Episode 73 : It's All About Data

Our next speaker who's actually part of the positive hire community. And when I say community, we have a community of women who are black, Latina and indigenous, who are experienced scientists, engineers and technology professionals. So, Angela is a business intelligence, analytics and data reporting executive with proving experience leading successful data projects with diverse teams. She has worked in industries such as banking, healthcare, higher education and marketing. Now, I'm gonna pause right there. So Angela went a couple months ago, like Michelle, I didn't even tell you what I did know. So for those of you that remember this company called Nielsen, where they used to send you $1, in the mail to do a survey. Angela didn't work for that company. We thought Nielsen was like the data company, right. Angela, actually worked for a company and her team helped develop data sets that Nielsen purchased them to do somebody Oh, work. So thank you thought I thought we also would like know, the person you want to go to. So Angela, actually is a St. John's University graduate with a bachelor Science degree in mathematics. Yes, just straight mathematics. But she minored in computer science. So Angela, thank you so much for joining.

Thank you for having me here. Yes, I love St. John's. I love Hofstra. I also attended there and I love Columbia, because I did some classes there when I worked there. So yes, I love Hi read.

The she forever gonna be in a class. She's forever learning and up leveling. But that's the other thing about tech and about data and data science is it's been changing, especially the last 15 years, we've seen a lot of movement and a lot of change and focus on it. So Angela, tell us how you got started with data because you're like, I'm just a math person. I'm gonna get a math degree. I love math. But how did you end up in the data space really early on before people really knew what it was?

Okay, so it starts with two stories. And they're hilarious, and they're long, so I'll cut it short. The first story is when I was a young child, I had an older cousin, she knows who she is. And she's gonna watch this. And she used to con me out of my money. So she used to say, Oh, you have five pennies. Five is more than one. So he had let me give you this. Let me know. She said, I have five pinnings. Five is more than one. And you have one quarter. Let me give you these five pennies for this one quarter. And yeah, I was like, Yeah, older cousins are not always trying to help you. So I was suspect. And I remember thinking in my head, I can't wait to go to school to count money. So yeah, I went to school with the attention of counting money. And that ended up being my job. And also I have a dad who worked in advertising. And so I always wondered what the data was behind that. And so I was good at math, because I went into school at first grade wanting to learn math. Because yeah, yes, she but I got a bad Delaware. And so yes, I still show up at her house for dinner. I'll be there soon. And so, yes, I was always inclined towards math and math related topics. And I just knew that was my major when that's what I took in college. I looked and I saw, I was like, Oh, I'm a math major. And there I was. And I just knew it was the right pick for me. I didn't know what I would do. Everybody asked if I would teach. And I was like, No, I'm not gonna teach you to something else. And, you know, as a black woman at that time, people like, Oh, she's not going to work at all. But there are a lot of jobs in the math industry. And the math, you know, being a math major, and you can work in any industry. So that is one of the best things about it. So yes.

I love it. I love it. I love math, but math classes get to the point where they don't have numbers anymore. It's just simple. That was more than what I needed. So I was for whatever we thought engineering would be better. No comment, no comment, no comment. That's
the good part two, that is the good part.

Okay, I want to get into people analytics. Because people often ask like you're talking about data and HR analytics people. What is this? What is people analytics? How do you describe it or define it? Well,

as we were getting, we were talking about this conference, I was actually thinking about a def, you know how I would define it, because it's so broad. But if I were to simply put it, I'm looking at what I said before, if I was to simply put it People Analytics is use of data to analyze techniques and understand and improve and optimize the, the business and the people side of the business. But that really includes so much, it sounds simple, but it's really a big thing, because organizations have so much data that they've collected for multiple purposes. So now, in the HR world, we're looking at that data. And we're trying to see how we can incorporate that data to hire and retain staff. And especially now when we're looking at ways to have a diverse workforce. As our workforce is changing, people, analytics is something that's really very important.

I love it, I love it. I'm sure I missed the best part. But um, so now that you've defined and share with us your definition, or how you view people analytics, why should organizations invest in it, and, and what I mean by invest, not just people, and money, but really invest in the data itself, like utilizing the power of that data.

There, again, there's so many reasons. First of all, it can improve the company, by that company, avoid missteps. There are so many cases that we've heard of where companies have not had diverse workforces. And they've had unfortunate consequences, they put out products that may have been offensive to some, because they didn't even realize because all of their staff looked the same. They've also produced products, that didn't work for everyone. Let's talk about health care. In the health care world, we have to fight to make sure that the health care products and medications that they're putting out, are good for minorities, people of color, I'm going to stop saying minorities soon because we know our demographic in this country is changing. We're in the US. And also for women. As women, some of the health care available to us was not tested on women. And if they had women on their team, that would have come up, they're going like wait a minute, all of our test subjects said this, we don't have a diverse group. So it's so many places, it's also illegal. I heard some of your price because I was on the side listening. And many companies get in trouble because of bias. And we're all biased in our own ways, because we're all human. And bias takes many forms is at the simplest level bias can be I like apples, but I don't like melons. But in the workplace. We know what bias looks like in the workplace, and it can be serious. So you can check for bias. I heard one of your price because talk about 360 reviews. There are methods to check the review before it gets to that employee. Because, you know, the process is usually the manager writes up the review, somebody else reviews it right. But what if we had systems in place that we had the technology to check for bias and those reviews before it went to that employee? Before there was an issue before there were potential legal actions. So there are so many ways that companies should be interested and the analytics of people analytics.

Yes. You brought up a very interesting and very important word as far as is. And so when we look, when we're looking at the data itself, let's talk about some of the issues and things that we need to be aware about people analytics, before we decide what what to do with it. Can you talk about that a bit?

Well, the biggest issue that I see is because you may have an organization that feels they're doing great. So why would they do this extra work, they don't really see that maybe there's an issue, or maybe there's a potential issue in the future. They're not have diverse board or diverse, senior staff, and everything is going good. The profits are good. You know, the employees around them are happy, they're happy. So why should they make changes? Why should they uncover information that could lead to potential problems? Because once you find that you have a problem, then you have to deal with it? Or else there's another issue?

Yes, absolutely. And I think along those lines, going in understanding that you have biases is really important. And acknowledging that, and you bought up something earlier about head to head diverse teams, when it came to doing product development, whether it's software or physical product, they would have known something. And there's something this is a major issue in healthcare, as you said, and so when it comes to medical devices, a lot of people don't realize they make medical devices for men first, and they're usually larger. And so a man's heart is usually larger than a woman's heart. So if you both have the same condition, you can't even get the same device until that manufacturer team designs a smaller version. And then it should work on women. But you don't really know that because it was never tested on women. And so you see it a lot. in different places. There's a there's a book that came out, I think in 2020, I can't remember the name of it. And it literally talks about how things are designed for men, and they end up harming women. And one of those is the seatbelt. Oftentimes, get in car accidents, women get hurt worse, because we're generally shorter than men, the average height of men, and it's all that adjusts, and it doesn't do enough or to see elevates, you still have to find the right, the right height and everything. Nobody teaches you that right, that manufacturer doesn't do that. And so then we end up being injured more in a car accident. So it's so much in the world, where especially around like Florida said gender equity, even what does that look like on the data side product development side? And there are so many barriers to bringing equity to women and and and underrepresented people as well. It wasn't my
favorite recent cases that is just mind blowing, is that AI systems didn't recognize black faces. Yeah. How is that possible? Ground faces are majority of this. But they were like, they, it just went right over?

You're going to get me into this post I saw about diversity of thought, and how is that just a dot? Why that's a dog whistle for really not addressing having black people having indigenous people and other people of color in spaces that aren't close to whiteness. And so yeah, it is so many issues, especially along with tech and anything, even services like health care that we see a lot of barriers. And I would love to see some data on which health care providers are seeing like for maternal health, having better results with black women, with Latinos, with Indigenous women, because as a health care provider, that is huge for maternal health, but I'm going to avoid in a whole other direction going but I love
it, you're you're hitting on something that is very important. That was what we were talking about the challenges that organizations face. So they may have all the data, okay, we're assuming they, there's so much that you could collect, but they may not be collecting what they need for that purpose for that research. And then how do they get the data that they have? It comes from different sources, it's in different parts of their organization. That is a humongous challenge. I worked on a project like that and All of the data was not sitting in HR. So HR was like, Well, you have to reach out to each department. Well, each department kept their data in different formats, they collected different data, they collected it at different times. So being able to create a system to grab all of the data, and we're talking to an international company. So it's not just five departments. It's a lot of data that you have to work in, put it together so that you can take action on it. And maybe some of those departments, you have to go back and say, well, we need you to collect this data. So all of our data is so consistent at an organizational level, and they're like, oh, but how will we do that? Maybe they don't have the staff to make those changes. They don't have the time. It's not important to them. It's not a priority. So that is a big challenge and organizations. I
thank you so much for pointing that out. It because we always assume HR has all of this information. And oftentimes they don't have it. Or it's or somebody else has it, and they have it in paper.

Oh, I wasn't even talking about paper don't scare me.

I know, I know, I'm some you'd be surprised a lot of engineering companies, they still do a lot of stuff via paper is printed out in a form but handwritten in the field. And then somebody's supposed to translate it back in office
it. So now we're talking about errors, because we're dealing with humans, and we have to accept that humans make errors. And humans program the system. So there's errors built into the system. But when you're dealing with so many automated systems, you're introducing a higher probability of error.

Absolutely, absolutely. I want to get to one of the question before we wrap up here. Why does measuring this data truly matter?
Why does it matter? Well, look at our look at our poll. Organizations are dealing with tremendous turnover, this, at this time, we're hearing this and in the news. And they're searching for ways to hire and retain employees. And you really have to take a look. Because in measuring these, this is some of what you talked about with your other speakers. And measuring these things you can find, well, why do we have more turnover in this department than this department? What's going on here? It's a way for organizations to be ahead, instead of having to work to keep up because our world has changed so fast. And technology changes. And we've we've just all come out of this humongous thing that changed all of our lives. So it's so important for us all to be ready. I could talk forever about what we need to do as data people but for the organization's they need to be ready. They they're doing training, but they're still having turnover. So what's going on with the training? What type of training, who's getting trained, as you mentioned in your last talk, is the training only for a certain demographic? And when I say you know demographic, I'm talking about the demographics of your employee base. So who's been trained? Why are they leaving? What are our salaries like compared to other salaries, for similar positions, there are so many things that data can help with and analyzing that data. And looking at it and just digging in the fun stuff that I love. It can give you so many insights to help your business grow. 

I love it. I love it. Thank you so much for this, Angela, how can people connect with you who are looking for people analytics consultant, looking for help with their data analysis and strategy in your organization?

Well, I am consulting now. And for right now, the best way to reach me is on LinkedIn. So do you have my LinkedIn,
your LinkedIn and yeah, we got you covered. Do you want everybody? 

Be sure you connect with Angela over on LinkedIn? Angela, thank you again for joining us and being a part of the positive heart community And long story short, I posted in our Facebook community, what were the conferences and events. Our members were going to community members were going to in 2022 Angelus comment was whenever you're hosting Michelle, so she ended up like, good. Now you're gonna be a speaker. She's like, Okay, what am I talking about? It's, this is what happens when you speak
up. Yes. And thank you for having me. Thank you for inviting me. And I do love your conferences, and I've been recommending you to so many people. So thank you.

Thank you. Have a great weekend. Angela.
Thank you. Bye bye.