've
got to move the data? Something else? We
kind of think about file compatibility issues around that.
Um, you know, it used to be your
it's not quite as bad as it used to be
, but, you know, we used to have
the whole uh, you know, Mac vs.
Windows battle that, you know, we generate something
in a Windows environment. You know, the backs
can't use it, we generate something back environment and
Windows can't use it and all that sort of stuff
, Most of that's kind of gone away, but
it is still a consideration um We think about the
sustainability and this is um probably get a little bit
more um into your area of expertise. But um
you know in a perfect world we go by software
package and then that software package would just be good
for the rest of our lives. But you know
we as I. T. People uh we've sort
of built our profession and our importance in the world
in upgrading software. So we you know, we
start off with this version, we have to upgrade
to a new version, we have to upgrade to
a new version. We have to think about what
the ramifications of all of that on the data that
we captured way back when. So you know,
my first spreadsheet was a product called Lotus 123 My
first word processor was Word Star, you know,
so the products don't exist anymore. Uh But if
we had data that you know, that was,
you know, it was stored in those things,
how are we gonna get that stuff back? Um
You know, the other thing about the sustainability is
not just the software, but it's also the media
. So again, remember my little thermal paper deal
, Right? Uh Well, I went from thermal
paper are graduated to a five and a quarter inch
floppy disk. That was a big step up.
I could get a whopping was 512 K. or
something on those things. Um And then uh and
then we went from that to a 3.5 inch little
disk, right? That was at 1.44 megabytes.
Uh Again, just astronomical standards for the day.
Um Actually about two years ago I had, somebody
had, if I remember called me and he had
a whole stack of these 3.5 inch discounts and he
wanted anyone to know if there was any way that
he could copy that data onto something else, like
you have kids, you actually found one of my
geeky employees that actually have a 3.5 inch of this
machine at home. So we were able to kind
of, yeah, here in the library, we
have support, yeah, they have machines that are
able to do that. And had, I know
that I was appointed him right here. Of course
, people have had to have driven people, but
, but again, you know, it's, you
know, it's not just captured, we've got to
think about, you know, historically, it's not
, you know, it's, you know, some
cases, you know, the books we have upstairs
, you know, we have a lot more sustainability
to them. Uh you know, we think about
it then we do some digital just along those lines
. People who kept their data on magnetic tape,
the tape, the tape will degrade unless you unless
you rewind them and read them and every, yep
. Exactly. So you know, these are just
, these are just considerations that we have to just
have to kind of keep thinking about. Um So
uh probably the uh the single biggest question I get
from faculty when they're coming to us about data stuff
is you know, they want okay, so how
am I going to be able to share this data
a lot of times? That's going to be with
you with a colleague at another institution? Uh Can
the colleague uh add to my data uh good store
here in Virginia Tech, can they edit the data
? Can I prevent them from editing the data?
So um so we have to remember that that a
lot of this stuff is now, particularly with more
and more of our grants, their their their interdisciplinary
their multi institutional. So um we have to consider
the cold collaboration piece. What are the uh what
are the forum? What are the forums that allow
us to collaborate with other individuals share our day,
allow the abuse our data add to our data modifier
datas on so forth. Uh I got to consider
sort of call this enhancing the science. Can't really
think of a better term before other than um there
are some cyber science missions out there to do that
. But it's it's thinking about how particular data uh
impacts other disciplines. He either on an intentional or
unintentional basis. And so uh you're one of the
examples from our college is his weather data. So
we may have say 10 years of meteorological data stored
somewhere. Probably wasn't generated by us, probably generated
by somebody else. But then what we can do
is we can use that data, say in our
in our plant pathology and wheat science department and our
Entomology department and develop models and do predictability of things
like pest infestations. You know, how does we
also look at things like how this temperature and uh
wind currents affect when we apply a pesticide or when
we don't apply pesticide? Um You know, there's
also been some studies where people use weather data for
predicting like influenza outbreaks and things like that. So
it's sort of thinking about how we can either access
or we can socialize the data that we have so
that other disciplines can take advantage of that data and
and a lot of cases using the ways that we
may have not, you know, not really have
thought about when we originally generated. Mhm. Um
Okay, believe it or not, we do end
up in cases where we want data removed. Now
I'm kind of a historical kind of guy by nature
. You know I just I love history and the
thought of like getting rid of something that's historical.
Uh it's something that kind of just sort of grates
on me but but we do have cases where data
removal is important. Um And it's you know,
I always like to think a lot of my staff
in previous years particularly um you know when we started
running up against ceilings on data storage, like people
just need to go in and delete some of this
junk that they've got in there that they really don't
need. And that is an absolutely wonderful concept.
And it will never ever ever ever happen because we're
a victim of our own success. It's not like
my basement that has a very finite volume to it
, right? You know we keep adding data storage
and we keep doing it cheaper and cheaper and cheaper
. And there's really not much incentive for you to
delete stuff because we keep finding places for you to
start. So the whole thing about the housekeeping deal
just probably didn't want to happen. Uh You know
you're just not going to go in and delete something
just because you're trying to be a really good that
citizen and you're trying to save any money and headache
. But there are things that really should be deleted
and they should be deleted on some type of plan
type of schedule and some type of based on some
type of guidelines and policy. So so legally we
have a term called s electronically stored information. Um
And when you get lawyers involved in no offense to
the lawyers that might be in the room. God
bless you. Um But a lawyer will tell you
that you how long how long should you keep data
? No You should keep data no more than one
minute longer. Then you're then you have to keep
it. Yeah. So uh how many of you
know Enron may have been before some of your time
. But so there was an Enron debacle right where
Enron was a what accounting firm And you know they
they got in really significant legal trouble um ostensibly because
they deleted some files. Well the problem wasn't that
they deleted the files. The problem was that they
deleted the files right after they were served with litigation
notices. When you went back and looked at it
, Enron had in place a company wide policy for
how long they keep certain kinds of data. And
the company policy said after this particular length of time
this data should be deleted had in Iran followed their
own data retention policies. They wouldn't have gotten into
trouble because they could have gone back and said you
know look our company policy says that we only keep
that particular kind of data for two years And two
years and one day we live. And from a
legal perspective that's okay. Mhm. What happens is
when you have a data retention policy that says that
we keep it for two years and you keep it
for three and then you get served with papers that
want to access three year old data and you go
back you go I don't think we want them looking
, we better go ahead and delete it. That's
not that's not good. So that's what really got
, that's what really got involved in control and and
you know, one of the, I guess unfortunate
events that that I was I guess somewhat apart,
I was, you know, at Penn State course
, you're all familiar with The issues that we had
in November of 2011. And uh I happen to
be unfortunate enough to have one of my faculties be
the chair of the faculty senate when the whole um
you know, when that whole episode was going on
. So um one of the things we had to
do is we had to uh immediately put uh put
a to holes on all of the faculty members data
. So every everything that he currently had plus everything
that he began to acquire. So it wasn't just
it wasn't just when just as old emails, but
it was all these new emails and all that sort
of stuff. So so there really are times when
uh when data removal is important, it's very important
to have data retention policies. It's important to understand
the data retention policies. And mostly it's important to
adhere to the data retention policies because it's when you
step outside of those things that you run into,
run into difficulty, run into legal trouble. Mm
. So it's been rattling on off on what's all
this mean? I would say without too much doubt
that most of us have a very um inadequate understanding
of the data for which we are Stewart's and what
all of these things that I've been talking about,
you know, up to this point, it's really
just understanding your data, understanding what you have,
understanding what kind of rules have also understanding how it
needs to be access, how it's going to grow
, how it's going to change, how it needs
to be sustained, how your recovery. Because if
we have a better understanding of our data, then
it makes all these other things that we've been talking
about up to this point much, much, much
, much easier. So when you went through this
exercise this morning where you're kind of looking at,
you're looking at the streets and all the different names
on that kind of stuff, you really kind of
got in there and understood that data pretty well,
right? You kind of got a sense of what
it is and how different it can be and how
you might categorize and stuff like that. So that's
really an exercise. And in understanding data and,
you know, perfect example for us is things like
Social Security numbers. We quit using those things identifiers
years ago. But they still come up and somebody's
machine gets compromised. And you ask whether you have
any Social Security numbers on there and Well, yeah
, you did. You have 1995 student data in
there and have students social security numbers. Now.
We got to notify, you know, the class
of 1995 that all their social security numbers. So
, so it's really important. I think that as
you work with work with folks is try to help
you try to help people understand their data. Mhm
. So the other thing that kind of that you
really need to keep in mind is it's not just
your data. Um Mhm. I think sometimes we
get a little bit we're sort of getting a little
bit loose with it when we think well it's my
stuff and yeah, I've got to back it up
so I'll try to get to that next week,
or R. O. I accidentally deleted it and
don't have backup copy. I'll just go in and
recreate it again. Uh, No big deal,
but it's not just short, you know, it
belongs to university, it belongs to the granting agency
, uh, gave money to generate that. It
may belong to the commonwealth Virginia because belong to the
federal government. So we have to, you have
to really understand that we're stewards of data that where
the ownership is really kind of shared for the most
part, you know, it's shared by our colleagues
and shared by our disciplines, uh, the people
that gave us some money to generate it. So
, you know, that hopefully, you know,
I know a lot of times when I it's one
thing to explain to somebody that's hurting themselves and they
can count and deal with that, but when you
explain to somebody that whatever they're doing might hurt somebody
else, sort of look at that little bit differently
. So you know, it's just an approach I
think we do with you know, with our data
. That's right, and speaking on behalf of the
community in general will help. Um I had a
really good friend of mine um at Penn State was
the director of something called Digital Library Technologies, so
um she was kind of like john Borthwick soy counterpart
U Penn State, but you know my rates,
you're very kind of you know, over you know
, drinks one day after some meeting or something and
it's like you know, you gotta understand, she
said this is what librarians do because we managed data
. I mean this is our is our job,
you know, just because it's digital doesn't mean that
it's any more important that we necessarily treated any differently
than we would paper stuff. There's different parameters trying
but this is kind of what the library professionals do
and so um yeah I think from a you know
from an I. T. Side um this is
where we could use help in you know assigning med
attacks the data. Uh I think Dublin core initiatives
kind of something that um you know where we can
have help understanding how that impacts the way that we
help our folks um use the data that they generate
. Um You know I think it's really um kind
of thinking about it more as a as a holistic
approach rather than just an I. T centric old
somebody's got a terabyte of data, let's put it
on dr somewhere else to make sure that we got
a firewalled off and they got fast access to it
. Then we're done because it's a lot more complicated
than that and in a lot of cases it's more
complicated to the extent that we use I. T
. Professionals might just be a little bit more prepared
to deal with. So that's it for me that's
kind of mild world as it comes to data.
Um It's an absolutely fascinating um job I've got the
best job on campus um uh Okay and then the
best college on campus to but but but it's you
know it really is it's a lot of fun but
it's also one of those things that more and more
I know about my job and I've been doing it
for a long time. Um The more I realize
that I'm never gonna be the expert in every facet
that the job really requires. And I think as
we look at data and data categorizations and all the
things are associated with it. Um you know this
is a place where you know where we need to
really reach out and try to form better partnerships with
you know the folks in this room and kind of
helping us manage it and deal with it and you
know and make it available for generations and generations to
come. So questions. Yeah. So as you're
working in cross college and interdisciplinary really, are there
extra challenges and then you can help and how to
bridge that gap? Are there extra challenges? Uh
. Oh yeah. Yes and no I mean you
know the thing about think about us I. T
. People is you get 10 80 people in the
room and they'll have 11 different ways of doing something
, right? So um so there is a struggle
of consistency in terms of the way that um that
people manage their data and the way that people permit
access and things like that, you know to their
data. In fact we were talking about the U
. I. That's the whole reason cuz I was
actually conceived in the first place was because um uh
oh and be kind of looked at all the all
the different federal agencies and they're like you guys have
like no consistency and how you manage some of this
data. D. O. D. Does it
one way and USDA does it a different way and
D. O. Doesn't another way. So that's
why they put all that stuff together. So you
know I do think um the challenges if you know
if I'm managing access to data one way and engineering
is doing it a completely different way. You know
, can Condell and I get together and kind of
figure out you know how that's going to happen or
even better yet can we all as as I.
T. Directors get together and kind of figure out
can we have some consistency in the way that this
stuff is managing weight is happy. That was one
part of it was another part of your question,
did that cover all that? Are there challenges and
then how do you bridge that gap? Can Yeah
, you know, and I think uh you know
I think the you all probably interact with maybe people
in a way that I don't for example. Right
. Um so it's I think it's one of those
situations where we just we communicate a lot more um
you know there may be things that you find you
bring it to me and say hey were you aware
of this? And uh I may do the same
thing and so I think it's um you know I
think it's having a little bit better understanding of what
we what we do individually and in how we can
kind of leverage that to do things better together jointly
. Um I'm I'm now just tell you I'm inherently
lazy by nature and so so I like partnering uh
it's a little bit more work on the front end
but it really saves me a lot of work on
the back and that's what I'm all about. So
any time we can leverage things with something that somebody
else is doing that's you know that's kind of how
that's kind of how I like to do it.
I'm not a typical I. T. Person I
don't like um uh I mean I don't like naming
my servers and going in every morning and petting them
and making sure that they're all one and all that
kind of stuff. Um You know I'm more like
to kind of figure out how do we leverage all
the resources we got? I don't like doing everything
by myself. Yeah. Um with regard to crash
plan, that sounds like something that I'd like to
pass along. A couple of other people. Sure
could you just either you may not have enough cards
to hand out. I don't even know what car
is anymore but yeah. Yeah you just put on
the screen special slide your contact. Yeah I'll write
it up here on the board. Something. Yeah
also be off a bit lee but yeah. Yeah
but I'll tell you I will give this a little
bit no bit of tip on its because everybody's gonna
want to know how much it calls. Um We
pay uh we pay$22 per person per year for
the license. That's if you put it on your
own storage and it's an additional$39 and something if
you put it in the cloud. So I guess
the way I explained it to everybody is for less
than$70 a year, you can have all your
data backed up reliably and be able to get it
back if you accidentally. Most people just quite kitchen
other questions. Thanks everybody. Yeah. Okay.
Mm mm.