Mike Boyle

Well, let’s talk about how companies can be more effective in managing that sheer volume of data we’re talking about here. How are they doing that these days?

Chris Hood

There’s really four key areas here.

The first one is you’ve got to start thinking about moving your data to the cloud and I’m sure we’ll, we can get into that a little bit more.

There’s probably a lot of different opinions about moving data to the cloud, but really you’re going to be safer, the the whole purpose of being able to scale, you know, going from 79 zettabytes to 100 over the next three years, you’re gonna have to be able to scale and you can do that more successfully in the cloud.

The other area is to start leveraging technologies, foundational technologies to help you manage data like APIs (Application Programming Interfaces).

APIs are not just about creating experiences directly inside of an application or building applications that can connect with each other, those integration points.

Obviously, it’s in the name, but it is also about helping you better manage that data across the cloud and across different platforms.

And then the third one is to begin thinking about how you’re governing that data.

What are your internal policies to help you track, manage, ensure that data is protected where the data is coming from? Those governance policies can be automated.

We’ll get into that as well but just defining what those governance policies are and building teams that wrap around those policies are going to be critical for the management of it.

But again, I think the scaling part, the critical first step is start looking at how you get this off prem into the cloud where it can scale more easily for you.

Mike Boyle

Well, as you know better than anyone else, when data is kept in separate siloed systems, it’s often very difficult to identify and consolidate it into a universal platform to speed up data driven choices.

So how would you advise people to go about conquering their multiple data storages?

Chris Hood

Well, when we think about multiple data storages, we’re also thinking about data that is siloed across the organization.

And there’s two areas that you should consider here.

The first one is solely the technical side. Yes, you can consolidate your data, you can unify the data platforms. That’s all fine. That’s going to help you centralize, get everything organized. It’s great. But today, you don’t really have to centralize the data itself. You can have data across multiple platforms and really having data spread out will help you in areas like security, redundancy, accessibility of the data.

Even innovation can happen more freely. When that data is across different platforms. We get into a lot of complex technical backgrounds of that, but you can look at it as either bring it all together or separate it.

That’s the technical side. But there’s probably a stronger argument to be made actually on the business side. And it’s not just that governance piece that we were just talking about. It’s recognizing that there are teams within your organization that are siloed and there are so many companies that are out there wasting time, money, resources basically duplicating efforts across their development, building new products and processes because their teams are siloed and then they’re trying to access different data sets or you’ve got team A that needs data from team B and team B is like, no, we’re, we’ve, we’re doing something different with that.

And that siloed mentality within your business is just wrecking any capability of managing the data better and delivering to your customer, what they want.

If you can’t quickly get to market through collaboration – collaboration inside of your organization – then it really doesn’t matter where your data is, you’re not going to be able to access it efficiently and be able to develop what matters.

And then there’s the issue of the quality of data, which companies freak out over, understandably how can companies overcome the quality of data hurdle?

Well, I think some of this goes back to what I was just saying, your quality of data is going to be diminished if you have siloed teams because ultimately, you’re probably going to end up with duplicate data, which is a problem.

But if we move away from those siloed teams, you know, the concept of garbage in garbage out is happening because of a lot of different scenarios.

So you’re going to have to have some form of continuous validation on that. And again, I’ll, I’ll just say it real quick, siloed teams, would you want to validate your data twice because it’s in two different teams?

No, no one wants to do that. So you have to build continuous validation checks.

There’s probably some automation that can be included in there. You can definitely start implementing AI to continuously validate your data against the record sets that you want to have, you know, we call it, you know, when we start thinking about generative AI and having a set voice, we can train the AI models against whatever data you want and, and how that should be validated.

So there’s automation that you can think of and then definitely it’s training across the board.

One of the interesting stories that I have was when I was working with a fast food company, a big brand that’s across the entire country and they serve more than chicken. But this story is about chicken. So I don’t think it’s like, you know, KFC, you know, it’s, it’s not a chicken company. It’s a fast food company that has chicken anyway, they have a database of all of their menu items and they allowed the different franchises across all of the businesses to set up their POS system, set up their database and enter their own terminology for whatever was on the menu.

And in this particular case, there were chicken strips.

The company as broad had over 16,000 different records for chicken strips. And you can probably think off the top of your head, all of the different ways that you could spell chicken and chicken strips combined to get to 16,000 records of chicken strips.

And so I always like to say if it’s a chicken strip, call it a chicken strip, don’t try to come up with some other name.

So that’s where this regulatory governance process comes in and that you have to manage.

But then you could obviously run some automated structure against that and say, well, we’ve seen another form of chicken strips here, let’s change it to a standardized naming principle.

Mike Boyle

You know, one of the most common data management problems. and frankly pain points for companies. is data integration. Talk to us about why it is so very important to get that part of the equation correct.

Chris Wood

Yeah, absolutely.

Proper data integration is pivotal for anything you’re doing – accurate analytics or streamlining your operation, consistent customer experiences across all of your platforms.

The biggest one though, I think, is that customer experience piece. You know, we think about personalization as an example. It’s critical today. Everybody wants something that’s personalized, but you probably don’t want to personalize an experience for Sam that says, ‘Hello Paul,’ right? I mean, that’s where we’re at and this integration ensures that you’re getting proper data delivered to proper places when it’s needed.

And it also helps drive innovation and partnerships.

So when we think about all of those customer experiences, the ability to innovate and get to market faster and deliver those new experiences to customers that demand those experiences from you all happen within integration. That’s the customer first now.

Yeah, there’s other areas of integration across multiple platforms. When you start thinking about internal pro processes like human resources and marketing and building analytics and sharing that data across different teams so that they can see how things are working.

But I would argue that the biggest opportunity for data integration is ultimately always going to be your customer first. And then secondary is understanding how those integrations and data management is going to help you improve operations, but you’re only improving operations to satisfy customer needs.

Mike Boyle

Well, Chris lastly here on the subject of data and data management, before we head into the next section here on digital challenges, let’s talk a little bit about the all important topic of security.

You brought this up briefly a little while ago… Data security is a significant challenge. Nobody questions how the companies look at that today. How do they go about tackling that issue?

Chris Hood

Yeah, we talked about moving to the cloud and I think most people, I don’t, I don’t know if most people who are listening are gonna think this, but there’s a lot of people out there who think, well, if I move to the cloud, it’s not safe, I’m at risk.

And unfortunately, that is a misnomer that has grown over the years.

The facts are simple. The cloud is going to be far more safer, far more safer than anything that you’re going to be able to build internally. Some of the biggest companies in the world who are concerned about data are on the cloud.

And I always find it amusing when I go into a company. And again, we were just talking about fast food. So I’ll use that as an example.

You go into a fast food company and they say no, no, no, no, we are different.

We have to do this internally because we’ve got some type of different system in place and different data and we want to control that all here and, and we are going to be able to protect that better than anybody else. And yet you have the IRS and the FBI and the CIA and banking institutes and health care institutes that are all in the cloud. And you’re going to argue that your data is more important or more secure than their data? It’s not and it takes time for them to understand this.

But all of the areas that we would typically be concerned with in terms of moving our data to the cloud such as a zero trust approach of data security or continuous monitoring of vulnerabilities or just the ever evolving data protection regulations that are out there.

All of that is happening in real-time when it’s in the cloud, your team cannot possibly keep up with all of those changes in real time. You would have to have a massive amount of people to be able to do it.

And what we typically see is the biggest culprit is version control. We’ve got version one installed. Well, it’s just upgraded to version two. So OK, we’re gonna upgrade to version two. Now version three, now we’re gonna upgrade to version three.

And at some point in time, version four comes and version five comes and now we’re on version 10 and you go in to do an audit and you’re like, oh, we’re still on version four because somebody dropped the ball and they forgot.

And then all of a sudden you’re open to a mass amount of risk and vulnerabilities because you just didn’t keep up with those ever evolving changes.

So moving to the cloud is your best option, not just for managing the data, but also to ensure that your data security is going to be in place constantly.

Mike Boyle

We’re talking data, digital and AI challenges with Chris Hood, who is a digital strategy expert… Let’s move into digital now, Chris. Why, when it doesn’t have to be, is digital transformation so difficult for companies?

Chris Hood

Well, here’s some stats for you… 

70 percent of digital transformation projects fail, 70 percent fail. OK?

Now let’s match that up to 92 percent of digital transformation projects are managed by technical departments so that potentially tells us a couple of things.

One, technology teams can’t deliver digital transformation, they fail 70 percent of the time. And I touch on this in my book: the wrong part of the organization is focused on digital transformation.

You have to really start to understand what is the purpose of digital transformation. And I have a personal opinion here…

Digital Transformation is dead. It’s over, you’ve missed it. It’s a lost cause if you’re still thinking about digital transformation, we’ve already moved on to the next era.

So I wish you best of luck because you gotta figure out something other than digital transformation, which ultimately is why I wrote the book, Customer Transformation.

And I argue that customer transformation is the next evolution of what you’re trying to accomplish. But ultimately, you have to understand that digital transformation was really never about technology.

And so why it’s failing 70 percent of the time when it’s in 92 percent of the technology teams is because it’s in the wrong part of the organization.

Digital transformation is about aligning and delivering technologies that your customers are asking for. It is ultimately a business strategy that is aligning with your customers.

You would not go and change a technology just for the sake of changing the technology. And yet that’s what people are doing. That’s what a lot of organizations are, are out there doing A I and we’ll get into it.

AI is a prime example.

There are thousands, tens of thousands of businesses out there trying to figure out how they’re going to implement AI when they don’t even know if their customer cares enough about it.

And so you still have to understand what the purpose of the technology is.

It’s the people, it’s either your internal people or more importantly, it is your customer and when you shift that mindset to customer first is why we are transforming, then you’ll understand how to be more successful in this area.

Mike Boyle

I want to talk a little bit about security here in this digital section. Talk about warding off cyberattacks. I know, you know, a little bit about that… What should companies be thinking about in terms of security and warding off things like cyberattacks?

Chris Wood

Yeah… Again, I think all this goes back to what we were talking about just a moment ago and it’s that you’re gonna get more protection when you’re in the cloud.

So if we want to really think about digital transformation as your transformation from an on premise organization to a cloud based organization, that could be one way you look at it and again, you’re going to get more protection when you make that transformation, you’re still ultimately got to ask yourself, why are you doing it in the first place?

It goes back to your customer. But the difference between on Prem and the cloud is going to be a huge differentiation for you to protect yourself against potential cyber attacks.

The example of version controls is a great one because when you’re out of compliance with whatever the current version is, you’re opening yourself up to cyberattacks.

When we think about even simple things… Look, I have a website. My website is run on WordPress, you know, that’s not any surprise. WordPress is on a lot of websites out there today.

I get notifications every day that says, oh, there’s a new vulnerability found patch, this patch this. 

There’s an entire community out there that is constantly watching out for each other, basically watching your back against potential vulnerabilities and cyberattacks that are happening.

And of course, there’s other tools that you can implement to further protect yourself.

I’ve got no less than five or six different tools on my website to do nothing but prevent me from being attacked, and do they work?

Yes, because I can see the evidence of it in the analytics and in the reports. But you have to be willing to make that leap and say, look, we don’t know everything in our business and we can’t hire enough people to be able to know everything.

So moving off into the cloud to help ward off those potential attacks is going to be critical for you.

Mike Boyle

Lastly, here in our digital segment, I wanted to talk to you about driving adoption within companies and you know, you go through the expense and the time and you give the company and give the people the new tools and the new processes. And oftentimes this wall goes up in companies about driving user adoption.

Can you talk a little bit about that and maybe the best ways to go about that?

Chris Hood

Look, when we think about adopting new tools, you could say we’re going to adopt this new tool and there’s businesses and companies all across the country who are dealing with this.

Probably right now at this moment they found some new tool. They’re like, ‘Yeah, that sounds great.’ They’ve bought it, they’ve invested hundreds of thousands or millions of dollars in it. And then they are sitting teams around saying,’Hey, this is a great new tool. It’s gonna help us do whatever it is.’  And you probably have people sitting there saying, ‘Why are we even bothered with this? Oh, another tool.’

I don’t know the stat, but I actually saw an article a couple of days ago that said something about employee frustration with continuous change that is happening.

Now, I think part of that is because we’re going through a lot of cultural and economical conditions that are forcing, obviously layoffs are happening and it’s impacting the culture of a lot of companies. And so there’s an increased amount of frustration that is happening with these changes.

So when we start thinking about tools, people are gonna just get us. You know, it’s like anything else that you’re trying to change… People get frustrated, like, ’I’m comfortable with this tool. I don’t need a new tool to do the exact same thing.’ And yet I think it still goes back to what I’ve been talking about related to the customer. You’ve got to understand why, what is the purpose. You don’t invest in technology.

If you don’t understand how that technology directly relates back to a customer value proposition. Otherwise you’re wasting time, money and resources, you’re not just going to go out and buy something and say, hey, we’re gonna bring this tool in if you can’t tell me how that tool is going to directly help your customers.

And so often we see executives who say, ‘Look, it’s not my money, it’s the company’s money.’ And you would think they would have more control over this, but there’s millions and millions and millions of dollars being wasted every year because some technology leaders think: ‘Bright shiny object, I want to play with that, not my money. I’m gonna bring it in and we’ll play with it,’ and you have to still be able to associate it to why.

So I think if you can come out and say, ‘Look, we have a new tool that is going to be implemented and it’s going to help us better communicate with our customers or improve the service for our customers or provide our customers with this new feature or functionality. And we believe that that new feature of functionality is going to increase our revenues because customers want it.’

That’s the approach you have to take. And then if you can satisfy the curiosity of why the tool is being brought in and align it back to a customer value proposition, I think the ability to adopt that tool inside of the organization becomes increasingly easier.

Too many employees out there are sitting in like, ‘I don’t even know what my job means. I don’t even know what I’m doing or what the customer is getting out of what I’m doing on a daily basis. I just show up, clock in, do my job. I have no direct understanding how my job relates back to the customer.’

And we know that if they do understand this, they are happier in their jobs and they’re more likely to adopt and use new technologies because they see the direct correlation back to customer satisfaction, success and loyalty.

And again, I think that’s how organizations have to think about this in order to be successful.

Mike Boyle

Before we wrap things up with Chris Hood, Chris, I wanted to ask you a question about AI. I was recently trolling around your WordPress website… You recently wrote an article, it was titled “Generative AI and Its Pivotal Role in Customer Transformation.” And again, folks can find that article on ChrisHood.com.

Chris, how do you see generative AI fitting into businesses today, and the challenges that lie ahead for it?

We’ve been doing nothing but talking about A I since I think the calendar changed, it came at us at record-breaking speed in January…

Chris Hood

Yeah, well, some reality checks.

We’re actually seeing a decline right now in overall AI usage. I think we’ve gotten past the hype, right? So obviously big hype, shiny new object, let’s use it.

And we are seeing a decline, we see a decline in user subscriptions for open AI for ChatGPT as an example. I think we’re getting more settled into what is AI and what is not AI. The perspective out there for a lot of people is that they have this vision of what AI is.

But that vision or even definition of what AI is is probably still 10 years away from today. We’re nowhere close to being able to do the things that I think people believe or it’s been hyped to do.

And so that’s why you’re starting to see a decline in usage of AI.

That doesn’t mean that no one’s talking about it clearly, it’s everywhere we see the government starting to take an active interest in it. We see a lot of companies trying to figure out how to use it.

But as I said earlier, if you cannot declare why a customer is going to benefit from the implementation of AI, then there is no point in doing it.

And unfortunately, there are thousands of companies out there that are AI-based. I get two to five emails a day saying, ‘Hey, we’re an AI company and we do this.’ Or. ‘Hey, I’m an AI expert and I do this.’

Well, I, I hate to break it to you, and I don’t wanna be negative, but roughly 90 percent of those businesses are going to go out of business by next year because they don’t have any foundation other than they are banking solely on the hype that is out there. There’s no substance to what they’re doing.

And you’re not an AI expert. You’re not an AI expert unless you’ve been working in the AI field for the last 10 years. AI has been around for over 50 years. You’re late to the party and again, you’re trying to overly market yourself.

So I would suggest that people don’t get overly hyped on AI and don’t feel like I have to bring in AI because it’s the end all answer to everything.

There are clear benefits to AI automation. We talked about security, we talked about data management, we talked about data governance. You can bring in AI to help you with those areas drastically improve your ability to manage your data leveraging AI.

We can also leverage AI against analytics to better understand who our customer base is and what they’re doing so that we can make better internal decisions about our products and services.

That’s a great space for AI and yes, generative AI is going to help with things like personalization service, right? Automation.

But here’s a great example. I’ll give you a use case for where we are with A I today and where we should be.

If we think about the traditional chatbots that are out there and you go to a website, you need support. We’ve all been presented with them. But, you have a problem. Most of us are getting overly frustrated by this. We recognize that it’s not a real person and they’ve only been trained to answer a handful of set questions with set responses. They are not going to be adaptive and continue to evolve when they are programmed to answer those questions in a specific way.

That’s what the chatbots are today.

But the amazing opportunity in front of us is to get the AI to actually recognize human emotions, which is the missing element and be able to adapt the language in the large language model to what the consumer is feeling at the moment.

So the example is, let’s say that you were just in a car accident. Heaven forbid we don’t want anybody in a car accident or maybe it’s a medical emergency. ‘Hey, my mom just found out from the dentist that she needs a root canal… these things cost money.’

And all of a sudden, we’re in a position where we need money and we’ve got a credit card payment due and our car loan or something and we’re calling in because you know what we just need to ask for an extension and we’re emotional and we’re frustrated.

We don’t want to be put into a loop. And especially when those loops are basically telling us, well, we can’t do that or call somebody and then we try to call somebody and we’re in another loop.

But imagine if the AI could sense that you needed an extension and instead of actually redirecting you to a human who could facilitate that, say, ‘Hey, I sense that you’re a little frustrated. How else can I help you? Maybe I can offer you an extension for 30 days?,’ and give that individual what they want. That recognition in real-time is the missing component today.

But if you could do it now, I know there’s probably a lot of sales people and chief financial officers out there saying, ‘What the heck? No, we would never automatically give someone an extension for a bill pay?’

But why not? You might do that internally if you get to the right person and you’ve empowered your support team to do it.

That’s where we have to get AI to, and again, I don’t think we’re there yet. We’re still five years easily away from that emotional recognition and human touch.

But if we can get there, then we’re unlocking a whole new world of opportunities for businesses.

Mike Boyle

Chris Hood… Thank you so much for joining us today.Just on this last topic here alone, AI, I need you to come back… We’ll continue this conversation another time. Would you?

Chris Hood

Absolutely!

Mike Boyle

Wonderful… And I wish you continued success, thank you.

And to the audience, if you’d like to learn a little bit more about what we’ve been talking about today, I will be placing some helpful links here in this episode’s show notes. I’ll also include a link to Chris’s website – ChrisHood.com and to his Chris Hood Digital Podcast show… Make sure you check that out as well.

Hey, if this is the first time you’re listening to our podcast, the Salesforce Simplified podcast, first, thank you. I’d be honored if you make it a point to follow us on your favorite podcast channel and you’ll get all of the past and future episodes, and I’d be very appreciative if you told your friends and colleagues about the podcast as well.

I’m Mike Boyle from Ad Victoriam Solutions. Thanks for joining us for the Salesforce Simplified podcast. As always, our next episode is just around the corner.

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