The Analytical Advantage: Bringing Data to Life for Entrepreneurial Growth.

Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Speaker 1 (00:11):
There was a time people counting me out put their
moup me. I'm walk inside out. I got to know
and know what they said.

Speaker 2 (00:26):
But what I believe that God's my bad.

Speaker 1 (00:32):
I don't be you to bother me. I know who
I am.

Speaker 3 (00:39):
I'm created that be, and I'm the reflect what my
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Speaker 1 (00:57):
Even know what us sea shine sixty should I get me?

Speaker 4 (01:15):
There is no time re stad for the outside doll
and the better window that voice the insults still itself
paid delitions of castay, all these simple my beads, bye

(01:36):
something not take your mind for later you'll buy your
pleasing one ball in this play head leave of die.

Speaker 1 (01:48):
All Wait, then that's nine neither struly.

Speaker 2 (02:02):
Welcome, Welcome, welcome. So another segment of the Seek Elevation
Experience with yours truly Attorney alikisha O Kelly. Yes, I
am an attorney, but I may not be your attorney.
So again, if you're new to this channel, please know
that I do give legal gems, but I'm not giving

(02:25):
legal advice because I don't know your situation. But you
do get the information and you are able to be
empowered to go dig deeper. But right here is where
real issues, real people, real conversations. They takes in a
stage because change doesn't happen in silence, growth doesn't. We

(02:47):
need to learn. The more you learn, the more you
can earn, and all the great things. But it's time
for us just to expand our minds. So whether it's
from sports and entertainment to business and community, right here
we elevate those voices that need to be heard and
not just heard for us to connect. There's a lot

(03:08):
of different avenues and platforms out there to just hear
and to just listen. That's not how I want us
to elevate. I want us to hear. I want us
to listen, but I also want us to take action
by connecting, connecting with those that come to the stage,
connecting with those that engage with comments. And there's been

(03:29):
a lot of connections. So it does my heart proud
that we are elevating together. Find your tribe and continue
to elevate. So we don't just talk here. We don't
just talk. We empower, we inspire, and we definitely challenge
the status quo. Things that a lot of conversations, a
lot of times that we leave off the table. I

(03:49):
put them on the table because we have to get
out of doing the same, old same that we have
to learn more. And that's why I expect for us
to do today to learn a little bit more the
analytical advantage when I talk about some of that just
bringing data to life for entrepreneurial growth. My goal is
to always identify those people that move in spaces that

(04:13):
can bring their expert authority in those spaces, but also
individuals who are like holistic people like those spaces. They
don't just define them, they're like real people that can
touch on so many levels. And the guests that I'm
bringing on today is just that can talk about this
error we're about to speak about. But I actually met

(04:35):
this individual I was given in a speaking engagement. He
was speaking, He does that as well, right, So there's
just so many gifts that pours out from our guest today.
So that's why he's one of the ones identify to
bring on here to talk about a topic that a
lot of us are intimidated by. But we cannot be

(04:58):
Data drives dreams, right, We can go by intuition, but
we have to be informed. We have to do more
than our gut. We have to look at numbers, we
have to have to look at information. We can't just
always do that. So that's why I felt this topic

(05:18):
is really important. But also I know we can go
a little deeper as well. So let's we're gonna go
ahead and get into it, and then I'm going to
introduce the guests. And this segment, I know it says
like analytical and data. You think entrepreneurship. Yes, this segment

(05:40):
is for those who are in business who are looking
for answers because sometimes the answer is not out there,
it's hidden in your own data. But this segment is
just for not just founders, but creatives, change makers. Sometimes
the way that we move you are a business within
yourself and what you're doing, and a lot of times

(06:02):
I think we parse that out. We go by labels.
I'm not an entrepreneur if it's not this. No, If
you are creative, if you're in sports and you're entertainment,
you are the service. Sometime you are the product. If
you're a change maker out here in general making changes,

(06:22):
data matters for you. So if you're in any of
those arenas and you're tired of just flying blind, right
and you're ready to make decisions. Again, like I just
said earlier, that's back more by insight than instinct, because
you want to see change. This is a segment for
you right here, And if you know that applies to

(06:43):
someone else, please share, please share. I know spreadsheet scare
some people and analytics feel like foreign language, but again
I identify someone where we can bring those things to
life today, and that's someone is Mychael Cortes. I just
want to talk a little bit about Michael Cortz. He's

(07:06):
the research and data analysts at Morehouse Innovation of Entrepreneurship Center.
He leads impactful data driven research to evaluate programs success
and he also fused strategic growth. He has a keen

(07:28):
eye on uncovering trends and opportunities. And that's why I
said it's important too when you look at whoever you're
looking towards to help you in a particular area, look
at what they bring to it.

Speaker 5 (07:42):
Right.

Speaker 2 (07:42):
So like even myself, yeah, may say attorney, but I
bring a holistic, a whole life, whole life experience to
that being an attorney. So I have a different eye,
I have a different mindset when I am uncovering, discovering,
protecting same thing with. Michael has a keen eye for
covering trends and opportunities because he understands that, and he

(08:03):
turns raw data into clear, actionable insights that supports the
entrepreneurial journey, but just journeys in general. Individuals are looking
for answers that can be tied to data. But he
does this especially for diverse business owners that are seeking
sustainable success. And that should be the key right there,

(08:26):
seeking sustainable success. A lot of us have successes sustainability,
though a lot of people do not have, and this
is one of the areas where we can get the
answers to sustainability. So Michael, as a true servant leader

(08:47):
that he is, believes that collaboration and adaptability are key
to lasting impact. And I wholeheartedly agree with that. So
whether he is speaking in a room full of founders
or working behind the scenes with stakeholders, his goal remains

(09:08):
the same, to empower all of you, to empower others
with insights that drive results and create real world change.
And I say that's what we're on here to do,
to elevate, to seek to create real world change. This
is why identify Michael. So with no further ado, I
am going to bring Michael to the stage.

Speaker 5 (09:33):
Hey, Lakeisha, how you doing.

Speaker 2 (09:35):
I'm good, how are you?

Speaker 5 (09:37):
Everything is good, Everything is beautiful.

Speaker 2 (09:40):
Also, And I forgot to tell him and the introduction
that when I said Michael is a speaker, obviously you
can hear from the amazing voice. He could do voiceovers,
do it all like just amazing. That is one of
his One of his gifts is the gift of voice. Literally.

(10:01):
So it's good to see you. It's been a while here.

Speaker 5 (10:07):
It has been admitted. You know, a lot of things
have changed since we last seen each other, but always
for the better. And I'm glad to see you are
constantly on your elevation.

Speaker 2 (10:17):
Ah, you know it. That's what we do constantly, This
essence the fabric of who I am. And we're going
to do this. And I'm even more honored and thrilled
to have connected with individuals like yourself that still is
always on your journey of elevating, but not just elevating,
elevating others in just different forms fashion in ways. So

(10:40):
I appreciate you and honor your light as well. Sure right,
so let's let's we're gonna get into it. But I'm
not gonna get all the way, jump right into it,
but I want to just just tell them a little
bit about yourself. I know I did an introduction, but
what I like for individuals like I'm mentioned in the
intro is that yes, people have their specialties and we

(11:02):
talk about different things, but people are still people. There's
a life, there's a relatability in those individuals underneath right
the surface of those specialties and why things become important
to them and why they choose several paths not just one.
I know I have several branches to pour back in,
So tell them a little bit about.

Speaker 5 (11:24):
Who you are, well, just around the way type of guy.
Came out of from the Bay Area, from the Bay
to the A in twenty eighteen and attended Morehouse College.
Upon attending Morehouse College, I saw myself immerged within the
ecosystem of entrepreneurs mostly who looked like me, who had

(11:47):
stood the test of time and matched hard times with
the resilience and eventually overcame, whether that was through civic engagement,
public policy, or entrepreneurship. And from there I had seen
what I exactly came to see. When I moved to Atlanta,
I wanted to move to a place where I would

(12:08):
be genuinely supported because somebody can't relate to my unique journey,
not because somebody was feeling guilty or felt like it
was their duty. But hey, you know, affirmative action. This
affirmative action, that's all good. But I came out here
to be genuinely supported, and I felt that that was
the decision, the distinct factor that was necessary for my

(12:30):
journey to learn about business entrepreneurship and support those who
want to learn the same with me. So upon moving
out here and attending Morehouse, I attended the Morehouse Business
Association meeting, and I was put in the right place
at the right time. Upon with ignorance at that I

(12:52):
did not know the magnitude of the place in which
I was standing in, or who the man was who
had erected it and his mind, mental presence and impact
upon the city of Atlanta and the civil rights movement.
But I was immersed into the Russell Innovation Center for
Entrepreneurs before it became publicly large as it is now

(13:12):
here in Atlanta, and it was hard hats and still
tow boots, still on the ground, the basement floor and
the second floor from what you entered was much unconstructed.
The third floor was mostly constructed and being rented out
to kid at the time. So myself, with ninety nine
other students, we had heard the CEO then will Still

(13:35):
Jay Bailey speak and tell us about the mission of
the Russell Center, And it was like music to my ears.
I said, Hey, this is what I moved out here for.
But out of one hundred, I was the only one
to approach him and say, how can I be a
part of this journey. I guess you could say my
peers were unimpressed with something still being constructed. But me,

(13:57):
I'm from the ground. I'm gonna work with you from
the bottom I could. I have vision. Therefore I can
see vision where visionaries speak, when entrepreneurs say this is
where we're going, when they're chart when they're sailing uncharted waters,
or when they're going throughout unpaved forests looking to make
highways that will create opportunities for more. So I spoke

(14:21):
to Jay and he told me to speak to his EA.
Then at the time, Chris Hill and I became the
first fellow and intern. And again I want to remind
viewers that I did not know where I was standing.
I have not heard of herm and Jerome Russell. Until
I moved to Atlanta. Out of the Bay, we hear

(14:42):
a lot about the Black Panthers, Eldridge Cleaver and whatnot,
of course, and of course Angela Davison and others, because Oakland,
California was the birthplace of the Black Panthers, But not
often do we get to hear about Southern civil rights leaders,
And not often we talked about civil rights leaders who
were also multi millionaires building most of Atlanta skylines. And

(15:06):
sometimes I think that's by design, if I mean being
fully transparent, more than likely is by design to not inform,
not informed marginalized communities about those are who come from
their communities and who have overcome systemic racism through its

(15:26):
capitalistic ways and became a conqueror, a champion up that
and used those finances and strategy and network that they
have to create more opportunities for more. So I guess
you could say ignorance was bliss in that moment, because
from there I had met so many c suite executives, founders,

(15:47):
global heads of organizations like Google, Microsoft, even over at
Disney I met Les Brown there as well, and I
met actors, I met athletes. I met some many people
just coming into the building, and more importantly, I learned
about j Russell's legacy, and that right there was most

(16:10):
important because for our children, for our people, for anybody,
because everybody needs heroes, no matter what community you come from.
I'd seen another possibility of self, which I had imagined
before I had moved to Atlanta, and now I was
seeing it in real time.

Speaker 2 (16:31):
That's beautiful, and that's why, that's why it's important for
I want people to tell their story and who they are,
because I won't even just back up to what you
had said. There's several things to capture in there, but
just being at the Russell Center, and I remember too,
I also grace that building. What was just cement floors.
It was nothing, It was just a vision, and I

(16:53):
took a tour there. We visited it with the Atlanta
Black Chambers at that time, many years ago, but they
seen that it was going to be something huge. I
know exactly what you're talking about. But while you was
there with other individuals, you did something you didn't You're
not a follow. You didn't follow. You said, I'm stepping up.
I have a question I need to ask and I'm
going to ask it. Also, what you stated was you

(17:16):
have vision right when things are You're not waiting for
the cream of the crop. You're not waiting for things
to be laid out. You said, I know there is
something here, and I'm a person who is a visionary
who can identifify with other visionaries as well. So you
completely moved on that. And then the other thing is, listen,

(17:38):
you're not just existing, You're trying to You're not trying,
that's the wrong word. You are striving to make sure
you operate in the highest energy of yourself because you
begin to learn and you recognized in oh, this is
siloed here and we don't do this, and you learn.
And so that's why I like people to say their background,
because when you go to people to help you within

(18:00):
the area, understand the essence of that person. There's one
thing I wholeheartedly disagree with, and this is probably unpopular opinion,
people say personal and business they're not the same a
personal life. I'm a business life who you are personally.
I strongly believe you bring that essence to your business.

(18:20):
At the end of the day, you can't if you're
someone who is this enlightened driven individual. It's gonna carry
over and it's gonna show and what you do business wise.
If you're someone that's not You're lazy, You're this, You're
you're quick, you're schemish type of person, it's gonna carry
over to you. Don't parse, It's who we are. And

(18:41):
so that's why I like people to get to know
who the person is that I bring to the stage
because connection and energy of connection is important, beautiful, So
thank you for that. First of all, I did a
lot of studying with the I love. I'm not a historian,
but I think I'm an honorary one because I am

(19:05):
a junkie for two things, documentaries and just history. History
and the more I learned about it, history is the
present and if we want to be different in the future,
it's got to change. I literally read, watch and study
things that I can switch the date out and you
wouldn't know if we were at that date or if

(19:25):
we weren't twenty twenty five. I think if a lot
of a study, history would change. But all right, so
so from curiosity to calling, So you did that, you
you made those connections at the Russell Center, and then
what kind of more from there? Let's go back more houseman,
And I don't know if a lot of people even
understand that one, but you know your research on that one.

(19:48):
So what did that all look like? So you now
in Atlanta and then you know you want an entrepreneurial journey,
you're learning some things. How did that continue to kind
of morph towards what you're doing now?

Speaker 5 (20:00):
Yeah? So when I was a junior at Morehouse, I
had became very good friends with a small business development
consultant at Morehouse, and he was like, Mike, what are
you going to do when you graduate? And I said, Man,
I don't want to go to the traditional psychology route
where I got to go get the masters, then the

(20:21):
doctor just to help people. I said, that's that's not
my goal. Like, why do I have to go into
debt to serve humanity? I don't believe that. I believe
that at any moment, anybody can serve humanity, whether that's
seeing somebody on the bus or the train or wherever
you're at and you just give them a smile and
that brightens their day. So but I truly didn't believe

(20:43):
that I had to do all that. So I said,
will I also have to be practical. How am I
going to provide myself with sufficient income post graduation so
that way I can start thinking about upward mobility for
myself and not be in the same place. Right, And
he said, Mike, and brother, brother name is Terrence Strong.
Great brother by the way. He said, Mike, you ever

(21:05):
thought about being a data analyst? And I said, no,
what's what's that? He said, You're already a data analyst.
I said, how He said, don't you do statistics in psychology?
I said yeah. He said, all that behavioral data that
you're looking at, that you're doing white that you're writing
white papers on and whatnot, that's data analytics. I said,
how come nobody's talking to us about this and in

(21:27):
the psych department like that? I think, now I'm gonna
be fair. I think there was maybe one or two
people who came and spoke about it, But the messenger
is just as important as the message, just to be
so honest. So so he came spoke to me about it,
and I guess to start ringing more bills. And I
was a junior at the time, so I was like,
all right, let's let's get a roll on. So from

(21:48):
there I had I didn't go home, uh for a
winter break, so I had just every day maybe like
four hours each. I took this Coursera program teaching me
about data analytics with Excel and Tableau. So I had
did that through Duke University and learned a whole lot,

(22:10):
and I said, okay, I could see myself doing this
and decided to take it up a step further. Post graduation,
I joined a nonprofit called the Knowledge House that helps
low income individuals learn in demand tech skills, and I
learned data science. So they had laced us with skills
in Python, a bit of power Bi in Tableau, and

(22:36):
then we start doing a whole lot of analytics on
like what skills are needed to really make it in
the data analytics marketplace, And it was like you didn't
need to know Python that much, but it was always
a nice to have, but SQL and Excel and just
a data visual data visualization tool like Tableau or power
Bi in your set. I mean I had the statistics

(22:59):
background already, so already knew how to formulate questions that
would lead to the insights that we needed. So that
was great for psychology. And then I had the stats,
so it was just about all right, Michael, Now you
got your batman belt on what part of Gotham you're
going to say, You're gonna fight the Joker, You're gonna
fight the snowman bang, you know, try not to get

(23:20):
your back cracked, you know what you're gonna do. So
from there, I started posting about my journey and I
had a couple interviews just from like people reaching out
to me on LinkedIn. I think that's one of the
things that people underestimate about LinkedIn is it's great to
show that you've been outside networking, but it's networking is

(23:43):
capitalistic in its nature, and that's no problem. It's just
that you're networking to learn about how you can make
money with somebody or get a connection to make some
more money, right, plain and simple. That's networking. So that
is what LinkedIn is for as well. So I started
posting my skills, saying, look what I built, Look what

(24:05):
I did. Here's some problems I'm solving, and then hiring
managers start reaching out to me and said, hey, we
want to interview you. And I got interviews and that's
how I actually landed my job with the m E C.
So doctor bussy, she said, Mike you used to work
with for us when you were a student. You're very
well immersed in the entrepreneurial community. A lot of people

(24:25):
know you, and now you've got data analytics skills. Would
you like to be a data analyst here? So I
jumped at the opportunity, and yeah, it had helped open
up my my mind more about qualitative analytics and turning
that qualitative to quantitative and vice versa, and doing that
for entrepreneurial programs.

Speaker 2 (24:46):
I love it. So let's back up. I know y'all, listen,
we're gonna get to the data park. There's there's gems
in the story, right there's when you come here again.
You wanna get more than just that title. Let's back up. Okay,
So one, I agree with you, right because my undergrad
is psychology and sociology. I never thought about statistics data

(25:07):
like I parsed different, like you think data and this
technology thing. But in order to get the statistical information,
you have to collect data. So you're right about that.
Put that together. That was a light bulb moment right there.
But I want to go back you when you decided
to start segueing in and spanding, you said that there
was a program that you were able to be a

(25:29):
part of. Are those programs out there for those who
may be seeking, you know, to expand their knowledge, just
like you did in data that may be underserved communities
or whatever. How did you come about learning about that
type of program?

Speaker 5 (25:44):
Again, going back to network, So who was it that
connected me? Was it? I believe it was Texts of Color? Okay,
they're very known within the tech community just period. And
with that I had I believe it was Mark or
John I had spoke to and they had told me

(26:08):
about some fellow named Christian. Could you hear me? I
know it's a little talking.

Speaker 2 (26:12):
Yes, yes, I hear you. You sound good? My thing's
cipping on my ear?

Speaker 5 (26:15):
Okay, great. So yeah, so they connected me with a
guy named Christian Lewis. He had graduated from Georgia Tech.
He was a data scientist over at Rubicon MD and
had helped like get the company from I want to say,
maybe two million to twenty million or something like that,
just in data science alone. So connected with him, had

(26:39):
a conversation asked him about like what is his day
to day look like? He showed me and he said, well,
if you're looking to learn these skills. I'm actually a
mentor part time over at the knowledge House, a volunteer there,
just teaching people about data science and you could apply
for their program. So I did that and timing was

(26:59):
right as they were launching a program in Atlanta, so
they needed people and then I got into the program
and Hey, that was that.

Speaker 2 (27:07):
See that's beautiful. So listen up, you guys. I mean,
if you know people that are interested, especially our youth,
younger individuals who may not have the same opportunities, there
are programs out there for different things. It's about network
and connecting and figuring those out. But also I say
connect with Michael just to get the insight on especially

(27:28):
if you know somebody young, the insight out how you
probably can navigate that and connect with me. That's some
of the things too. There's some things that I got
when I was younger as well, like capling different type
of study programs I had to be a part of
when I was taking ACT and SAT I had at
that time. My coach Slash mentor Slash Brother found that

(27:49):
there was a program with Kapelin for underserved people that
if you write this essay about why they should waive
all the fees or help you you can get in.
It was there back then. It's probably plenty of other
things too, So please do not let what appears to
be lack of opportunity to stop you or those that
you know from moving forward. All right, So you got

(28:10):
in there, That's how you got it. Now you got
this connection. But also one thing that you said is important,
We're not going to bypass it. You always showing up.
People already recognize either from direct engagement or involvement with
you that this is the right fit, or they're watching
how you're showing up on your platforms. I tell people
that all the time, you never know who's watching. So
always show up in the manner in what you want

(28:33):
those opportunities to happen. All right, So now that we
walked all the way up, tell us a little bit
about now when you're working with people now and you
understand data, what is some of your biggest challenges because
you're dealing with who what is the group of people
entrepreneurs are know diverse poop, but do you deal with

(28:54):
some that come from underserved communities or don't learn you
know know much and what are some of the biggest
challenge that you see when you're sharing this information.

Speaker 5 (29:04):
So in the m I e c S program, we
have a mix of folks. Some are entrepreneurs turned business owners.
And what I mean by that is that they have
recurring revenue and millions plus so they're they're not under
the one hundred k mark or they are then you know,

(29:24):
still fine. But the main thing that I and all
of them aren't from like underserved communities too. The m
I e c. We have various programs. Like a lot
of people think just because it's more house, it's just
for black people. That's a fallacy. We have Latin folks,
we got white folks, we got really everybody. It's just

(29:44):
about the industry that you align in. But we primarily
do have black people within the within the as participants
in the programming. So when it comes to conveying the
insights to them, they they don't really get to see
it much. So like let's say if I were doing

(30:05):
a consultation with somebody, which I have done before, and
I was just talking to this entrepreneur like Okay, what
do you know about your data? I was like, okay,
Shopify tells me this about my data. I said, okay,
that you know that's great that Shopify has these built
in metrics for you, but what are you doing outside
of Shopify telling you what your data means and optimizing

(30:26):
on That was like, well, I'm not really doing much.
I'm just letting Shopify take care of it for me.
It's telling my sales volume, any chargebacks. I got. Where
my customers are, I could segment them based on ethnicity, race, age, gender,
geographical location, things of that nature. And I was like, okay,

(30:48):
well you ever thought about segmenting them based on what
times of day they buy? I was like, Nah, I
ain't really think of that. I was like, yeah, if
you maybe if you think about segmenting your customers no
matter where they are in the country, and then you
could start to do one for the entire country then
based off state, but basically on what times the day
that they buy, then you can start to have optimized

(31:11):
or specific marketing plans towards those times in which those
people buy within those states or just the country as
the wholes like I redly think about that. It was like, well, yeah,
if you test, just do a b test, like just
to just choose two of your top performing states of
customers that you serve wait.

Speaker 2 (31:33):
Way back up, what's AB test? What's that mean?

Speaker 5 (31:35):
Yeah? AB test is just like option A option B. Yeah,
and option A could be like the control group, and
then option B would be the experimental group. So then
we're experimenting seeing. Okay, I'm actually going to do a
targeted campaign for those in the B group and see

(31:55):
if they're going to buy more at this certain time
that they've been buying more. And then the control group,
we're not doing nothing with them, We're just seeing what happens.
So with that AB test, then you would see if
there is a true difference in this hypothesis, in the
hypothesis being I believe that customers will buy more based

(32:17):
on the times that they've been buying already. So let
me offer some maybe a ten percent discount if they
buy between now and then, or whatever that may be.
It could be like from the hours of eleven am
to three pm. Let's see and then boom, if you
notice a sale a different in your sales, like, okay,
we look at it at the historical data. Now, boom,

(32:40):
we look at last month, there was no test, but
sales were consistent. Now we look at this month for
the AB test, and we see It could be something
as small as as a fifteen percent increase in sales.
That's still a high number. I don't care where you
at business. If you did a test and you got
a fifteen percent increase in your sales just off of

(33:01):
that test, and it didn't cost you nothing. But just
to look at your data and see what times people
buy and where they're buying from. Hey, you just you
just came up. You take that little fifteen percent, start
putting in other areas of business where it's most needed,
and build from there. But when I suggested that to
the entrepreneur, they are very taken. Aback, I don't know

(33:24):
if they took it. They didn't ask me for any like, hey,
would you could you do this for me? I just
gave them the game and left it at that. But hey,
we'll see.

Speaker 2 (33:36):
So that entrepreneur were speaking to said, I use Shopify,
use this. So when you look at the diverse people
that you actually serve in thinking about that person too.
Is there a stark difference between who's using what, who
understands what? And what is the business misunderstanding or is
it You don't care. You just see all entrepreneurs, no

(33:59):
matter what background they come from seem to be missing
the power of data and they just don't understand are
you seeing it differently? Like what are you seeing?

Speaker 5 (34:09):
Well? Based on what I've been seeing now? Is that
across the board people just don't comprehend data that much. Okay, yeah,
they know how important it is. But AI has got
people in the chokehold right now that it used to
be data was the big thing. But now I was like,
oh AI is ai thatt and I'm like, okay, well,

(34:29):
you need healthy data to create healthy AI. That's just
that's just the proof of It's like how our body
needs healthy food to be healthy or's he gonna be
walking around all day sluggish, tired, anemia, you know, kidding,
stones or whatever.

Speaker 1 (34:45):
Mix it too.

Speaker 2 (34:45):
I'm happy you brought that up. You said you need
healthy data to create healthy AI. So how because first
of all, artificial intelligence has always been here, we're morphing
and we're using it more. Of course, it's getting better
and then it's starting to you know, oversaturate. But how
do you since that is the direction we're going. Good point,

(35:05):
if data is important in whatever we do, how do
you marry the two? How do you now set AI's
here and this is what we need to do. We
don't lose the power of data to AI.

Speaker 5 (35:19):
So you want to ensure that your data is cleansed,
meaning that you take out the null values, that there's duplicates,
you clean that up too, if there are any like
no values, Like all right, let's say I got somebody
that that genuinely I mean not genuinely but buys most
of the time. But now they're missing and I can't

(35:39):
see where they are how much they bought. Then I
could again go back to the historical data and be like, well, bom,
plug that number in there. They usually buy that seven
dollars product. So sometimes you will have to fill in
where there are any no values. Then there could also
be something as small as capitalization within I'm trying to

(36:02):
think of the word again, within the qualitative side of
the data. Because you got your qualitative, you got your quantitative.
A lot of people think that just for data, they
need to go towards the quantitative, and that's not the case.
A qualitative data such as self reports and open ended questions,
I think, tend to be more powerful, especially when engaging

(36:24):
your customers. You want to know what they will honestly
say which is why the platform Reddit is so important
for companies because people will go on Reddit and just
speak their hearts out. It don't matter everybody speaking French, okay,
and they gonna say what's on their mind. Yes. So
then let's say if it's a large corporation and then

(36:46):
there's a Reddit chat on that corporation, I don't know,
home depot, somebody says the shovel suck. Home Depot is
gonna go to that Reddit chat be like, tell us more,
why do our shovels suck? And then that person may
not even though that they're interacting with a home Depot rep.
They're gonna spill the beans. Every time I use it
it breaks, or every time I buy something from here,

(37:10):
it just breaks. It's just dysfunctional. So then they take
that qualitative data, they report it back to the team. Hey,
they're saying that our shovels suck. Let's do some tests
and figure out what's the problem and why it keeps
on breaking. So then they find out they do the test,
they find out why it keeps breaking. Problem solved new
marketing campaign if they saw a decline in sales on

(37:32):
the shovels too, like are new and improved shovels with
extra strength around the breaking point, and people notice that
in the name, I'm gonna try out these new ones.
It don't break cool. We're back where sales are healthy again.
We're back in the black or just on that product.

Speaker 2 (37:48):
So how do we transfer that to services? So if
I'm not selling the product or the service, and I
do my data, like, okay, sometime I collect testimonials, and
I'm starting to do better with this because me, I
just like to do what I do and go. And
then I started saying, you know what, I have all
these people telling these things. It's important to collect it
because other people are searching and seeking and they want

(38:10):
to hear these things to help filter through. So instead
of just testimonials, is there something service wise to ask
instead of saying, hey, just give me your test just
give me a review, m is it something else to
kind of pull data to find out here's a review,
but plus what I need to work on or so.

Speaker 5 (38:28):
One thing you can do is take all those testimonials
and reviews and then you could plug it into an
l l M and then ask for the trend that
it's seeing within that because sometimes it's hard for us
to identify the exact trends that are being.

Speaker 2 (38:42):
Spoken of LLLM go back to me, is that an AI?

Speaker 5 (38:46):
Is that ar?

Speaker 2 (38:46):
Is that just like?

Speaker 5 (38:47):
Yeah, it's like a like a check GBT or Google's Gemini. Okay,
you upload that data into an LLLM. Of course, you
got to anonymize it so that it's not specific to
your business if you want to keep your data anonymized
and then just be like, you know, ex business, here
are the reviews for X business instead of saying, my

(39:10):
business seek elevation. So now you could upload that data
into the LLM and say, find the trends in what
people are saying about this service or this speaking engagement,
and then what it'll do. It will read through it
and say, based on what I have read, the trends
are people want to see more I don't know, I'm

(39:31):
just throwing things out there, but they want to see
more authenticity. They feel like you weren't engaging them on
a level that they're at. They felt like you were
speaking to them maybe condescendingly or whatever it may be,
or they felt like you weren't doing enough interaction with them.
You were just speaking with your hands closed and looking
all tight something like that, and then these are now

(39:54):
data points for you to improve on as a speaker
providing your service or if it's something like we could
say like doing cosmetics things of that nature. And I'm
speaking to the black community now because we have a
lot of cosmetic based products. We are one of the
shiniest people walking the planet. So I was like, all right, cool,

(40:15):
you know what, what what is it that my customers
are really saying about my shade, butters, olds, hair sprays, etcetera, etcetera.
Like some some are saying there's a consistent trend that
boom is too oily, or that there's too much preservatives
or whatever it may be. But you could even ask
it too to give you a percentage aggregate as well,

(40:38):
like say, okay, review the trends, but give me, like,
for example, twenty twenty percent, make it one hundred percent,
but tell me what is twenty percent this, fifty percent
that or whatever, and just do it like that and
then it will tell And then now you're like, okay,
I need to focus more on they on this area
rather than that area. But all areas need improvement.

Speaker 2 (41:01):
So actually you could use it in a flip so
First of all, great, you now explain how you use
AI with data, because now you have AI to help
you cut through some of that work in stend and
manually doing things. You can plug it in, but you
have to know to ask the right question. That's just
in anything. Right answer, right question, get the right answer.

(41:21):
So it's about testing question testing as well to get
the right thing. But it sounds like you could use
that in a reverse and you gave me this idea
by what you just said. You said for anonymity, you
instead of saying, hey, seek elevation, I'm gonna just ask
it like a company. But that can actually happen if
you look into your competitive field. It's sound like you're

(41:43):
saying you can go pull other people's reviews that's within
the field of your interest that you're in, and then
find out what people are saying, are not saying, or
getting or not getting from people within the field or
industries or companies within your industry, and then you become
the one that solves that problem.

Speaker 5 (42:02):
Mm hmm.

Speaker 2 (42:03):
That's what because that's what the first thing I thought
about when you said, I.

Speaker 5 (42:05):
Said, oh so, and what's cool about the ones that
I mentioned chat, GBT and perplexity. Perplexity is more like
research focused as well. So you can upload a link,
let's say too, like a Reddit page, and say, hey,
tell me what with the with the with the crowd
or the group is saying about this company. Tell me

(42:29):
the trends that you're picking up from you. That way,
you don't have to sift through everything and start to
generate it, think about it yourself. And no, no, don't
get me wrong, I'm all about independent thought.

Speaker 2 (42:39):
Wait, go back and say what you said, though I
might have missed it. They picked up. Say okay, you
can do what now?

Speaker 5 (42:44):
Yeah you can. You can take the link like you
just copy and paste the link into the lll MS. Okay,
they read this page and tell me the trends that
you're seeing based on this page, and then it will
tell you what it's reading and those trends based on
that page. And now you can start to think, Okay,
now I could build a whole campaign around what my

(43:07):
competitors aren't doing and say this is what I can do.

Speaker 2 (43:11):
Did y'all hear that? Did y'all just pick that up?

Speaker 4 (43:15):
Like that?

Speaker 2 (43:16):
Just cut through like he just explained, Like you can
even use AI the next level from what I just
explained and just say hey, pick this up and say,
you know, ask it. What are the trends in this
particular area. Also, that's where some individuals, because you know,
I do courses and things, people ask like what do

(43:37):
I do? People even want what you are doing? Sometime
a lot of us are stuck because we believe what
we think people want is what we're spending our time doing.
And it may be that they want it, but not
at that time. There may be other things in real
time that's happening that is actually a need. And you

(43:58):
have a skill and ATA not your gut because your
gut is right. You don't put your gut to the side,
but your gut saying this is a need, which is true.
But data's telling us this is what's needed right now.
I like how you just okay, so that's for service,
that's for products. So why you knowing this? Why do

(44:21):
you think so many entrepreneurs overlook odatato it like a how?

Speaker 5 (44:27):
I think it's mainly because they're they're looking at that
next contract or they're looking at that next sale. And
I don't I don't. I don't blame them for if
you're in business you're in business to make money, just
plain and simple. But the way to make money is
to learn. Like there's a direct correlation between learning skills

(44:50):
and wealth accumulation. The more you learn and the more
skills you have, the more wealth you can have. But
we'd even turn it on a more personal note and
not a capitalistic note. The more you learn about herbs exercises,
breathing techniques, stretching, chiropractice, you know things like you don't

(45:11):
need to know everything, but if you learn just enough
that is specified to you and what your body needs,
what your mind needs, what your spirit, your emotions, everything
else needs, everything becomes wealthier in your life. Health and
wealth are one and the same. But for a lot
of entrepreneurs, they're just trying to get to that next mark.

(45:33):
It's like, the way you get to that next mark
is by either you learn it, or you hire somebody
that knows it, or you hire somebody to learn it,
or you got somebody who's already hired and you pay
for them to learn it.

Speaker 2 (45:47):
Absolutely so that way.

Speaker 5 (45:49):
You can delegate.

Speaker 2 (45:51):
And that's why I say AI can help. That could
be your as system long as you train it, train
it properly. That can help when there is financial constraints
to bring a body, you may have to use artificial intelligence,
but again it has to be done right. It only
can give you what you input back, right, So it's
always testing and learning how to utilize it properly. But

(46:12):
so I want to go back, like you said, it
may be the numbers of the check, but from your
experience and teaching individuals about data and what it can do,
do you see a lot of aha moments like even
I had tonight and if you do, is it possibly
also that they just really there's no connect. It didn't connect.
There's no we hear this word. You know how you

(46:33):
hear words sometime and they just become words. Yeah, but
there's no breath or substance to that word data until
you hear somebody like you. So could it be that
tools that we just never connected?

Speaker 5 (46:45):
I would say yes, because, especially now since AI is
changing a lot of industries and particularly within data, a
lot of data folks when it comes to AI, they're
going to be more AI strategists within the coming years,
and then they'll be doing more of what I'm doing
right now on your podcast, which is explaining how we're

(47:08):
going to leverage this AI and this data and then
putting the AI to work as like a bit of
a surrogate for us, so that it could give us,
give us and the company the outcomes that we're looking for.
So I think when it is contextualized, it paints the
picture for people a whole lot better than just giving
them the content. You know, it's like it's like here's

(47:31):
a sandwich, but here's not how to make a sandwich.
Or here's a fish, but here's not how to fish.
You know, you don't have to be a data endlest
data AI strategic to really know how to use it.
But if you learn from the right person about just
some just a few ideas, you don't need a whole book,
just three ideas on how you could optimize your data.

(47:53):
I think that'll start steering people in the right direction, right.

Speaker 2 (47:57):
And I also think not just the what and the how,
but the why.

Speaker 5 (48:01):
Right.

Speaker 2 (48:01):
So here's the fish, here's how you get the fish,
but this is what the fish is doing for your body.
I think that last part is what you're doing, is
gonna be good, right because that last part, hearing the
why why why? Okay, not just hearing that is important

(48:22):
for you to be successful. That's not the why I'm
talking about, right, people hear that, but the why. It
can answer specific questions that you have. And let me
explain the right like you just did today. And I
think the more that spreads, the more that will click
for us. And we need all the information that we
can get in these times and days on how to

(48:45):
continue to excel this. This is not the time to
be complacent, to be stagnant, to go backwards. It's time
to move forward. And because we have these these tools,
use the things that were already in place, and that's
that's data. So what does success look like for you
when you think of entrepreneurs and they're really using the data,
what do you envision that success looking like when they

(49:08):
get it? And again, you guys, real quick, I'm using
the word entrepreneurs. I hate. I don't want us to disconnect.
Data is important for a lot of questions and answers
you need for you to do whatever it is that
you're absolutely doing. But what does that look like to
you that you envision.

Speaker 5 (49:23):
That we can have a conversation that builds rather than
just one that unilaterally teaches about how to optimize data.
So now we can bounce ideas off of each other
about how we can optimize their data. When having a conversation,
they can help me think about it in ways that
I haven't considered because they have more subject matter expertise

(49:43):
within their industry, and I can help them think about
how to optimize that data further in the ways that
they've already considered or maybe have not based on what
they've presented to me thus far in a consultation or
just conversation.

Speaker 2 (49:59):
So how does that start? How how do you work
to change it? How does that start from what you're
doing and them to completely understand that? So how do
you start that trajectory that chen? So they get data,
they get that from you, they get why it's important.
But I guess I'm asking how do they get to
the point where they can completely understand it in the

(50:20):
area and what they are in which they are providing
products or service and be able to bring that conversation
back to you.

Speaker 5 (50:26):
Yeah, So everything that they do is a data point,
whether it's sending an email, I mean even down to
reading an email and crafting an email, like everything is
a data point within the universe, plain and simple, but
making more microscopic and looking at their business. Every click,

(50:46):
every font, every font, every color, every sales funnel, every
marketing campaign, every video posted, just text posts to everything
is data. Wow, and it could get hard to capture
all of that data and optimize all of that data.

(51:07):
It's like it's like you're at a buffet, but your
plate can only take so much food, right, and that's fine,
and then when you go down to eat, you may
get seconds, but you may not have room for thirds,
So you gotta let it digest after ingesting it, and
then you could go back. And I think that's where
a lot of us are like data it can help

(51:29):
you with consumption, but if you can only eat so
much and digest so much, there's only so much you
can do with that data. So it really turns into
education first, like how we're having on this podcast workshops
speaking with consultants, data professionals, hiring data teams. People to

(51:51):
enjoy the buffet with you, because it shouldn't be all
on the CEO or the CTO or whatever. Like everybody
has to know a bit about technology moving forward. Technology
and media. Really those are the two things that I
see for everyone. You're going to need technology and you're

(52:12):
going to need media because media is going to help
you connect with your community. Technology is going to help
you constantly engage them and help to maintain them. But
they work side by side. They work like a Venn diagram,
and if you're not making them work like a ven diagram,
then you are doing your business and self a disservice.
Because that's the times we are in now. So as

(52:35):
you build out that community and you're sending out these
surveys to them and you're capturing that data from them,
or they're providing reviews and testimonials on your services or products,
what tools are you using, software's, ais whatever to help
you get to the next level? And if you're not

(52:55):
sure about what tools or software is to use, go
ask those who are using them, or even asks the
AI itself. Hey, here's what I'm doing with my business.
I want to take it to the next level. Here's
the data that I'm capturing, and from where what can
I do? Help me create a data action plan? Boom

(53:16):
it will help to give you something based on that.
But particularly if you've been having like you have your
own account with one of the llm's and you've just
been using that same time after time. Now it's learning
based on that same chat that you've been speaking to,
and it will give you more customized recommendations rather than

(53:40):
having to go out to the internet for everything and
then bring it back. Now it's like I know you better.
I'm gonna take what I know out here and what
I know in here and then give you something more unique.

Speaker 2 (53:52):
You brought a good point. Consumption because you know there's
the lessons and the and everything. Because we're going greatly. Also,
it could cause us to consume more than what we're
ever used to.

Speaker 4 (54:06):
Right.

Speaker 2 (54:07):
It becomes so we're in a stage of a stage,
we're in the space of major consumption that it can
become so overwhelming we shut down almost like a computer
like overload and it's like like everybody's crash out, like
we can get everything. So to your point what I'm hearing.

Speaker 4 (54:25):
Then.

Speaker 2 (54:27):
The old school way, the way we were intentional with
writing a vision down. We have plans, we have tasks,
we have goals, we have milestones. Those can be the
things to kind of simplify how we're going to use data,
right like what's important now to execute and to reach
those goals the things that we write we can start

(54:47):
here now I'm gathering data to master and understand these
things right here that help me with these particular goals.
Once you digest all that you have mastered, that you
became full from that, you're hungry again. That's when you
come back with new goals, new things written. And then
now you're using the data and the AI. If I'm

(55:07):
hearing that correctly, right, because that because we consumption. If
consumption is a big thing, which it is, we have
to now use the old school of we're writing things
down and what are you trying to get? Remember, we
say what do you want the next year, three years,
five years? Right? So we start with the we can
right all the way out, but start right here in
the year. What are we doing gather data to accomplish

(55:30):
those things, and then we go on from there two year,
three year, four year. So is that okay?

Speaker 5 (55:36):
Yeah? And we don't want to get consumed with our
own consumption that right there, it can be blinding.

Speaker 2 (55:42):
What does that mean?

Speaker 5 (55:43):
Getting consumed by your own consumption? I'm not just speaking
about data right now, but just anything. If you're constantly ingesting,
what are you digesting? Right? Which is why I brought
up the example of the buffet or the metaphor of
the buffet and your play can only take so much,
but you get full too. It's like, no, don't get

(56:04):
consumed with the consumption. Sit back, take a step, you know,
eat slow if you need to.

Speaker 2 (56:11):
And like jebrah I said, a lot of clear, I
hear you lout of clear, Go ahead, I go ahead,
finish that point.

Speaker 5 (56:16):
That's big, Eat slow. It's it's not a race, it's
it's a marathon. Or like Nipsey Hustle used to say,
it's a marathon. So if you don't have enough data,
that's fine. Just learn how to optimize what you got.
And that's what entrepreneurship is all about. I mean, that's
what that's what life is all about. Even back when

(56:39):
we were just single cell organisms, it was about optimizing
what you had and then becoming something greater than that.
So same thing with your business. Sure you may sell
one product, but that's fine. Optimize that one product. Samel
Jackson said, all I need is one great ten dollars
product that ten million people love or a million people

(57:02):
love or whatever. That's all you and do the math
from there. That's one hundred million right there. You say
a ten million.

Speaker 2 (57:09):
Im about to say, if you have, we'll think we
don't have enough data, because, like we just said, there's
so much out the consumption that one thing you have,
you can keep slicing that down so much. It could
be more data than you think that you don't have,
that you do have right in front of you. And
then we already know. This is where it comes important.
You said, don't be consumed by the consumption. The more

(57:32):
you find out what you don't know, you find out
that you don't know, and you can stay in the
constant seeking phase, right you stay instead of now that
I got this, I'm gonna do something with it. You
keep just drilling down. And as Debra said, consumed by
your consumption, does that mean the tapeworm.

Speaker 5 (57:53):
Facts people got, they got the tape worm out here.
That's that's why you get greed, avarice and whatnot. Like Nah,
just I seen a man cut an avocado into fifty
slices just by his technique. But me, I was cutting
the avocado into maybe what ten? But when I learned
his new technique, I said, oh, my salage is about

(58:15):
to be owning popping now. I'm about to have a
whole lot more off one now and I ain't gotta
buy ten dollars for four avocados anymore. So, but it's
just about how insame. Thing with money, same thing with anything.
It's not about how much you've got, but how you
use it, how you spend it. Because it's like I

(58:35):
think about all the rappers and whatnot. That inflects all
their money and all their jewels and whatnot, and their
cars and stuff and houses, right, and how much money
they got. Cool, looks good, right, But what if they
would have bought a bitcoin when it was ten thousand
dollars or less, you think they'll still be rapping or
you think they'll just have more stuff. Nah, they if

(58:56):
they would have took that little five hundred k that
they had and buy some bitcoin back when it was
ten thousand dollars, oh, they would have been up by now.

Speaker 2 (59:04):
Yeah, it would have.

Speaker 5 (59:05):
Been on and popping. So everything in life is about
how you use it. Because self defense, hey, that is
martial arts. That's self defense. You ain't using it to
just go antagonize people. But it's a deadly art. But
you're not using it to be a murder, right, So

(59:27):
saying with people are super smart, they could be out
here hacking everybody bank account and stuff like that if
they won't causing economic terror whatnot. But they don't want
to use it that way, so and then there's some
that do and then they go to jail. So that's right.

Speaker 2 (59:42):
But to your point, you're speaking about the relationship with
the thing we talked about a couple of seconds ago, right,
And it's the mindset, your relationship with it, relationship with money,
because you said it's not how much is how you
use it? If that comes how you use it and
understanding how much comes with the relationship you have with money.
There was a snippet that was released of an interview

(01:00:04):
that was being done with Fat Joe and somebody else
I can't remember who it was, but they were talking
about how you know a million dollars two million dollars,
that's not enough. It was like speaking to you know,
other artists saying, you know that million dollars, not that
we're gonna run through that, we will run through that.

(01:00:25):
You got your change or this or that, but you're hearing.
And then you had I can't remember his name. He
has his Instagram pays hip hop a critical and he's
always speaking against being hypocritical using hip hop, and he
was saying, I can't believe fat Joe, and I think
it was Jada kiss. I want to say, you guys

(01:00:45):
put this out here. That's not the gospel. Like you're
you're saying it like it's not enough money. It is
plenty of money. The thing you're talking about needs to stop.
The thing you attached to saying why it's not enough
is the problem. The one million, two million is the
problem to your point, It's about the relationship. So how

(01:01:08):
you using whatever and what you think has to be
used for. You could have the chains, you could have
the fifty thousand cars and everything else and not go
bankrupt if you would have used your money to make
more money and make more money and make more money.
Relationship says I got something I never had. Everything else

(01:01:28):
I don't have. Now you have nothing.

Speaker 5 (01:01:31):
It's like there. You could buy dividend paying stocks that
would depends on how much you buy. That is, but
and how much you have and what stock you choose,
and if they pay quarterly or annually. You know, so
there's more copyasts that go into it. But you could
buy dividend paying stocks that can pay you up to

(01:01:54):
one hundred K a year and then you're living off that,
and now that frees up a whole lot more time
for you to figure out how to maximize your income.
And again we're still talking about data analytics. It's just
now it's just data analytics within commerce, entrepreneurship and money
and wealth management and and scaling that money and wealth

(01:02:18):
because like again, you get you get two million dollars
look at us two million data points. Okay, so now
I want to go buy some dividend stocks that can
pay me up to one hundred k year. What stocks
are going to pay me? Is that stock high?

Speaker 4 (01:02:32):
Is it?

Speaker 5 (01:02:33):
Is it? Is it a high yielding stock? And just
pay anally quarterly? Has it been performing well within the
past five years? And how long am I looking at
holding onto this dividend paying stock too? Just in case
I ever want to back out if they start to
go down or whatever. But you're being incentivized just for
holding the stock. So you do your review, or you

(01:02:53):
talk to a stock trader and say, hey, what's a
great dividend paying stock for me. I'm looking to make
make one hundred k year off of that. They give
you the information now that they're a bit of a
data analyst in that way. Now they give you the
information on that stock, and boom, you go pay for it,
and let's say it costs you one million out of
the two not a problem, because you still hold a

(01:03:16):
million dollars worth of stock, and now you're being paid
one hundred k annually just to hold the stock. So
now you're actually at two point one million. And what
you do is, let's say you want to be more frugal.
You want to say, okay, now I want to chop
that down. I want to just be paid eighty k
a year, and then that other twenty k I want

(01:03:37):
to reinvest into the stock, so boom has grown over time.
Or you take that twenty k year you think, okay,
well now I want to start a new business using
that twenty k. It could be something like vending machines.
You buy five thousand dollars worth of vending machines and
then you put a at least let's say ten within
the inventory for it and place it that apartment complexes, gyms,

(01:03:58):
whatever it may be. And now you're making let's just
ball park it twenty thousand a month just off vending
machines alone, and maintenance brings you down to just pure income.
Editor brings you down to an EBITA of eight thousand
per month. So now I mean you're you're winning eight
with eight times twelve?

Speaker 1 (01:04:18):
What is that?

Speaker 5 (01:04:19):
So now you're making a whole lot more. That's why
everything is just about, like one, what you know, how
you perceive it, what your relationship to it is, and
how are you using it? Because two million dollars that's
a lot for anybody. I don't care what you say.

Speaker 2 (01:04:36):
It's a lot. So we said that's not enough. But
see what you just said, because I was going to
ask you how do we shift from us being intimidating
and ignorant when it comes to data to being empowered?
But I think you kind of just answered it what
you just said here we got to change our language.
That just so I thought our language. You literally went
from we're talking about money. But wait, we didn't shift

(01:04:57):
from the cop we didn't shift from the topic. We
didn't shift We'll talk about data. So everything it just
clicked to me what you just said, everything is a
data point. We literally talked about getting these reviews, getting
these you know, talking about products that Solda went out,
But then we went to talking about money we're spending money,

(01:05:18):
we're gaining Everything is a data. So is it fair
to say to get us to be more empowered and
not ignorant intimidated, is to know that the language we
need to say, speak and think of is in data.

Speaker 5 (01:05:34):
I would say it's in the person's mindset. Data can
be a great way for you to transform the way
you think and evolve the way you think. So I
would say earnings before interest, taxes depreciation.

Speaker 2 (01:05:51):
Oh, she just explained what EBITDA is.

Speaker 5 (01:05:53):
While it's a financial metric used to assess the company's
profitability and operated performance by excluding operating Okay, yeah, so
thanks Debra. So yeah, I would say it's more within
the person. Data can definitely be used as a as
a pathway to looking at things more because when you
do realize, like even companies that have remote workers, sometimes

(01:06:17):
they'll send them laptops that track how many pushes on
the keyboard are they striking, so the way they can
gauge if they're really working or not. Yeah, it gets
like that, So then they start to do data analytics
based on that person. Now and like okay, ex worker
says they're working eight hours a day, but based on

(01:06:39):
those pushes, they'll be doing three. We need to have
a conversation with them in HR or whatever.

Speaker 2 (01:06:45):
Then where so then biases can creep into data then, right,
because what if you have someone who types sixty words
permitted eighty in and a hunted how do you that's crazy?

Speaker 5 (01:07:01):
Yeah, And that's the thing what I've learned with when
I was doing the behavioral studies in psychology was that
not everybody is going to be honest in their answer,
and that's just what it is.

Speaker 2 (01:07:13):
Okay, that's true too. There's gonna be biases in the
answers to Yeah, So you just have to look at
the trend of the bulk of what you're getting and
see what it is saying to.

Speaker 5 (01:07:26):
You, because you'll have that bell curve. Okay, on the outliers,
you'll have the lower end the upper end. So let's
say mostly everybody agrees that strawberry ice cream is good.
On this end, they say it sucks. On this end
they say that they that they love it. Okay, well,
why do we have these outliers of love and suck.

(01:07:48):
Let's go talk to them because on the middle park cool,
they said they're good, they're fine with it. But then
you go talk to people that says that it sucks.
Could somebody just be dissatisfied with I don't know. It
could have been the packaging, It could be something as
small as that. It could have been the spoon they used.
They could have just been having a bad day, or

(01:08:08):
maybe strawberry ice cream triggered a memory for them that
they didn't like. You know it when it comes to
these research studies, these product research studies, it could be
some crazy stuff. And it's like, all right, let's go
to talk to the people that loved it. Were they
hire that day? Like, listen where they are that day
when they came in, and you know, are they just

(01:08:29):
saying this because they want to be chosen for the
next study when they come back. So it's in so
many considerations that it can quickly become convoluted and sometimes
even make you very cynical of the data. So the
thing for you to do is just take what you got,
usually that middle, that middle part part of the Bell curve,

(01:08:50):
and then work with that. But don't forget about those
two on the outliers. Still have more in depth conversations
with them. Still talk to the people in the middle.
Why is it good? Why did it suck? Why did
you love it? So?

Speaker 2 (01:09:04):
Using data right now right so we we know we
could use data to uh give best better customer experience.
We heard you say that you could also use data
to get more customers or also not to waste your
time to you know, make sure you're giving the product
or service that is needed at that time. How can

(01:09:26):
data be used just for breakthroughs for people right now
in this in this time right now? What if you
have some who are entrepreneurs but also working nine to
five and they're trying to find opportunities? Is there way
to use data to help set them apart in this
time where it may be hard doing it the traditional
way looking for stuff? Is it something a little cheat

(01:09:50):
sheet of saying we're in everything's data, this is what
you should possibly look at data wise?

Speaker 5 (01:09:56):
Mm hmm, I would say, because a general cheat sheet,
I would say the best thing for them to do
is to leverage their data with AI like anonymize it. First,
know what you've anonymized, and then figure out which AIS
are you going to use. Doesn't this have to be

(01:10:16):
an LLM or generative AI. It can also be Let's say,
like again, a data point could be a video you
could use like an AI that chops up a video
and the clips for you and then you can leverage it.
That way. You could use an AI that can help
you build an app for your community, such as Lovable.

(01:10:38):
Let's say if you don't want to use a discord
or a slack or something like that, now you just
go make one that's customed to your community or a
custom to the specific metrics that you wanted to gather
within within your community. Because let's say a discord or
a slack isn't providing you all of the capabilities that
you want for your community. So boom, you go to

(01:10:59):
love a bull teller. What you need, what you won't,
it will create the AI will create that app for you.
You're not coding at all, so it could be something
like that. I would I would be more focused on
just what I have and how I can leverage it,
and then talking to people who are in these fields
and asking them their opinions on my data too, because

(01:11:24):
never forget the human factor within technology, right.

Speaker 2 (01:11:28):
We can't forget the human factor. There is no there
is no technology without the human factor. How you're tellingividual,
I mean, that's what drives it First of all, the
humans created it, but that's what drives it. Again. It
only knows what you tell it. And to your point,
it's learned. If you create a platform that you're using

(01:11:49):
all the time, it's starting to learn based on everything
that you put in there. It learns your language and
learns what you're looking for. It learns, It learns a lot.
But I was thinking more along the lines, is they're cheap,
not necessarily like it was cheat sheeting quotes? But is
data helpful for those who? In my mind, I'm saying yes,

(01:12:10):
because you said data is everything who may be looking for?
Because if you look on LinkedIn, there's a lot of
individuals that are saying for so many reasons or layoffs,
some way to use data of what companies are looking for, Well,
there's a gap. You could use that data and then

(01:12:32):
you're now not wasting time. You're addressing these companies on
the specific based on data, the specifics that they may
be looking for or needed. I guess that's what I'm
trying to get at. Does that make sense?

Speaker 5 (01:12:43):
So like looking to solve the company's problems to enhance
the likelihood of being hired by them.

Speaker 2 (01:12:49):
Yes, because I'm saying we're saying old traditional way, which
is all I have skills, and I've been applying for
these jobs, and you know I've seen not people say,
I've been on thousands of interviews. But maybe I'm wondering
if we're doing it in the traditional way, but if
we're using data and being a little more intentional on

(01:13:10):
whatever information may be out there, why jobs are letting off,
what positions, what is the need, and then we're curating
that information to be more intentional on how we show up.

Speaker 5 (01:13:21):
Now, it can be a bit difficult to figure out
specific companies problems because that would be more internal. But
let's say let's say an article was released about what
the company is going through. Because chaos is always an opportunity.
So let's say an article was released about what the
company's going through. You read that article, you connect with

(01:13:44):
a hiring manager, recruiter from that company, or you got
somebody on the inside, and you say, here's what my
skills can do to address this problem. I'd like to
speak further about it. That's one way. Another way it
could be you could look at what industry that they're in,
and based on that industry, you could find market research
reports and look at those market research reports and say, Okay,

(01:14:09):
do the skills that I have align with the problem
that this company is looking to solve. If yes, then
you could go again hiring manager, company whatever, or go
to an event if they have any events and say, hey,
I recently heard about so and so going on in
the market. I know that XYZ company addresses that problem.

(01:14:32):
I would like to speak more about how my skill
set can add value to the company and solving that problem.
Something like that is going to make people's ears per cup,
like boo, they're talking about the only time for millionaires, billionaires,
business people like very successful people. When I've had a
conversation with them, they were really tuned in if I

(01:14:55):
added value in somewhere.

Speaker 2 (01:14:56):
Absolutely, that's periods and playing.

Speaker 5 (01:15:00):
You don't you don't want nothing in your life that
detracts value from you, like you only want things that
add value. So it would it would be the same
like that somebody who has a business wants the same.
We're having this conversation because it adds value. So find
the problem that they're solving, see how you can address

(01:15:22):
it with them, or if it's something that they haven't
thought about, it thought about then go about it that
way too. You can make a slide deck presentational would
a bit on yourself and how you would go about
this problem and go from there, but talk about skills
that you're learning as well. People want to know what
you can build and at the end of the day,

(01:15:43):
it's about what can you do when it comes to business.

Speaker 2 (01:15:47):
For them exactly. And that's I wanted to pull out
of you because I wanted people who may come across
this and listen. Those who are listening now, listen later
watch it audio. I want them to hear because sometime,
well turn away from something if it feels like it
doesn't feed our situation. And I wanted to pull in

(01:16:07):
data where it's with someone who say, Okay, I don't
even relate with being an entrepreneur, creative change maker. I
just work and I'm struggling right now. What can be
done now? That's why I wanted to hear how data
still speaks to no matter what era you in and
what it can do if we just get out of

(01:16:29):
the you know, the learned way of doing things, but
if we try to apply something new such as data research.
So that was huge. Thank you for sharing that. That's
how I was trying to get out its way better
to me.

Speaker 5 (01:16:41):
Is when people hear data, they just think numbers.

Speaker 2 (01:16:43):
That's what I'm trying to say. I'm trying to take
this word that has been trained to mean something different
in a lot of our ears, right and do what
you're doing right now. You just made it a real
It's life is data. Everything is a data point. I'm
calling that right, right, So what you just said, you
come out here and said, no, look at data points.

(01:17:07):
And that's when they met even ask that question.

Speaker 5 (01:17:09):
Yep, life is data. The brain is a computer, the
body is a machine that our thoughts are algorithm. Same
thing with the mindset and do what it what you want.

Speaker 2 (01:17:21):
So does that mean When I was an undergrad the
first undergrad course, I thought I was going to do
computer science until I sat in front of the computer
and had to do the homework assignments where you had
to fill in the language and if you was missing
one thing, it wouldn't come out the way it was
supposed to come out in the end. It could be

(01:17:42):
a question mark estimation point. I was like, this is
not my thing. So does that mean with life being data?
If we fill a lot of things off there's something
we're missing in that language. There's something we're missing in
that data point.

Speaker 5 (01:17:58):
It's translation is everything is about translation. You got to
see how this can connect to that and that can
connect to this. But for most people, they don't have
a core comprehension of what they're this can be to that,
and what they're that can be to their this. So
for example, let's say you're let's say you love it

(01:18:22):
could be you could love sport, right, Okay, So let's
say you love a sport. You know the rules of
the game, you know how to get the you know
you know how to get the points, you know what
skills and strategies are needed to get the points, So
you know everything there is to be no right, You've
got a core comprehension on how this works. Now, as
you move throughout life and you're engaging in other compext

(01:18:46):
other topics that seem to be more complex to you,
you use that core understanding of that game that you
love as a translator to everything else. Okay, that was
a foul, Like I see that as a foul, although
it was let's say what they're saying. What they're saying court? Uh?

(01:19:09):
When man, what they be saying in court. See look,
I'm trying to remember.

Speaker 2 (01:19:13):
When in the court or.

Speaker 5 (01:19:15):
The other one objection objection, Yes, objection is a foul
because like no foul on the play, and I gotta
stop you and tell you why it's a foul. So
we go, we read the tape. Yep, that was an objection,
that was a foul. It's explained objection, rule or overrule,
whatever it may be. So now people need to figure

(01:19:39):
out that because what they do is when they learn
something new, is they forget the thing that their core.
They forget that court comprehension and try to learn this
new thing all on its own. Not saying that you
can do that, because you can, but it becomes harder
to learn it if you don't have a foundation to
learn it from, which is why when you're learning a

(01:20:00):
new language, we use objects as the foundation. See that
and see memonia. But in English hand you know, right,
but we use the symbol, the the image.

Speaker 2 (01:20:16):
Of the thing that we all know. We use the
thing that you do know, and you're connecting it with
the new language or that you're learning exactly, So.

Speaker 5 (01:20:26):
That that right there can help people a whole lot,
just in everything that they do.

Speaker 2 (01:20:34):
That was good because I I I.

Speaker 3 (01:20:38):
Do that.

Speaker 2 (01:20:39):
I didn't know that was It's just natural for me.
I'll use a lot of language from track sports whatever.
I'd be like athlete in life for staying your lane.
You just you just got dqed from doing right. So
it was just transferred. But I didn't even realize. And
because that was that helps me to community kate the

(01:21:00):
essence of what I'm trying to say because that thing
I know is relatable. And then I may now translate,
but I'd be like you say the cute, but I
go there. I didn't even realize that was yeah, a
human part of us, and that helps a lot. Like
you said, don't try to go completely into this language
and you're not connecting it to something that you're already
familiar with. And that is how we learn new languages.

(01:21:22):
They show you stuff images, mom, dad, sister, brother. You're
learning those words, right, gotcha.

Speaker 5 (01:21:30):
I remember being a baby, I play I played the
Sonic video game and then I'll hit the little box
above the coconut tree. But it was a had a
question mark on it. So then when I started learning
about grammar, I would call the question mark a coconut.
I said. I was like, I was like, okay, ABC, whatever, coconut.

(01:21:53):
And then teacher would be like, you're so cute, Michael.
Why is the question mark? Why are you calling the coconut?
I said, because when I play the game, I see this,
I see the I see the thing like I like, like,
that's what I was calling a question mark. I said,
I see the thing in the game on top of
the coconut tree. So it's a coconut. O my gosh.

Speaker 2 (01:22:13):
And that's how you associated. That was your word association.
Question mark was a coconut.

Speaker 5 (01:22:20):
So those developmental years they never go away from us.
They just plain and simple.

Speaker 2 (01:22:27):
Okay, So and wrap it up. I want to know
what is your legacy if you think about what you're
doing on this side of the realm, because your legacy
is tied to a lot because there's a lot of
different things that you're doing. But when you think about
you know, it's the long term impact of what you're
doing and helping individuals understanding that data points this life.
Everything in life is data points, Like what do you

(01:22:50):
see yourself leaving or want to change? When you've got
the growth and data and what we need to learn.

Speaker 5 (01:23:00):
I would love to leave a book that translates data
to life one where people can comprehend data within business technology,
but then it could also be a self help book
two and help you comprehend your life through its data.

(01:23:20):
Oftentimes we think that our unclean and unorganized data is
the problem, but it's not the problem. It's just unclean
and unorganized, and we can see that as the trauma
in our life, the backgrounds that we had, the things
that we didn't like that made us into who we
are or made us do things that weren't in alignment

(01:23:42):
with the most high All you got to do is
clean that data up and organize it and then start
to put it into a pipeline and extract transformed a
load pipeline. So you've extracted that unclean data in your life,
start to go to therapy, start to realize that all
these things that happened to you aren't really who you are, whatnot,
and you start to heal from it. That's the cleaning

(01:24:03):
of the data. Now with that healing, it's also the
transforming of the data. Now that I am healed and clean,
who do I want to become? So you start thinking that,
you start looking at other algorithms or thinking in other algorithms,
seeing other images of what you can be out there,
because now you're free. So now you want to load

(01:24:23):
that image, that image or new images of self that
you want to be into this program or this dashboard.
And now you're looking at your metrics based on me
extracting all my dirty data, transforming it and doing what
I needed to do with my life and loading it up.
I can see now that I have changed from this

(01:24:44):
to that, and my life is so much better now.
So at the end of the day, with everything that
I do, it's in service to humanity and alignment with
the most high and always seeking ways to just be
of value in service from a heart space. Uh, it's

(01:25:06):
never an ego. I'm not not trying to have people
praise me and build monuments of me and whatnot, you know,
just because I want that. If I want that, I'm
just gonna do it myself. To be honest, I don't
need nobody to build me statues. I will build my
own statues. That's fine with me. But if I if
I do it and it turns out to be out

(01:25:27):
on a grand scale, like now, millions of lives are
being changed because the things that I've done. Then I'm
grateful that I have helped to change lives, and not
for it just to venerate me.

Speaker 2 (01:25:41):
That that was deep. When you're just talk about the book,
first of all, can't wait for it. You need to
connect with you. Can't wait for it because I can
see where you can go so many ways with this.
That's why I'm happy, That's why I sit. You're chosen,
you're here. Data's not just about these numbers, and business
is life. But again, when you tell these stories and

(01:26:04):
you put it together, you make a light bulb go
off in my head at least when you say it,
because you just said, it's not mismanaged bad data. Just
rearrange it, clean it up, put it where it's supposed
to go, and it changes the whole outcome, that whole
algorithm situation. And it made me think immediately. And we
talk about language, We always talk about limiting beliefs that

(01:26:25):
comes from me, right, like, oh, these limited beliefs people
tell you this that's downloaded. This is translation to limited
beliefs from other people. Is a downloading, a downloading of data,
and you're taking what's downloaded, and you may be a
virus something that needs to be clean up you know, malware,

(01:26:46):
get that into get it together, a malfunction, whatever. You
help me to understand that and to think about it
in that way of my the language I know, and
now put it to what the language you're just even
talking about right now? That download and clean up the data,
you can switch it. And I'm I'm a visual person.
I can actually visually see that when it comes to

(01:27:08):
limited belief. So I can only imagine how amazing the
book can be when and where you can go with
several chapters sections and you can talk about data from
here to data to here and how it just ties
into life as a whole. That would be absolutely amazing.
How you don't sit on.

Speaker 5 (01:27:25):
That, Oh no, I've already wrote it in my mind.
There you go, it's already completed. It's the way my
algorithm works.

Speaker 2 (01:27:35):
Is that algorithm.

Speaker 5 (01:27:39):
We're talking about rhythm now, you know. That's it the
rhythm of life. Life is a cosmic dance. And that's
a whole other conversation right now. The way my algorithm works.
If I say, if I see it, I say, is done.
There is no I want to have. It's just I have,
or there is no I want to be it's just

(01:28:00):
I am, and I let it be there.

Speaker 2 (01:28:02):
It is because we don't want to be sitting waiting
on that, because that is that's a self help book,
it's a inspirational book. It's a business guy is all together.
This is what you're talking about, the data and how
it is. So thank you. I knew it, Michael. I
knew you would help bring this in, hon it in,
and I can't wait to continue to share more and more.

(01:28:24):
I knew you would do it. I appreciate you so
much doing that. I know for me, I've learned a lot.
I'm going to walk away and implement some things that
you said, but also help me learn a new language
and when I see different things and how I move
and how I think about it. So I appreciate you
so much everyone. First of all, how can I connect

(01:28:46):
with you before I do that? It's a wrap. How
do you want people to connect with you?

Speaker 5 (01:28:49):
Yeah, connect with me on LinkedIn Michael Cortes and my
cha well spelled as it is on the screen. Just
connect me on LinkedIn. You'll see me like a red
f from I need to get it profile, update, some
new headshots and everything. As I've been growing a lot
you got, I.

Speaker 2 (01:29:05):
Would say, you're here grew a lot and you locked
it too, is a lot yep.

Speaker 5 (01:29:10):
Oh wow.

Speaker 4 (01:29:12):
So yes.

Speaker 5 (01:29:14):
So it's been a lot of growth, clarity, transformation, guidance,
gratitude and direction around here.

Speaker 2 (01:29:22):
So I'm just grateful and I feel it. You even
have to say I felt, I feel it. I feel it,
I feeling in your comments, I see it in your light.
I love it. So connect with Michael. And that is
a rat for today's segment of Seek Elevation. I mean,
as you clearly see that data does not have to
be something that is dry. It's not dry. Actually we

(01:29:46):
clearly see that it is. It is a river, it
is it is a flood that we need today to
understand better. And Michael showed us how to turn that
information into inspiration to inspire me the inside of us
to outside of our life. So it doesn't matter if

(01:30:07):
you're talking business, if we're talking just life. Data is
important for us to start understanding it from a different perspective,
the perspective that he laid out today. So keep asking
the right questions, stay curious, and everything that you do,
let the data guide you do. Not be consumed by

(01:30:28):
your consumption. But let it guide you and get ready
for your next move. So until next time, continue to
stay elevated. Thank you for tuning in, thank you for engaging,
and just remember, the more you know, the more you grow,
the more you learn, the more you earn. But when
do you.

Speaker 5 (01:30:48):
Share?

Speaker 2 (01:30:49):
So please share this segment that Michael has so graciously
poured into was on. When you share, you show care.
Don't keep this knowledge to yourself. Spread the wisdom and
connect with Michael. Remember when you come on seek elevation,
we're here to keep connecting. My vision, part of my
legacy is to see an entire empire that's built with

(01:31:12):
connected individuals who sought elevation and reached it. So until
next time, keep driving, keep growing, and most importantly, keep
seeking elevation to next time, peace and progress, all.

Speaker 5 (01:31:27):
Right, everybody, have a gool one.

Speaker 2 (01:31:29):
Thanks for having me all absolutely

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