172: Tesla Data Analyst: This is how to land a data job (Lily BL)

Avery Smith: 00:00:00

This is my Tesla, but I've never worked for Tesla.

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Luckily, one of my accelerator

students has Lily BL, and today I

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had the chance to interview her.

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I got to ask her what it was like to work

at such a unique, cool company, what it

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actually took to get her there, and what

advice she'd give to those of you watching

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who are interested in working at Tesla

or other really cool tech companies.

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So let's go ahead and

get into the episode.

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Lily, your career has taken you

to Tesla, pg and e, Intel, and

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now the city of San Francisco.

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But your first full-time

data job was at Tesla.

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What was it like working

on data projects at Tesla?

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Uh,

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Lily BL: it was nerve wracking

and exhilarating at the same time.

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I was not sure what to expect because

when I took the role on, it was blended

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between a couple of different areas.

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But as, uh, I worked more and

more like day after day, I could

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see what their data needs were.

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They had data in multiple systems for

multiple reasons, and it was just so

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much in volume that they couldn't keep

track of how to look at it concisely.

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They had to go through embedded

records to get an answer.

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So I think when I first got fired on,

the word on the street was she's like an

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admin assistant to the district manager.

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If you have administrative stuff

you can't do, just give it to her.

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Once the boss saw what I could do.

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It completely changed, and I

was monitoring everything for

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data flow to determine what

kind of visuals could be built.

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The scary part was like, I don't

know, and the exhilarating part

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was, but I can figure it out.

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Avery Smith: That's awesome.

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And I'm glad to hear that.

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Uh, an employer like that was, you

know, first off recognized your talents,

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but then second off was like, okay,

Lily, Lily can, uh, do this stuff.

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Let's give her some, some more tasks.

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Did you feel like what, what you were

doing, like was, was super cutting

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edge or did you feel like it was

more like regular, regular tasks?

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Like, um, did you feel like

challenged in what you were doing?

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Lily BL: I did feel challenged in

what I was doing because it had a

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lot of impact once it was completed.

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The technology and the how to itself

surprisingly was very basic, so it was

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continuously searching for the specific

thing that will fix this specific problem.

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And then gathering all the solutions

to say, Hey, this is how you can

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improve your data situation overall.

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Avery Smith: That's interesting.

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And, and correct me if I'm

wrong, uh, you know, Tesla is

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obviously a, a large company.

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I worked for ExxonMobil, a large

company at these large companies.

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You hear this phrase, I'd never

really heard it before, this word.

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Um, it's called disparate.

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Uh, and basically, or siloed, I

think is the other thing that they

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said a lot at Exxon that data is

siloed or that data is disparate.

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Basically, I think you kind of said

something similar where, you know,

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at these large companies there's a

lot of data, um, and there's a lot of

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systems, but the problem is this system

doesn't necessarily talk to this system

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and this data is kind of stuck here.

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And you know, this data, they only

enter it in an Excel, uh, database.

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So it doesn't really like

integrate with anything else.

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And so it sounds like your job was almost

like you were like data analyst glue.

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To try to tie in all these different

data sets from this different systems.

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Did I, did I get that wrong, or

is that kind of what you did?

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Lily BL: Yeah, you're

nailing it on the head.

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It was very interesting because

a large portion of the actual

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engineering work was done inside

of a software called Jira, which is

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meant for tracking, uh, the project.

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That's the data that was needed

to be reviewed and the company's

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decision was not to view it.

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Outside of that, I would run validations

in Excel to make sure the numbers it gave

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me were accurate to what I visualized,

and so I had to actually learn Jake to

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be able to put together what I needed

and I was limited by the visualizations

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preselected for project management.

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At the end of it, it was super cool

because I kind of created a grid.

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Um, it was like a large,

uh, standing rectangle.

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When you looked at it up and down.

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That was the information for the district.

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Managers.

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Managers.

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So each team and all of their staff.

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You could look at what they

did and when they did it, what

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was still pending Vertically.

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Each team had a, a call.

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When you looked at it horizontally,

those were all the KPIs my

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district manager had requested.

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So they were subject to some standards

and it was so cool because I didn't know

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how to do that till I was done with it.

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Um, and so then I was like,

yeah, this is what I wanted.

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Uh, but I needed some help from

the management team because to

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make sure the data had integrity,

I didn't set the conditions.

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So I did push for them to tell

me, this is how this is defined.

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This is the threshold

to, uh, determine this.

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And once I had them deliver to me,

uh, some definitions, I used those

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decisions to build out the, the

visual and they ended up loving it.

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'cause I was able to color code

it and I assimilated it to red

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light, green light, yellow light.

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So if it's marked green,

don't worry about it.

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You don't have to look.

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If it's yellow, you kind of

need to keep an eye on it.

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But if it's red, you need

to go in and investigate.

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That was just relative to the

data produced by the teams.

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Then there was the data produced

by the hardware and that

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those were different systems.

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So then in those systems, I took

that one to Excel and I was able to

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create a chart that had a threshold.

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So I had them, again, define

what the threshold was, and

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let's say it was like 10.

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Once you got 10 of these things,

the chart would go from being green.

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To now being red 'cause

it crossed the threshold.

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So the division manager could look

at all of these things at a glance

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and be like, oh, red is where I need

to be, and figure out what happened.

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Um, and then, uh, separate from that, I

was also, uh, building into Tableau, uh,

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master portfolio so that the division

manager could just look in there at

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all of the teams and all of the things

that were of interest to him because he

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would take that information back to the

meetings with the rest of management.

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They would decide what would come

next based on what was there.

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So like if a lot of equipment was

failing, they would say, Hey, your

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team is under producing because you

have 10 of these different kinds of

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machines and you're only putting out

like half of the results we expected.

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Or based on the analysis, half of

the machines were not available

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for one reason or another.

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So it's like, this is why our numbers

are lower than what's expected.

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Per what is available.

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Only half is available, which you

couldn't really tell any other

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way is they were kind of, uh.

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Digging constantly before we were

able to build the visuals, uh, to

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determine, uh, what was really going on.

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So it really facilitated

the manager to manage.

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And he was actually a really good manager,

so he knew where the weak points were.

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He just was not a data person or a data

analyst to be like, I need this, felt

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like this, and like that to get this.

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But he knew what he wanted, so it

was a perfect partnership because

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I could build it and he could tell

me if it worked or didn't work.

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Avery Smith: Lily, this is super cool.

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Thanks for sharing all of this.

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Um, I have so many places I, I want

to go based off what you just told me.

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Uh, the first was, I had never

heard of J Quill, but I had

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a chance to look it up here.

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So that's, that's Jira Query

Language or Jira, I don't know

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how to say that, but for those who

never heard of that, it's JIRA.

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Um, and it is owned by

Atlassian, I'm pretty sure.

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Um, and it is like a project management

software that a lot of, uh, software

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companies use to develop software.

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So you're, you were kind of

looking at project data, um,

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which is, which is really neat.

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Uh, and it sounds like you were working

like really close to these, you know,

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kind of higher up stakeholders who,

you know, they need a bird's eye view

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of what's going on in their business.

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It's, they kind of, like you said, have

like a gut feeling of this is, you know,

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maybe this part is struggling right

here and I have a feeling why, but I'm

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not exactly a hundred percent sure.

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What it sounds like is you tied up a

bunch of loose ends and you know, this,

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these disparate data sets, and you're

able to create a data visualization, uh,

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that helps these managers see, you know,

maybe what's struggling in the business,

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maybe what's doing good, um, what they

need to worry about and what they need

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to maybe put their, their focus on.

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Um, so it sounds like you were almost

giving them like supervision goggles to

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like look into their business and like

actually see is everything, is everything

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going the way it's supposed to go because.

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You know, as someone who runs a

business, I obviously do not run a

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business close to the scale of Tesla.

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Like one division of Tesla, I'm sure is

a hundred times bigger than my business.

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Uh, but even now I have, you know, Trevor

Maxwell helping me out with coaching.

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I have Isaac Ania who's

helping with my community.

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I have, uh, a podcast producer

and editor, and I don't know

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what's going on half the time.

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They're just awesome.

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Uh, employees doing a great job,

but I do wanna be like, okay.

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How do I get above the business and like

actually look down on it and see like,

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okay, what's going well and what's not.

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And it sounds like you were able

to do a bunch of analysis to kind

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of produce that for these managers.

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Lily BL: Yeah.

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And uh, one project, uh, that I was at

hair away from completing, 'cause I was

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missing one definition, uh, which I think,

uh, would have had a huge impact, is,

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um, I worked on their hiring process.

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So I would sit in on their

meetings and see how they went

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through their hiring processes and

would sit in on the interviews.

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And then I would also look at fubu.

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They had already hired because of the

way they, uh, did bonuses there, the

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managers had to divide a percentage of

bonus among all of the existing teammates.

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So I was able, based on watching

the data flow, um, I was able to

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determine what the standards were.

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They already knew that they had tiers.

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Like we have engineer 1, 2, 3, 4,

5, a lead, whatever, a, a manager.

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Then I was able to zero in on what

are the standards, uh, that this

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person needs to complete or being

knowledgeable in, in order to ascend

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to the following tier, which translates

to more money for the employee.

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And so then, um, we got, I got, I

reviewed everything and I set it

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up, but what was missing was the

metrics associated to each tier.

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And so I left it alone

to not push a project.

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Uh, and hack the pay be incorrect.

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At the very end of the, uh, contract,

the HR published the standards

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or the, the pay for each scale.

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So that was the missing piece I needed.

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But with that, it would've facilitated

all of the yearly reviews of the

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management team to enable or to

determine very quickly, oh, this person

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hit these projects and these projects

are labeled within this category.

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So while they were working as

an engineer too, their work.

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Function was actually an

engineer, four or five.

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So they qualified for the bonus

and potentially a promotion.

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Um, we were also very proactive there with

kind of working with the, um, employees

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being more, um, uh, how do you call it?

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Um.

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It's not affirmative, but it's being

more proactive about their findings

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and stressing their good works.

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So with so many people on the team, I

don't know every single thing you did,

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so take the initiative and tell me,

Hey, I completed these multiple things.

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That way it's fresh on my mind.

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So I didn't get to the see there.

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But after I would have completed

that project with the raids, my goal

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would've been to work with the team

one-on-one and have them pitch me.

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Their successes and then I

could categorize it for them.

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Like, okay, what you said

falls into this or into that.

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Do you agree or disagree?

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And then teach them how to make the

argument for their good works better.

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Um, it's delicate to do in business,

but it's like a negotiation.

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So you actually need to

practice it in order to get it.

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And this particular company was

open to that they wanted to hear.

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So, uh, that was like the

icing on the cake for me.

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We didn't get to finish it, but

it's something that would've

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been proactive for everybody.

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The company would've had a very,

well, a very articulate staff, which

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is needed for problem resolution.

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And then with the market as it was

constantly laying off and whatnot,

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this employee would have had the

skills sharpened to then go right

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into another position if they were

laid off and quickly get another role.

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Avery Smith: Hmm.

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Very cool.

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Um, while you're at Tesla, what

tools did you use the most?

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Lily BL: Um, I think I used Jira the

most and excel for the validation.

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Um, I got heavier into the

administrative side of, of the

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software because for Tableau and Jira

I was bringing in add-ins to make

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them more functional for analytics.

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Uh, so for companies, uh, you have to

connect the Tableau software to what,

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wherever your data is in the company.

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When you use Tableau as an

individual user, you just

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connect it to your worksheet.

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You can't connect it to something

else if you have it, but typically

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you just use a worksheet.

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So that was different.

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And it was a full host

of security clearances.

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Um, so I did a little bit of the

administrator stuff, but, uh, JIRA

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and Excel round my validations.

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Avery Smith: That's awesome.

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I think that's true.

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And, and you mentioned JQL.

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Is that kind of like SQL or

how, how are those related?

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Lily BL: Yeah, so it's very similar to

the commands in sql, so that's why I

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was able to learn it pretty quickly.

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Uh, but then, um, some

things are specific.

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It uses a lot of, uh, a lot

more keywords than you would

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expect, and they're different.

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Um, the software itself

does try to help you.

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Like it lets you click on buttons

and produces the code for you.

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Uh, to an extent, but then you

have to have modifications.

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So I would allow the software to allow

me to click to build some of the stuff,

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but then I would review it and determine,

oh, it still needs this functionality,

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or this, or this other group of people.

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And you would have to manually put that

into the existing code to make it function

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Avery Smith: super neat.

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So it's basically SQL for Jira, and

they try to make it a little bit easier

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for you to actually write the code.

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Um, okay.

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I'm actually not sure the

answer to this question.

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How did you get this job at Tesla?

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I remember you messaging me when

you got the job offer and you're

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like, Hey, these are the details.

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What do you think?

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Should I take this job or not?

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You know, it's one of the things I try

to do with my accelerator students, but

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I don't remember off the top of my head.

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This was a couple years ago now.

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Um, how you ended up landing this job?

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Lily BL: Yeah, I think it

was through networking.

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Um, at the time I was an instructor

for co-op and I had a cohort that

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I would teach in the evenings.

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One of my cohort students actually

got hired by them about a month or

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so before I helped them finalize his

work that they wanted him to see.

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So then about a month or so later, I got,

uh, contracted by a recruiter on LinkedIn.

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I checked with him.

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It ended up being the same

person that contracted him.

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So I think what happened is that I

popped up for her in association to him.

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But she never said that.

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But that's, that's what we

think the connection was.

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And so then she interviewed me.

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I actually was like number three or

four, because three or four other

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people had said yes and then backed out.

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And so then it was really easy

to consider, oh, you know what?

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I just won't take the job.

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You know, it just seems really hard.

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But I just kept saying, well,

if the, if the manager wants to

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interview me, I'll be available.

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If the position comes back open.

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So after that is how everybody

else, 'cause there was a lot

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of things that it required.

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And then I ended up not

doing most of those things.

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Uh, so I ended up, uh,

hanging in for the interview.

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And then in the, in the interview,

um, he asked me some questions that

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I think everybody else struggled

with and I answered very confidently,

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uh, because of the work that I

had done inside of your bootcamp.

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Avery Smith: That's,

that's awesome to hear.

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So, uh, what I was kind of hearing was.

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Basically you were, you were connected

to the, to a right person, someone

301

: 00:15:21

kind of similar, one of your peers,

um, looking to inundate a job.

302

: 00:15:25

Uh, and then you had a good

LinkedIn because the other thing

303

: 00:15:27

is, uh, on, on LinkedIn, right?

304

: 00:15:29

Like you don't get recommended if

you have kind of a crappy LinkedIn.

305

: 00:15:32

So making sure your LinkedIn was

up to date with all the right

306

: 00:15:34

keywords, all those projects you

had done inside the accelerator,

307

: 00:15:37

I'm sure that helped, uh, as well.

308

: 00:15:39

Yeah.

309

: 00:15:40

You, you nailed the interview.

310

: 00:15:41

Okay, that makes sense.

311

: 00:15:42

So what advice would you give to someone.

312

: 00:15:44

Who's listening right now who's like, wow,

I wanna be cool like Lilly and work for a

313

: 00:15:48

cool company like Tesla in the data space.

314

: 00:15:51

Like what advice would you give them?

315

: 00:15:52

Lily BL: Um, I would recommend that they

kind of determine what part of the data

316

: 00:15:57

portions they like to do, and then after

they figure out, oh, I like building

317

: 00:16:00

the data structures, or the pipeline

or the visualizations, dive into that.

318

: 00:16:05

I do get, uh, a lot of requests for

like, how can I kick off figuring

319

: 00:16:10

out my data stuff and actually

recommend them to your free content?

320

: 00:16:13

'cause I find it really helpful.

321

: 00:16:15

I think you do a good job organizing.

322

: 00:16:17

Okay.

323

: 00:16:17

You gotta go get the data.

324

: 00:16:19

Once you get the data,

you gotta clean the data.

325

: 00:16:21

Well, once you clean the data, you gotta

figure out a quick way to deliver it.

326

: 00:16:24

And also the visuals, how you

build your visuals is gonna kind of

327

: 00:16:28

determine what you can say, um, as

the Bluff Fund, like right up front.

328

: 00:16:32

I know these are things we do to build

the projects, but those directly translate

329

: 00:16:36

into the interview and also into working

with, uh, people on site in the jobs.

330

: 00:16:41

So if you can find a material that helps

you hone the skills you naturally want or

331

: 00:16:47

like inside of your data, uh, career, it

will make it easier for you to get that

332

: 00:16:52

and it will make you a natural to post it.

333

: 00:16:55

'cause you'll actually

be excited about it.

334

: 00:16:56

Like, oh, I had a hard time

learning this particular function

335

: 00:16:59

in Excel, but I nailed it.

336

: 00:17:01

Let me show you guys how I did it.

337

: 00:17:02

You'll naturally be like, oh, we

had overtime with sql, but I figured

338

: 00:17:06

this out and now I'm gonna post it.

339

: 00:17:08

And people actually do look at it.

340

: 00:17:10

They might not comment, they might

not like, but recruiters and also

341

: 00:17:14

other people, uh, interested in data

will come and look at your projects

342

: 00:17:18

because if you had an issue with

it, likely someone else did too.

343

: 00:17:22

So if you're constantly posting your

projects and how you solve the problem.

344

: 00:17:25

Uh, they will naturally gravitate to you.

345

: 00:17:28

And one thing I always stress

is to try to frame my projects

346

: 00:17:32

into a problem and a solution.

347

: 00:17:33

So the purpose of this project

was to address this specific

348

: 00:17:38

problem and here's the solution.

349

: 00:17:40

Maybe they won't care for looking

at the problem, but maybe they're

350

: 00:17:43

interested in just a solution.

351

: 00:17:44

But that's interesting.

352

: 00:17:45

They'll go back and look at the

problem and then read all of the work.

353

: 00:17:48

Avery Smith: Interesting.

354

: 00:17:49

So yeah, projects played a big

role for you, it sounds like, like

355

: 00:17:51

you really believe in, in doing

projects and then posting them on

356

: 00:17:55

places like LinkedIn to get noticed.

357

: 00:17:57

Lily BL: Yeah.

358

: 00:17:57

And in the interview for Tesla

specifically, uh, I think the

359

: 00:18:01

question that sealed the deal for

me was that, uh, the bus had asked

360

: 00:18:04

me what I would do in inside of sql.

361

: 00:18:06

So he asked me just the general

stuff, like, you know, how would

362

: 00:18:09

you get something to come up?

363

: 00:18:10

What would you call the tables?

364

: 00:18:12

And he goes, it was like

his, his secret question.

365

: 00:18:15

It was supposed to catch me off guard.

366

: 00:18:17

He says, what if there

isn't anything in there?

367

: 00:18:20

Like you asked for it and it doesn't

give, like there's nothing in there.

368

: 00:18:23

What seat we're going to do then.

369

: 00:18:25

And we, I had done a module, uh,

to the bootcamp, uh, that you have.

370

: 00:18:29

And I had picked the short data

set instead of the large data set.

371

: 00:18:34

And because I picked the short data

set, my results were different.

372

: 00:18:36

And in fact, missing.

373

: 00:18:38

Mm.

374

: 00:18:39

So I distinctly remember sitting

there for like, what, what happened?

375

: 00:18:42

Did I do it wrong?

376

: 00:18:43

Rewatching the video, redoing

the thing, and trying and trying

377

: 00:18:45

until I got very frustrated.

378

: 00:18:47

And then I realized, oh, I picked

a different data set than he did.

379

: 00:18:51

So our results are probably not the same.

380

: 00:18:53

They're likely missing from mine.

381

: 00:18:55

So then I manually went in and checked,

and that was exactly what happened.

382

: 00:18:59

So when this manager from Tesla asked me.

383

: 00:19:01

I knew exactly what

happened when that occurs.

384

: 00:19:03

And so I was like, you

get absolutely nothing.

385

: 00:19:06

It's the most frustrating

thing in the world.

386

: 00:19:08

Uh, but it's good because you

don't have to keep looking.

387

: 00:19:10

There's absolutely nothing there.

388

: 00:19:11

You, you're just gonna get a, and

because I was so confident about it,

389

: 00:19:15

having sat in the frustration, he

laughed and then was like, I, I think

390

: 00:19:20

that she will be able to figure out

whatever she doesn't know and what

391

: 00:19:23

she does know will benefit us anyways.

392

: 00:19:26

And I think that's what sealed the deal.

393

: 00:19:28

So as you're working through the

projects and honing your skills.

394

: 00:19:31

Think about what you experience.

395

: 00:19:33

'cause that's what's gonna make

you shine in the interviews.

396

: 00:19:35

Avery Smith: I, I love hearing that.

397

: 00:19:36

I love hearing that the experiences

you had inside the accelerator

398

: 00:19:39

program, uh, worked out well

for you in, in an interview.

399

: 00:19:41

And it's interesting because, uh, I

obviously try to design the accelerator

400

: 00:19:45

and we're constantly updating it so that

people have less and less problems, right?

401

: 00:19:49

Like, we wanna try to make it as easy

for people to learn data as possible.

402

: 00:19:53

But the silver lining is

when those problems happen.

403

: 00:19:56

It puts you in a real life

scenario 'cause you're gonna have

404

: 00:19:58

problems when you get on the job.

405

: 00:20:00

And figuring out how to solve

those, figuring out what's

406

: 00:20:02

going wrong, uh, is a skill.

407

: 00:20:04

It's kind of a hard skill to teach.

408

: 00:20:07

But it's a very valuable skill to have.

409

: 00:20:09

So, uh, I love hearing that,

you know, a lot of data skills.

410

: 00:20:13

I'm curious here what order you

learn them in, and if you have any

411

: 00:20:17

tips for anyone who is learning

these different data skills?

412

: 00:20:20

Because there's a lot, right?

413

: 00:20:20

There's Power bi, there's Tableau,

there's Excel, there's sql, there's

414

: 00:20:23

Python, there's R Like what order did

you learn those in, and what advice would

415

: 00:20:26

you give to someone else learning those?

416

: 00:20:27

Sure.

417

: 00:20:28

Lily BL: Uh, I think the order I

learned them in was first Excel

418

: 00:20:32

and the Microsoft Office Suite.

419

: 00:20:34

Uh, I actually was certified through

them, um, to use Word in Excel.

420

: 00:20:38

However, I didn't understand it

as much as I would over time.

421

: 00:20:42

So then with the Excel basic knowledge,

I was able to navigate most data

422

: 00:20:47

and then I realized everything's

trickling into information systems.

423

: 00:20:51

So when I realized that I went back to

school and I got a degree that focused

424

: 00:20:57

in information systems and there I was

introduced to, uh, data visualizations

425

: 00:21:03

where we used a variety of tools.

426

: 00:21:06

Uh, the one that stood out

the most to me was Tableau.

427

: 00:21:08

So from there I joined an apprenticeship

where they used that tool.

428

: 00:21:12

'cause it just was visually stunning.

429

: 00:21:15

The rest of the stuff could get

the things done, including Excel,

430

: 00:21:18

but they were kind of grainy.

431

: 00:21:19

But with Tableau.

432

: 00:21:21

You could just floor somebody

by just the visual alone.

433

: 00:21:24

You wouldn't have to say anything.

434

: 00:21:25

They'd just be looking at it for a while.

435

: 00:21:27

So I was like, I'm really

interested in that.

436

: 00:21:29

So I did that.

437

: 00:21:30

And while I was in that program,

we also covered, uh, Python,

438

: 00:21:34

uh, more basics and sql.

439

: 00:21:36

And, uh, we also did, uh,

presentations, um, of the findings.

440

: 00:21:41

After I had, uh, those things under my

belt, I discovered your bootcamps and

441

: 00:21:47

then went back to square one with Excel.

442

: 00:21:49

Was like, okay, this is how you use

Excel specifically for data analysis,

443

: 00:21:53

not the other stuff I was doing.

444

: 00:21:55

So it redefined, like, it really

sharpened what I knew how to do.

445

: 00:21:59

And from there, uh, I went back into sql.

446

: 00:22:02

A lot of the companies I worked for didn't

use SQL as intensively as I expected.

447

: 00:22:07

So I was more so, uh,

using Tableau frequently.

448

: 00:22:10

And then Power bi.

449

: 00:22:12

Uh, power bi, um, is

like a full stop shop.

450

: 00:22:16

For analytics because it allows

you to do the visual component.

451

: 00:22:20

But to do that you need to

be able to pull in data.

452

: 00:22:22

To pull in the data, you need

to understand like the, uh, the

453

: 00:22:27

stakeholder request, and then also how

to clean the data and it uses Excel.

454

: 00:22:32

So, um, the skills were the same

in all of the software you just

455

: 00:22:36

clicked in a different spot.

456

: 00:22:38

So throughout the software per uh.

457

: 00:22:41

Phases or processes.

458

: 00:22:43

What I was continuously

sharpening was what is the data

459

: 00:22:46

process independent of the tool.

460

: 00:22:48

So if I had to start all the way over,

the way that I would learn these in

461

: 00:22:52

is Excel, uh, power bi and then uh,

Tableau and last sql, unless it you

462

: 00:23:02

are company that you're targeting does,

is focused on sql, I would do Excel

463

: 00:23:06

and then sql because if you understand

what you're doing in Excel, like, um.

464

: 00:23:11

V lookup, a next lookup, an H lookup.

465

: 00:23:13

They're essentially joining data.

466

: 00:23:15

So if you know how to join the data in

Excel and you can articulate it, then

467

: 00:23:19

you can look at any other software.

468

: 00:23:21

Here's um, a sql, let me go

ahead and join data here.

469

: 00:23:26

This is how I do the

joins in this software.

470

: 00:23:28

Okay, now I have Tableau,

how do I do the joins here?

471

: 00:23:32

And you are specifically honing your

skill for joining data, which is like

472

: 00:23:36

the backbone for, uh, data analytics.

473

: 00:23:39

And then that will parlay you

into engineering if you want.

474

: 00:23:43

Uh, but I would go Excel first and

then whatever you learn in Excel,

475

: 00:23:48

mirror it in whatever software

you can get your hands on next.

476

: 00:23:51

I did have the cases sometimes

where I didn't have certain

477

: 00:23:54

software, so I've had to wing it.

478

: 00:23:56

Um, I did a lot of G docs and

the, all of the Gmail suite

479

: 00:24:01

documentation when for some time I

couldn't afford the office software.

480

: 00:24:05

So even if you can't get

the most premium thing.

481

: 00:24:08

Do what is affordable, but focus on

the skill you're trying to sharpen

482

: 00:24:12

and you'll be able to figure it out

even if you've never used it before.

483

: 00:24:15

I

484

: 00:24:15

Avery Smith: think that's a really cool,

uh, point there is like, you know, we use

485

: 00:24:19

different software at different times,

but really a lot of them do similar stuff.

486

: 00:24:24

They get data from places.

487

: 00:24:26

You clean data with them, you do some

sort of aggregations or analysis or

488

: 00:24:30

make some charts obviously, like SQL

doesn't really make a lot of charts.

489

: 00:24:33

Like a pivot table in Excel is pretty

much just like a group buy in sql.

490

: 00:24:38

Um, so there is a lot

of, uh, overlap there.

491

: 00:24:41

So that makes a lot of sense.

492

: 00:24:44

So Lily, when you were trying

to break into data, there's

493

: 00:24:47

obviously a lot of data roles.

494

: 00:24:48

Um, there's data analysts,

there's business analysts, there's

495

: 00:24:51

operations research, which is

what I used to do at ExxonMobil.

496

: 00:24:54

Um, and each one of those jobs,

uh, is kind of complicated.

497

: 00:24:59

They, they're all data analyst roles, but.

498

: 00:25:02

They have different domains,

they have different industries,

499

: 00:25:05

they have different focuses.

500

: 00:25:06

They may use different tools, they might

have different vocab and, and customers.

501

: 00:25:11

So one of the things I really love, um,

that, uh, you sent me was like this matrix

502

: 00:25:15

you made of a couple different, uh, data

analyst titles and what you'd be doing

503

: 00:25:22

slash what tools you'd be using based

off of how experienced you you were.

504

: 00:25:27

So tell me about this matrix you made.

505

: 00:25:29

Why did you make it and, you

know, what does it do for you?

506

: 00:25:32

Lily BL: So I wanted to share this,

uh, with you and, uh, potentially

507

: 00:25:36

to anybody trying to break into data

or further career in data, because

508

: 00:25:40

this is how I was able to do it.

509

: 00:25:42

Uh, pretty much when you start

at the beginning, you don't

510

: 00:25:45

have a bunch of experience.

511

: 00:25:46

Um, in my case, I just knew Excel,

but not specific to analytics.

512

: 00:25:51

So the way that you leverage, uh, the

tool I gave you is that you kind of.

513

: 00:25:55

Set up your goals by a five year plan.

514

: 00:25:57

And the reason why is because by the

fifth year of any profession, you're

515

: 00:26:01

considered a professional 'cause

you've been in it for five years, you

516

: 00:26:03

have enough working hours to do this.

517

: 00:26:06

At a professional level,

you're not guessing anymore.

518

: 00:26:08

You should know, uh,

concretely what you're doing.

519

: 00:26:11

So, uh, depending on what kind

of analytics you wanna do,

520

: 00:26:16

the matrix can kind of guide

you to where you would start.

521

: 00:26:19

Let's use me for an example.

522

: 00:26:20

I started with Excel and I

wanted to be a data analyst.

523

: 00:26:23

My first data rules were

not titled Under Data.

524

: 00:26:27

So what I did is that I said,

Hey boss, I know you want me to

525

: 00:26:30

take care of these appointments.

526

: 00:26:31

And it was clerical work, but it

was, uh, handling a lot of data.

527

: 00:26:36

So I said, Hey, you have an

opportunity here, uh, to figure out

528

: 00:26:40

why your patients are dwindling.

529

: 00:26:42

So I took it upon myself to

offer a project so that I

530

: 00:26:45

can gain the skills I needed.

531

: 00:26:47

So in that project I recovered about half

a million dollars, uh, of lost payments

532

: 00:26:52

because somebody clicked the wrong button.

533

: 00:26:54

And there I secured my Excel experience.

534

: 00:26:57

I secured, uh, the patients being

able to return the company, gaining

535

: 00:27:01

the money, uh, that had originally

been lost because, uh, I used Excel.

536

: 00:27:06

That's what I needed in order to begin

to say, Hey, I have six months work.

537

: 00:27:10

With Excel, I have a

year's worth with office.

538

: 00:27:13

Um, at the time it was very popular

to use the Microsoft Office Suite.

539

: 00:27:17

Uh, let's say you secure the

the time you need with Excel.

540

: 00:27:20

Now you can say, Hey, in Excel.

541

: 00:27:23

I've also executed Pivot charts

and VLOOKUPs so I can join data.

542

: 00:27:27

I'm ready to go onto the next thing.

543

: 00:27:29

Hey, boss.

544

: 00:27:30

Uh.

545

: 00:27:31

We have a lot of data in

a lot of different places.

546

: 00:27:34

We already are integrated with Microsoft,

so we can use Power BI to pulling

547

: 00:27:38

all the data sets into one location.

548

: 00:27:41

Uh, can I get some time to be able

to make that happen so that I can get

549

: 00:27:44

you some support with your recording

and then you start figuring that out?

550

: 00:27:48

You might, when you, when you do this,

you don't have necessarily somebody

551

: 00:27:52

coaching you, so you need to rely

on the bootcamps or the knowledge

552

: 00:27:56

you already have that gives you the

confidence that I can execute this.

553

: 00:28:00

If you can't execute in the

software you're reaching for, don't

554

: 00:28:03

nominate yourself to do the project.

555

: 00:28:05

In there, you do it 'cause you

already know you have, you know

556

: 00:28:08

how to use that software, but the

company's just not implementing it.

557

: 00:28:11

So then you would jump into Power bi.

558

: 00:28:13

Maybe not its most advanced things,

but just enough to get your feet wet

559

: 00:28:17

so that you can figure out, this is

how I use it, this is what I like.

560

: 00:28:20

Once you get in there,

you can be like, Hey boss.

561

: 00:28:22

Uh.

562

: 00:28:22

We're in here with the Power bi.

563

: 00:28:24

We have these simple reports, but we have

a lot of stuff inside of SQL as well.

564

: 00:28:28

I was wondering if you can get me access,

uh, to request permission to join them

565

: 00:28:34

into the Power bi and that way I can

access more data and goes from there.

566

: 00:28:38

Right.

567

: 00:28:39

Well, one of the visualizations in

Power BI is a table, so you can actually

568

: 00:28:44

organize and clean all of your data inside

of Power BI and then export that sheet.

569

: 00:28:49

Put it into something

stunning like tablet like.

570

: 00:28:52

It's hard because as you're working

on it, it's not inherently clear

571

: 00:28:55

what you're doing, but that's how

you use the document I sent you.

572

: 00:28:58

You look at the title you want,

what software or what knowledge do

573

: 00:29:01

I have now and what can I reach for

based on my hidden skills that I

574

: 00:29:06

can start to attribute to my career?

575

: 00:29:09

And that's why you slowly grow it.

576

: 00:29:11

Now, sometimes the companies will say,

no, we don't need any work in Power bi.

577

: 00:29:15

We just want it done in Excel.

578

: 00:29:16

For me that translated to, I need to

find another company because I really

579

: 00:29:20

wanna grow more skills, uh, to get to

the next level because I have five years

580

: 00:29:25

to make it to that professional status.

581

: 00:29:27

And if I hit five years and I don't

have all the things in my tool belt,

582

: 00:29:31

I gotta do more than five years.

583

: 00:29:33

That's what I used to

get into the next thing.

584

: 00:29:35

Um, also, if you don't wanna grow

your career, like you're happy with

585

: 00:29:39

what you're doing, don't volunteer

the projects or the software.

586

: 00:29:42

Um, hone on what you, or focus your

skills on honing what you already

587

: 00:29:47

know and that will make you sharper

and sharper with what you have.

588

: 00:29:50

Avery Smith: Well, I think that's one of

your skills is that you're really good at

589

: 00:29:54

figuring out how to link business to data.

590

: 00:29:58

Uh, and I think a lot of

business and operations people

591

: 00:30:01

kind of struggle with that.

592

: 00:30:02

Um, so it's really cool that you

were able to be like, Hey, I see this

593

: 00:30:05

business need, uh, here's how analytics

could help us, uh, in this case.

594

: 00:30:10

Um, and I think that, you know,

you've done that as well with building

595

: 00:30:13

dashboards for, for stakeholders that

aren't necessarily, uh, data experts.

596

: 00:30:18

Um, I guess how do you have like an eye

for where data can help these businesses

597

: 00:30:23

and how do you, uh, help these maybe

non-technical, non-data folks be excited

598

: 00:30:30

and interested and ready to, to help

with these data analytics projects?

599

: 00:30:36

Lily BL: Well, that's such a,

that's such a good question.

600

: 00:30:38

Um, because you kind of

have to actively listen.

601

: 00:30:41

So, uh, it's almost like

speaking another language.

602

: 00:30:44

Somebody can say, oh man, like in real,

a real life example, a boss that I had

603

: 00:30:50

said, oh, I just want this inside of

Excel, and I'll be happy if we could

604

: 00:30:55

just get it from where it is into Excel

so that I can analyze it, I'll be happy.

605

: 00:30:59

So I got it done.

606

: 00:31:01

After it was done, they were so

happy that they decided, I want,

607

: 00:31:04

I wish everything can go in there.

608

: 00:31:06

And I said, what?

609

: 00:31:07

What you want, sir?

610

: 00:31:08

Is a warehouse of data.

611

: 00:31:10

Mm.

612

: 00:31:10

So what he said is, I want everything

in there, or I want this in Excel.

613

: 00:31:14

But what they're asking for

is an accumulation of data.

614

: 00:31:17

They're asking for a pipeline.

615

: 00:31:19

If you understand the data portion of

that, you can translate the regular

616

: 00:31:23

English into what that looks like in data.

617

: 00:31:26

And that's how you can determine

I can fix that or I can give you

618

: 00:31:29

something to help you hit that goal.

619

: 00:31:31

Or you can determine, oh, you know

what, that's just outside of my reach.

620

: 00:31:33

'cause your Google and you have a bunch

of data, I can't handle that much stuff.

621

: 00:31:39

Like I need servers, I need a

bunch of other stuff, but these

622

: 00:31:42

portions I can handle for you.

623

: 00:31:43

And that's how you determine I can

do this versus I can't do that.

624

: 00:31:47

I should offer you this 'cause

I know I can execute that.

625

: 00:31:50

Avery Smith: Lily, that is awesome.

626

: 00:31:51

I think that is a superpower

that, that you have.

627

: 00:31:54

Thank you so much for giving a glimpse

into what your career was like, telling

628

: 00:31:58

us what it was like to work as a data

analyst at Tesla and give us some

629

: 00:32:01

good, uh, advice and feedback for.

630

: 00:32:04

Trying to learn these data skills and

trying to maneuver in our data careers.

631

: 00:32:09

Is it okay if we put your, uh, LinkedIn

in the show notes down below and if people

632

: 00:32:12

have questions they can reach out to you?

633

: 00:32:13

Sure.

634

: 00:32:14

Okay.

635

: 00:32:14

Awesome.

636

: 00:32:15

Lily, thank you so much

for coming on the podcast.

637

: 00:32:17

It's so good to have you

and, uh, good to catch up.

638

: 00:32:20

Lily BL: Thank you.

639

: 00:32:20

Likewise.