Cloudflare TV

📊 Influencing with Data

Presented by Nikole Phillips
Originally aired on 

Join Nikole Phillips, Data Analyst Manager, as she discusses how data impacts decision making and how to leverage Business Intelligence.

English
Womenflare
Business Intelligence

Transcript (Beta)

Hello everyone. Thank you for joining me today here on Cloudflare TV as we continue our BI takeover this week.

My name is Nikole Phillips and I am a Data Analytics Manager here on the Business Intelligence team.

Today I will be talking to you about influencing with data and how data impacts us both from a professional level as well as a personal level.

So before we get started, I want to provide you with a high level overview of who we are in the Business Intelligence team.

The BI team is a little over two and a half years old and we are distributed across Austin, San Francisco and we are currently building a team in our Lisbon, Portugal office.

Now the BI team is broken out into three different areas.

We have the Big Data Engineers.

Now they are like the foundation of the team. They ingest data from multiple sources, they build out data pipelines, they optimize data and then they really put the data into a format that can be consumed by end users like the Data Analytics team, the Data Science team and some of our more technical users, I'll say power users to be able to surface.

And then we have the Data Analytics team. The Analytics team is the team that I lead and manage and what our role is, is we engage with the business.

We want to understand what use cases are you trying to address? What business problems are you trying to solve?

And then we take that information back and synthesize it into a format that can be consumed by our end users so they can make data informed decisions.

And then we have the Data Science team. The Data Science team engages with the business as well as the Data Analytics team.

But their approach is they may use a more scientific method by applying machine learning algorithms, creating predictive models and this is so they can automate forecasts and predictions as it pertains to the business because our business will continue to evolve.

The tech sector will continue to evolve and just things that we don't know about will occur and we want to be able to get ahead of that.

With that being said, even though we are three different areas within BI, we are a highly collaborative team because we all need each other in order to build the overall product.

And that product is business enablement and helping our business to uncover those things they would not have known if they didn't have a big data engineering team, an analytics team and a data science team.

We are here to enable the business so they can make data informed decisions.

Now before we get started and to add more context to where we're going with this, I want to let you know a little bit about who I am, how did I get here, why did I decide to enter the tech sector.

So I was raised by a single parent, my mother, and I grew up outside of Philadelphia, Pennsylvania, so of course I love everything Philly.

I am a true northeastern. I was driven to beat what statistics said that success look like for people that come from my background.

This will make a little bit more sense as we continue to progress through the presentation today.

But in addition to that, I wanted to be able to use skills that I was already good at.

So in high school, I was good at math and I was good at science.

So I knew those were some of the fundamental skills that I needed in order to major in computer science.

So I wanted to leverage and lead with my strength the skills that I already had while I continued to learn new skills.

But I want to take a step back and help you understand the analysis journey.

When you make data informed decisions, you have your data, you identify key takeaways and insights through trends and patterns that we may see, but then there are non -quantifiable factors that also are partnered with those data points.

That may be your domain knowledge or just life experiences.

In my particular situation, in addition to the statistic I will see a little later, my non-quantifiable factors were what you see.

So you know when you're in high school and you are working with your guidance counselor and they're helping you to determine what your next steps should be in life.

So my guidance counselor told me people like you don't go to college.

Why don't you just take a trade or something? So that was one of my non-quantifiable drivers.

But in addition to that, the positive side of that is my mother was my number one cheerleader naturally before I got married and had my daughter.

Now I have three cheerleaders that are always in my corner. But she would always tell me people could take anything away from you, but they can't take your brain out of your head.

So use it and it's free. And that's so true. God gave me a brain so I use it to the best of my ability.

And that's exactly what I did. So you see here where we've outlined how does this all fit together.

You see become a statistic, overcome a statistic, and test and learn.

When we think about analysis, it's a myriad of all three of these things and it will always continue to go into a cycle.

But to add context to this, let's define Webster's Dictionary of what become and overcome means.

So based on Webster's Dictionary, become means to come, change, or grow to be.

Overcome means to prevail over or surmount. And then test and learn.

This is not a statistic, but it's just a definition as it pertains to doing analysis and identifying key takeaways and insights.

So we don't know what we don't know.

We approach an analysis based on a use case or a business problem that we're trying to address.

And then that analysis will unlock if it was true or false on what we were approaching.

But then it will also uncover unknowns that we didn't know about.

And that's where the what if questions come about.

And then based on the end result, we're in that test and learn.

Because based on our domain knowledge, what we see in the analysis, we are now testing out the actions that we take.

And as we learn, we continue to iterate on that.

And we continue to go through a process. So in analysis, you may become a statistic or you may overcome a statistic.

And you will continue to go back and forth in the process.

But you will always be in that test and learn. So let me provide an example on what that all means.

So based on society's criteria of what success looks like for people that come from my background, let me provide clarity to that.

At the time, and I graduated in the early 90s. So forgive me right now, I don't have the source that this came from, because I read it in a magazine, it wasn't digital at that time.

And but when you're doing any type of analysis, you should always be able to cite your sources.

So I just want to keep that as a side note to know.

So my apologies for not having a source for what I'm about to state.

At the time, children from the African diaspora were considered successful, if they only graduated from high school.

Now, there is nothing wrong with just achieving your high school diploma, or going to a trade school.

But for my own personal growth, and what my goals are, I wanted to move on and get my college degree, my college degree, because that was what success looks like for me.

I'm a first generation college graduate, I didn't want society setting what success looked like for me.

So that's the stat that influenced me, and made me driven to be what success society says success looks like for me.

But in addition to that, I did, I wanted to set my own standards.

And I wanted to set my own trends. But the beauty of it all was I was surrounded by people that were doing the same things that I was doing.

So we were pretty much standing on the shoulders of our ancestors and those that fought before us, so that we can collectively start shifting the narrative and shifting the trends of what success looks like for those that come from our background.

I'm going to provide another example. So you can really get the context to what I'm trying to say here.

So another example is statistic state, and this was based on an article I read in the medium, that 80% of startups fail.

Now, when we think about Cloudflare, obviously, Cloudflare did not fail, they overcame the statistic of 80% of failures.

Now, I don't know what they did, I wasn't there when they were making decisions.

But the only thing I can assume is the actions that they took based on seeing that and not wanting to become that was, they listened to people that may have gone through the same things that they were going through more experience, they looked at the mistakes that other people made, they continued to iterate and make changes and pivot as the industry changed, and many more decisions that they probably made.

But overall, Cloudflare is a very innovative company.

So I'm not surprised that we're still here. So switching it back to myself, and success criteria, and what that baseline was for those that came from my background.

Over the next couple of slides, we're going to get an understanding on we have the use case, but what else does the data unlock for us to be able to take action or become more curious about, because that's the beauty of analysis.

So I'm going to focus on educational attainment. And I'm going to focus on STEM, because that's the field and the degree that I had.

And I'm going to get more insight into how that looks by race, and by gender.

This is based on from Pew Research on the 2015 and 2016 academic year.

And when we look at graduates that have a degree in STEM, broken down by race, we see that 18% of them were achieved by whites, Asians earned 33% of degrees in STEM, and then Hispanics earned 15%, followed by blacks in 12%.

So key right there, I overcame the statistic of getting my high school diploma only, and now I've become a part of the statistic of getting a STEM degree.

If we move a little bit further and see what else does the data tell us about STEM degrees and educational attainment in this analysis.

So this is also based on the same data from Pew Research on a 2015 and 2016 academic year.

So we see when we look at all degrees in that academic year, there is a 16 point difference from males and females that achieved a bachelor degree in any field.

And you see that females are outpacing males by about 16 points. However, when we focus just on STEM, and we break that down by gender, it flips dramatically, where males outpace females with a 20 to 28 point difference.

Now we see that there is a double digit difference between males and females, when we break that down by race, where there's an 11 point difference between black males and black females.

And the largest gap is between white females and white males with a 32 point difference.

And then when we look at Asians and Hispanics and Latinx, there's about a 23 point difference.

So you see how we approach the use case with one baseline, and now it's unlocked things that I did not know.

So when we summarize an analysis, the use case was post -secondary education completion was considered successful for individuals from my background.

But as you can see, I overcame that statistic, and now I am a part of that 12% of black or African American STEM graduates.

And other nuggets that we were able to uncover were black females outpace all females in STEM degrees by an average of 28 points.

This stat was very surprising to me, because when we look at now the tech sector itself, women as a whole make up about 26% of the tech sector.

But then when we break that down by race, black women only make up 3% of the tech sector, Hispanic or Latinx make up 2%, Asians make up 6%, and then white women make up 15%.

So if we are actually meeting black women getting degrees in STEM, that is not reflecting in the tech sector.

So that's some more research that I would like to do to see why we see that disconnect, because the education is there.

But then we also see, as I stated, that the smallest gap between males and females was between black STEM graduates with 55% versus 44%.

And as I stated, the largest gap was between white females and white males with a 32 point difference.

So this is when we ask those what if questions, what other analysis can we do?

What are the contributing factors to what we see?

But we see that the baseline of what I was driving to do was to overcome the statistic, I then became another statistic.

But let me help you understand how I almost became a statistic.

So in our careers, we go through different things, and a whole bunch of stuff happens.

I've been in an industry over 20 years. So I've seen a lot, been through a lot, experienced a lot.

And based on the 2017 study by Kapor Center for Social Impact and Harris Poll, you see, I wanted to see why do people leave tech?

Like, what are those driving factors?

Because I was almost this statistic, and I'll tell you why.

So people may leave tech due to unfair treatment, they may be seeking new, better opportunities, like you want, you may have hit a plateau at your current job, and now you want to be able to move on, dissatisfied with your work environment, recruited away, people are always reaching out LinkedIn, indeed, you're always getting pinged about different job opportunities, or you're dissatisfied with your job duties, meaning you may want to transition into another career.

I have number one and number three highlighted, because at the time, and this is prior to Cloudflare, those were the points that resonated with me at the time.

And that was how I almost became statistic.

But then when I get a little deeper on why women may leave the tech sector, as you can see, women leave tech at a rate 45% more than men.

And the reason that is, is it's due to lack of career growth opportunity, gender pay disparity, which I'm not just going to make that a tech thing that is across all industries.

And that's definitely something we need to continue to work at.

Because if we're doing the work, we need to be paid for it.

But it is not a binary issue. It is the issue that is multi layered, and we need to approach it that way.

And then people also leave due to work life balance.

So this is something I'm also interested in, because, as all of you know, we're in a pandemic.

And a lot of the data is showing that women are really taking the brunt end of that.

So I'm really curious to see how that's that changed, based on the pandemic.

So I'm going to do some more research on that. But based on what you've seen in the prior slide, and now, this is these are the things that were resonating with me on why I wanted to leave.

And it was due to a lot that was going on.

Like I said, I've been in the industry for over 20 years. So I was frustrated.

I was angry. And I was scattered, because I did all of the things that the textbook says I need to do in order to maintain relevancy in the tech sector.

But it still subjected me to toxicity, micro and macro aggressions, no opportunity for upward mobility.

So I basically felt stagnant. And I felt like I hit a plateau.

And I felt like my answer was to leave the tech sector. And let me help you understand how serious I am about this, and how this really almost happened.

When I interviewed at Cloudflare, I had an offer on the table at another company for a non technical role.

So I was serious about leaving. However, I refocused. And there were non quantifiable factors that influenced me.

And that was my family. Remember, I said, I have some cheerleaders that are always in my corner.

And my husband and my daughter really helped me to refocus.

And they helped me to realize I've never had it easy.

I've always had to figure out how to make a way out of no way.

And I had to continue to keep that mindset. So right now, I'm still here. I'm still in tech.

I'm still in that test and learn mode of what do I need to continue to do to keep relevancy.

But now, it is even deeper than that. It is what do I need to do to make it easier and better for those that come behind me, so that they don't have to deal with the things that I had to deal with that could possibly push them out of the tech sector, which is something that I'm good at.

So I'm going to move on and help you understand how from an analytics perspective, sometimes, when dealing with stakeholders, and just in life, we will make assumptions.

We have the domain knowledge. And we're always saying to ourselves, oh, based on what I know, this is what I expect to happen.

Based on what we see, this is what I expect to happen.

So we almost write the narrative of what the outcome is going to be before we even see the data.

So we should always approach any type of use case, any type of business problem with a neutral view, and allow the data to dictate and tell the story.

And then we add our own business knowledge or life experience to that.

So to use an example, society's assumption is that teens and young adults use social media more than any age group.

Why do people think that?

It's for a variety of reasons. So let me see how they can come up with those assumptions.

Some of those assumptions could be based on who you to get feedback from.

You can end up telling a story you want to tell by targeting the cohort that you want to give you that outcome.

So in this case, the cohort here was teenagers at the age of 15, I'm sorry, 13 and over.

And the question was, why do they use social media?

So when you see this data surface, you can assume the reason why Pew Research focused on teenagers is because they assume that they're the biggest consumers of social media, which can then make people think the biggest consumers are teens and young adults.

And the reason why they do use social media is 40% of them use it because they want to stay connected with family and friends, which is the core functionality of why all platforms were created, to stay connected without physically being connected, but feeling like you're connected.

And we're doing a lot of that right now in the pandemic. And then 16% use it to get news and info.

Now, in this case, I understand that because there's products and brands that I found out about on social media that I would have never known about.

But then on the contrary, we see that 27%, they experience bullying or spreading rumors.

And when we see a lot of stories in the news and read a lot of stories, it is focused on our teenagers and young adults.

So again, that's another way we can make a bias assumption.

And then they also miss that in-person contact because it may harm a relationship, which is true.

A lot of our young people have grown up and been born into a virtual world.

So this is pretty much all they know.

It's not like it was when I was growing up. We would be outside with each other, jumping double dutch and playing hide and seek and all that stuff.

It's a lot different now.

But is it true?

Do teens and young adults consume social media more than any other age group?

So as you can see, Facebook is the largest social media platform that is used.

Like 45% of people use social media. Now, my daughter will laugh at that because she says nobody uses Facebook anymore.

And research does show that the more popular social media platforms are Snapchat and Instagram for teens and young adults.

So for her, that is true. So that number probably comes from that 45% is mostly driven by people that are most likely in their late 20s and I would say even 30s and older.

But as you can see, when we look at the difference, 26 to 35 year olds actually use Facebook, I mean use social media five points more than those 15 to 25 years old.

And it may not all be for leisure because as you get older, you get a job and you get more responsibility.

So you could have started a business.

So you're trying to build your brand. You're trying to promote your business and your new products.

So you're using social media to do that so you can reach a broader audience.

So it's not all about leisure and fun. That could be the contributing factor as to why we see that in the 26 to 35 year old age group.

This was also another surprising factor when we break it down by gender because males use social media 16% more than females across all platforms.

And I'm saying just the platforms that are within this analysis.

And the largest engagement is at 19% for those that are 26 to 35.

And again, it goes back to my point of growing more responsibility just in life itself.

So was that hypothesis true that people, I would say teens and young adults are the largest consumers of social media?

No, the hypothesis was actually false and we approached it with a neutral view.

But then we also uncovered additional insights where now we see that 26 to 35 year olds actually consume social media at 42% on average and males use it 16% more than females across all platforms.

And where we see the largest gap in gender is within the Twitter social media platform at a 17 point difference.

So let's bring this all home and what this all means.

So when we do analysis, as you learn today with the examples that I've provided, the unknowns cause you to become a statistic, but the data can drive you to overcome a statistic.

And like I've said, you will continue to go back and forth.

And the synopsis and summary of what I am saying is that data can take you in different directions.

We always have to approach it with a neutral view to avoid any type of bias outcome and allow those key takeaways and insights to help drive our decisions based on our own domain knowledge and life experiences.

You are never going to avoid the test and learn. That is what analysis outcome encourages you to do.

And it also encourages you to be able to make change as things are changing based on what you're seeing.

So you can be proactive versus reactive.

So I would like to thank everyone for joining me today. I encourage you to continue to watch Cloudflare TV this week as some of my teammates continue with the BI takeover and also some additional other segments that will be on Cloudflare TV today.

Thank you and have a good one. You run a successful business through your e-commerce platform.

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