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May 26, 2023
Moderator:
Joel Martin, Executive Research Leader, HFS
Panelists:
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This transcript was auto-generated from the original recording and lightly edited for readability. We've done our best to catch errors, but names, technical terms, and company references may be misspelled or imperfectly captured. For the definitive version, please refer to the original audio or video. Views expressed are the speakers' own.
This is actually a very exciting panel for me to run. Samir and I share that similar passion around data. I mean, all my research for the past 12 months has been around data modernization, how organizations are using data in a more practical way. And while we’ve spent a lot of time over the last few days talking about the theoretical, the aspirational, the autonomous enterprise and the role data plays, this panel with Phil and Samir is something that Samir and I have been discussing for a while, which has led to a study. And I’m going to start by revealing some of those insights to get the conversation going, really centered around what do we need to do today with our data to empower all these things that we’ve been talking about. To empower employees to make decisions and action things faster, to empower our customers to want to do more business with us—not just transact but be loyal, be engaged—and then how do we use that data to build better ecosystems. So really covering all three horizons, but we’ve got to start very practically before we get to all the aspirational aspects. What’s going on with data in the organization right now? So you’re going to get a sneak peek of the study that Sonata Software, one of the fastest growing firms that we’re working with right now, and us have basically launched. 500 business and IT decision-makers in North America, so the US and Canada, where I live, as well as Western Europe and the UK—which Phil’s proudly supporting the Arsenal colors today, which I’m very proud of. So let’s start with data. What’s going on? Quantifying why we’re talking about data, why we’re talking about everything from why we need things like generative AI solutions to get to that data through to just better data processes. Well, one of the keystone questions that we asked in the study that we’re doing with Sonata was, do people feel they have good data in their organization? Whether you’re in business or in IT, you can see from the slide up there—no. People don’t trust the data. And if people don’t trust the data, everything that we’re talking about will never happen. So the goal, if you take nothing away from this, is to basically find out where can you build trust in that data, and that’s where you put your efforts. Because right now, only 16% of companies felt they have good data. There’s a lot of people out there that are just fundamentally saying we don’t have good data, and we need to do something about that. And then we looked at, so why don’t we have good data? And fundamentally it came back: we’re not capturing the right data, we’re making it difficult for people to enter data, or for the systems to bring that data into a way that can be insightful and actionable. So that was more so than outdated architecture. We talked a lot about legacy modernization, very important, but if you modernize your legacy but you don’t take into consideration what it takes to actually bring in the data your organization needs, again you’re going to be stumbling along. And we’ve been stumbling along with this data journey now for pretty much three decades, back when we went from client server all the way to now where we’re hiring fewer app development people and a lot more data scientists. And a lot of times that’s because companies continue to take it on themselves to try to drive themselves through the strategy versus leverage a trusted partner. We’ll talk about that in a little bit more in a second. This slide was another interesting data point. Of course, we asked, what’s stopping you from getting there? This to me was like, hmm, let’s go hide behind a reason. Security and governance—very important in getting good data and implementing your data strategy. But to me it’s kind of a cop out if you really think about it. It’s very important, but it’s easy for somebody who’s tasked with modernizing their data to say, well, we can’t do that because we have security policies. Make improving your security and governance part of the data journey. Don’t make it something you thrash about afterwards, or implement something and then get pushback from your CFO or chief risk officer. More important is capabilities and resources. That’s one of the biggest challenges right now that I’d like to zero in on and talk practically about how we’re going to get the capabilities and resources needed to get to a better data state, with both Phil and Samir. I’ll also bring to this the mom and dad equation, or conundrum I should say, because it’s really about who owns the data strategy. And when we asked, again the same 500 companies, very fragmented. Is it the chief data officer? Well, often, as you can see, 21% said yes. So not a small amount. But also a very IT-centric way of looking at data, when, as we’ve talked about for the past day and a half, data is about making the business better. And that’s typically the CTO’s role. So it’s nice to see the CTOs listed as number one, and the CEO is also up there, so they are engaged in these conversations. But when basically 26% of the people are saying the CIO, you’re looking at how do we make data optimized versus how do we make it powerful for the business, that a CTO and CEO would be driving. So where do we go from here? That’s a sneak peek of the data. If you took pictures, that’s great. We’ve made them available now. The report will be coming out later this month. But let’s get really practical with these problems about who owns the data strategy. What should the data strategy look like to overcome the challenges they have with security and governance, people, the talent they need? So Samir, I’d like to just start with you. You’ve built a whole business. It’s growing very quickly. You just acquired a Texas-based company, one of your biggest investments, to bring more of your expertise near shore in North America. What’s driving you to build a data practice, and what are you learning from your customers that you’re using to build out with all the new customers that are coming to you traveling the same data journey?
Thanks. And, Joel, first of all, thanks for the study that you did—I think it’s very insightful. From our point of view, across clients globally, what we see is data is really becoming more mainstream in decision-making and everything that customers do. I served banking and financial services customers quite a lot of my life, and it’s very interesting to see that the business heads in most organizations now are more technical people who are defining the strategy. And as the more technical people are coming up and taking business decisions—because technology is defining the future—data really forms a critical part of their core strategy. And this is a problem which has not been solved. I don’t think even today most enterprises have solved the issue of data quality, which is perhaps one of the first slides that you had. And I think it really starts with alignment in the organization and the enterprises. I often give this example to many of our customers: if you think of a call center environment, what does the call center agent charter to do? Their main job is to drive customer satisfaction. But if you look at it from a CFO lens, what does the CFO expect a call center agent to do? Drive more productivity. If you look at the same call center agent from a sales head’s point of view, what do they want? They want them to drive more cross-sell and upsell opportunities. And the story goes on and on. If you really think of that one single example, there are competing and conflicting priorities that people are driving at. And what happens is the CFO office or the executive team then start putting rules and say, I want to capture this data, and when you’re capturing data, put these various rules and mechanisms in place. And that’s where the journey starts to go south pretty quickly, because these rules are at cross purposes in many cases. And that’s when the story starts to get complex thereon, and data just grows beyond a point. Every day the call volumes are coming in, if I take that example, and it gets more and more complex. So what we’re really trying to solve with most of our customers is this alignment issue. You have to take some executive decisions—which is why I like your chart about the CXO office having to take a role in this—to drive that alignment across various functions and say, how do we make metrics not at cross-heads to each other, but really drive that alignment. And unless you do that, you can drive data modernization programs, data cleanliness programs, and all that, but you’ll come back to the same square again, because the alignment in the company has not been set right. That’s why we believe that’s a fairly germane issue, which some progressive companies have taken more bold steps on than others. And that’s why we are really wedded to this thinking that can we bring a more consultative way to solve this data problem, which is beyond technology. Because this is not about data cleanliness, this is not about just data dictionaries. This is about driving alignment from an MBO or KPI point of view across the organization, which will ultimately result in the enduring value of this data modernization journey.
And I think that’s a great point. When you think about it, this journey is a journey for all of us. It can’t be led by just IT. It can’t be just pulled by the business. It has to be shared. And I think, Phil, we’ve talked about this a lot with regards to how we all come together and drive this. What are you seeing from an executive standpoint, when you’re meeting with our customers, the enterprise users, about what they’re practically looking to do to get more buy-in from all parts of the business?
Yeah. Ultimately, you talk to any enterprise executive, they have two problems. It tends to be one, data, two, talent. And the data they need to be successful in their job could be supply chain, it could be CX, could be internal operations, whatever, and they need to figure out how they’ve got the right people to actually get that data. So it’s one thing looking at end-to-end process, but then how do you actually get the understanding of the interactions around the processes and between people to get the data you need to be successful? So how do you build an audit trail of getting to the data? How do you have a team that understands that, that can build it for you? And then once you get to the point of saying, right, we’ve now figured out how to interact and process the way to get to the data, then you’ve got to figure out where does that data go? Where can it be accessible so it’s centralized? You’ve seen the number one chart we just showed in the previous session and earlier—lack of centralized data is the biggest problem. So you’ve got to house it, you’ve got to secure it. Every touch point you have outside of the organization creates governance and compliance issues, as we know too well as a research company. People access our research, we put our research on big intranets and massive companies like IBM and Accenture, and they go crazy that this is touching my stuff. But then it’s also, how do we figure out how to get that data centralized, automated, trusted? So only 16% trust the data. It’s not just your own employees that need to trust the data, your customers need to trust it, your suppliers need to trust it. Everyone in the value chain needs to trust it. If they think you’ve got bad data, it’s like putting bad ingredients—like putting sugar into your spaghetti bolognese, right? Well, it shouldn’t be that bad. And then ultimately you get to AI, which is data patterns. How do you get the data patterns you need to make decisions to be successful? So it’s a whole cycle, and having the right mindset of people around you as part of that cycle—you should be working together. We were talking earlier about silos: I work in finance, I work in CX, I work in marketing. Ultimately, what data does everyone need? How do you get it? How do you have the right mindset to get it? I think GBS is interesting because it’s a centralized pool of people who could possibly come together to do that. You may outsource a lot, so you might say, hey, I’m going to use Sonata or IBM or whoever. They could do a lot of that for you as well. Why not move to a trusted third party? They can maybe fix a lot of things you can’t do yourself. That was one of the big problems with automation—just getting political buy-in to do a cross-company, cross-function automation strategy is really hard. So how do you actually get buy-in? How do you get people to work together? I think if you can’t do it internally, seriously consider getting a third party to do it for you. Honestly, otherwise you’re going to spend decades trying to figure this out, and then it’ll be, yeah, we need to upgrade SAP—that’s usually the biggest excuse to do nothing for another five years, right? Some of it helps, but it’s true though. Figure out how to get the data you need and then move backwards from there. Don’t go to the very end and say let’s pull us all together and try and do it from a sort of inside out; it’s got to be outside in.
So Samir, building on that thought about needing to get buy-in from the organization—obviously, given your growth and what you’re driving with the company, you’ve been very successful working with your clients to get that buy-in, helping them to curate. Can you tell us what’s made you successful, or what’s your special sauce for doing that, for getting the company involved and working with you collaboratively to solve their data issues?
Yeah, it’s a great question. I think of it in two different bookends here. One bookend is to solve for the current mess, if I can use the expression, which is relatively easy to do—it’s a one-time effort, you can get there. But the bigger part is to stop the origination of the mess itself: what organization and processes do you need to put in place so that you don’t create the mess again? And that’s really where our capabilities around data consulting that we have created really help us. And there is a duality there, if you really think about it, to the point that you made earlier as well. You want data to be democratized, but you also want centralization from a governance perspective, because if you do too much centralization you become quite a rigid organization, and if you become too democratic you become an anarchy to some extent. So that fine balance is what companies are really after, which is what we try to bring as part of our consulting practice—to help customers think about what can be federated and what has to be centralized. It’s a fine balance, and it comes with years and years of experience working with large and small enterprises across the globe, which is what we have harnessed over the years. And that’s really what we bring to the table, because the risk and compliance officer has a different requirement—I go back to the same thing of alignment—versus sales and marketing, which has a different requirement. You have to think about how do you not make it too central and kill the complete entrepreneurial spirit behind it, and make the data environment more scalable. And that’s really our secret sauce—it’s engaging in those conversations. Beyond closing the one-time effort to modernize the data from on-prem to cloud, or whatever you might be doing—those are fairly mainstream at this point in time, I don’t think that’s a real differentiation in my mind, many companies will be able to do that. The real differentiation is, can you consult with the business stakeholders and drive the alignment from a data KPI perspective, a data prioritization perspective, keeping the duality in mind of what is centralized versus what is federated.
Yeah, that’s fine. I’d be interested to look at the data—and that’s our data—but look at it by where the responsibility lies for the data strategy. So it’s the CEO saying, I want better data in my company, I need it clean, I need it centralized. It wouldn’t surprise me if you’d correlate that with trusted data and centralized data more when it’s been pushed down to the CTO level. I think it’s coming from the top. Leadership need data more than ever to be successful, and then they’re pushing down hard on their team to say get it together. For sure—stop working in your little silos and fighting with each other and actually figure out how to.
If I can just quickly add a quick point on that. We talked about the language; I think there’s another dimension. Information with context becomes knowledge, and knowledge with reflection becomes conviction. And I think one of the parts that IT can do well going forward is to really drive that reflection along with knowledge to make it a conviction with the business users. So if you really peel it down and say, how does it really happen, the way you do that is really the art and the science of analytics that you drive in the company. So yes, you can do the data modernization, you can do the alignment, but you have to also focus on the analytics—and really do analytics in a way that you’re providing information so that people start to draw more reflection out of it and more conviction out of it. Because otherwise there’s information or knowledge out there, but you’re really not living that information in the day-to-day decisioning cycle. So the analogy I often give is that information plus context is knowledge, but knowledge plus reflection is really the important part to make it more of a conviction, more of your belief. And I think in a world driven by—I’m not going to use the most often used word these days, generative AI, it sounds so cliched these days—but if that’s the expression you want to use, the problem will get compounded if you don’t solve for that analytics engine in the process as well.
No, it’s great. I mean, we can’t just throw it all in a large language model. Who’s up for that? I have more questions, but I really want to open it up as well to get some inputs from the audience. How many of you are struggling with data in your business? You saw this slide that I had with that pie chart. Show of hands—does everybody trust every bit of data they have? No. Do you trust half the data that you have? That’s half. Do you trust 75% of the data you have? There’s a fewer people kind of nodding, but no, it’s exactly right. We believe we’ve got enough data to make a gut decision too often, and I think that’s unfortunately the way we operate as human beings, trying to jump ahead and make decisions. But given the amount of data growth—and we’ve talked about it in numerous sessions—and now how it’s accelerating faster with the new tools that we’re getting, the new software solutions we’re using, it’s a huge challenge. The other challenge, and Samir, I’m going to come back to you unless there’s a question—does anybody have a question? You brought up the word cloud. So that’s another one that’s caused a lot of heartburn with companies when it comes to data. Either they embrace the cloud, throw everything in Snowflake, and then suddenly get the bill, or they’re worried that they put their data in the cloud, especially if they’re a bank or healthcare provider—what is the risk? So from a practical standpoint, when you go in and you work with a bank or a healthcare provider or another highly regulated organization—even a manufacturing company, for instance, with environmental regulations—how are you helping your clients peel back that and really get to, don’t let the regulations get in your way, use it as a framework to build a better business model? So how do you approach that?
Yeah, I think there are multiple dimensions you can solve for that. One, the whole private cloud and public cloud is a very mature infrastructure at this point in time. You can really do a lot of data privacy tools and techniques which can take care of your data, and I think we specialize in that, and the recent acquisition really helps us in that direction. It’s a fairly mature practice—I don’t think that’s, it’s almost like a table stake at this point in time. But beyond that, if you fast forward that conversation, what happens is, again, the CXO mandate comes: move everything to cloud. And that’s where the problem actually starts, because the execution engine then starts and you put whatever you had on prem—now you have the old mess in the new estate. And unless you put again the guardrails around how do you optimize the data and how do you do it differently, you’re going to pay a higher bill than you were probably paying earlier. And that’s where most of the clients are today. They’re now looking back and saying my bill with AWS or Google or Azure is higher than what it was on prem, because they never took the time to solve for this estate modernization in a meaningful way. And I think that’s really a critical part of the step that gets missed out, because these things can’t just run on mandates. And even if you run it on a mandate, you have to have a back-end plan which says how do I solve for that. The second is you’ve got to have a sequence of solving these issues as well. And I go back to the same point about knowledge plus reflection is conviction: you really have to think your analytics story right up front and say, what am I really trying to solve for, what are the metrics that are really important for my business to thrive in this environment? Is it—am I really after productivity? Am I really after cross-sell and upsell? Am I really after customer satisfaction? I guess the answer is all, but you’ve got to take a priority call for each function and say what takes a beating, because these are conflicting priorities, whether we like it or not. Functions and organizations which take a very deliberate step tend to be more successful about it than others. And that’s really what we at Sonata try to bring to the table—can we facilitate this conversation and really drive alignment towards those, so that you get maximum value out of moving to the cloud? And I think that’s the value a partner brings: the domain, the experience working with a lot of other companies to help you set those priorities. And that’s critical.
So thank you, Samir. Thank you, Phil. I think we’re going to wrap here to get us back on time—25 minutes flew by. Samir will be here; if you have questions about your data strategy, I encourage you to talk to Sonata. They’re doing some really cool stuff, bringing partners in, really bringing some fresh ideas—that’s what I’ve been most impressed with—to the data modernization journey. So thank you very much, Samir. Thank you.
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