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July 12, 2023
In this edition of Unfiltered Stories, Don Ryan, Chief Strategy Officer at HFS Research discusses with Rex Ahlstrom, CTO & Executive VP- Innovation & Growth at Syniti, and Allan Coulter, Distinguished Engineer & Global CTO – SAP Services at IBM the business case and requirements for effective data management. Our research shows Perception isn’t reality when it comes to data.
Rex Ahlstrom leads the company’s product strategy and development roadmap and drives customer adoption of its technology. Whereas, Allan Coulter drives value-engineered outcomes for organizations transforming with SAP technology and IBM offerings.
You can listen above or watch this HFS Videocast here:
Syniti and IBM have a strong history and long-standing partnership, working to solve complex data challenges for some of the world’s largest organizations. Data has become a core element of the strategy for every organization.
HFS Research recently conducted a global survey of 300+ executives from large enterprises to understand the state of data management and uncover areas of opportunities and challenges. So, in this videocast, they are going to review the findings and share examples of how companies can improve data management and data quality to drive business success. To read the full report, click here.
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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.
Hi everyone, I’m Don Ryan, Chief Strategy Officer at HFS Research. Today, we’re going to discuss the business case and requirements for effective data management. Our research shows perception isn’t always reality when it comes to data. With us today is Rex Ahlstrom, the CTO and Executive Vice President of Innovation and Growth at Syniti. He leads the company’s product strategy and development roadmap and drives customer adoption of its technology. We’re also thrilled to have Allan Coulter, Distinguished Engineer and Global CTO for SAP Services at IBM. In his role, he drives value-engineered outcomes for organizations transforming with SAP technology and IBM offerings. Syniti and IBM have had a strong history and long-standing partnership working to solve complex data challenges for some of the world’s largest organizations. Data has become a core element of the strategy for every organization, both large and small. HFS Research recently conducted a global survey of over 300 senior business and technology executives from large enterprises to understand the state of data management and uncover areas of opportunity and how organizations need to face their data challenges. Today, we’re going to review the findings and share examples of how companies can improve data management and data quality to drive business success. So, Allan and Rex, the research is showing that data management is maturing and has increased executive and senior management focus. All of our studies confirm this. What do you attribute this to?
Yeah, first of all, I think it’s great that we’re hearing that statistic. It means that the whole conversation about the importance of data has come out of the back office of companies and has really escalated to a level of understanding within executive teams that it is critical to how their business runs. So, impacts to company performance, impacts to a company’s ability to navigate in highly competitive markets, and the ability to really advance and not stay behind in the marketplace, is all driven by effective use of data. And the fact that executives are recognizing that and driving decision making around it is a really positive outcome.
Yeah, I think from my side, there are two big items. I spent my life really driving SAP programs, and there was always, at the go-live event, that typically challenges were all down to data quality, and less the actual real proper configuration. So I think it’s simply a reflection of how the importance of data has evolved over the years, from just simply a last-mile data migration activity just to go live, into really understanding what it means to have a proper data foundation. I think the second major shift we can see is that when companies are starting to think about data not just from a data quality standpoint but actually as a foundation for analytics and insights, there’s got to be more emphasis on the underlying data foundation, the data engineering. We use the old phrase “rubbish in, rubbish out,” so if your data quality is poor, you can’t drive meaningful, consistent insights and analytics from data. Then the business impact of that transformational change is compromised. So I think people are starting to make far more informed relationships between transformational value and the importance of the data management underneath it.
It’s interesting. This idea of analytics being the lifeblood and foundation for companies going forward, especially with new applications such as generative AI, it just puts a complete emphasis around data as something that drives company success. I just wanted to add that the research shows a clear disconnect between perceived trust and usability of data. So we talked before about the importance of data management. It might not be driven in organizations as much as it should be, but interestingly, 80% of executives suggest they trust their organization’s data, and this was across all different user groups. But there’s a significant delta, as we’ve been discussing, between trust and usability. So when asked what percentage of their data they deem consumable or usable, more than half of the respondents indicated this level is 60% or less. So roughly half are only using 60%, they think 60% is usable, yet they trust it. What do you attribute this to, and what’s driving this disconnect?
I think it’s a clear indication that the executives, again, see the importance of data. They may think that is translated into breaking down the silos inside a company to actually operationalize and internalize how data management is executed, but the research clearly shows that gap still exists. So while it’s important to the executives, I think the findings clearly show that it’s still being managed in silos within the company. And if an analytics program is going to be really successful — and I know this is a particular passion of Allan’s — you have to look at data more at a semantic level, at a higher level. But if how data is being managed is still siloed, you’re really not getting that holistic view of data at an operational level that allows companies to achieve the goals they have through their analytics, through their generative AI programs, through all the other things they hope to achieve. So there’s a big gap between desire and sponsorship, which is a really positive thing, and the actual execution of what it takes to do effective data management.
There’s also that aspect where there’s data that you own and generate as your own company, and then there’s data that you acquire or consume. So where’s the control point? How can you provide proper data management when you’re only really impacting or influencing a fragment of that data? I think a really good example of this is the orientation towards sustainability. When you look at data in the context of Scope 1, Scope 2, Scope 3, companies can understand and put some control points around their Scope 1 and Scope 2 to drive some meaningful insights, but most of the sustainable action really comes in Scope 3, and that’s where the data is externalized and is being consumed. So how can you guarantee the quality of the data to drive the meaningful insights around sustainability that you would then use as a company to take downstream action to improve ESG operational capabilities? Data is becoming a complex topic. It’s not just the old days where you generated your own data set, you put in your own data management practices, you tied it to the applications, and hey presto, you’ve got a good data framework. Today what we’re seeing is more and more companies managing data, acquiring data, trying to consume data to provide these types of insights and analytics, so it is becoming more complex. That’s when you see companies challenging the usability of data or information to drive these types of insights and analytics. I wouldn’t think it’s only down to your own management framework, it’s also down to the fact that reality today is you’re buying and consuming data from inside and outside the organization to drive the new meaningful insights that you need as a business going forward.
Yeah, and I think that’s perfect, Allan, because you’re talking in the context of a business process. And that’s the other area where things typically break down when it comes to data quality. Here’s the person responsible for customer master, material master, the other objects that are deemed important to the company, but what we’re really talking about is impacting business processes. And until you impact the business process at a meaningful level, you’re not achieving the benefits you can get from understanding levels of data quality. Whether it’s sustainability, whether it’s supply chain, maintenance and repair operations, there are so many different use cases you see with customers where, again, they’re thinking data quality siloed by object rather than looking at the overall business process that’s being impacted.
So would you attribute the root cause of poor data management or poor usability to this lack of addressing data in silos and this underlying lack of business process to really understand and drive the data through the organization? Or are there other things as well that you would attribute this to?
I think it’s one big contributor, and I can give you a few examples. When we go in and look at data quality from a customer perspective, we’re looking at business processes together with data quality. So, for example, a company may be going in and trying to get rid of vendor duplicates. We want to clean up our vendor file, so we’re going to clean up our vendor master. But when we look at it, we also look at things like vendor discounting. If they do have duplicates, they may actually not be getting all of the discounting or rebates potentially available to that company, because they’re not aggregating all the information in one view across a vendor. So there are unrealized savings. Same thing with things like order-to-cash processes. If I’ve got payment terms that are all over the place, those payment terms were decided in the moment, to be able to get something out the door, and are retained — it may not follow the corporate policy. And so you may not be driving cash collections the way you should be, which is going to impact cash flow. So it’s not just these discrete elements as they relate to the data quality pieces, but how they’re related across that continuum, that process that you’re trying to drive.
Allan, do you want to comment on this? The research shows there are major upsides to data quality. Generally, if the quality is twice as good — we asked this question in the survey — companies would be much more competitive, more innovative, and make faster decisions. What was interesting in the study was that the respondents, senior managers for the most part, could make this correlation between good data and company performance. But what do you see? Rex highlighted a couple of hurdles in fixing bad data and keeping it clean. What might be some others to drive this upside that data can bring?
No, I think Rex called out some of the key ones. For us, it’s driving that association of the data and the business value. As I said before, rubbish in, rubbish out — if you have a poor underlying data engineering foundation, then the way you consume it to drive meaningful business insight is compromised. As Rex said, there are some obvious basic examples around discount leakage and things of that nature. So we see the same thing, and this is why we’ve been working with Rex and the team, because for us the outcome isn’t about getting data to high quality — that’s simply the enabler. The ultimate goal is actually how do we fundamentally improve the business performance? How do we start to really impact the cash flow or the bottom-line behaviors? That’s why we have this work undertaken. But I still come back to the trust aspect. A lot of times, when we talk about trust in AI, if you’re driving a recommendation to an organization — let me take an example. I remember one really good example where we were doing some work around precision mining, to say this would be the place where you would get the right type of yield or the outcomes. So we were generating some recommendations through AI insights. Now, if that geologist doesn’t have the same reaction to that AI outcome, is it because he doesn’t believe in it, or is it because the data’s not high quality? So this association of the data accuracy, the trust in that data, with the ability to consume data — there’s a phrase I always use, that any idiot can create an insight, but it’s only got value when the insight is consumed by the business consumer and does it actually drive value. That’s really where the trust aspect comes in, and that’s why, for us, the root cause of that trust starts with good quality data engineering at the foundation.
Interesting. Does governance play a role in this? People have to have the trust in the data, but is there something from an organizational standpoint and from a governance perspective that drives this trust and usability as well, from your experience?
I think Rex picked up on that just before. When you get to the situation where you may have a process but you end up, for whatever reason, with thousands of payment terms, that all comes down to proper good governance mechanisms. Having that meaningful governance in place allows you to make sure you don’t get to the point where your data quality is so eroded that the value becomes almost redundant when you actually start applying it to a business process outcome. So the two things go hand in hand. You can’t have good data management practices if you don’t have the effective governance in place. And we’re not just talking about systems like MDG systems, we’re talking about proper operational governance mechanisms.
Yeah, and well said, Allan. I think part of that is also understanding that governance is a pretty broad term. But part of your governance processes has to really focus on what data really matters, and what quality is really required. There’s a point of diminishing returns — what is the difference if my data quality on my customer master is 97% versus 99.9%? What elements of the data actually have an impact on a business outcome? And so governance strategies aren’t just an implementation of technology, it’s really developing a much better understanding of how data impacts the business, and therefore where can I get the highest impact if I were to focus on data quality, and what do I expect that outcome to be?
So who’s making those decisions, Rex, within organizations? Is there a data quality group that we point to, or is this a functional management responsibility?
It should be shared. When we opened up the session, we mentioned the siloed nature of data quality, but this is the problem. You have somebody who is the domain owner for a particular data object, and they’re working on improving that data object, but they may not have the full context of how that ties into other data objects and how ultimately that boils up to the KPIs that the company is managing. And I think it would be valuable for Allan to touch on the semantic model concepts, because that’s the silo that we’re trying to break down. We want to understand the impacts of data at the business level, but too often the data quality problem is looked at in silos.
Yeah, it was interesting. I was actually with a customer last week, and they said that their data quality is horrible. And they were asking me the root cause behind it, and a lot of it was, to your point, Rex, that it’s seen as a very siloed domain exercise. So one of the things we talked about is that when we start to evolve the way we address this, it’s not just trying to force down more data quality, it’s more about why data is important, and really looking at the business term rather than simply the procedural term of someone saying, “I’m a data management function, I need to do X, Y and Z.” It just becomes routine, and they lose the intimacy between what they’re trying to do and the benefit of why they’re doing it. One of the things we started talking about is that today, even beyond the domain sometimes, data’s locked into an application management function. So I’ve got a big SAP system, I’ve got all of these data objects, I need to manage this because SAP says this data has to be of a certain quality or a certain schematic to drive this kind of transactional activity. Where we’re at today, obviously, is when we start to really decouple the data from the underlying application, when we have this more semantic view, that data’s actually treated as an object across the entire business. So what a customer means is consistent, not just to an ERP application but to a CRM application or a pricing application. You’ve got that semantic management mechanism, and then you use that to drive the foundation for the insights and analytics. So this decoupling of data from the system and actually managing it as a more semantic, holistic activity is one major trend that we see going forward, Don, that more companies will buy into. I think it will become necessary as we move from big ERP-centric philosophies to leaner, more heterogeneous landscapes. And I think that in itself will improve the actual function and meaning and approach of how companies actually treat data management.
So does this drive a new way of organizing and managing data? Today, in many organizations — not all, but many — data is still largely under the purview of IT and is seen as a technical tool, or at least the management of it is. What you’re arguing with the semantic model is that this has to be more distributed and have more business focus. So this is kind of a big shift. How do you see this moving forward? What are the steps that companies really need to take to drive this?
I couldn’t agree more that it needs to be done, but what needs to change? I can give you an example. I work with a lot of our large enterprise customers, along with Allan, and when you look at both Syniti and IBM, obviously we’re out there solving some of the largest data problems for some of the largest companies in the world. And to Allan’s point, they have very heterogeneous environments. It’s not uncommon for us to go in and see 200 different ERP systems. A lot of times that’s driven through merger, acquisition, and divestiture activities in these large enterprises. I’m working with a company out of Germany that implemented what they called a migration factory, but the concept was, look, we’re going to be doing a lot of buying and a lot of selling. And every time we do that, there could be an impact to business process. Every time we do an acquisition, maybe there’s something new that we would learn that we want to incorporate into our business process. So they established multiple levels within the company, as peers — the person who would be responsible for the technical artifacts and making sure that we can get data loaded and moved and transformed, but also the peer who understands the business process and the impacts that data will have. And so they have a council that will meet, so if they do a new acquisition, they’ll understand what new opportunities we may want to incorporate into our global template, our global policy, or what things need to be changed to conform to our global policy. And that decision cannot be made purely by technologists, because obviously it has big impacts on how the business runs. With that, they’ve been able to achieve a much faster time to completion on both divestiture and acquisition opportunities, and it’s had a really positive impact on the business, because it’s elevated data not just from the executives wanting it to be good, but into something that’s being operationalized by a joined team.
And was this driven by the C-suite? What’s been the role of the C-suite in making these changes? Is this a vision, or is this more hands-on?
This is real. The C-suite wanted it for a lot of good reasons. When you do an acquisition, you typically have an agreement in place from the selling company that says, hey, we’ll operate this system and this operation until you’re prepared to run it. These agreements have deadlines on them. If those deadlines are missed, there are very punitive cost impacts to the company. So the C-suite knows there is a huge financial impact if we don’t do these things as planned and integrated into the business. There could be lost revenue because they’re not able to optimize why they bought that company in the first place, on the acquirer’s side. So absolutely, it has a lot of visibility at the C-suite, and so the reporting goes all the way up in the scenario I described.
Yeah, a lot of the things we saw — I remember we were discussing this with a few companies — during COVID, for example, when companies had to make some really quick decisions about how they simply survived. They actually got to the point where there wasn’t a mechanism of consistently measuring the business, because the way that every organization or division was providing measurement insights meant that you really couldn’t make a determination about where you should continue operations, where you should shut down, where’s my biggest area in the market where I can drive the biggest profit. So I think COVID was also an inflection point that brought home the fact that companies had, in some areas, lost control of the foundational element of business measurement — never mind business value, but just baseline comparative measurement. And therefore I think that was a real trigger point to start looking at the foundational mechanics of how do I measure, how do I compare, how do I report, and that really put the emphasis on data management and quality so you could manage that consistently. That was a retrospective view we saw from COVID. But to your point, Don, about the C-suite — let’s be honest, for years we would never have heard this phrase of a chief data officer. So the relationship of data and value, because the CDO is typically also the one who’s got the transformation ownership — transformation is tightly coupled with the importance and relevancy of data. The two things are really tightly coupled, the CDO and the transformation officer. So companies are really starting to see that as part of that transformational journey, to get that 1%, 2%, 3% extra benefit, to have that quality, trusted data to drive the more meaningful insights, to drive cash flow optimization, to drive operational optimization, you’ve got to have that solid foundation. But to your point, you said earlier that it’s not just about an object that you manage. This whole democratization of data — it’s the business saying that if I have this quality of information or this insight, I can drive another action. So you’ve got that interrelationship between the business, a data scientist saying here’s what I want to do and the data I must have, and then IT providing the baseline engineering capability to provide those insights. So it is becoming more of a collaborative mechanism in how we manage and optimize data day to day for organizations.
So we think about data in terms of application accessibility and implementation and operations, but if we take it up a level, everybody talks about digital transformation, which incorporates many different aspects. It’s the number one objective of the Global 1000 in all the studies we’ve done. So I think people, as you’re saying, Allan, are making this connection to these higher-order objectives and driving change management around that. But are there any specific examples around data and digital transformation that you see at some companies, and maybe bring in the SAP example, some of the things you’re working on at IBM where SAP data and digital transformation are all working together?
So again, coming back to it, let’s look at some examples. When you’re looking at things like digital transformation, it’s really locked into better experiences in the value chain. How do I understand not just my customer master, but what is the information I need from that customer to drive the highest level of intimacy regarding buying behaviors or preference behaviors? So it’s not simply the data objects that drive the actual transactional information, it’s also the aspects of that customer we need to know to drive the highest level of customer insights, customer intimacy. We talk about this 360-degree view of the customer, which sometimes is a bit twee, but the principle behind it is that the more you know, the better you can serve that customer’s attitudes. It’s not just about transactional excellence, it really is about value chain improvements rather than just process improvements, and the same goes for suppliers. What we’re seeing today with some suppliers is how can I turn that supplier insight into cash flow optimization behavior? I’ve been working with companies like Taulia, for example, that are really looking at the data behind the supplier and the way we improve those supplier relationships to improve cash flow optimization. So there are many examples, Don, around discount leakage, cash flow optimization, customer intimacy — they’re all the things that are really important to customers. They all have their foundation in largely an SAP backbone, but it’s more than just the data we need to perform the actual functional task of an order entry or order execution when we get into the supply chain downstream. So digital transformation for us isn’t just about transactional compliance, it’s more about insights, and insights come not just from SAP sources but other sources that we need to use to drive this. I’ll give you another example. Today we do a lot of work in industries like mining or oil and gas, or any company that’s an asset-intensive business, where there’s a direct correlation between the use of that asset and revenue. And it’s more than just predictive maintenance — we’ve been doing predictive maintenance for years — but what’s the causal effect of that machine being disruptive? Is it weather information, is it operator information, as well as just the trending analysis? So data today, as I said earlier, isn’t just transactional data, it’s data from all sources like weather information, other OT system information, that we need to truly drive those digital transformation outcomes. So data for me today is a step beyond how we’ve always looked at it, which was just a compliance function. Today it’s a total view of how we can manage that data to drive those insights and optimizations through AI, or whatever, to drive those digital transformation outcomes that everybody talks about.
A lot of what we see as well, and very well said, Allan, is that companies that are really tackling the digital transformation journey oftentimes have 10, 20, 30 years of systems and legacy that are holding them back from achieving that. And again, it’s not uncommon that we’ll see companies with 200 different applications and lots of old data, and they’re not sure what to do with it. They don’t know what they can get rid of. Their auditors maybe won’t let them get rid of it. So they’re dealing with this mess, and very simply, as they modernize those applications, as they look at lean ERP, as they look at how they can optimize at the business level as Allan described, that type of digital transformation requires data transformation. You can’t get to an effective digital transformation scenario without considering how data needs to be transformed and purposed and understood and governed as part of that digital transformation journey.
I think the risk you’ve got to also count is that it’s easy to say we want more and more data. But Rex, you pointed out earlier that there’s a balance between data that’s actually valuable versus data that’s simply acquired and managed. One of the things we’re seeing with a lot of companies going forward is this emphasis on data right-sizing. What data do I need to drive true operational or meaningful business intelligence, versus what data do I need to have infrequently, versus data that I can simply carve out, freeze, archive, whatever you want? What we’re seeing, especially as we’ve been moving a lot of these big applications to cloud computing and also to HANA, is that the more data you have, the more costly it is to own and operate. So going forward, I think we’ll start to become far more precise around the data that’s actually needed in the fabric of the organization — the data that’s really needed to drive the real, meaningful business impact — and then we’ll drive those right-sizing behaviors. In the last year or so of conversations we’ve had, this is becoming a real situation for customers, that the TCO of running their systems in cloud, that cost of data ownership, and also the cost of running HANA, is becoming punitively high, because they haven’t done the due diligence of that data management foundation. It’s not about quality or consistency, it’s actually about volume that we’re addressing now.
So it’s not just about access, it’s about this right-sizing notion too.
Yeah, and when considering right-sizing, it’s multifaceted as well. Because as Allan said, the cost of managing data that really shouldn’t matter to the business anymore is high. Yet again, companies are fearful of changing what they believe they need. Which means you have to go to the business with insights to help them make that decision on how best to right-size. What do I truly need? What is being used by the business today that’s critical, and can you prove that to me by assessing how data is used at our company? So the first part of getting into a right-sizing effort is really having a business-process, business-outcome-driven assessment that says, we can prove to you what data actually matters to your company, based on how your company runs. And then from that, build the case for decommissioning systems, for archiving data, to get to the right amount of data, the right-sized environment that not only reduces your costs but is going to optimize your business, because then you’re focused more on the business process. You know you have the data you need, you’re not carrying around all the extra baggage. We always like using the fun analogy of, hey, we’re selling our house. We’ve been in this house for 20 years, and we’re going to move to a new house. You don’t take absolutely everything that’s in your house. You’ve got lots of boxes you probably haven’t moved since the last time you moved. You’re not going to just take it all and shove it all in your brand-new house. You’re going to right-size, and there’s no difference from that to what needs to be done within these businesses on the data side.
That’s funny, we started with the discussion of business process and we’re kind of ending up at that point.
Well, for us as well, when we defined our clean core philosophy, it really wasn’t what SAP were talking about, it wasn’t about simply fixing the code. When we looked at it, it was really about good process, good data, and good system engineering. Because to have a really proper clean core foundation, you’ve got to drive the association of all those three dimensions. If an engineering system is a bottleneck to business value, you’ve got to fix that; but if your data’s poor, you’ve got to fix that too; and if your process is bad, it doesn’t matter if your system’s good or your data’s good, you’re not going to get the best outcome. So our clean core philosophy really is around merging those three worlds together, and data really is the middle way between that system engineering and the process consumption.
Interesting, that’s interesting. We’ve talked before about the work that you’re doing with Heineken. Can you bring that up, or maybe talk about that in light of the process discussion we’re having, and the data migration and middleware?
I think the Heineken one was a really great example of two things. When we started to see companies that had spent years on a big ERP-centric philosophy starting to move into this world of what they call a leaner ERP, what became obvious is that the data can’t be locked into the system, it has to be elevated, it has to be moved into a proper enterprise data management framework. Because if you lock it into the system, there’s so much disruption in that system as we’re moving to this leaner ERP philosophy that the value becomes eroded. So one of the foundational things done at Heineken was really truly recognizing that data truly is an enterprise product, and therefore it’s managed as an enterprise product. There’s complete semantic management regarding customer, supplier, material, products, and so on. So that was a real foundational thing that Heineken did, and it was really advantageous to help them go from this ERP-centric world into a leaner ERP philosophy and then start moving towards more composability. So for me that was a really great lesson learned around when companies are making this shift in their application philosophy, that abstracting that data from the system and managing it as a semantic object or semantic capability was foundational to the success as well.
Yeah, and I think it’s a great example too of the partnership that Syniti and IBM have together. We’ve been working there for a number of years. The other thing that has made us successful is that if you look at the traditional ways these problems have been tackled — ETL tools, Excel, something that gets used for a lot of use cases it probably shouldn’t — when you try to achieve this goal of bringing data up to the business process level, you’re involving more stakeholders. And if everything you’re using is a highly technical tool that technologists can use, but you’re not empowering business users to be part of the process and the product in how you actually achieve the goals, then you’re really not achieving the result. Either quickly, or you’re using brute force, throwing a lot of IT people at it. And if they’re going to be a partner at the table, you have to consider how that actually works. What are the techniques that we will use? Does it respond to the different personas involved in that operation? And again, that’s something I think we were able to achieve at Heineken. That was pretty special.
Yeah, great example. I think we’ve discussed all the questions I wanted to get into, but I want to ask each of you, what’s the one piece of advice that you’d give to our audience that’s come out of the research and also your experience? So Rex, do you want to go first?
What I would say is, data is critical, as we’ve all discussed, but it’s also complicated. And so what we see often is that customers will think it’s just a technical problem, and they’ll just try to solve that with technical tools. It’s like anything else in life. If I have a really complex plumbing problem happening down in my basement, or I’ve got to rewire something in the house electrically, yeah, I could give it a go, but I’d rather not drown or be electrocuted. So bring in an expert, bring people in that do this for a living. And that can probably expose you to tools that not only they can use, but that can be leveraged by the business to become a partner in how you go and solve the problems.
Interesting. Allan?
No, I agree. As we go forward, we’ve got to think of data as a democratized capability. It can’t be locked into technology engineering, because we’ve got to drive the association between data and business transformation, and technology in itself can’t achieve that. So the more we think of data as a true asset, in the way that it drives real meaningful intelligence and real business value — to get the business involved in what data’s important, what data do I need, how do I then consume it, whether it’s inside or outside the organization — these are the things that are vital for success for any customer going forward in this whole next generation of business transformation philosophies.
That’s great. Well, Allan and Rex, thank you very much for this insightful discussion. And for our audience, go to Syniti or the HFS website and download our research report, “Perception Isn’t Always Reality: The Case for More Effective Data Management.” So thanks again, and we’ll talk soon.
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