so when we’re plotting our points in life either as new parents new graduates
life either as new parents new graduates
life either as new parents new graduates newlyweds we always think that path is
newlyweds we always think that path is
newlyweds we always think that path is very clear and pretty straight but we
very clear and pretty straight but we
very clear and pretty straight but we know better we know that it’s windy and
know better we know that it’s windy and
know better we know that it’s windy and a little confusing and more like a
a little confusing and more like a
a little confusing and more like a journey and an adventure what we’re
journey and an adventure what we’re
journey and an adventure what we’re doing is having experiences that
doing is having experiences that
doing is having experiences that culminate into the next great thing so
culminate into the next great thing so
culminate into the next great thing so when I’ve completed my undergrad degree
when I’ve completed my undergrad degree
when I’ve completed my undergrad degree in math this is how I felt I was
in math this is how I felt I was
in math this is how I felt I was searching for jobs with the title
searching for jobs with the title
searching for jobs with the title analytics in it because I knew I wanted
analytics in it because I knew I wanted
analytics in it because I knew I wanted to work with math but I wasn’t in the
to work with math but I wasn’t in the
to work with math but I wasn’t in the applied math position those first few
applied math position those first few
applied math position those first few jobs in analytics were really putting
jobs in analytics were really putting
jobs in analytics were really putting numbers into spreadsheets and then doing
numbers into spreadsheets and then doing
numbers into spreadsheets and then doing brace yourself really simple formulas in
brace yourself really simple formulas in
brace yourself really simple formulas in Excel adding up numbers across the row
Excel adding up numbers across the row
Excel adding up numbers across the row and columns the people were great the
and columns the people were great the
and columns the people were great the companies were nice it just wasn’t the
companies were nice it just wasn’t the
companies were nice it just wasn’t the right fit so I kept looking for work and
right fit so I kept looking for work and
right fit so I kept looking for work and eventually landed in higher education
eventually landed in higher education
eventually landed in higher education now in higher education the process of
now in higher education the process of
now in higher education the process of onboarding me and getting me started
onboarding me and getting me started
onboarding me and getting me started into work was slightly different here
into work was slightly different here
into work was slightly different here they had me sit in on meetings learning
they had me sit in on meetings learning
they had me sit in on meetings learning about business processes business
about business processes business
about business processes business processes like how a student would
processes like how a student would
processes like how a student would enroll into a class and how we would
enroll into a class and how we would
enroll into a class and how we would count that enrollment and then I would
count that enrollment and then I would
count that enrollment and then I would sit in meetings learning how to take
sit in meetings learning how to take
sit in meetings learning how to take that information out of the system that
that information out of the system that
that information out of the system that big black box and put it into a pretty
big black box and put it into a pretty
big black box and put it into a pretty PDF and send out so that people could
PDF and send out so that people could
PDF and send out so that people could see how many people were enrolled it was
see how many people were enrolled it was
see how many people were enrolled it was fine it was again good work but I got a
fine it was again good work but I got a
fine it was again good work but I got a degree in math math to me is is
degree in math math to me is is
degree in math math to me is is beautiful take this agave plant from
beautiful take this agave plant from
beautiful take this agave plant from Tucson where I live it represents to me
Tucson where I live it represents to me
Tucson where I live it represents to me a fractal a fractal is a geometric scuse
a fractal a fractal is a geometric scuse
a fractal a fractal is a geometric scuse me
me
me geometry shape that repeats itself and I
geometry shape that repeats itself and I
geometry shape that repeats itself and I know it’s not a perfect fractal so I
know it’s not a perfect fractal so I
know it’s not a perfect fractal so I don’t want my undergrad advisor to come
don’t want my undergrad advisor to come
don’t want my undergrad advisor to come yelling at me but it’s still beautiful
yelling at me but it’s still beautiful
yelling at me but it’s still beautiful math is also certain two plus two equals
math is also certain two plus two equals
math is also certain two plus two equals four every day it never changes this is
four every day it never changes this is
four every day it never changes this is what I love about math so here I am
what I love about math so here I am
what I love about math so here I am putting information into spreadsheets no
putting information into spreadsheets no
putting information into spreadsheets no big deal and again like I said it worked
big deal and again like I said it worked
big deal and again like I said it worked it wasn’t until our system went through
it wasn’t until our system went through
it wasn’t until our system went through in a complete conversion that it had a
in a complete conversion that it had a
in a complete conversion that it had a difference in perspective and when I say
difference in perspective and when I say
difference in perspective and when I say a system conversion I’m talking about a
a system conversion I’m talking about a
a system conversion I’m talking about a legacy mainframe to a PeopleSoft system
legacy mainframe to a PeopleSoft system
legacy mainframe to a PeopleSoft system and if you don’t know what that means
and if you don’t know what that means
and if you don’t know what that means it’s essentially switching from like
it’s essentially switching from like
it’s essentially switching from like Samsung to iPhone or back and forth it’s
Samsung to iPhone or back and forth it’s
Samsung to iPhone or back and forth it’s a completely different way of pulling
a completely different way of pulling
a completely different way of pulling out your data out of this big black box
out your data out of this big black box
out your data out of this big black box I didn’t get drugged into a ton of
I didn’t get drugged into a ton of
I didn’t get drugged into a ton of meetings cuz I had only been there a
meetings cuz I had only been there a
meetings cuz I had only been there a couple years but I did know enough about
couple years but I did know enough about
couple years but I did know enough about critical information that needed to be
critical information that needed to be
critical information that needed to be reported to the state and federal
reported to the state and federal
reported to the state and federal government so in our staff meetings we
government so in our staff meetings we
government so in our staff meetings we would talk about how we’re going to
would talk about how we’re going to
would talk about how we’re going to report in this new world one of the
report in this new world one of the
report in this new world one of the critical pieces of information in higher
critical pieces of information in higher
critical pieces of information in higher education is whether or not a student is
education is whether or not a student is
education is whether or not a student is a new first-year student freshman if you
a new first-year student freshman if you
a new first-year student freshman if you will we didn’t have a way of recording
will we didn’t have a way of recording
will we didn’t have a way of recording this or reporting this in the new system
this or reporting this in the new system
this or reporting this in the new system just yet so somebody had to spend the
just yet so somebody had to spend the
just yet so somebody had to spend the time figuring out how to identify these
time figuring out how to identify these
time figuring out how to identify these students I was ready for a new challenge
students I was ready for a new challenge
students I was ready for a new challenge let’s try this out so I approached my
let’s try this out so I approached my
let’s try this out so I approached my boss and mentor and asked if I could
boss and mentor and asked if I could
boss and mentor and asked if I could give it a go and she said go for it so I
give it a go and she said go for it so I
give it a go and she said go for it so I met with my colleagues Bob and Harry and
met with my colleagues Bob and Harry and
met with my colleagues Bob and Harry and asked them questions
asked them questions
asked them questions how does this work in the old system how
how does this work in the old system how
how does this work in the old system how do you think it’s gonna work in the new
do you think it’s gonna work in the new
do you think it’s gonna work in the new system
system
system I spent time face to face with these
I spent time face to face with these
I spent time face to face with these people really trying to understand how
people really trying to understand how
people really trying to understand how the data worked and get that knowledge
the data worked and get that knowledge
the data worked and get that knowledge from them in a way it was collecting
from them in a way it was collecting
from them in a way it was collecting data myself in a different way then I
data myself in a different way then I
data myself in a different way then I had to meet with different departments
had to meet with different departments
had to meet with different departments because every department also cared
because every department also cared
because every department also cared about how this data went into this big
about how this data went into this big
about how this data went into this big black box or they cared about how it
black box or they cared about how it
black box or they cared about how it came out so what I learned in this
came out so what I learned in this
came out so what I learned in this portion of data analysis path is that I
portion of data analysis path is that I
portion of data analysis path is that I had to spend time with people to really
had to spend time with people to really
had to spend time with people to really understand the data process next I had
understand the data process next I had
understand the data process next I had to spend time with the data itself
to spend time with the data itself
to spend time with the data itself because as much as I’d love to say that
because as much as I’d love to say that
because as much as I’d love to say that data goes into a system and it comes out
data goes into a system and it comes out
data goes into a system and it comes out perfect every time that’s
perfect every time that’s
perfect every time that’s not true there’s tons of errors and
not true there’s tons of errors and
not true there’s tons of errors and quirks and exceptions and timing
quirks and exceptions and timing
quirks and exceptions and timing differences so I had to spend time with
differences so I had to spend time with
differences so I had to spend time with the data to really understand how it
the data to really understand how it
the data to really understand how it works
works
works so now let’s talk about bias this little
so now let’s talk about bias this little
so now let’s talk about bias this little boy is my seven-year-old son he has what
boy is my seven-year-old son he has what
boy is my seven-year-old son he has what I like to call condiment bias
I like to call condiment bias
I like to call condiment bias he has never tried mayonnaise but if he
he has never tried mayonnaise but if he
he has never tried mayonnaise but if he has to put it on a knife and put it on a
has to put it on a knife and put it on a
has to put it on a knife and put it on a sandwich to help me make lunches for the
sandwich to help me make lunches for the
sandwich to help me make lunches for the week for his dad he holds it as far away
week for his dad he holds it as far away
week for his dad he holds it as far away as he possibly can
as he possibly can
as he possibly can he’s never tried mayonnaise my daughter
he’s never tried mayonnaise my daughter
he’s never tried mayonnaise my daughter on the other hand will only eat ketchup
on the other hand will only eat ketchup
on the other hand will only eat ketchup she has a ketchup bias so the bias that
she has a ketchup bias so the bias that
she has a ketchup bias so the bias that I brought whenever I was doing data
I brought whenever I was doing data
I brought whenever I was doing data analysis for a couple the first is that
analysis for a couple the first is that
analysis for a couple the first is that if something happened in a prior report
if something happened in a prior report
if something happened in a prior report in the prior term in the prior year even
in the prior term in the prior year even
in the prior term in the prior year even the prior week I would assume that I
the prior week I would assume that I
the prior week I would assume that I already analyzed why that happened I’m
already analyzed why that happened I’m
already analyzed why that happened I’m not gonna worry about it anymore and
not gonna worry about it anymore and
not gonna worry about it anymore and moving on you make a lot of mistakes you
moving on you make a lot of mistakes you
moving on you make a lot of mistakes you miss a lot of crucial evidence when you
miss a lot of crucial evidence when you
miss a lot of crucial evidence when you do that the second thing is when I was a
do that the second thing is when I was a
do that the second thing is when I was a newer analyst especially I was raised to
newer analyst especially I was raised to
newer analyst especially I was raised to make yourself irreplaceable the only way
make yourself irreplaceable the only way
make yourself irreplaceable the only way you’re gonna keep a job especially in
you’re gonna keep a job especially in
you’re gonna keep a job especially in that job climate that I was in was if
that job climate that I was in was if
that job climate that I was in was if you couldn’t be replaced my perception
you couldn’t be replaced my perception
you couldn’t be replaced my perception was that if I looked like I could move
was that if I looked like I could move
was that if I looked like I could move fast do a lot quickly and I knew what I
fast do a lot quickly and I knew what I
fast do a lot quickly and I knew what I was talking about even though I didn’t I
was talking about even though I didn’t I
was talking about even though I didn’t I would not be replaced so when I needed
would not be replaced so when I needed
would not be replaced so when I needed to spend time face to face with people
to spend time face to face with people
to spend time face to face with people and ask questions I was very hesitant I
and ask questions I was very hesitant I
and ask questions I was very hesitant I kept to myself or I did as much research
kept to myself or I did as much research
kept to myself or I did as much research as I could on my own when all that
as I could on my own when all that
as I could on my own when all that knowledge was in the cube right next to
knowledge was in the cube right next to
knowledge was in the cube right next to me but I didn’t want to look like I
me but I didn’t want to look like I
me but I didn’t want to look like I could be replaced
could be replaced
could be replaced I also made really dumb mistakes because
I also made really dumb mistakes because
I also made really dumb mistakes because I was rushing through processes and
I was rushing through processes and
I was rushing through processes and reports dumb mistakes like using a
reports dumb mistakes like using a
reports dumb mistakes like using a greater than instead of a greater than
greater than instead of a greater than
greater than instead of a greater than or equal to but some people may say so
or equal to but some people may say so
or equal to but some people may say so what well it costs us time the company
what well it costs us time the company
what well it costs us time the company time me time other people’s time trying
time me time other people’s time trying
time me time other people’s time trying to fix this error trying to explain the
to fix this error trying to explain the
to fix this error trying to explain the same
same
same and it costs a little bit of your
and it costs a little bit of your
and it costs a little bit of your reputation rushing through and instead
reputation rushing through and instead
reputation rushing through and instead of spending ten more minutes on this
of spending ten more minutes on this
of spending ten more minutes on this side cost me three more hours on this
side cost me three more hours on this
side cost me three more hours on this side and a little bit of trust so after
side and a little bit of trust so after
side and a little bit of trust so after you know seven eight years and data
you know seven eight years and data
you know seven eight years and data analysis I started learning new patterns
analysis I started learning new patterns
analysis I started learning new patterns learning new things to try based on what
learning new things to try based on what
learning new things to try based on what I’d learned from other people and from
I’d learned from other people and from
I’d learned from other people and from my own experiences so instead of just
my own experiences so instead of just
my own experiences so instead of just diving into a project I take a 30,000
diving into a project I take a 30,000
diving into a project I take a 30,000 foot view I bother people I look at the
foot view I bother people I look at the
foot view I bother people I look at the data and I ask questions I look for
data and I ask questions I look for
data and I ask questions I look for patterns and then I start slicing and
patterns and then I start slicing and
patterns and then I start slicing and dicing so it’s at this point in my
dicing so it’s at this point in my
dicing so it’s at this point in my career that I thought I had a pretty
career that I thought I had a pretty
career that I thought I had a pretty clear path I know what’s going on I know
clear path I know what’s going on I know
clear path I know what’s going on I know where I’m going and I’m pretty good at
where I’m going and I’m pretty good at
where I’m going and I’m pretty good at this data analysis thing well two years
this data analysis thing well two years
this data analysis thing well two years ago my husband received this
ago my husband received this
ago my husband received this unbelievable offer and my family and I
unbelievable offer and my family and I
unbelievable offer and my family and I picked up and moved from Columbus Ohio
picked up and moved from Columbus Ohio
picked up and moved from Columbus Ohio to Tucson Arizona it was a big change
to Tucson Arizona it was a big change
to Tucson Arizona it was a big change but it was an exciting change and we
but it was an exciting change and we
but it was an exciting change and we said to ourselves you know what if we’re
said to ourselves you know what if we’re
said to ourselves you know what if we’re gonna do this we’re gonna lay the chips
gonna do this we’re gonna lay the chips
gonna do this we’re gonna lay the chips down where we want I wanted to go back
down where we want I wanted to go back
down where we want I wanted to go back to grad school so why not I’ll go back
to grad school so why not I’ll go back
to grad school so why not I’ll go back to grad school I’ll work part-time as an
to grad school I’ll work part-time as an
to grad school I’ll work part-time as an analyst well I was going from full-time
analyst well I was going from full-time
analyst well I was going from full-time worker and parent and juggling a family
worker and parent and juggling a family
worker and parent and juggling a family and home to working part-time from home
and home to working part-time from home
and home to working part-time from home it’s very different it’s good but it’s
it’s very different it’s good but it’s
it’s very different it’s good but it’s different also a full-time student in an
different also a full-time student in an
different also a full-time student in an online program and still a parent and
online program and still a parent and
online program and still a parent and mom oh and our family and friends were
mom oh and our family and friends were
mom oh and our family and friends were all 1,700 miles away in Pennsylvania
all 1,700 miles away in Pennsylvania
all 1,700 miles away in Pennsylvania Ohio oh and I was going into a different
Ohio oh and I was going into a different
Ohio oh and I was going into a different field completely I was going into a
field completely I was going into a
field completely I was going into a master of learning technologies program
master of learning technologies program
master of learning technologies program something that had always been a passion
something that had always been a passion
something that had always been a passion of mine but I just didn’t make the time
of mine but I just didn’t make the time
of mine but I just didn’t make the time for my first semester in this master of
for my first semester in this master of
for my first semester in this master of long learning technologies program I got
long learning technologies program I got
long learning technologies program I got to work with some excellent people the
to work with some excellent people the
to work with some excellent people the way that we introduced and met each
way that we introduced and met each
way that we introduced and met each other was a little
other was a little
other was a little fun though it was through group work
fun though it was through group work
fun though it was through group work everybody loves group work we did a
everybody loves group work we did a
everybody loves group work we did a project on evaluating learning
project on evaluating learning
project on evaluating learning management tool we formed team tank
management tool we formed team tank
management tool we formed team tank which is Tim Nunn myself
which is Tim Nunn myself
which is Tim Nunn myself Natalie Gunter and Karen North and when
Natalie Gunter and Karen North and when
Natalie Gunter and Karen North and when our project was completed we spoke with
our project was completed we spoke with
our project was completed we spoke with dr. ana paula crea who was our professor
dr. ana paula crea who was our professor
dr. ana paula crea who was our professor for the class and she suggested that we
for the class and she suggested that we
for the class and she suggested that we present our project at a conference well
present our project at a conference well
present our project at a conference well I had never been to a conference let
I had never been to a conference let
I had never been to a conference let alone presented at one but this sounded
alone presented at one but this sounded
alone presented at one but this sounded like okay if they’re gonna be with me I
like okay if they’re gonna be with me I
like okay if they’re gonna be with me I can do this I immediately became a
can do this I immediately became a
can do this I immediately became a conference junky I wanted to attend and
conference junky I wanted to attend and
conference junky I wanted to attend and present and just be a part of this I
present and just be a part of this I
present and just be a part of this I wanted to gather data and it be face to
wanted to gather data and it be face to
wanted to gather data and it be face to face with more people I wanted to learn
face with more people I wanted to learn
face with more people I wanted to learn more experiences and then I wanted to
more experiences and then I wanted to
more experiences and then I wanted to share what I had to say because after
share what I had to say because after
share what I had to say because after you present people come up to you
you present people come up to you
you present people come up to you because they have questions and they
because they have questions and they
because they have questions and they want to know it’s exciting at that same
want to know it’s exciting at that same
want to know it’s exciting at that same conference I met a lady named Katie
conference I met a lady named Katie
conference I met a lady named Katie Stroud Katie Stroud likes to talk about
Stroud Katie Stroud likes to talk about
Stroud Katie Stroud likes to talk about the power of story and like any human
the power of story and like any human
the power of story and like any human being when I heard her information I
being when I heard her information I
being when I heard her information I related it to myself and I thought
related it to myself and I thought
related it to myself and I thought that’s what a data analyst is really
that’s what a data analyst is really
that’s what a data analyst is really you’re taking information from this big
you’re taking information from this big
you’re taking information from this big black box and you’re telling a story
black box and you’re telling a story
black box and you’re telling a story with it based on a question you may be
with it based on a question you may be
with it based on a question you may be asked or something that you want to
asked or something that you want to
asked or something that you want to share
share
share I also learned at these conferences and
I also learned at these conferences and
I also learned at these conferences and in this new world that instructional
in this new world that instructional
in this new world that instructional designers teachers and educators really
designers teachers and educators really
designers teachers and educators really don’t have a lot of information or data
don’t have a lot of information or data
don’t have a lot of information or data when they’re trying to make an
when they’re trying to make an
when they’re trying to make an assessment of the success of their class
assessment of the success of their class
assessment of the success of their class or a tool they’re trying to implement or
or a tool they’re trying to implement or
or a tool they’re trying to implement or the teacher themselves they really have
the teacher themselves they really have
the teacher themselves they really have a hard time measuring that qualitative
a hard time measuring that qualitative
a hard time measuring that qualitative data I really began to appreciate the
data I really began to appreciate the
data I really began to appreciate the fact that I had these massive data
fact that I had these massive data
fact that I had these massive data points in my comfort zone job and then I
points in my comfort zone job and then I
points in my comfort zone job and then I started thinking that maybe data
started thinking that maybe data
started thinking that maybe data analysis is something that we should all
analysis is something that we should all
analysis is something that we should all start to embrace we should all start to
start to embrace we should all start to
start to embrace we should all start to try and figure out where we can pull
try and figure out where we can pull
try and figure out where we can pull this information from at another
this information from at another
this information from at another conference that was more academic based
conference that was more academic based
conference that was more academic based I met a lot of graduate students and
I met a lot of graduate students and
I met a lot of graduate students and professors that were presenting on their
professors that were presenting on their
professors that were presenting on their research remember that I’m new to the
research remember that I’m new to the
research remember that I’m new to the world of graduate school so when I would
world of graduate school so when I would
world of graduate school so when I would listen to their presentation afterwards
listen to their presentation afterwards
listen to their presentation afterwards I would say things like how it’s
I would say things like how it’s
I would say things like how it’s wonderful so what’s next what are you
wonderful so what’s next what are you
wonderful so what’s next what are you gonna do with this
gonna do with this
gonna do with this everybody’s response was to get
everybody’s response was to get
everybody’s response was to get published and I thought well that that’s
published and I thought well that that’s
published and I thought well that that’s awesome let’s do this get published
awesome let’s do this get published
awesome let’s do this get published thing but what else are you going to do
thing but what else are you going to do
thing but what else are you going to do with it what problem are you trying to
with it what problem are you trying to
with it what problem are you trying to solve who are you trying to help what
solve who are you trying to help what
solve who are you trying to help what are you going to look into next and I
are you going to look into next and I
are you going to look into next and I realized I was coming across a bit of a
realized I was coming across a bit of a
realized I was coming across a bit of a aggressive and backed off and just
aggressive and backed off and just
aggressive and backed off and just stopped asking and started listening one
stopped asking and started listening one
stopped asking and started listening one presentation by a dr. Thomas Reeves
presentation by a dr. Thomas Reeves
presentation by a dr. Thomas Reeves started talking about a rift between
started talking about a rift between
started talking about a rift between educational practice and academic
educational practice and academic
educational practice and academic research the teachers feel like
research the teachers feel like
research the teachers feel like researchers aren’t looking into the
researchers aren’t looking into the
researchers aren’t looking into the things that they need or sharing
things that they need or sharing
things that they need or sharing information with them and then
information with them and then
information with them and then researchers feel like they provide all
researchers feel like they provide all
researchers feel like they provide all this information and do all this
this information and do all this
this information and do all this research but it’s not applied and that’s
research but it’s not applied and that’s
research but it’s not applied and that’s when I realized that I have a different
when I realized that I have a different
when I realized that I have a different perspective I’d been a data analyst all
perspective I’d been a data analyst all
perspective I’d been a data analyst all this time and these pressures from
this time and these pressures from
this time and these pressures from graduate school are completely different
graduate school are completely different
graduate school are completely different and I thought again there’s a gap here
and I thought again there’s a gap here
and I thought again there’s a gap here this is data analysis this is one system
this is data analysis this is one system
this is data analysis this is one system in one system and nothing’s happening in
in one system and nothing’s happening in
in one system and nothing’s happening in between this is where I started to think
between this is where I started to think
between this is where I started to think there’s purpose in data analysis to
there’s purpose in data analysis to
there’s purpose in data analysis to bring these two people together to bring
bring these two people together to bring
bring these two people together to bring these groups together to find a way to
these groups together to find a way to
these groups together to find a way to share the information so when you think
share the information so when you think
share the information so when you think about data analysis think about spending
about data analysis think about spending
about data analysis think about spending time with people think about asking
time with people think about asking
time with people think about asking questions thinking about lifting the
questions thinking about lifting the
questions thinking about lifting the heavy rock before the presentation look
heavy rock before the presentation look
heavy rock before the presentation look underneath things really appreciate the
underneath things really appreciate the
underneath things really appreciate the time that can be spent and the data
time that can be spent and the data
time that can be spent and the data that’s out there data analysis is as
that’s out there data analysis is as
that’s out there data analysis is as much an art as it is a science anyone
much an art as it is a science anyone
much an art as it is a science anyone from any background can really
from any background can really
from any background can really appreciate its beauty
appreciate its beauty
appreciate its beauty and anyone from any background can
and anyone from any background can
and anyone from any background can really appreciate the power it can bring
really appreciate the power it can bring
really appreciate the power it can bring to our relationships thank you
to our relationships thank you
to our relationships thank you [Applause]
”
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