Measured Direction Podcast

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In this episode, we discuss THE INTERNET OF THINGS! This is a wide-ranging discussion that delves into the enabling technologies for wider customer data capture and aggregation.

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Google BigQuery
Amazon Redshift

Transcript

Jason Rose:
Welcome, listeners, to the fifth episode of Measured Direction. I am Jason Rose, a content strategist here at Digital Surgeons. I'm joined, as always by Tom Miller, the leader of our analytics practice. What's up, Tom? Hey, how's it gone?

Jason Rose:
As always, this podcast is a audience driven podcast. So we answer the questions that you listeners submit to Bitly slashed measured Direxion. Once again, that's Bitly slash measure Direxion or using the hashtag measure direction. So today's question. I want you to elaborate on a recent article that you just posted, Tom, about winning I and how it's not about the analytics of things, but instead about the analytics of customers.

Tom Miller:
Yeah. Well, thank you. And, you know, thanks for to our listeners for continuing to submit questions. We're starting to get to a point where we're going to start setting up individual shows around individual topics, which is pretty cool. So thank you for that. And I take this question out of the pile this week just because, you know, I did fairly recently published article. I mean, by the time this comes out, it's going to be like three weeks prior. But I wrote an article about the Internet of Things and the quote unquote analytics of things since somebody requested that we get into the topic a little bit more. I think that's a great topic for today's conversation. So let's have it.

Tom Miller:
Let's start a really high level and just talk about fundamentally what the Internet of Things is and kind of how marketers are leveraging it or moving in that direction. Sure.

Tom Miller:
Well, it's a product set, right. So, you know, my article focuses on the consumer Internet of Things, but really what the Internet of Things in general is and I think this is sort of a different definition based on who you talk to. But it's Internet connected devices, right? It's devices that are taking inputs from their local environment and in some way, either processing them locally and doing something with that or processing them in a cloud based environment, sending data to the cloud and then getting data back. That could be the case of like a remote control. Right. So my car I have an app on my phone and I can start my car with my phone. Is that Internet of Things? I mean, possibly. I may say so, yeah. And my app and my phone, I can actually open up by app right now and I could tell how hot it is in my car. Right. So that's that's a little bit more of a two way. Right. Communication movement. Right. It's it's there are sensors in my car that are telling me like where my car is and how hot it is in my car and things like that. And then I can also send back to it, I can say startup and then it continues to track. Right. And, you know, that's pretty interesting. And I think this is all come about because of and I mentioned in the article, it comes about there's this really great convergence of technology that's occurred in the last really the last twenty five years. Really the last 10 years that has made these sensors, these small processors.

Tom Miller:
You know, there's been a whole lot of standardization of code to be able to run these processes. There's been a major, major decrease in cloud infrastructure cost, which we're going to talk to, I think, in the second half of this conversation. You know, batteries, memory, all this stuff has has combined to create a technology environment where these types of devices can exist. Right. And, you know, if you think about it, there's also lots of other really useful things, really useful applications for Internet connected devices that extend way beyond consumer devices. Right. You think about like roads and infrastructure. Right. It's like the whole concept of driverless cars is certainly an Internet, Internet connected device in Internet of Things type of problem or type of solution. But even just understanding road surface temperatures for a state agency that's in charge of cleaning the roads of snow. Right. Understanding, you know, even traffic flows and understanding that if traffic all of a sudden gets backed up in a spot where it doesn't normally get backed up, that there's probably an accident that's occurred on your highway there. Right. And, you know, it extends way beyond public sector stuff. I mean, you think about health care and you get a pacemaker monitoring that is phoning home. Right. Things like that. So, you know, it's sort of a interesting, quote unquote, new world when it comes to these devices. But on the consumer side, I think probably one of the Keystone Technologies is certainly smartphone adoption. Right. Because pretty much every Internet of Things device that I can think of.

Tom Miller:
Even though it probably could be controlled through a traditional wind up interface. Most of them are controlled through an app or even an app protocol that is sort of phone or mobile device based. Right. So you think about Apple's home kit. You know, Apple wrote an entire protocol for integrating connected devices to their platform because they understand that controlling that platform is going to control their market share with this device. That's right. They need to have the device manufacturers working with Apple because that's a major differentiator for them when it comes to selling their devices.

Jason Rose:
Right. So that the position themselves as the central hub. Otherwise, someone else is going to and people will stop it and move from an Apple device to, say, androids. And now the Android devices, the center of your home, your car, the entire connected world that you kind of create around yourself.

Tom Miller:
Right. So, I mean, what do you think? You know, I am extremely bearish. Nope. That's the wrong word.

Tom Miller:
Bullish. I'm extremely bullish on the future. I would say I am, too. I t I mean.

Jason Rose:
I think it is. I think it's very interesting.

Tom Miller:
I, you know, I'm not like I don't necessarily completely believe the hype because I'm not you know, I think we've sort of already evolved a lot of the way into the I.T. world, even though the devices themselves haven't really caught up with us, you know. I mean, I think the cultural shift maybe has already happened in a lot of ways. But I also think that there's some fundamental things that will change with with us and our thinking as we shift into this world. You know, there'll be one or two sort of major changes to human behavior that occur as a result of these devices in the next twenty five years.

Jason Rose:
I think, you know, that's a pretty big shift, I think. Obviously, driverless cars is probably one of big ones health.

Jason Rose:
How far do you think we are out from fatherless cars?

Tom Miller:
I don't want to make a prediction. I'm terrified. I mean, for me, I think I'll be an old man.

Jason Rose:
Yeah, an older man. By the time. By the time we're in driverless cars.

Tom Miller:
But, yeah, I mean, you know. But the point of my article is just to get get into it. And that's I think that's a pretty good background, is that there's been a lot in the press referring to what's called the analytics of things. And Thomas Davenport, who's an author, you know, he's like he's like the professor in business school where everybody wants to take his class and nobody can get into it. Right. Because everybody wants to take that. You know, I would say that he's one of the most brilliant minds in business analytics today. And he coined this term the analytics of things that refers to how the things themselves can have, what I would call logic baked into them. Right. And that that logic can be upgradable over time to basically have a response based on, say, a stimulus, a sensor stimulus or or an external stimulus. And the that's basically part of the definition. So the analytics. Right. I'm putting that in air quotes, which doesn't really work for a podcast. But the analytics is happening based on the programming of the device and the sensors. Right. And I don't think that that's analytics. I think that that is data collection. No, I mean, it's just it's it's the the device functions. It's the product feature. Right. It's it's just like I push a button on my microwave and the microwave turns on. That's that's it. I mean, I have an oven in my house that I can put a piece of steak in and I can say there's a steak in the oven and the oven knows that based on its sensor data. How long to cook that steak for? Right. And that's a pretty nice piece of kitchen technology. But that's not that crazy, right? I mean, that's just something that's baked into my oven. I mean, it's the same with my refrigerator. Like, my refrigerator is a thermostat of it that turns it on when it gets below a certain or gets above a certain temperature. That's nothing that that is that crazy, right?

Jason Rose:
It's like the technology of like almost like a check engine lighter that inflate your tires like they have in your car.

Tom Miller:
Right. So that's boring. The the second sort of way that we talk about the analytics of things are the way that these devices are providing feedback to people. Their owners, I guess, is a good way to say that their users on their usage. Right. And so the best example is like the Nest thermostat. Right. Or like the Fitbit. Right. And it is a device that is based on its stimulus, based on its. Based on sensor data, right? Is giving you feedback on your own behavior or perhaps even to put it better, its own contexts write its own context as it is affected by you. That's interesting. Right. But I think that that's also sort of missing a huge part of the conversation around what the analytics of things really means for the business of the Internet of Things. Right. And I think that it's in you know, I think this phrase has been coined and then sort of some you know, some men, some people within the blogosphere, the popular what I call a popular business press blogosphere, kind of glommed on to it. And it's just it's a little bit painful to read a lot of the articles around here. The analytics of things and the phrase itself to me now, it's sort of like I cringe a little bit when I hear it, because it's it's really far off of what they're they're completely burying the lead on how awesome the Internet of Things will relate to a customer analytics practice, particularly when it comes to companies that are within the Internet of Things space.

Jason Rose:
Yeah, what they're really they're speaking to the personalization of things, almost like technologies that just allow you to have your world spit back at you as opposed to what you're talking about as prediction.

Jason Rose:
It's like the next step of letting data work to figure out your needs before you can figure them out yourself.

Tom Miller:
Yeah, yeah. And not only that, but figuring out how to leverage the data to have a better customer experience with your devices and also how to sell more devices. Most importantly, how are you going to how are you going to move those devices, how you can get people active on the devices? Because we're in this period right now where these devices are pretty cool. But I think you're seeing it like in the thermostat market right here.

Tom Miller:
The NSC came out. Everyone loved it. It's like a 250 dollar thermostat right now. You can go buy. You know, I won't I won't drop any brand names, but you can go buy a similarly functioning thermostat for like 100 bucks.

Tom Miller:
Right. And, you know, maybe you're getting 80 percent of the feature set up the nest. But is it the 20 percent that you're missing? Are you really missing it? Right. And so where I think where I think the the common definition of the analytics of things, it was pointed was towards a product scope.

Tom Miller:
Right. So you're you're really using sensor data to inform product scope. And you know what? What I posit in my article is that I think that you should be using sensor data and product activation data and tying it in with your customer data and using that data to inform your sales and marketing activities. Right. To inform your customer experience models, to inform all of your, you know, your customer segmentation.

Tom Miller:
And that's really what was missing in the conversation. I also think to flip that, to flip that a little bit. What you're also missing with these companies, because these are companies, right.

Tom Miller:
And they're all fairly established companies that have a really good handle on how they're distributing their product. Because one thing that you have with the analytics or the Internet of Things is that most of these products are tied to a single serialized product I.D. number. Right. So you have a serial number on your product or a Mac address. Right. That product is dialling home to a cloud based server. So you see you're tracking, sir, products by activation. And then in a lot of cases, if you're selling directly to the customer, you can actually tie that product activation back to an individual customer. Right. And so I also think that the flip side of the tying that all together and using it to inform your marketing activities works. I think that tying that customer data to your to your product and your sensor data is is a much more powerful way to also improve your product. Right. So actually improve your product programming to improve the iterations of your product going down the road. Right.

Tom Miller:
You know, you think about a company that is selling these devices. You know, they have distribution data. They have retailer data. They're selling direct to consumer. They have direct consumer file data. They have product registration data. They have products, support data. Right. They have whatever data they're collecting to it whenever you register the product and activate it. Right.

Tom Miller:
You also have all of your Dejoy analytics data. You have all the data that you're getting from individuals that are logged into your app on your phone and how they're using it. And so I'm looking at all this. I'm thinking about all this. And I'm like, dear Lord. You know, in Internet of Things company that is selling. Call it thermostat's has a gigantic advantage from a marketing data standpoint on a a company that is selling traditional thermostats. Right. Selling traditional thermostats, even direct to consumer or through a big box, you know, home improvement retailer. It's like night and day. So I think the winners in the space are going to be those companies that are able to take that data, aggregate it, combine it with, you know, voice of customer data and really get to an understanding of how their devices are being used. You know, what are the use cases? What are the emerging behaviors of people using those devices? There are people using their nests the way that Nest thinks they're using them. Well, Nest is going to know because Nest has all of this data at their fingertips. Right. And, you know, I think that emerging behaviors are hugely important for marketing. Right. For how your positioning the product. And then, you know, again, to flip that coin to the product side. Also hugely important for your product roadmaps. So, you know, these are the companies that are going to win with this are going to once again be able to integrate that data and leverage it fully for insights.

Tom Miller:
And I think the key piece there is the sensor and the you know, the being able to key on an individual user is huge. Right. And be able to track that in some manner back to a sales data, either direct sales transaction or being able to track by device to retailer.

Tom Miller:
Right. So we sold a thousand devices through Lowe's. We sold a thousand devices through Wal-Mart. How are our usage is different. Right. Are people from Wal-Mart more likely to not even ever activate their device? If you're a smart I.T. company, you've got that tracked, right? You're sending devices with serial numbers, zero to a thousand to Wal-Mart and thousand and one to two thousand to Lowe's. And you're looking at those differences right in your data warehouse. You've got your device level data tracked in that manner.

Tom Miller:
I mean, when you pair that up with a DMP or any other kind of digital based marketing analytics that you're doing, you can almost follow the complete path of purchase and see how your marketing affects how and users are actually using the product.

Tom Miller:
Yeah. And honestly, if you're if you're smart about how you're doing, your voice of customer research, you don't even have to you don't even have to have all of your users tracked in that manner. So, you know, if you're doing your VSC smart, you know, what you're doing is you're making assumptions about where they are and their customer journey and how they're using your product.

Tom Miller:
You know, those those segmentations, you know, if you're doing like a two tiered segmentation, you're making news inferences based on how they're using their products. Right. So you get you get an idea of like how and why. And the context of what people are doing with your products through VSC data, through survey data. Right.

Tom Miller:
And then what you can do is say, OK, out of all the people that we clustered into this this stage in their customer journey or this particular use case, they are all sort of doing the same behaviors. Well, then what you can do is you can infer that most of the other people doing the same behaviors are also within that segment.

Tom Miller:
Right. And so you don't even really need to have everything be one to want one. Now, with I.T., in a lot of cases, you have the opportunity to. Right. And you really should. And if you think about, you know, sort of transitioning this to the second part of the conversation. Some of the same tech. And, you know, there's there's also been sort of a similar evolution in data technology, in marketing technology, similar to that which has enabled this Iowa tea explosion. The tech for marketing has evolved. Right. And really, where I see the excitement in the. Enablement of these Iowa tea firms to really fully leverage his data is in. You know, I think the biggest one is cloud based data infrastructure. So, you know, people call it software as a service or actually platform as a service, right, PNAS. You know, Amazon Redshift is someone that immediately comes to mind for me. But also, you know, you've got a competing Google product, even competing Microsoft product. You also have some other players in the industry.

Tom Miller:
There's also been a group of emerging enablement technologies that is really allowing these companies to very easily get their data into either cloud based or, you know, localized data, warehouse data infrastructure, get it organized in to reasonable data groups.

Tom Miller:
Right. So you group data based on product group data based on customer you group data based on, you know, individual device. Right. Or device type whatever. And there's also been a fairly recently a really big change in tools that enable companies to leverage that data. So business intelligence tools, data, discovery, statistics, tools, and that, you know, we've sort of we're sort of reaching a pretty fun moment in business intelligence where the next gen business intelligence tools are all sort of emerging.

Tom Miller:
Right. So you've got Domoto, which also does your data infrastructure looker periscope. But I call all these out. I mean, this is by no means an exhaustive list, but this is just sort of what comes top of mind. To me, mode is a big one.

Tom Miller:
And then, you know, obviously for more hard core data manipulation or discovery tableau for discovery for sure, are Excel Power, B.I, all of these tools, you know, working together have created almost a new data environment.

Tom Miller:
And it's not like these tools weren't around a few years ago or five years ago or you know, it's not like you couldn't replicate this with some previous Gen B.I tools 10 years ago. But just the cost is so much less. And the the ability for a small company to you know, if you're a small tech company, you can really, really leverage some very powerful data aggregation data, discovery, business, intelligence, cloud based data infrastructure tools for very low cost. Right. And so what you're doing is you're empowering your product teams with this data and you're certainly empowering your marketing teams with these data, with this data.

Tom Miller:
So, you know, that's that's basically the article. That was my it's my break. I feel like I just spoke more about the article than I certainly wrote at the same time. I you know, I think it's sort of an interesting topic and really kind of summarize it up.

Jason Rose:
It's really just about separating mechanical features. Yeah. And sets from customer insights and thinking how those all can be applied to product scope. And when you mean product scope, it's the features and benefits and the outcomes of the actual product itself.

Tom Miller:
Right. Right. And in you know, in you know, I talk about product and I talk about marketing, you know, positioning and, you know, the seven piece of marketing. Right. And I talk about those things as if they're separate. And a lot of cases they are in a lot of companies as they are. Right. So if you think about it, Nyati Company, you know, your product is really engineering driven. Right? So so you're your product is really the device, but it's also the app. Right. It's also the way that the app integrates. It's also the analytics of things when it comes to how people are using the device. Right. So it sort of goes to hardware. It goes to internal software and it goes to user interfaces. Right. So it's it's sort of a pretty interesting product set because each one of those things is independently important when it comes to the product success.

Tom Miller:
And they also sort of grow on each other. Right. So it's like be it when you think about the nest, what do you think about. Do you think about the cool thermostat on your wall? Do you think about the cool app? Right. I don't know. Right.

Jason Rose:
I mean, you're a user, a user based on what appealed to them and why they purchased in the first place. Right.

Tom Miller:
Right. And then, you know, you think about the marketing of the product in that sort of is a whole it's a whole different conversation.

Jason Rose:
But to understand, you know, this data enablement happens on both sides of that.

Tom Miller:
And, you know, I think it's I think it's a. Very interesting data paradise. Now, if you just take the device and sensor data out of the picture, it's not really that different than a lot of other consumer goods right there, particularly anything where you still have where you're getting that good to retailer. And you've got a really good grasp on the retail environment for your goods. Right. But what I'm talking about applies to not biotech companies. Right? I mean, most companies are not. I have two companies still and will be for the foreseeable future. But these enabling technologies are massively enabling companies. I mean, you know, I think that the adoption of sort of these data platform as a service technologies, you know, some of this is speculation, but I know that a lot of software as a service sort of start ups and technology startups are very heavy users of these these types of services, which makes sense. I mean, a lot of them are using the same services and the same concepts as part of their product, not just to enable them to sell more product. Right. But, you know, I think that what we're going to see is major sort of corporate, you know, classic corporate. And perhaps we're entering the beginning of the late majority stage of companies that are really dumping most of their data into the cloud.

Tom Miller:
I mean, we're probably not entering late majority, probably entering the early majority stage at this point. But, you know, sort of where my head's at is like it's late majority. It's like this happening for well. But, you know, these companies are performing their ETR processes and using the cloud for their data warehousing and really allowing large groups of individuals within their companies to share the data. Right. And also having more control over the data. So having a high degree of data governance where the data is being preprocessed in a way that makes the most sense for the business in a way that metrics and dimensions are being well defined and well policed. Right. And it's you know, it's it's sort of a very interesting and fun time because these new technologies are are sort of constantly coming out and they're all very inexpensive for the value they can drive to business. And there's also a little bit of a race to the bottom. I mean, to be honest, like some of them are perhaps too inexpensive. Right. But that's part of the fun of it, too. Great. Yes.

Jason Rose:
I'm going to add and the only thing that immediately comes to mind to me when I mean, this is kind of out of left field, but just as we talk about this, as our world becomes increasingly connected, it was that first kind of earliest.

Jason Rose:
Now, even I would say it's still very much described as a fragmented consumer journey that we're not sure exactly how consumers are interacting with all these different digital touch points that are now happening. It almost seems like we're now kind of getting at least towards the other side of the coin words. We're gonna have way, way more insight as a world increasingly becomes connected into exactly what the consumer journey looks like. Once again, I almost have a I will never look exact like the old sales marketing funnel. But yeah, I'm moving back and I kind of directional. We can really start to map things out.

Tom Miller:
Yeah, I mean, that's a loaded question because I mean, I think people been saying that for 15 years. I mean, my entire career that, you know, things are going to get much cleaner. And I when it comes to how we're looking at data and I think that, you know, it really depends. And I think that where we are getting to a place of more clarity is, you know, I think where we've been is we've been focused more on, you know, we used to at least in digital marketing, we used to be focused really on like interfaces. Right. And I think that interfaces are way less important than they used to be. And, you know, you speak about the customer journey.

Tom Miller:
And I think that understanding sort of the you know, the journey and the intent and these questions and really, you know, using the thinking about the fundamental customer questions before we think about the tools is where we're going right now.

Tom Miller:
And I think that that is going to bring clarity. Now, when it comes to having a clean data set, that's never going to happen. Right. And when it comes to having a very clearly defined sales funnel, I mean, in some cases that's the case. Right. I mean, B2B is sometimes the case. You start really interesting problems with, you know, sort of understanding. OK, so I have a lead and that can be B2C or. I've got somebody that's interested in purchasing my product. I think qualifying that in TED is difficult, right? I mean, I think we're to the point where it's like, okay, I know that this person is expressing some type of purchase intent or product interest. But let's get beyond that, let's say. All right. So how can we scale that? Like in when I say scale that I don't mean grow it. I mean, how do we actually measure the level of that intent and classify it? Right. And that's that's difficult to do.

Tom Miller:
And I think, you know, one of the things that is I think one of the technologies that's, you know, I use that term loosely that is going to come into the forefront once again is voice of customer right into doing more user surveys. I mean, you know, we do tons of survey work here, but doing more user surveying and getting to sort of those fundamental questions, you know, before we get to. OK. What are all these cool tools that we're going to leverage? It's really OK. We have these tools. We have these data sets. What is fundamentally what we're trying to get to or how can we fundamentally segment our customers and understand that our KPI should be different for each customer segment? Right. And that's that's sort of a a shift for smaller businesses up like sort of the the sophistication model. Right. The maturity model for how they're looking at customer analytics. But that's I see a technology enablement of smaller and smaller companies just because the cost scale to to become really, really much more mature when it comes to their customer customer analytics.

Jason Rose:
Yeah, yeah. We're going to get to the point where the small mom and pop shops, suddenly it's got sophisticated analytics tracking the local FCL.

Jason Rose:
I mean, there's endless potential there as opposed to.

Tom Miller:
Yeah, I mean, the area to a big marketer, you know, and it's weird because I think about this a lot and I think about, you know, I think about if I started a company. Right. If I started like a sandwich shop in Farmington, Connecticut. Like, how much as I would kick at market.

Jason Rose:
Right.

Tom Miller:
Because I would just I would go full bore analytics on it. I would go full bore, you know, one to one technology, enable marketing with everybody in town. Right. And it wouldn't be that hard to pull off and it wouldn't be that expensive to pull off. Right. That's sort of where where I see it. A pretty big shift happening. So my answer is yes. You know, and I think about, you know, when you talk about local, what does that mean? And, you know, I see a lot of disruption happening in sort of technology providers that can address the local small business marketplace with so much better structured, less expensive customer Alex. Right. And so small businesses can run CRM. Right. And CRM is is also becoming increasingly less expensive. But, you know, if you're a a small business that's doing some retailing on the side or however works, you know, there's just so much more technology that's available to you just because the prices, you know, the cost is there and the cost makes sense. You know, different obviously different technology makes sense at different cost. But the technology's become a lot easier and a lot of ways, and it's become a lot less expensive in a lot of ways.

Jason Rose:
Kind of come full circle here. It really goes back to the interesting convergence of technology.

Tom Miller:
Yeah, it really is. It really hasn't. And, you know, I sort of tried to draw that out in the piece that, you know, this Internet of things, you could you could probably call it a technology revolution. Right? In some ways. I mean, certainly an exploding industry. And and I would say that this, you know, integrating device and sensor data into an overall data view, right. Into sort of how you're looking at your customary analytics. It's really just another channel. It's really an evolution. And there's also a similar evolution happening in those enablement technologies. And it's an exciting one. And I think that, you know, this this space is opened up to enterprises of much, much wider variety of enterprises because of this enablement technology, because of the cost. But I you know, I'm not going to go so far as to call that a revolution. I think that that was predictable, you know, 10 years ago when, you know, we were running.

Tom Miller:
You know, billion dollars of server hardware to run our FBI tools, right, and doing, you know, doing million dollar data integrations with six month, 10 person consultant teams just to get all of our internal data ETF, GLD into a local file store that, you know, 10000 people across my organization were using B.I tools on. Right. Or running single queries against. So it's exciting. And, you know, I think the scale, the scalability is the same. The costs are exciting. But I don't necessarily see it as a revolution right now. I think we've just evolved to being able to to actually afford to get our data into the cloud and afford to hook tools into that data that are accessible across enterprises of a lot more various sizes.

Jason Rose:
So chances are 15 years from now, some content strategies, not advertising agents here in some kind of marketing function is going to say something very similar to what I just said and pronounced that now we're finally almost at the point where we understand a customer.

Tom Miller:
Let me make a move forward looking statement here, because I think this just came to me and I think it's important. So if you think about if you go work for a large company, let's say you work for G.E.. Right. There is a certain expectation that if you're at a certain level. Right. If you're at a manager level or above that, you you really understand what's going on externally and internally at G.E.. Right. On a daily basis. You do want to be caught with your pants down. If somebody you know, the day after earnings come out in, the companies missed earnings, like, you should know that, right? You should know that. You should know what. You should know the fundamentals of your business. You should know all the business units. You should know generally, I mean, GS and big companies. That might not be the best example, but you should know generally what the revenue is of those business units, how they're doing based on their recent history. You should probably know who the top executives are at each business unit. Right. I mean, these are all things that you should know, right? I think that in the next call it five to 10 years that.

Tom Miller:
But I'll call burden of context for people that work in these organizations with technology like this enabled, the scope of that is going to massively increase. And that if you are working at an organization, you should have a much better context of the call it recent operational metrics for the entire organization. So you should know that last quarter and quarter is too is too broad of a it's not a find enough code. You should know, you know, how the sales process works. But you should also know how the sales mechanics are working. Right. You should know your customer satisfaction scores over time. You should know about your products and how your products are selling relative to each other currently. Right. Not historically, but currently. And I think that these technologies are going to create environments within large corporations where this type of information is going to be more readily available to more people. And it's also going to raise the bar of expectations on how much each individual team member within those organizations knows about the context of the operation of their business in real time.

Jason Rose:
They I mean, I'm almost picturing it's almost like a, you know, the floor of an investment bank.

Jason Rose:
And you're watching stock prices on the wall to the point where people are just like literally in real time seeing the success of the business units, that it becomes this game A5 kind of thing. Yeah.

Tom Miller:
I mean, maybe but maybe more like, you know, in much the same way that again. And I think is a great example. So, you know, these companies are you work for X, Y, Z Corporation. Right.

Tom Miller:
And know again, the expectation is, is that employees should know what's going on with earnings. Right. Like, you should know that, like if you have a public earning statement at a certain level within the company, you should you should understand the context of the overall organization. You should understand what the context are of the executives that you deal with, what their context is. Right. And I think that these technologies are going to broaden the ability for individual. Theaters within that business to have a broader context, say broader 99. It's going to increase the ability for people within the business to understand those key business metrics in a much deeper way.

Tom Miller:
So we're moving towards a dashboard world? Pretty much, yeah. Yeah.

Tom Miller:
You know, I dashboard's just to type of report. I mean, I think that it's a and I think that that's actually probably a good way to describe it. To be honest with you, it's a you know, we're talking about business intelligence tools, self-service, but the ability to to card and collaborate on reports. And if you think about you know, when I think about B.I tools based on the context of how I've generally worked with them, it's either been producing reports that go way up the chain. Right.

Tom Miller:
Or producing tactical reports that are being used almost in, you know, in near real time by a team that is focused on a tactical execution. Right. And I think it's a little bit of a paradigm shift.

Tom Miller:
But really what you could do is you could bake company wide or division wide or, you know, group wide reports into a way that when you're talking about how the business is doing. You don't have to waste time talking about it anymore. It's like, you know, imagine, you know, in some of these tools, a lot of these tools like mode or periscope, they sort of big this n but imagine slack where you know what you're doing is on slack is the slack bot is giving real time business context data so that people are wasting time communicating that business context when it comes to meetings or other other cops. Like there's no reason to waste time at that anymore. Now, there might be a reason to contextualize that. Right. But there's no reason to create extra time because everyone is armed with what we'll call near real time business context information and a much, much more.

Jason Rose:
Holistic way. Right. Yeah, it really just flattens an entire organization when you're not either using data on a super prescriptive way or in a super, you know, interpretation for the executive team.

Tom Miller:
And I would almost say it doesn't even need to flatten the organization. What it does is, you know, if the organization's a pyramid, it's just building like a floor underneath it, like literally raising it. One hundred feet. Yeah. Right. And everywhere you put it, you know, I think that that is that is sort of what, you know, this technology. And I think it's always sort of been the it's been my experience at least, that it's been sort of the failure of traditional B.I in that, you know, you sort of have this, you know, the paradigm I discussed before. And you don't really have like a broader contextualized view, like a shared understanding. Right. You have a top down and a bottom up understanding. But really, I think that the greater the more accessible these technologies become, the more that, again, individual constrict contributors are going to be expected to have a broader context of business understanding and understanding of the metrics that are driving their business. Maybe not within their department, but overall over time. So that's that's sort of where I see us going. Like ten years down the road.

Jason Rose:
Great. Yeah.

Jason Rose:
All right. Let's say we cover the article. Do you?

Tom Miller:
Yeah. I mean, I feel like I just talked about that V.I. stock was great, though. So that was it.

Jason Rose:
So that was the fifth episode of Measure Direction once again. I am Jason Rose. My Twitter handle is J.

Jason Rose:
J a y t r OSCE and Tom.

Tom Miller:
Yeah. My Twitter handles at t. L. L r.. And the article that we've been discussing for the last long time is you can find it at ti m. L. L. R. Dot u. S. That's t l. L r dot u. S slash. I t. That's my you are Elshaug.

Jason Rose:
And if you have anything that you'd like to submit for us to answer the next time around.

Jason Rose:
Once again, the link for that is Bitly slash measure direction. Keep the questions coming. We really appreciate it.