996: Bringing data to LIFE by asking – and SOLVING – the right business questions w/ Bradley Boldenow

WEBVTT

00:00:00.119 --> 00:00:01.082
Hey, what is up?

00:00:01.082 --> 00:00:04.331
Welcome to this episode of the Wantrepreneur to Entrepreneur podcast.

00:00:04.331 --> 00:00:08.106
As always, I'm your host, brian Lofermento, and I'll tell you what.

00:00:08.106 --> 00:00:16.210
As we race to the end of an epic year here in 2024, the conversation on so many people's minds is AI.

00:00:16.210 --> 00:00:25.393
Everyone wants to talk about AI, but we often forget about the underlying ingredient to AI, which is data, and so that's why we've gone out.

00:00:25.452 --> 00:00:35.576
We found an incredible guest here today that we're all in for a real treat to learn from him, because this is someone who understands data in a really human, understandable way.

00:00:35.576 --> 00:00:50.734
This is someone who brings these complex topics and brings it to the human level so that we can actually understand it, make sense of it and use it to all of our advantages in our businesses, because data touches so many different aspects, including as I kind of tease a little bit about AI.

00:00:50.734 --> 00:00:52.206
So let me tell you about today's guest.

00:00:52.206 --> 00:00:53.987
His name is Bradley Boldenow.

00:00:53.987 --> 00:01:01.209
Bradley is a seasoned data consultant with seven years of experience helping businesses turn data into a strategic asset.

00:01:01.209 --> 00:01:11.352
As the founder of Bureus, bradley has led successful projects spanning data integration, modeling and advanced analytics, delivering exceptional data-driven solutions.

00:01:11.352 --> 00:01:24.846
He has worked with cross-functional teams across various industries, helping them understand and leverage complex technologies like large language models and machine learning, hot buzzwords that we're all hearing about, even in the mainstream media these days.

00:01:24.846 --> 00:01:33.429
Bradley's expertise lies in making data simple for teams to implement and act on transforming complex data pipelines into accessible insights.

00:01:33.489 --> 00:01:36.385
And if you're thinking, what the heck does all that stuff mean?

00:01:36.385 --> 00:01:38.671
Well, that's why Bradley's coming on today.

00:01:38.671 --> 00:01:41.168
I love the headline on his website.

00:01:41.168 --> 00:01:43.640
I'm going to tease that before we dive straight into my interview.

00:01:43.640 --> 00:01:50.111
As soon as we came across the Boreas website, where it says at the top we help bring data to life.

00:01:50.111 --> 00:01:55.322
There's so much that we can unlock in our businesses with what we're going to learn today, so I'm not going to say anything else.

00:01:55.322 --> 00:01:58.671
Let's dive straight into my interview with Bradley Boldenau.

00:01:58.671 --> 00:02:05.147
All right, bradley, I am so very excited that you're here.

00:02:05.147 --> 00:02:06.209
First things first.

00:02:06.209 --> 00:02:08.961
Welcome to the show, thank you.

00:02:08.981 --> 00:02:10.106
Brian Excited to be here.

00:02:10.520 --> 00:02:10.700
Heck.

00:02:10.700 --> 00:02:15.513
Yes, so many technical things that you're going to introduce us to and walk us through here today.

00:02:15.513 --> 00:02:18.407
But before we get to those, take us beyond the bio.

00:02:18.407 --> 00:02:19.248
Who's Bradley?

00:02:19.248 --> 00:02:20.973
How'd you start doing all these cool things?

00:02:22.759 --> 00:02:24.127
Yeah, absolutely so.

00:02:24.127 --> 00:02:27.221
Actually I started out in marketing and public relations.

00:02:27.221 --> 00:02:35.349
I never knew that I had a passion for data until I got into the professional world, started out in marketing and public relations.

00:02:35.349 --> 00:02:46.825
Through that role I was kind of introduced into how data can be used to really drive a business and help people within a business learn and understand what's working.

00:02:46.825 --> 00:03:02.090
And from there I kind of slowly made the transition fully into the data world and now, having spent the last seven years being essentially a data nerd, I do love every second of it and excited to see where we can go from here.

00:03:02.941 --> 00:03:04.228
Yeah, Bradley, I'll tell you what.

00:03:04.228 --> 00:03:10.068
As you heard me tease in the intro to today's episode, there are so many different corners of the data world.

00:03:10.068 --> 00:03:18.049
When we talk about business and data analytics and all of the ways that we could take our conversations today, Obviously AI is a big cornerstone.

00:03:18.049 --> 00:03:24.849
When we start introducing terms like large language models and machine learning, All of that is powered and driven by data.

00:03:24.849 --> 00:03:28.807
Where's your intersection of this world of data?

00:03:28.807 --> 00:03:30.493
Are we talking sales data?

00:03:30.493 --> 00:03:32.439
Are we talking AI and machine learning?

00:03:32.439 --> 00:03:37.872
Where do you really enjoy getting into the nitty gritty of how data powers businesses?

00:03:39.600 --> 00:03:45.807
Yeah, I mean I think my favorite part of the process is really kind of being the intermediary between the data and the business.

00:03:45.807 --> 00:03:53.605
You know, I love just businesses in general being able to take a product or service and use that to help make people's lives better and the flip side.

00:03:53.605 --> 00:03:55.979
I am very much into the data.

00:03:55.979 --> 00:04:05.602
I love being able to take raw data and transform it into insights and information that can drive a business, into insights and information that can drive a business.

00:04:05.602 --> 00:04:20.447
So I love really that intersection of the business and the data and being able to really assist businesses in using the data that they have to understand what's working, what's not working, and use that to drive their investments and ultimately see the most return on their business.

00:04:20.447 --> 00:04:31.761
I've seen so many different businesses take their raw data and use it to better themselves and I just feel like there's just so much power in being able to use data effectively within a business.

00:04:31.761 --> 00:04:37.483
So I really love being kind of that intermediary between the business and the data that they have.

00:04:38.305 --> 00:04:56.305
Yeah, bradley, I want to push you a little bit for examples, but I want to interject here of one example that we're all very familiar with as consumers, which is, of course, it's the largest online retailer, amazon, and we, as consumers, we see it so frequently that you know product products similar to what you're looking at or based on what you've bought in the past.

00:04:56.305 --> 00:04:58.271
Here's something that we think you may be interested in.

00:04:58.271 --> 00:05:00.583
You know we chalk a lot of it up today.

00:05:00.583 --> 00:05:04.927
I feel like today we kind of confuse AI with data, as we think that they're one in the same.

00:05:04.927 --> 00:05:14.528
But Amazon, target, best Buy all of these big box retailers they've been using data for more than a decade at this point to drive consumer behavior.

00:05:14.528 --> 00:05:25.569
What are some real life examples, both big business and small business, bradley, because I'd love for you to show our listeners what's possible on all sides of the spectrum, so that this isn't reserved just for the enterprise level.

00:05:27.132 --> 00:05:28.494
Yeah, absolutely yeah.

00:05:28.494 --> 00:05:35.983
I mean you mentioned the product recommendations that we as consumers see on Amazon and really now almost any e-commerce website.

00:05:35.983 --> 00:05:41.762
That's really kind of been the gold child kind of that gold standard, I'd say, of machine learning.

00:05:41.762 --> 00:05:49.033
That really kind of kicks businesses saying, okay, amazon's doing this, how can we follow and use data to the best of our abilities?

00:05:49.033 --> 00:05:53.790
And it's fun because there's so much you can do with data.

00:05:53.790 --> 00:06:07.790
And so, for instance, we're working with some smaller brands and retailers who are selling on Amazon and oftentimes when companies think about data or people think about data, they jump straight into those more advanced applications like machine learning, ai, predictive analytics.

00:06:07.790 --> 00:06:16.163
But there's such a foundation, such a base that is so valuable and impactful to especially smaller and medium-sized businesses that we love to focus on.

00:06:16.163 --> 00:06:28.475
So, for instance, the seller that we're working with on Amazon, it's as simple as taking their sales data and visualizing that data so that the brand can understand, ok, are our sales trending up or are they trending down?

00:06:28.475 --> 00:06:42.216
And let's say they're trending down, giving them a dashboard, the resources for them to understand what's driving our sales up or down, so that they can take actions to either lean into what's working or fix what's not working.

00:06:42.216 --> 00:06:48.668
So oftentimes you know what people like to jump into, the very fancy applications of data like machine learning and AI.

00:06:48.668 --> 00:07:08.067
But there's so much low-hanging fruit that you can do and simply taking the data that your business has access to and visualizing it to just get a base understanding and having that context of how your business and Trent is trending, then be able to take actions to, like I, capitalize on what's working and mitigate what's not working.

00:07:08.067 --> 00:07:18.853
But then you know so once you have that foundation, once you're able to kind of put that data to use, that's when you start to ideate and find those more advanced applications of data.

00:07:19.680 --> 00:07:32.850
So, for instance, one recent project that we've been working on for a larger media agency that specializes in TV advertising is helping them take their media logs.

00:07:32.930 --> 00:07:50.310
So every single week they get a log of all of the ads that they're going to be running on their client's behalf and what they'd like to do is, before actually placing those media logs and buying those TV spots, they want to understand okay, what can we expect to see in return for each of these TV spots that run?

00:07:51.213 --> 00:08:02.122
And we're able to essentially build a predictive model that takes those TV spots and then tell them in terms of the, ultimately the number of people that are coming to the website, whether it's e-commerce sales.

00:08:02.122 --> 00:08:14.475
We can predict using that data what their expected outcome would be, and then they can align that with the goals and objectives of each client to determine okay, are the expected results below or above?

00:08:14.475 --> 00:08:22.365
Where we want to be with these clients and then make adjustments back in that media log to ensure that they're doing whatever they can to meet the goals of their clients.

00:08:22.365 --> 00:08:36.302
So it is fun because there's just so much that can be done with data from, yeah, obviously the very fun and shiny applications like AI and machine learning, but really just the most base levels of putting data to use can provide so much return for businesses.

00:08:36.302 --> 00:08:41.865
So it's fun having such a variety of different applications that we can work through with our different clients.

00:08:42.528 --> 00:08:48.333
Yeah, for sure I can see that it must be fun, because you're talking about so many different outcomes from the data.

00:08:48.333 --> 00:09:04.231
You're talking about making decisions based on data, you're talking about predictive analytics based on data and obviously all of these things are relying on inputs and you're generating the correct amount of outputs and the correct outputs that you're even gearing towards so that you have some actionable outcomes.

00:09:04.231 --> 00:09:11.327
Bradley, let's enter AI into the conversation at this point, because it's something that is on the tip of everybody's tongue these days.

00:09:11.327 --> 00:09:21.344
From my perspective, it's made those outcomes even easier, of course, based on the inputs, and I want to hear your perspective on both inputs and outputs and the correct way to do both of those.

00:09:21.384 --> 00:09:28.254
But I'll throw a real life example into this is that for the podcast, we realize, you know, we have almost a thousand episodes at this point.

00:09:28.254 --> 00:09:33.230
All of those episodes have transcripts and show notes and guest bios and all of that.

00:09:33.230 --> 00:09:42.086
We feed that into various AI tools and we say, hey, let us know if you identify any synergies and connections that we should make between our guests for business networking.

00:09:42.086 --> 00:09:57.822
And it's cool that it's drawing these lines and these parallels that we, with so many different variables at play, we don't even see some of these things from our vantage point, so it's really helpful to have AI as that conduit to deliver the outcomes that we're looking for.

00:09:57.822 --> 00:10:07.490
Talk to us about how all this plays together and especially, I'd love to hear your emphasis on those inputs and outputs and how it really drives the quality of all the things data.

00:10:08.812 --> 00:10:09.434
Yeah, absolutely.

00:10:09.434 --> 00:10:43.964
I'm glad you bring up inputs and outputs when it comes to AI and large language models, tools like ChatGPT and Gemini, because I think often we focus too much on the output of what can we get out of these large language models, but what we oftentimes lose sight of is that what we get out of the models is usually dependent on what we put into it, and the more information we can provide a large language model, the better the information that we can provide a large language model, the more effective the outputs are going to be, and so that's always where we want to start, with a client that says, hey, we know we should be using large language models.

00:10:43.964 --> 00:10:48.273
What are some ideas, some ways in which we could potentially use these and what are the outputs we can get?

00:10:48.273 --> 00:11:03.445
Oftentimes we say, ok, let's pause for a second and let's think about what data that we have and how can we use that data to either automate process, to provide internal teams with better information, to provide customers with better information.

00:11:03.504 --> 00:11:27.609
We always want to start by looking at okay, what data and information do we have and how can we put that to use, because oftentimes clients will have these grand visions of how they could potentially be using large language models and say, hey, we could potentially use a large language model to catalog all of the comments and reviews on our products and then get better ideas of how we can adjust maybe the copy for our products on our e-commerce, then get better ideas of how we can adjust maybe the copy for our products on our e-commerce catalog.

00:11:28.669 --> 00:11:40.157
But what they realize then is that, oh, we don't have that standardized, structured set of data that contains all of our product reviews or all of our product comments, and so at that point there, well, what can we do?

00:11:40.157 --> 00:11:49.850
We can't really do anything without the data that we need to be able to get the outputs that we desire, and so that's kind of where I feel like there's not enough attention being put on.

00:11:49.850 --> 00:11:54.196
What data do we need in order to use a learning to his model to the best of its ability?

00:11:54.196 --> 00:12:17.102
And then, on top of that, then how can we build out the infrastructure so that we can have a more automated process, so it's not just team members having to constantly go to chat, gpt, enter an information and then do it with a will, but rather have process in place that can help automate and streamline the usage of these large language models so that it's fully baked into how they are going about their business.

00:12:18.086 --> 00:12:22.864
Yeah, bradley, I'll tell you what it's immediately apparent to me how intentional you are about.

00:12:22.864 --> 00:12:27.375
When we're talking about AI, you emphasize the fact that they are large language models.

00:12:27.375 --> 00:12:30.924
That's what is actually driving these tools that we just lump in as AI.

00:12:30.924 --> 00:12:31.967
You mentioned a few of them.

00:12:31.967 --> 00:12:34.365
Chatgpt and Gemini are two of the huge ones.

00:12:34.365 --> 00:12:35.631
I've personally switched to Cloud.

00:12:35.631 --> 00:12:43.849
I love Cloud for so many different reasons, but obviously they're a dime a dozen, but what makes them all common is that they are large language models.

00:12:43.849 --> 00:12:49.456
So, bradley, you're the first ever in this show's history to talk about the technicalities of this.

00:12:49.456 --> 00:12:51.366
What the heck is a large language model?

00:12:51.366 --> 00:12:56.750
Why do we hear that term so much and why is it important in understanding how these tools even work?

00:12:58.200 --> 00:12:59.634
Yeah, I mean large language models.

00:12:59.634 --> 00:13:01.865
I mean they're amazing so what they are.

00:13:01.865 --> 00:13:05.642
So think of a chat GPT amazing so what they are.

00:13:05.642 --> 00:13:06.243
So think of a chat GPT.

00:13:06.243 --> 00:13:12.721
And essentially, what a large language model is doing under the hood is it's essentially a mathematical model that is looking at an insane amount of data.

00:13:12.721 --> 00:13:17.557
So it's going online and reading almost any website on the internet.

00:13:17.557 --> 00:13:27.565
It's reading every single post that's on Wikipedia and it's, under the hood, it's training itself to be able to predict what word would come next, given a previous series of words.

00:13:27.565 --> 00:13:45.751
So if the start of the sentence is the dog, the large English model is essentially going to predict, okay, what word is most likely going to come after the words the dog, and then it's going to essentially then reuse that to provide an output in which it sees as sufficient, based on the prompt that a user gives it.

00:13:46.200 --> 00:13:47.563
So it's kind of interesting to think that.

00:13:47.563 --> 00:13:49.028
You know, today you can ask it.

00:13:49.028 --> 00:13:50.591
You know, provide me a recipe.

00:13:50.591 --> 00:14:03.980
I have these five ingredients and I want to be able to cook it in 20 minutes, and it's going to actually be able to provide you with that information, information down to this specific list of ingredients that you provide it and all it's really doing is just predicting which word comes after the other.

00:14:04.049 --> 00:14:12.745
So it's, the technology is pretty amazing, especially for the value that we are all being able to get from this tool.

00:14:12.745 --> 00:14:14.854
It's just, it's just fascinating.

00:14:14.854 --> 00:14:21.596
And now that sort of the some of the hype, at least, is starting to die down with large language models and generative AI.

00:14:21.596 --> 00:14:30.879
So it's really starting to see, it's interesting to see now how these tools are being used at a more base level, for both companies as well as individuals.

00:14:30.879 --> 00:14:39.794
It's just really, and it's crazy to think this is just the beginning of kind of maybe you want to call it an AI revolution or whatever but the adoption of this technology.

00:14:39.794 --> 00:14:45.924
We're still so early on in its phase that it's just so exciting to be able to even think about what's going to come next.

00:14:46.789 --> 00:14:59.494
Yeah, piggybacking off of that, I'm definitely going to throw some shade right now at both mainstream media and all of the entrepreneurs out there who are using all of this as an excuse, saying that, well, ai is just going to replace every single job.

00:14:59.494 --> 00:15:08.451
Generative AI art is already going to replace graphic designers and ChatGPT has already replaced content creators and copywriters and all of these things.

00:15:08.451 --> 00:15:20.817
Bradley, you seem very comfortable in the corner of the world that you're in and obviously, having seen your website, I think it's so cool the way that you structure your services and we're going to talk about what all of those look like to bring that data to life.

00:15:20.817 --> 00:15:24.964
But what's your take on it when people say, oh, ai is just going to replace people?

00:15:26.649 --> 00:15:29.096
No, yeah, I mean I've now put in.

00:15:29.096 --> 00:15:34.710
I mean I've used both the API to be able to leverage large English models in more automated ways for businesses.

00:15:34.710 --> 00:16:00.054
I use ChatDBT myself every single day to help just whatever task I'm working on, and you know, I feel that in probably 90% of the time a large language model will get you 80% of the way completed with a task, and the last 20% of completing that task that the human is responsible for is by far the most important piece of that test.

00:16:00.054 --> 00:16:08.817
So, for instance, let's say I want to write an email to a client and I say, hey, chatgpt, can you write me an email to this prospective client telling them about our services?

00:16:08.817 --> 00:16:11.320
Chatgpt will provide me with an email.

00:16:11.320 --> 00:16:14.075
That's 80% of the way there before I'm ready to send.

00:16:14.075 --> 00:16:27.176
But that last 20%, that last 20% of me going in, adding on more of a human touch, adding in some more personal notes to that email, will be what makes that email that I'm sending ultimately successful.

00:16:27.330 --> 00:16:34.558
If I were to just take what that chat, gpt or large language model provided me and just send it out, people are going to look at that and be like a robot wrote this.

00:16:34.558 --> 00:16:35.695
This is not personal at all.

00:16:35.695 --> 00:16:38.495
I don't want to have to deal with this email, I'm just going to throw it in my trash.

00:16:38.495 --> 00:16:50.816
So I think large language models won't replace jobs.

00:16:50.816 --> 00:16:52.480
They'll enhance jobs by making it easier for people to complete tasks.

00:16:52.480 --> 00:16:58.576
It'll save them time and allow them to focus on more strategic things like how can I make this email as resonating as possible for this client?

00:16:58.576 --> 00:17:06.098
So I don't personally have a few, you know there there always be some major changes with a technology like this.

00:17:06.098 --> 00:17:14.221
But again, I think overall, the the net net benefits will be extremely positive because it'll allow people to do a lot more with a lot less.

00:17:15.070 --> 00:17:22.020
Yeah, I really appreciate hearing your perspective on that and actually, to me, what I'm hearing is it's just making your superpowers even stronger.

00:17:27.349 --> 00:17:28.796
You can do so much more than what one human typically could do.

00:17:28.796 --> 00:17:43.359
So, even just looking at the depth and breadth of your services, I mean I'd love for you to talk about this stuff, because a lot of listeners probably have always heard about the world of data and analytics within business, but they've always wondered what does it look like to have someone on my team who's actively helping me with that?

00:17:43.359 --> 00:17:52.836
And, bradley, I love the fact that you focus on everything from hey, let's make sense of the data, let's put it into the right visualization, let's harmonize the data so it actually makes sense to us.

00:17:52.836 --> 00:17:53.880
Let's develop things.

00:17:53.880 --> 00:18:07.701
But even as simple as the way beginning end of this entire process of integrations making sure that data is feeding from places where we want it, where it comes from, to where we want it to be Bradley, walk us down that road path.

00:18:07.701 --> 00:18:12.162
What does this look like to work with someone who is so adept with data and making sense of it?

00:18:13.250 --> 00:18:14.634
Yeah, absolutely, it's a great question.

00:18:14.634 --> 00:18:17.990
And yeah, you kind of alluded to it earlier in the introduction.

00:18:17.990 --> 00:18:38.570
We really focus and we really want to be the best at making data as easy to use for people who are not necessarily data people or even technological people themselves, and so the kind of the process that we really like to work through is starting with a question Everyone within a business has a question that they need to answer to be able to go about their business.

00:18:38.570 --> 00:18:42.359
So, for instance, we typically work in the marketing space.

00:18:42.359 --> 00:18:57.622
So we work with a lot of marketers who have questions as simple as is our marketing working, and we work with that business partner, that marketer, to start with that question, and then we work backwards, essentially then enabling them to, like you said, obtain the data.

00:18:57.622 --> 00:19:05.265
So then, for this marketing case, we'd go to the different channels in which they're marketing, in which they're communicating about their product or services.

00:19:05.846 --> 00:19:19.949
We can ingest that data into, you know, a data storage tool like Snowflake or Power BI, and essentially model that data and get it into a state in which it resembles how the business operates.

00:19:19.949 --> 00:19:24.561
So we're able to store that data and get it into a point in which they have the attributes.

00:19:24.561 --> 00:19:27.436
They have the metrics that match up with.

00:19:27.436 --> 00:19:29.942
Okay, this is our question, this is how our business operates.

00:19:29.942 --> 00:19:55.406
Then, ultimately, then we can take that data and whether it's visualizing it in a dashboard, building a model, building a tool, leveraging that data that allows that marketer to use it to answer their questions and go about their business with more context, more information to make better decisions is kind of the general process which we follow to ensure that businesses that have questions are able to use data to answer them and continuously improve.

00:20:01.250 --> 00:20:03.218
Hearing you talk about this, bradley, though I would imagine that you see data where others don't.

00:20:03.218 --> 00:20:08.855
And even just using this example that you've introduced us to of the marketing study, the marketing question, let's roll with that for a little bit.

00:20:08.855 --> 00:20:13.195
A lot of people will probably think okay, here's my sales data, here's what it is.

00:20:13.195 --> 00:20:14.038
Make sense of it.

00:20:14.038 --> 00:20:27.069
I would imagine that you go beyond what we all traditionally view as data in our business, because there's so many different ways that data shows up through reviews you talked about earlier in our conversation today, through website traffic.

00:20:27.069 --> 00:20:38.701
There's so many different places for us to gather data that I would argue most of us business owners especially those of us who aren't technical or data focused we probably don't even realize the data that we have.

00:20:38.701 --> 00:20:43.593
So give us some examples of those sources of data that you look at that others may miss.

00:20:45.117 --> 00:20:46.402
Yeah, absolutely, it's a great question.

00:20:46.462 --> 00:20:49.973
So obviously there's, yeah, the known data within a business.

00:20:50.015 --> 00:21:05.730
So things like point of sale data for an e-commerce or brick and mortar business, for marketers, it's, you know, they run campaigns on Facebook, google, trade, desk, tv, so they have all of their campaign data from all of those different source systems.

00:21:05.730 --> 00:21:14.664
But then there are a lot of these ancillary data sources, data sets that are impacting how their customers operate, how their customers think and feel.

00:21:14.664 --> 00:21:51.453
You can include even things like weather data that may not be directly impacting the operations within a business, and then being able to think creatively and work with them to identify some of those potential hidden areas, that in which they weren't thinking of.

00:21:51.453 --> 00:22:10.461
But then we can go out and find that data to be able to provide them with the insights that, again, they may have not thought of initially when they thought about how their business operates, but in a way, in a roundabout way, maybe impacting their business, and then, by collecting that data, giving them those insights just allows them to have that context they didn't have before to really drive better decisions and better outcomes.

00:22:11.470 --> 00:22:31.134
Yeah, it's so cool hearing about all of this stuff because, also, I've had the pleasure of looking over just some of your clients that you showcase on your website, and it's everything as diverse as a joint relief practice to a vegan jerky company, to coworking spaces, to service-based businesses all of these different types of businesses.

00:22:31.134 --> 00:22:39.002
Bradley, and you've picked on e-commerce a little bit today, because that is an industry that is very driven by metrics and analytics.

00:22:39.002 --> 00:22:44.616
Give us some examples of some of these more different industries that a lot of people might convince themselves of.

00:22:44.616 --> 00:22:47.769
Oh, I build websites or I'm a social media agency.

00:22:47.769 --> 00:22:50.096
What am I going to do with data and analytics?

00:22:50.096 --> 00:22:53.915
Give us some examples as to how it plays into decision making and growth.

00:22:53.915 --> 00:22:55.140
That can happen there as well.

00:22:56.651 --> 00:22:57.372
Yeah, absolutely yes.

00:22:57.372 --> 00:23:02.990
We're actually working with a company called Golf Forever right now, which is a very interesting client.

00:23:02.990 --> 00:23:24.887
They provide both products as well as an application on mobile devices that golfers can use to essentially build a workout plan that's catered around the muscles that's needed to maintain their golf game long into their life needed to maintain their golf game long into their life.

00:23:24.887 --> 00:23:48.006
And you know, you wouldn't think, especially when it comes to golf, you can think of data probably much at all but it's so interesting to see how this current company is using data as people log into the application, as they complete workouts, as they submit assessments as to what they want to work on and the areas of the golf game that they want to improve on, how data can be used to essentially improve a golfer's game.

00:23:48.790 --> 00:24:16.902
So being able to understand okay, this golfer fits this profile, they're this, you know, they are maybe 50 years old, they golf three to four times a week and they have lower back pain and being able to use data to then surface up recommendations for this user to say, hey, I think these are the workouts that you should focus on in order to improve your golf game, while fixing or trying to help aid your lower back pain and then focusing on these different areas of your swing that you feel are lacking.

00:24:16.902 --> 00:24:22.609
It's just so interesting to see how data can be used really in so many different applications.

00:24:22.609 --> 00:24:38.698
Especially as a golfer myself, I never really thought of you know much data behind my golf game, but now being introduced into this client and seeing how they're using data and how we're helping them use data and enhancing their customer experiences, it's been a really really amazing, uh kind of fun project to work on.

00:24:39.380 --> 00:24:45.521
Yeah, Bradley, I've always kind of argued that the sister sport of golf is my favorite sport to play, which is tennis.

00:24:46.104 --> 00:25:13.853
And it's really cool because in tennis we've got a similar app it's called Swing Vision where I just throw my cell phone up on a fence Every single tennis court is surrounded by a fence and after the match I've got data on my forehand, my backhand, how much spin I've been generating, what my error rate is, my serve percentages, and it's really cool because you're right, that's actionable data to me to get better, which is obviously the pursuit of everything that we're doing in our businesses and in our lives.

00:25:14.256 --> 00:25:24.682
Which leads me to ask you this question, Bradley, because, knowing that we're being listened to by business owners all over the world, at all different sizes and all different industries, they're probably thinking Bradley, this sounds great.

00:25:24.682 --> 00:25:32.711
However, I have to also worry about getting sales and doing marketing and doing accounting and managing my staff and there's so many other things.

00:25:32.711 --> 00:25:40.098
How do you embed this stuff a data-driven focus and decision-making process how do you embed it into your clients?

00:25:40.098 --> 00:25:41.586
Is it in a dashboard?

00:25:41.586 --> 00:25:43.913
Is it weekly or monthly meetings with you?

00:25:43.913 --> 00:25:50.155
How do they actually get aboard the train of having data move their business forward in a really tangible way.

00:25:51.839 --> 00:25:58.713
Yeah, it's a great question and, honestly, it varies from client to client depending on how they operate, depending on where they are with their data literacy.

00:25:58.713 --> 00:26:02.567
But what we always encourage is starting small.

00:26:02.567 --> 00:26:20.189
So instead of thinking about, okay, how can our business as a whole become more data-driven, start with a much more refined scope of a project that you know could benefit from having more data data drive the progression and the process behind that more specific area of your business.

00:26:20.189 --> 00:26:30.364
That is extremely valuable in that you don't bite off more than you can chew and that then the results of a data project then don't just fall off towards the end.

00:26:30.805 --> 00:26:34.827
By starting small, you're able to become more targeted in your approach.

00:26:35.269 --> 00:26:39.669
It also makes the project more manageable in itself, in that you're not going out and getting all of the data.

00:26:39.669 --> 00:26:44.983
You're just getting a very defined set of data and then putting that data to use with a smaller set of stakeholders.

00:26:44.983 --> 00:26:48.340
That allows you to get more regular feedback.

00:26:48.340 --> 00:26:59.964
It allows you to adjust and iterate how that data is being put to use, like you said, whether it's a dashboard, or maybe your team would rather prefer having you know a weekly report generated and shared.

00:26:59.964 --> 00:27:06.403
All of that will most likely be uncovered as you're going, it most likely won't be known before you get that project started.

00:27:06.403 --> 00:27:30.540
So by starting small and having a more defined scope of how we're going to put data to use for this specific area of our business or this specific project, it just allows for again that better feedback and learning as you go and then, by the time the project is completed, those valuable insights can then be used to start again and continue to just add data into more areas of the business in a much more manageable and iterative way.

00:27:31.364 --> 00:27:33.211
Yeah, I really like that grounded approach.

00:27:33.211 --> 00:27:37.443
Bradley, I think that you're the epitome, in so many ways, of one of my favorite quotes of all time.

00:27:37.443 --> 00:27:41.275
It's from Albert Einstein, where he says if you want to impress someone, make it complicated.

00:27:41.275 --> 00:27:43.621
If you want to help someone, make it simple.

00:27:43.621 --> 00:27:48.784
And I love the fact that, even when we talk about the implementation of it, you say, hey, let's just focus on one area.

00:27:48.784 --> 00:27:53.940
We're not trying to make your entire business a data powerhouse, let's just focus on one aspect.

00:27:53.940 --> 00:27:57.423
Or, even earlier in the conversation, you alluded to just one question.

00:27:57.423 --> 00:28:00.347
Let's ask one question and start to solve that with data.

00:28:00.347 --> 00:28:02.230
So I really appreciate those insights.

00:28:02.530 --> 00:28:11.520
I also love in these conversations to bring it not just about your subject matter expertise, but also entrepreneur to entrepreneur, that you are one of us, bradley.

00:28:11.520 --> 00:28:16.137
You are not only incredible at what you do with data, but you are a fellow business owner.

00:28:16.137 --> 00:28:25.061
So, with that in mind, knowing that you had a career in data leading up to you going forth and serving clients under your own business brand, what's that transition been like?

00:28:25.061 --> 00:28:26.787
Is it everything that you hoped and dreamed for?

00:28:26.787 --> 00:28:29.663
Are there aspects of the entrepreneurial journey that have surprised you?

00:28:29.663 --> 00:28:31.135
I'd love to hear some insights there.

00:28:32.458 --> 00:28:33.219
Yeah, absolutely.

00:28:33.219 --> 00:28:40.829
I mean, yeah, it's kind of got to pitch myself and to think about how I'm being a full-time entrepreneur right now, and it's been, at least to me.

00:28:40.829 --> 00:28:50.008
It kind of seems like a unique journey to get to the point where I'm at today, where it actually started about three years ago as I was working full-time as a data solutions engineer.

00:28:50.008 --> 00:28:51.460
It was during COVID.

00:28:51.460 --> 00:28:52.779
I had a lot of time on my hands.

00:28:52.779 --> 00:29:05.133
I liked what I was doing, so I said, hey, why don't I see if anyone else needs help with a project here and there?

00:29:05.133 --> 00:29:09.359
So I essentially started freelancing as I was working at a nine to five, doing essentially what I was doing for my nine to five, but just for other clients, and did that on a part time basis.

00:29:09.359 --> 00:29:14.086
And over the course of a couple years that side hustle slowly grew.

00:29:14.086 --> 00:29:23.838
I added on a couple more team members and it got to the point where we said, well, essentially, you know we have enough business to do this full time, we love doing it, so why not just jump in?

00:29:23.838 --> 00:29:26.244
You know, off the deep end and do it?

00:29:26.244 --> 00:29:28.576
You know, as a full-time entrepreneur.

00:29:28.576 --> 00:29:37.603
So it's been really rather slow progression to get to this point, but that has been just fun to be able to see the business expand.

00:29:37.603 --> 00:29:49.628
You know we started out in just one very specific data-related platform and then slowly over time we started to grow and expand the different projects that we were working on, the areas of different businesses we were working on.

00:29:49.628 --> 00:30:00.159
So it's just been a really fun transition and journey to see how this business has grown and just gather these different experiences and clients that we've had to this point.

00:30:01.082 --> 00:30:11.903
Yeah, I love that, and it's that sort of organic and intentional step-by-step growth that I think is on display not only in your entrepreneurial journey but in the way that you address business problems and solutions.

00:30:11.903 --> 00:30:14.777
So huge kudos to you with the consistency in your approach.

00:30:14.777 --> 00:30:24.497
I really admire that about the way that you operate in all the ways even you and I just exchanging over emails how understanding you are in flexibility and us getting this scheduled.

00:30:24.497 --> 00:30:30.041
It's something that is so apparent to me in the way that you work, so it's something that's really cool to see from the outside.

00:30:30.041 --> 00:30:47.385
Looking in, Bradley and I always love to ask this question at the end of episodes, because I never know which direction guests will take it in and that is your one best piece of advice or takeaway for listeners Knowing that listeners are at all different stages of their business journeys and some of them are still stuck on the sidelines as entrepreneurs.

00:30:47.385 --> 00:30:51.861
Bradley, what's that one thing that you want to impart on them at the end of today's episode?

00:30:53.955 --> 00:30:57.086
Yeah, I think the biggest thing for me is going back to that question.

00:30:57.086 --> 00:31:02.659
You know, I think when it comes to data, people get writer's block in terms of okay, how are we going to use data?

00:31:02.659 --> 00:31:05.281
And I would say, don't even think about data.

00:31:05.281 --> 00:31:12.207
Just think about the questions that you have as a business and think about the questions that if I had an answer to this question, I would be better off.

00:31:12.207 --> 00:31:17.652
And that's really the heart and the soul of any data related project is that question.

00:31:17.652 --> 00:31:31.307
So I would really recommend again focusing on the questions that if you had the answer to your business and you as a person would be better off, and then, from there, work backwards to the rest.

00:31:31.307 --> 00:31:34.805
From there, Once you get on that point, the rest of the data pieces of the project will fall in place.

00:31:34.805 --> 00:31:41.867
As long as you have that question and the answer that you know you need, you'll be well off in terms of using data to the best of its ability.

00:31:48.154 --> 00:31:50.729
Yes, really well said and, once again, so consistent with your approach in all the things, which is why I'm really excited.

00:31:50.729 --> 00:31:57.067
I've teased your website a few times throughout today's episode, but your headline at the top of the website we help bring data to life.

00:31:57.067 --> 00:32:02.246
You've certainly delivered on that and right underneath that, you detail your approach and there's a diagram of hey.

00:32:02.246 --> 00:32:04.282
At the center of it is your objectives.

00:32:04.282 --> 00:32:08.217
Let's create a flow that gets us to where it is that you want to be.

00:32:08.217 --> 00:32:10.163
So, bradley, you've been a wealth of knowledge.

00:32:10.163 --> 00:32:15.580
I'm super excited for listeners to go deeper into your website and all the great things that you're putting into the world.

00:32:15.580 --> 00:32:17.103
So drop those links on us.

00:32:17.103 --> 00:32:18.905
Where should listeners go from here?

00:32:20.788 --> 00:32:21.971
Yeah, feel free to reach out.

00:32:21.971 --> 00:32:24.273
I'm on LinkedIn, happy to chat.

00:32:24.273 --> 00:32:28.181
I love talking about this stuff, so happy to chat on LinkedIn.

00:32:28.181 --> 00:32:29.443
Bradley Bolden now.

00:32:29.443 --> 00:32:45.058
Or yeah, breas wwwbreascom is where you can get that information on kind of how we operate, how we like to focus our work but ultimately love to have conversations with people, how we like to focus our work but ultimately love to have conversations with people about how they want to use data, their business and how we can grow and partner together.

00:32:45.840 --> 00:33:04.218
Yes, listeners, and if you thought that this was going to be an intimidating and technical episode, you see how much Bradley not only really loves this stuff I think that that's so evident here today but also the fact that he makes it so actionable for us, so understandable for all of us, whether you're technical or you're not technical at all.

00:33:04.218 --> 00:33:15.961
It's just so cool that he's focused on the business, the problem, the question at hand, and that's the way that we want to approach all the things, whether we're talking data, or marketing or sales.

00:33:15.961 --> 00:33:18.307
We don't want to talk about it from the technical components.

00:33:18.307 --> 00:33:19.678
So reach out to Bradley.

00:33:19.678 --> 00:33:25.780
He just dropped two of those locations His LinkedIn, his website is at thebureuscom, but you already know the drill.

00:33:26.041 --> 00:33:31.462
We're putting both of those links down below in the show notes, wherever it is that you're tuning in to today's episode.

00:33:31.462 --> 00:33:32.695
So feel free to reach out to him.

00:33:32.695 --> 00:33:34.300
He's super easy to get ahold of.

00:33:34.300 --> 00:33:39.156
You'll see a contact form right on the bottom of his website, or just personally on LinkedIn.

00:33:39.156 --> 00:33:40.500
You'll find those links down below.

00:33:40.500 --> 00:33:46.084
So, bradley, on behalf of myself and all the listeners worldwide, thanks so much for coming on the show today.

00:33:47.448 --> 00:33:48.128
Thank you, Brian.

00:33:49.192 --> 00:33:54.756
Hey, it's Brian here, and thanks for tuning in to yet another episode of the Wantrepreneur to Entrepreneur podcast.

00:33:54.756 --> 00:33:58.727
If you haven't checked us out online, there's so much good stuff there.

00:33:58.727 --> 00:34:07.941
Check out the show's website and all the show notes that we talked about in today's episode at theentrepreneurshowcom, and I just want to give a shout out to our amazing guests.

00:34:07.941 --> 00:34:16.721
There's a reason why we are ad free and have produced so many incredible episodes five days a week for you, and it's because our guests step up to the plate.

00:34:16.802 --> 00:34:18.786
These are not sponsored episodes.

00:34:18.786 --> 00:34:20.360
These are not infomercials.

00:34:20.360 --> 00:34:23.865
Our guests help us cover the costs of our productions.

00:34:23.865 --> 00:34:34.755
They so deeply believe in the power of getting their message out in front of you, awesome entrepreneurs and entrepreneurs, that they contribute to help us make these productions possible.

00:34:34.755 --> 00:34:43.315
So thank you to not only today's guests, but all of our guests in general, and I just want to invite you check out our website because you can send us a voicemail there.

00:34:43.315 --> 00:34:44.677
We also have live chat.

00:34:44.677 --> 00:34:48.487
If you want to interact directly with me, go to thewantrepreneurshowcom.

00:34:48.487 --> 00:34:50.697
Initiate a live chat.

00:34:50.697 --> 00:35:00.143
It's for real me, and I'm excited because I'll see you, as always every Monday, wednesday, friday, saturday and Sunday here on the Wantrepreneur to Entrepreneur podcast.