Alexis
Hi everyone. Welcome to Data Dave Dives Deeper. My name is Alexis. You know me as the host of this podcast and of Talk Tech with Data Dave. Today, I am here with Data Dave and Cher Fox, The Datanista. Yes, we invited her to join the podcast because I really wanted to have The Datanista and Data Dave on a podcast.
I’m super pumped, actually. I’m really excited to hear your story. But before we get started, I always like to let our listeners know that you can always submit a question to Data Dave by sending an email to talktech@d3clarity.com or by reaching out to us on the D3Clarity website. Or if you’d be interested in joining us for one of these Data Dave Dives Deeper episodes, you can always reach out to us that same email- talktech@d3clarity.com. We’d love to have you on the show.
Dave, Cher, thank you both for being with me today. I really appreciate it. Dave, how are you?
Data Dave
I’m very well. How are you?
Alexis
I see we have on our matching shirts today. I like that.
Data Dave
Yes, we do. That’s good. And welcome, Cher, welcome. President and founder of Fox Consulting must have been in data for a while and the name Datanista has got to have a story behind it, so we’ll drill into that as we move forward. Welcome, and thank you, everybody, for listening.
Cher Fox, The Datanista
Thank you for having me. And I’m so excited to share stories. Life of consultancies, yes.
Alexis
That’s where I want to start. Cher, the name, The Datanista. I would love to know how you got that name.
Cher Fox, The Datanista
It’s a good story. When I professionally started speaking in 2016, I was working with a colleague on a co-presentation, and we were submitting our talk and our presentation to various conferences. And one conference would not let me register without a Twitter handle… in 2016.
And I thought, gosh, it just wasn’t another social media platform I wanted to get on. But it became a roadblock. You know, it was really tricky. So, I started kind of digging into myself a little bit and kind of what my brand is. And I had a client, Nordstrom, for about four years, and I had purchased during that time a pretty healthy wardrobe that was pretty nice and fancy. So, I kind of took the fashionista because I was always getting complimented at conferences and at events and things like that, that I was fairly well dressed. I took that and thought, what about The Daatanista? It still resonates. It still follows kind of my brand. It’s, you know, what’s hot about data instead of what’s hot about fashion and what are the trends in data, as opposed to what are the trends in fashion.
So, I picked a Twitter handle. It took me a couple days, I think we barely got our presentation submitted in time, but we committed to it. It’s been my Twitter handle, which is now X, and it’s my handle on most of my social media. When Twitter got purchased and switched over to X, I kind of wasn’t sure what the future of that was. So, I started using it a little bit more on some of my other social media platforms, and it stuck. Some people might not even remember my actual name, which is unique on its own, but they’re really. Oh, The Datanista. Yeah, I know your stuff.
Alexis
That is my dream for Dave. I want people to know him as Data Dave. I have been, like, blasting it all over our social media. I just registered him to attend a couple of conferences, and when they were like, what’s the name of the presenter? I wrote Data Dave Wilkinson. I was like, everyone needs to know the Data Dave name. So, that’s what I’ve been pushing. So, I dream for that. I’m really happy to hear that that works so well for you.
Cher Fox, The Datanista
Yeah. And you have it on his LinkedIn profile. If you have to search, I mean, you can still find him under Dave Wilkinson. But if you search for Data Dave, he’s the first guy that pulls up. So that’s good. Excellent. It’s a good start.
Data Dave
Excellent.
So, how did Cher Fox get into data?
Cher Fox, The Datanista
Gosh, I started programming in 1985 when I was twelve years old. My dad brought home a Commodore 64 computer, and I spent some time shoving those floppy disks in and going through the manual to learn how to use it.
It was something that was really comfortable for me and was kind of my jam. Like, it was just super symbiotic in how I started programming. Three years later, while I was still in high school, I got my first job programming. My high school allowed us to leave school. If you were ahead on your credits, you could leave school and go work and actually get paid. At 15 years of age, which in Iowa, you’re allowed to drive. If you’re 14, you can drive to and from work, and you can drive to and from school. Yeah, it’s tractors. You know, you have to be able to do some things there farm-wise. So that’s where I was living at the time.
So I went to work for a company that programs those light up coasters that you get for your table reservations at Chilis’ or at Outback that was actually, those were invented in Cedar Rapids, Iowa. And that was my first programming job.
I graduated high school a little early. I graduated at 16 because I started a little early. I started at the age of four, and I started temping. It was really the easiest way for me to learn about the companies in Cedar Rapids. I also spent about three weeks in the temp agency’s corporate office learning, self-teaching programs. I learned the entire Microsoft suite. I learned the entire Word Perfect suite, and I learned the entire Lotus suite in about three weeks. And you had to test really high in all of them, well over 90%, in order to be placed out into the world.
So, I had the opportunity to start going into companies and seeing what they were about as a temp. And it seemed like my aptitude for data and working in spreadsheets always seemed to rise to the top for me. So, I was always doing analytics. Learned actually how to do analytics in Lotus 123 for DOS, which is super, super nice.
Data Dave
That takes me back. That takes me way too far back. I don’t want to remember that.
Alexis
I remember you saying Lotus 123 before Dave, but that was absolutely before my time. DOS 95. I definitely remember, but I don’t remember Lotus 123. It was the original form of Excel, right? Am I making that up?
Cher Fox, The Datanista
Well, it was the original. I think it was more the original spreadsheet.
Data Dave
Yeah, it was the original spreadsheet because that’s where the inventor of the spreadsheet actually worked. He created it at Lotus.
Cher Fox, The Datanista
And Lotus was really popular, not so much in the States, but abroad. And one of my temporary opportunities landed me with a cash management group called Lefebure at the time. And they produced coin counters and cash machines and things that you might find behind the desk in a bank that the tellers would be interacting with. They ended up getting bought out.
I was on a project developing cash management software, and I started with it as a technical writer. And then, the more I learned, I became a programmer, and I became a trainer. And when the acquisition happened, they let my entire team go and kept me. I’m sure that was because I was young. I had kind of learned how to do everyone’s job, but I’m sure it was the cheapest route, unfortunately. But it was a really great opportunity for me to stay on because I got to support that software solely for the parent company. And then they started putting me out at other mergers and acquisitions to learn about how data and how money was being handled at companies that they were purchasing.
I spent five years with them. I got promoted every year. I was really blessed. I had some really great mentors that believed in me and gave me a lot of great advice. One mentor at my last promotion, he said, “Well, what do you want to make?” And I undersold myself, and he said, “I’ll double your salary, but you have to buy a house. You’re 24 years old. You need to stop renting homes. You need to buy a house. And you’ll be able to buy a house if I give you that kind of raise.”
And back in the day, I mean, we’re talking 1996/97. I’m 24 years old. I was making about 30 grand. He bumped me up to 60. That was pretty healthy for, that’s pretty good of my age and of my aptitude. But realistically, I’d been in the corporate world for ten years, so if you balance that out, I’m probably in the right spot. So I bought a house, and I worked at that capacity for about a year before Y2K hit; about almost two years before Y2K hit the division, the parent division out of England closed our doors.
They turned off our 24/7 help desk; they shut off all the beepers. They turned off the 800 number because I had risen to the top. I was the customer service manager for the Americas, which was Alaska, all the way down to the tip of South America. Most of my clients had my telephone number, so they called me at home, and they’re like, hey, the 800 number’s down. And, oh, it’s worse than that. I’m afraid I’m out of a job.
They don’t have 24/7 support. They don’t have a Y2K-compliant piece of software. They said, what are we going to do? And I hadn’t signed a non-compete. I was out of a job, too. I consulted with a lawyer and started a company at 24 years of age as a single woman. I just bought a home, for Pete’s sake. A lot of responsibility.
That’s kind of always been my trend since I was younger. I did that for a couple of years, helped everybody get through their data migrations and their data integrations and pick out new software and gave them the 24/7 support that they needed. I basically worked myself out of a job because once Y2K was over and they’re all on new platforms, there’s really no need for Chet anymore.
My husband and I decided to move to Colorado, and when I got here, I started temping again because it was what I knew, and I built this beautiful network here. Soon, clients didn’t want to pay the temp agency anymore. They just wanted to work with me directly, and we have waiting periods with non-competes and things like that. So just things started to really snowball for me, and I was able to build a successful practice here in Colorado. And I don’t just service Colorado clients. I mean, we have global clients. One of our largest clients is based out of France.
So that’s how it’s happened. And gradually, you pivot from just being a programmer and being responsible for data to the data migration, to the data integration, to data quality, to being a BI developer and being a data engineer. And, you know, we’ve all seen the new titles that they’ve come with over our career spans.
So, I’ve been really blessed to have had wonderful clients who I developed really deep relationships with, who have allowed me to stay on and do some really remarkable things for them. With data, once you find a resource that you can really rely on and that you trust, sometimes it’s easier to let them try something new than to try to go find a new resource and integrate them into the fold.
So that’s been my career. It’s been really great, just amazing opportunities. And I’ve met so many people, and I’ve made so many really close friends. I’ve been to weddings of clients and children’s graduations. I helped a manager at a client pick out an engagement ring because I was the only woman on his team. And he’s like, “I don’t need any more dudes. I need a woman’s perspective on this.” I’ve been really blessed to have been integrated into a lot of people’s lives as a consultant, and I don’t think all consultancies are like that or have those kinds of opportunities.
Data Dave
No No.
So, where’s your focus now? What would you say your focus is right now?
Cher Fox, The Datanista
We are really focused on preparing organizations for next-generation tech, getting them ready for artificial intelligence, getting them ready for large language models. We all go to conferences, and AI is the buzzword. It was the buzzword at DGIQ that Dave and I both spoke at. The following week, I spoke at a conference in Denver that was a cybersecurity conference. It was all the buzz and all the rage, but there’s a lot of caution around that, that you can’t just jump into that pool and to the deep end, that you need to have some structure and some foundations in place.
So, we’re really working through speaking engagements and through client visits and opportunities to possibly work with them about data quality management, about data governance, even mentioning and having the conversations about data security and privacy that there are stepping stones that they need to have in place before they do jump into that deep end. Because the amount of data that artificial intelligence and the LLMs is generating and is going to generate is going to be like drinking from the fire hose. And if you already have bad data problems or you already have a lack of trust in your data, it’s just going to exponentially get worse with the next-gen tech.
Data Dave
So, yeah, it’s going to expose all that, isn’t it? Because the way I often see it is that we second guess a lot of our own decisions as people, as having real intelligence, or I think I have real intelligence. We second guess, and we gut-check. So we look at a set of data, and we make a decision, and we say, is that right? AI can’t do that, can it? It doesn’t have that second wave. So if your data is wrong, that you’re making decisions on the, then it’s going to just make that decision.
Cher Fox, The Datanista
It is. But I think, Dave, you can agree with me, most organizations know the status of their data and their level of data maturity. At least one person really, really knows the hard truth.
It’s not usually a surprise, right?
Data Dave
No, I completely agree with that. There’s somebody who knows, there’s somebody that is nervous, there’s somebody that is second guessing some of this construct. But if that person doesn’t see it all if that person is not the voice, then how do you clean up the data? How do you make the data so that it’s making the right decisions based off of that?
Cher Fox, The Datanista
Well, I mean, you have to have data quality controls in place and business rules. And there’s so many new tools today that are promoting data observability, which is a very proactive practice.
Alexis
Hold on, data observability.
Cher Fox, The Datanista
So data observability is looking at the data as it’s coming into our ecosystem, it’s evaluating it and possibly putting it up against business rules that we’ve already set. Back in the day, in olden times, we had a reactive process called test automation, which was the data coming into my ecosystem. I’m maybe still presenting it out through BI or analytics, but I have this little thing over here that’s keeping an eye on it and it’s letting me know, “Oh, gee, you’ve got some errors. Oh, that’s not right. We don’t accept that. That doesn’t fit the rules that we’ve set around this piece of data.” And then there’s a retroactive cleanup, which is still not necessarily retroactive.
Most companies only close their books once a month so they can keep an eye on their data. If they get their test automation output all cleaned up before the end of the month and they’re happy with it, they’re still going to be okay. But the data observability piece is trying to build data trust sooner in the process as the data is coming into our ecosystems and that could be into our CRMs, our ERPs, our data warehouses, really anywhere that we’re collecting data. And they have to be those data observability tools have to be multifaceted so that they can handle all the different vendors.
You know, Microsoft, they have a data observability tool, but it only works in the Microsoft ecosystem. Oracle is the same, SAP is the same. They have their proprietary tools that work within their own ecosystem. But then you add JD Edwards or you add Salesforce, you start adding all these other things that are outside of the ecosystem. And those tools, they don’t integrate as well or at all with some of those other tools. And you know, we all know most of our organizations today, they have 101 tools in the tool shed, whether they’re using them or nothing. Some of them are collecting dust, some of them are still sharp, some of them we’re still paying licensing on that we’re not even using. So having those tools available that are well rounded for all the tools that everyone uses today is really important in that proactive nature to try to manage your data quality, but you still have to tell the tool what’s right, whereas Dave is saying AI can’t make that decision for us yet.
Data Dave
So you just made a key point there, because you just talked about moving towards proactive. So getting more to using tools to get proactive monitoring of data quality, proactive monitoring of the data. Proactive feeding, is the data good enough for purpose? Not right before you use it for the purpose, but right after it’s created. When does that data become good enough for purpose so we can get out of reactively fixing it into proactive observation and monitoring? Is that what I just heard you talk about?
Cher Fox, The Datanista
You nailed it. I mean, data fitness is something that we have to really be paying attention to you and the fitness part of it is, is it fit for purpose? You know, we have to train our data, just like I’m a competitive bodybuilder, so I have to train my body to do the activities that I do that are associated with bodybuilding. Whether that’s competing on stage, whether that’s doing a Tough Mudder, maybe doing a Spartan Race. And I’m not just necessarily training with weights, because some of those obstacle course activities like Spartans and Tough Mudders, there’s running, there’s jumping, there’s a lot of other things that you have to do with that. And I remember I had a coach in the early 2010s and he always trained us. He said an athletic body will always do well on stage because it’s well most prepared from so many different facets.
And we have to look at our data in that same way too. We have to be training our data for the sport it’s going to do if that’s bodybuilding if that’s obstacle course races. And I think that’s where a little bit of the disconnect is on the importance of having data foundations like data quality management, or data governance and data security and privacy, which now is becoming heavily regulated. There’s compliance and a whole bunch of other things around that for risk mitigation. So, it just gets more complicated every year.
If you still know in the back of your mind that there’s data problems that keep you up at night, you have to attack those first before you start jumping into all these other things.
Dave and I, we go to a lot of conferences, and sometimes I’m coming back from the conference, or sometimes I know an executive that’s coming back from a conference, and they’ve heard all these amazing things, and they’re super excited, and they heard all these wonderful case studies and best practices, and they come back, and they start just throwing out buzzwords. I call it Buzzword Bingo. You know, oh, we’re going to have AI and, oh, we’re going to have data scientists, and you know, what’s the hot new verbiage of the year. We’ve just slapped lipstick on the pig. In essence, it’s still the same pig; it’s still the same stuff that we were doing. It just has a new title.
We don’t really call anyone a programmer anymore; they’re a developer. All of our titles have evolved over time, but we’re still doing the same things that we’ve always been doing. But when they come back with all that hype of all the buzzword stuff, let alone all the buzzwords we know of “boiling the ocean” “guardrails”, we have all these buzzwords.
I worked at a client’s once, and we actually made bingo cards, and we would go into meetings, and if we heard people say it, we would hit it, and then someone would yell BINGO to try to stop ourselves from doing it. It wasn’t to make fun of any executives, but it was for us to stop doing it, to stop saying those things and just speaking in English to each other because when we invited outsiders into our tech talks, we were killing them with buzzwords.
So, I think it’s still trying to communicate that best practices still need to be in place before we jump the gun on these fun, new, exciting technologies that everyone wants to harness. And a lot of companies are harnessing it in a variety of ways, whether they’ve pulled chatbots in. Gosh, I can’t think of an application that I use today that doesn’t have an AI plug into it now, whether it’s Canva for marketing or LinkedIn has AI help now. I mean, somebody’s selling a version of AI to us every day, right?
Data Dave
There is so much of it. There’s so much that, and it’s so big now. Whereas when I started creating something for a computer to do, something that they didn’t do before was actually relatively easy because nobody did anything with them, right?
Cher Fox, The Datanista
Even the technology we have today, we all remember when a TV show or a movie came out with video phone. We’re like, “Oh, we’ll never have that.”
Data Dave
That’s Sci-Fi that’s not going to work. Nobody will ever do that. Look at us now.
Cher Fox, The Datanista
I have it on my cell phone. I have it on, you know, I have it on my computer. I don’t even need a phone if I don’t want to. There’s plenty of applications that make that up for me. So, I mean, we don’t all have robotic dogs and robotic maids in our houses, but we all thought it was a good idea when it came out in the Jetsons cartoon. We’re all like, oh, we’d love to have a Rosemary. That’d be great.
Alexis
I have a robotic vacuum, and I love it.
Cher Fox, The Datanista
Oh, but you know what? It maps your house. And from a data privacy and security standpoint, it’s not the best thing to have in your house.
Alexis
If someone wants to come and vacuum my house, they can come anytime they want to map it.
Data Dave
They don’t always mix with your dog very well.
Alexis
Well, that’s a different point.
Cher Fox, The Datanista
Yes, but they’re hilarious for cat videos when the cats are riding them in shark costumes. You know, maybe some of that tech is more amusing.
Alexis
I liked hearing that perspective from both of you. But the one thing that I walked away from is the idea of curiosity. And maybe the advice we would give to that twelve year old girl is be curious about what you can do and remember that you can do it. And then there are people around you who can offer you mentoring and assistance and stuff like that. And just to go find it, would you guys say that’s kind of the recap?
Data Dave
I would say something else as well. I would also say that tech is evolving. It’s changing. When we grew up, tech was an end in and of itself. In other words, we became programmers. We made a conscious effort to become programmers and learned how to program. If I look at my son now and others, especially my daughter’s boyfriend, tech is now a tool, and he has to learn to program. It’s not that he became a programmer. He had to learn to program in order to be an aerospace engineer. In order to be one of these or one of those, he had to also be a programmer along the way. And I think you have to remain, as you said, curious within everything. But you must not be intimidated by it and just use it as a tool to go and solve those bigger and better problems.
Alexis
Thank you so much for joining us today, Data Dave, as always, it’s been a pleasure. I’m so happy that we got to have this conversation. I got to hear both of your perspectives as long-term data professionals who own and run their own businesses and are really cool people.
Data Dave
So thank you, Alexis. Thank you for coming up. And thank you, Datanista, for sharing your perspectives with us today.
Cher Fox, The Datanista
Thank you. It was a good get-together with Data Dave and The Datanista.
Alexis
That’s what I wanted all along.
