In this webinar, hear Julia Fadzeyeva, Senior Consultant at Forrester, and Louise Baldwin, Solutions Director at Tamr, discuss the benefits of moving to a modern MDM solution and the impact of mastered customer data on business outcomes.
- Hello and welcome to today's webinar, Getting Maximum Value From Cloud-native Master Data Management hosted by TechCrunch. My name is Ashley [INAUDIBLE] and I'll be your host for today's event. Joining me are two great speakers, Julia Fadzeyeva, Senior Consultant at Forrester, and Louise Baldwin, Solutions Director at Tamr. During today's session, Louise will kick off with an introduction to building the business case for MDM. Julia will review the Forrester total economic impact results. And Louise will wrap up by sharing a few customer examples.
We'll then move to the Q&A portion of the session. If you have a question, please submit it in the chat box. We'll do our best to answer all your questions live and follow up with those we aren't able to address after the event. I'll now hand it over to Louise to begin the presentation.
LOUISE BALDWIN: Fantastic. Thanks, Ashley. So when we think about building the business case for a master data management, it's important to set it in the context of the broader data agenda at the moment.
So we're all used to the big data agenda being the center point. We've had 15 years of it. But when we look at the underlying stats-- and there are many-- they're normally pretty disappointing. So here is a survey from McKinsey done in just January this year. It looks at how much customer data is actually informing business decisions and being used by users throughout the business. 86% of respondents believe that they could do much better with the data. This is looking at it from the perspective of actual businesses. But often we see these stats told from data science's time wasted not doing the core of their job, focus on things like data cleaning and data cleansing.
So how do we get here? Why does the problem exist? Well, after 15 years of a big data agenda, the big data part isn't really the problem, bad data is. And when we look at the sort of data management landscape today, there has been tremendous progress made in bringing together data at scale. So we typically think about this in the transition from traditional data lakes and data warehouses, to seeing the benefits of cloud data platforms and the benefits of cloud elastic compute that go with it. And we've always seen this in the growth of the major cloud providers and cloud data warehousing providers like Snowflake.
But while many organizations have now overcome the challenges of data at scale. And they've really optimized with huge investments in infrastructure throughout the business. Often the quality challenges remain. Seemingly simple targets, such as single views of customers often seem impossible. Analytics are often incomplete or really just out of date. And what ends up happening is that the data can't be trusted by the business. And we're here to talk about the business value of data. These are really key factors that influence at the end of the day, is the data driving impact for the business?
So why do we have to tackle this problem? Well, what's been really interesting to see is the growth of DataOps that is essentially there to address both the speed and scale of addressing data quality challenges. And within solutions that targeted from a dataops approach, they're kind of key principles that are involved typically agility, continuity, collaboration, interoperability, which is always key to thinking about data at scale. And this best of breed approach, we think, is key to ultimately driving value from data.
Now that's putting DataOps in the broader scheme, but we're here to talk about master data management. So I think within the context of business value, it's good to hone in on what we really mean.
So always good to start with with a definition and lay things out. And so when we think about what are the objectives of master data management, it really boils down to to a few things. So consistent and uniform data. So the ability to rely on it by the business. Those identifiers and attributes. So that creating that robust, holistic view of entities.
And master data is addressing the core entities of the business. So we've listed some of them out here. Customers, suppliers, products, parts and materials, spend data. Really the entities that are most critical to the business from understanding it from a value piece perspective. And typically, the type of data that feeds multiple business processes. So lots of end systems and lots of end users.
So really, master data is about tackling the core entities for the business and ensuring that it's putting the data in a format that's usable by the business. And that's really kind of the heart of where the value comes from within it.
So when we think about the transformation within it, master data management is a pretty old term within the industry. What has changed and how does this impact business value? Well, we like to think about it in the context of time to value. And here it's really about measuring outcomes in weeks and months, not years. And there are few key components that really emphasize the importance of a modern approach to master data management.
And so a couple of elements that at Tamr we really emphasize in our approach, one is being cloud native. So the importance of harnessing the elasticity of compute for true enterprise-wide use. The second is machine learning, which is a key part of how we think about master data management. It's really important to be able to capture that subject matter expert input from the business. And at the same time, reduce the manual effort. So capturing their input, but doing it in a scalable way.
And it drives two things really from the business side. One is just the efficiency at the end of the day, right? You're getting access to that data in a much faster-- in a much faster frame. And the second is the data usefulness. Because you're getting, again, the input from those that know the data best. And that's key to master data management and it's key to business value is really ensuring that entities are available in the format that the business makes decisions.
And the third kind of principle that underlies how we approach master data management is the DataOps principles. So we touched a little bit on this already, but ensuring that agility is at the heart of the approach and that interoperability is so key. So for many who have been working on executing the perfect data architecture, it's really important that the master data management solution is able to ensure that it's interoperable for the longer term.
So I'm really excited that we have Julia here as an expert in the fields to chat about how to capture the value of master data management solution and to deep dive on Tamr. We spend a lot of time thinking through why it's so important to capture the business value and how it's key to our solutions. And so here are a couple of points that we really try to think through when we're approaching a new master data management problem. And when we're trying to discuss what it actually means to the business at the end of the day to be able to communicate it.
So starting out, prioritizing projects is just a key part of it. So in the intro, we described master data management and the potential that it has, right? There are many core entities within the business that you can tackle. And naturally, time is a critical resource, right? There are trade-offs that you have to face. With the ability to look at at the end of the day, why does it matter to the business? It helps inform that prioritization. Are you starting with your customer data? Are you starting with parts or suppliers? But to really make sure that not only within the context of master data management, but also potentially other data projects that are on your agenda, that you're able to weigh up and quantify and compare projects to make sure that you're focused on what is driving value for the business as a whole.
The second reason we focus on it is really that we want to focus at the end of the day on the outcome objectives. And sometimes talking about the business value really forces that focus. You have to think through the timelines that you're working to. Who at the end of the day is consuming the data? And so we use it as a useful forcing mechanism to really prioritize at the end of the day what we're actually trying to achieve with the data.
A third benefit is really highlighting the work of the data org. I think, often, data and analytics teams, it's difficult sometimes to be able to communicate that clearly. Not everyone understands sometimes the value of reducing false positives, for example. But when you put it into dollar terms, it always translate.
And that leads to our fourth point, which is creating that universal language for success. So really ensuring that the value is understood by everyone throughout the business and that everyone's on the same page.
So how we typically think about value is on three levels. So at a high level, the three are driving those business outcomes, which is key, improving efficiency, and reducing infrastructure costs.
I'm going to start in reverse. I think reducing infrastructure costs is sometimes one of the most visible, especially we don't have a centralized master data approach. It often leads to an explosion of systems to support, which often leads to more teams being set up to support those systems and third parties being brought in.
The second, improving efficiency. So really streamlining those workflows across data users and trying to reduce the manual effort so that teams can focus on the core of their work and the scalability over time.
And then what we think is the most important one, but the neediest, driving those business outcomes. So thinking through how to drive revenue, how to lower costs, how to reduce risk. I'm thinking about that from the perspective of how the data is actually being used. So in operational systems and also the analytical use of the data and what decisions it's driving.
So at a high level, that's an introduction to how we think about the value and the importance of master data management. But really looking forward to getting into the detail with Julia and hearing how she approached total economic impact.
JULIA FADZEYEVA: All right. Well, thanks Louise. It's a pleasure to be here and have the opportunity to walk you all through the findings of the TEI study. You may be asking yourself at this point, what is TEI? So TEI stands for total economic impact. We built this methodology about 20 years ago because Forrester found that over 90% of all IT decision makers in North America see value in a business case. And so the question, of course, became, what was the effective business case study, right?
Early on, those studies focused primarily on the total cost of ownership, and that included IT cost and IT savings. Going one step further, the ROI analysis took into account tangible business benefits such as efficiency or incremental revenue or that infrastructure cost savings, right? But today, for TEI case study is we look at the ROI. But we also look at the risk and uncertainty that comes with any technology investment. We evaluate long term outcomes, such as scalability and flexibility that the solution brings to the table.
So the TEI framework provides a very realistic and conservative technology assessment that you should feel really confident using. Here, within the TEI framework, we look at the benefits, and those are improvements to productivity, efficiency, cost avoidance, or it could be incremental revenue. We also look at the costs, such as implementation or ongoing support or training. And we consider flexibility. Now that you have a certain technology, what are the long term paths that are opening to you and what impact they may bring to your business?
And as a final step, we look at the risk. And we account for certain variations in benefits and in costs. All of these considerations come together for the total economic impact analysis.
Here is a high level visualization of the process. Starting with due diligence, this is where we talk to subject matter experts, industry analysts and professionals. Then we conduct interviews with Tamr customers. And then based on these interviews, we create a composite organization with the accompanying financial analysis. This all gets rolled into a written study that I highly recommend you read. And then it goes through a rigorous review process, and that includes the analysts, but also it includes the people we interviewed. So they are confident that the story that they told is accurate.
So let's get to the good stuff, shall we?
Data management today-- and I'm probably not going to surprise anyone with that-- is increasingly critical to the organization's ability to win, serve, and retain its customers. Business users are always looking for quicker access to trusted data. And they want to use it to make a [AUDIO OUT]. From the technology perspective, the user is aimed to simplify data management. It's kind of a given.
But with the investment in Tamr, customers achieved an accurate and reliable single source of truth for their customer data. And they could easily scale with increasing data volume and complexity. The platform served as a solid data foundation that helped improve efficiency for data mastering teams, increased productivity for sales teams, and bridged the gap between the data and the business outcomes to drive future business opportunities.
Let's break this down further.
The financial model in the study covers three years of benefits and costs. And we did the math to make sure that those benefits and costs are measured in today's dollars or the present value. So the big takeaway from the document is that the composite, which is an anonymous representation of the customers that we spoke with for the study-- and we'll talk about it a little bit later-- receives a return of investment of over 600%. Benefits of over $8.8 million. And then if we take out the cost of the solution and the cost of rolling it out, we come to this figure called the NPV or net present value, and that's $7.6 million.
While the numbers here are really impressive, I personally find that stories and the sentiment of the interviewees are equally as important. And I'll point to some of those throughout today's conversation.
To give you an idea of how we arrived at all of these findings for the study, Forrester interviewed five companies. All of them obviously were the Tamr platform customers and we saw a variety of industries. We spoke with different organizations in finance, in retail, media, manufacturing, all of them shared their stories and they brought in very unique perspectives to how Tamr provided value for them.
But despite the variety in the industries, we saw a lot of similarities when it came to concerns and the challenges that these customers experienced before they came to Tamr. I want to say that most importantly, they were dealing with inaccurate or incomplete customer data. Before they started using Tamr, these companies collected customer information from multiple touch points and they had no established process to consolidate the data into a single record. With no single source of truth for customer data, they really ended up with incomplete, duplicate customer records. Undoubtedly, you know what this means. The number of customers and number of customer records doesn't match, and that's really far from ideal in any business scenario.
The second challenge was that prior to using Tamr, these organizations were unable to consolidate [? clients ?] and categorize their data at scale. Some of them relied on legacy rule-based point solutions or very basic internal tools. But both of these approaches meant having manual involvement from the data engineers and from the analysts. And it would not be a surprise that when the processes are that manual, you can't accelerate the data management operations.
And finally, all of these organizations that we spoke with saw the need to become more data driven. Unifying their customer records was seen as a stepping stone for these transformations. But as it was back then, the business users could not trust the data to inform their daily actions and strategic insights. Without the single source of truth, these companies struggled to propel the business forward. And I'm speaking for the five customers that we've interviewed. But, Louise, I wonder if this is an overarching story that you hear from more than these customers.
LOUISE BALDWIN: It truly is, Julia. I feel if you interviewed every single one of our customers, most of these themes would apply. And I really like the quote because I think it brings it home in terms of people weren't sure of what the source of truth was. So many issues linked to it that you mentioned in terms of the silos and what that leads to in terms of duplicates and errors and stale data sources. But ultimately, that is the real issue that the business is facing. That single source of truth and being really sure of what your entities are and having that data view on them.
JULIA FADZEYEVA: Right. Right. So, you know, knowing these issues-- again, not going to be news for everyone that they're looking for a new approach to manage their data. They were primarily focused on finding a solution that would serve as an accurate and reliable single source of truth for their customer data. They wanted a solution that would reliably provide a cloud-based machine-driven data foundation for future modernization and data quality initiatives.
They wanted to deliver consistency at scale. When they came-- when it-- especially when it came to increasing data volume and to increasing complexity. They wanted to improve risk management because it is all dealing with customer information. So making sure that the information stored properly and connected properly was critical. They wanted to provide a foundation for data-driven organization. And also, last but not least here, reduce manual processes and take out the inefficiencies throughout the organization.
Anything you would add here. Are you seeing any other objectives for your customers?
LOUISE BALDWIN: Yeah. I think there's a lot that comes from that and kind of builds off these core overviews. And so I think these are a lot of the sort of key ones that come into investment objectives. But I think you can break it down further and think through at the end of the day, how it links back. So within each of these meaty topics, I think there's so much. I'm going to pause there, though, because I think even as you get in to it, into more of the results, I think it starts to show it from the customer context of what it means. What does providing a foundation for a data-driven organization mean in a customer context?
JULIA FADZEYEVA: Yeah. Yeah. Absolutely. Before we move to talk about the results, I do want to summarize what we were just talking about with the words of one of the customers that we interviewed. "We needed to innovate and modernize our digital assets. As our organization has grown, a great deal of home grown tools and off the shelf products have been incorporated into the ecosystem. And over time, there has just been a confluence of too many things. And so our goal was modernization, cost savings, optimization, and really finding opportunities to be much more efficient without resources." And I couldn't have said it better.
So we looked at all of the commonalities and the differences between the interviewed organizations and we created a composite that was representative of the stories that we heard. The case study and the rest of the slides for today will focus on the benefits that are accrued by this specific composite organization.
So for the purposes of the study, this is a global company with about $15 billion in annual revenue and 30,000 employees. The composite has a database of over 10 million customers that is sourced from more than five different data sources. It has 1,000 B2B focused sales representatives. And the average deal size for the B2B sales is $15,000.
So now that we've set the stage, let's look at the most interesting part. What are those benefits that the composite organization saw as a result of using Tamr? The highest benefit comes from improving productivity for the data analysts and for the engineers. The composite also increased productivity of their sales team. And that's a second benefit here. And the sales and business development employees got access to better quality customer records. They now could generate net new opportunities from these leads and from the data. And so that's the third benefit we were looking at. I'll walk you through some highlights for each of those. But even more so you can learn the details from the study.
So the leaders in the data services and data management and operations told us that the Tamr Platform for them connected the disparate data sources across the organization. And the heavy lifting of cleansing and categorizing the data could not be done without the heavy reliance on machine learning from Tamr. Before they invested in Tamr, ingesting testing the new data set and reconciling customer records was all a manual process.
Think about that. The engineers had the responsibility to provide that custom logic for integrations, and data analysts conducted manual data entry, review, reconciliation, and cleaning. This inefficient process could take months and resulted in data that had errors, was not readily available to business users.
I have some examples from the industries that we spoke to. So, a media company told us that they rely on the data operations team of 30 to 60 people to just clean the data. Their VP of data management said that they were basically scrubbing the data and it was a very expensive way to do it. Once the cleaning of the data deduplication was run automatically with Tamr, the organization significantly reduced its reliance on that manual effort to clean the data and could reassign those people to more meaningful roles.
As the financial services organization began using Tamr, they were able to reduce the effort of data entry and manual processing by at least 10 FTEs from the very beginning. And they plan to further grow their efficiency to a reduction of 40 to 50 full-time roles over time.
I like to allow customers to speak for themselves, and so I will quote the director of the business and product strategy here. "The ability to introduce machine learning with Tamr was critical for us, and the efficiency we gained was tremendous. We were able to reduce the number of engineers that were actively sitting and coding and trying to make sense of millions and millions of records that we had. That was by far the most impactful thing, and we felt that the impact was coming right out of the gate."
Based on these conversations with the industry professionals, Forrester modeled that the composite organization reduced the engineering effort that was related to data mastering by 70%. That's huge. And by 80% for the data analysts who were manually processing and cleaning the data. And that amounted, for this organization, for just above $4 million in reduced costs over the course of three years.
Any thoughts on this, Louise? Do you see it for other customers as well? Does it ring true for the majority?
LOUISE BALDWIN: We do. And hearing the quotes from the customers, it's almost like pulling back the layers of an onion in terms of the different layers of efforts that are going into the tasks. And I think often it starts with those sort of core engineers and that core engineering team that's spent.
But then often the ripple effects across other teams, from a data entry, from a data cleansing efforts, are often so, so key. And it really does become this huge sort of cross-team effort often, and where a lot of the sort of immediate value, often the most visible value I think, ends up coming from.
JULIA FADZEYEVA: Right. right. Well the efficiency that we just talked about is definitely critical for the organization and always came up first in every conversation that we had. But they also certainly wanted to make the data, now that they have it, they want to make it work. And interviewed companies told us that they wanted to empower their sales teams to be more efficient in generating leads, in interacting with customers. And to do so, they needed to make sure that these customer records were high quality and they were easy to access.
Prior to using the Tamr platform, the data that landed in the hands of the sales agents often contain errors and it often required them to do some additional research once they got the data. Again, I don't think anybody will be new to a scenario that I want to describe where a single customer could be represented by multiple records, which then resulted in sales team duplicating their efforts. And if they called the same customer twice from different salespeople, it could provide a very subpar customer experience.
So Tamr helped create that single, unified customer profile. And it allowed the sales folks to focus on their core job of conversing with customers, of understanding the customers' needs, and helping them resolve their problems. We heard financial services organization experience a 50% to 80% reduction in time that it took to connect salespeople to the correct customers.
The director at a retail company described how Tamr has allowed sales agents to optimize the use of their time. And again, I will quote here. "Having sales agents hunt for clients is wasting valuable time for them. The best use of their time is to be engaged in conversations with customers, to understand their pain points and what problems they need to be solving, not trying to find someone to call in the organization's records. Putting that information in front of them and allowing them to understand what type of conversation they need to be having with this customer puts them in a position of strength."
So to sum up, for this study when we evaluate the time savings from not having to search through the customer records, that was roughly several hours per week for each of the sales reps which resulted in $2.6 million over the course of three years in terms of just saving the sales representatives' time. Anything you would add here, Louise?
LOUISE BALDWIN: Yeah, I think it's great to go deep on it from the perspective of sales. I think anyone who has spent time in a CRM, like a Salesforce or a Microsoft Dynamics, can probably relate to this challenge. and just how painful it can be to actually go and source the data you're looking for. And what is it it can be, right? When you're a salesperson trying to reply to a lead, trying to follow up with a customer, these are the things that really can make an impact at the end of the day.
And I think this is a beautiful job of capturing the sales impact and going deep on it. I think we see similar benefits when think about the same problem, really, customer data from the perspective of marketing teams. Or when we think about risk or reporting teams and how they also tend to go through a lot of the same processes to get the data they need at the end of the day for general business operations.
JULIA FADZEYEVA: Right. Right. Undoubtedly. So, with the sales organization saving time, now we have a chance to see what they're doing with this time, right? Once Tamr helped them make customer records more accurate and available to the sales force, they now have more time to generate leads into sales pipeline opportunities. And we had one interview at a retail company who noted that the organization used Tamr to comprehensively build out customer profiles, enabling their sales to better discern between new and existing customers. And that defined their strategy from there.
A manufacturing organization was able to shrink the pool of customer records in their system by 20% due just to using Tamr, and this allowed their sales team to optimize their campaign to target the verified customers. An interviewee from a media company told Forrester that through improved customer data and less time spent in research, their salespeople could make eight additional phone calls to all of their customers per week. And that created additional pathways for the sales ventures.
I'm sure you can read the quote here, but I do want to sound it because it's important. "Sales agents appreciate having a single customer view with Tamr because they're better prepared to talk to their customers. They can speak with confidence about a customer's position, about their account and balances because all of the customer's previous profiles across the company are right there in front of the agent, not split across 10 different accounts. To be able to aggregate and disaggregate, to talk their same language in the same context, that's key food for relationship and service and creating future business opportunities."
So again, for the composite, Forrester model that with Tamr each salesperson receives one opportunity every two months. And the outcomes of them having those additional opportunities amount to $2.1 million in three years. I'll pause here before we move into the next section. Is that similar to what you're hearing, Louise?
LOUISE BALDWIN: Yeah. For sure, Julia. And this is the element of business value that I get most excited about.
JULIA FADZEYEVA: Right.
LOUISE BALDWIN: Because at the end of the day, how the data is actually being used to drive decisions. And I think it ties into the previous benefit in thinking through enabling people to do what they do best, you know?
JULIA FADZEYEVA: Right.
LOUISE BALDWIN: Why the sales folk are hired in the first place. To drive those results. To better look after their customer. To make informed decisions around cross-sell and upsell. I'm excited, later actually we'll have the chance to kind of touch on some of our customers and describe in a little bit more detail. But a lot of these kind of value propositions, in terms of driving value for a new opportunity creation, is definitely a core benefit when we look at customer data mastering as a whole.
JULIA FADZEYEVA: Right. So as you saw, we were able to quantify a lot of the value of that Tamr provides to organizations. But beyond the ones where the customers were able to put the number to, they talked about a lot of the benefits that they couldn't necessarily quantify but they were critical to them nonetheless.
So they noted that using Tamr resulted in shorter customer onboarding times by at least 50%. And similar to onboarding their customers faster, with Tamr organizations could develop more streamlined processes to ingest internally the new data sources and new lines of business, which was big for them.
Before Tamr, they've also reported struggling with providing high-quality and accurate data to their internal users. We talked about that. The errors ultimately resulted in lack of trust in the customer's own data. But after they've implemented Tamr, business users realized that they were seeing reconciled and consistent records now from a single source of truth. And so their confidence in data improved, and their ability to use the data improved.
On the flip side of that, when people saw the errors they had to do something, so they've submitted support tickets. And so with fewer errors, there was a reduction in the number of support tickets that were seen by the data operations team. And consequently, there was a reduction in time that was needed to investigate and fix those data errors coming at different parts of the organization.
According to the head of digital transformation at a financial services company, regulatory reporting or internal reporting or forecasting budgets became very tough when they didn't have the data that they could rely on. But with Tamr serving as a single source of truth, the organizations could now ensure that their reporting was accurate. And it posed no risk or violation of compliance for them, which was very big especially for the financial services organizations.
And again, the last bullet here may not apply to everyone, but prior to Tamr a handful of the interviewed companies invested in building data lakes. However, without a good way to navigate across the ecosystem that fed these lakes, the lakes ran the risk of sitting idle and ultimately turning to sunk costs. So with Tamr, the platform made the data lakes an asset for data users in the organization because now they could navigate it, they could use them. And instead of the data swamp, they turned it to be a useful resource.
I think this is it here. In terms of the other side of the equation, you probably know that there are costs from adding a new technology piece to an organization. So of course there were annual fees that were paid for Tamr as a platform based on the scope of the project, and that included the number of data sources, workspaces, integrations, things like that. The organization also assigned resources to implement the solution and also to train the team to use Tamr. And Forrester accounted for all of these costs as we built the financial model, so you can take a look at those in more detail in the study.
So once more the, ROI for the Tamr platform amounts to 643%, with benefits nearly $9 million and a net present value of $7.6 million, which is an excellent outcome. As I mentioned earlier, this already accounts for the risk, so it is a conservative estimate and a realistic one as well.
This is where I want to conclude the brief summary of the TI study. I know I talked a lot. The full version of the study definitely has more content. And I think what's more important, it has a lot more real-life examples of how customers use the platform. There is more of customer voice coming through, there are more quotes and more stories that you may find useful to your organization. So I highly recommend reading it.
But this is enough of me for today and I want to pass it back to Louise to talk more about other customer stories.
LOUISE BALDWIN: Fantastic. Thanks, Julia. Yeah, I think that was the perfect scene in terms of setting those kind of core benefits that we've seen across so many of our customers. So with that, wanted to bring it to life and chat about some of our customer experiences. And we do work with a wide range of industries, from public sectors such as the US Air Force, to finance leaders like Blackstone, or life sciences health care providers like Novartis or J&J.
So really getting into it, and let's take a couple of examples and kind of chat through what it meant to them. You know, I think starting off Santander is a great starting point. They're a great customer case that speaks to the value of master data from the perspective of business transformation.
And so with Santander, we were working with their UK business to create a holistic view of customers. For Santander the core challenge was really unifying 45 data sources across millions of customer records. I guess, at the end of the day, what is it what does it mean to them to have siloed data? What was their starting point?
Well, for them a core focus was the credit exposure that comes from customers, thinking about it within the commercial banking context. And it was slowing down lending decisions at the end of the day. So as anyone, even from a B2C perspective, when you think about waiting on that response time when you apply for new credit, it can really be critical to retaining and expanding customers. And also as a bank, making the right decision about who to lend to at the end of the day.
For Santander, it was a rodeo. It wasn't their first rodeo, but it was a rodeo. They had multiple failed attempts before this. They had gone through sort of rules-based approaches, and they were definitely trying to overcome, as well, the business fatigue that kind of goes with that, of having tried many different approaches before.
Working with them, a key part of it was really creating that single view of the customer through a unique ID. For them it composed several sort of core sources of business. So across corporate data, sales force data was a key source, and global business data. As well as looking to how they expanded then to the retail business. Within that, a core part of it was clustering the data. So looking at the underlying way that we can cluster data records so that there are business-ready segments. So you think about that by products, by region, really making sure that you have the views of the customer that you need.
Again, our customers always say it best. So this quote from Jonathan really speaks to the critical business benefit in terms of the faster lending decisions. And for them, they were about to implement a new system, Encino, and mastering the data ahead of that was really key to the system being successful. The data at the end of the day through Encino, it's also going into reporting. So typically as a bank, you inevitably have to do a lot of it, and it's key to make sure that the data is accurate. As well as coming back to empowering sales, that's another key benefit that we see again and again from the perspective of a mastered data.
So maybe jumping to a different example and maybe changing industry to mix it up, but Littlefuse is, again, another great example of a customer that leveraged their data on their customers to drive value. And as a high-tech manufacturer, often the third-party data sources are so critical. It's not only their own sort of direct sales channels but indirect sales channels. And often controlling data from external sources is even harder from a governance perspective.
So for Littlefuse, it was really things like the actual underlying data quality. So the inconsistencies in how different distributors were capturing it, which meant that they had these limited views into the corporate relationships at the same time. So there would be the [? B ?] business. And we all know the complexity within that. Thinking through how you're defining a customer, at a what level even within the sort of hierarchy of the corporate are you looking at the customer. And so for them, it was key to be able to create that view.
In the scale context, they had just over a million, I think was 1.4 million, customer accounts. And it was about unifying that down to the true underlying half a million customers that were actually represented through golden records. In their case, they were looking at having those primary identifiers in SAP business warehouse. So again, just speaking to sort of the diversity in terms of the systems that we end up seeing our customers use.
From a business perspective the value is a little bit different to the Santander case in that Littlefuse were very focused on those kind of end insights and the analytics that they were driving. So for them, it was about advancing from one-off reports that were taking so much time and so much effort. And looked pretty for that one-off use case but just could not be repeated in a scalable way. So for them, sort of the repeatability of reporting and analytics was key.
And within that, what they were focused on was really cross-selling products. So upselling and cross-selling products to make sure that they were expanding their customer relationships and really informing, again, the sales team's decisions about how they reached out, how they contacted and communicated with customers.
So that's a little bit of a taste here of some of our customers kind of, again, their perspective on where the value is coming from. But we'd love to open it up to Q&A, and maybe hand back over to Ashley to see what questions there might be.
- Thanks, Louise. I have been looking through the questions. We've got some great ones coming in. So to start off, I think this one's for you, Julia. How can I use the case study to determine what my costs and benefits and ROI might be if my company invested in Tamr?
JULIA FADZEYEVA: Great question. I mean, this is what we were talking about, right? So we developed a case study specifically in a way that each organization should be able to evaluate what applies to them and what they need to adjust when they think about their own benefits or ROI.
So the best approach I would recommend is to look at the financial model that is shown in a lot of detail in the study. And then see how your organization is similar or different to the composite. And then think about what you need to just to evaluate your own outcome. So thinking about the numbers, some of them may be really easy to scale, right? So for example, you may plug-in the number of engineers and the analysts that you now have, looking at the data and see how much they can save from the manual data management removal, right?
But you may need to think a little bit harder if you're just trying to adjust the number of sales opportunities that your salespeople will gain from better data. But essentially, it's just taking the model and putting your numbers in, your own numbers in.
- Thank you. This question's on a somewhat related topic. How do you make a compelling case for business drivers that might not be quantifiable, like reduced risk and customer satisfaction? Louise, would you like to take that one?
LOUISE BALDWIN: Yeah, it's a great question. It's often so hard to actually get down to the numbers in many of those benefits and other ones that Julia touched on in terms of those unquantifiable ones. I think bring it to life in those cases, often it's really useful to kind of ground it in the business context of what teams are going through and what they've seen from a risk perspective. Unfortunately, in some industries, you can see it translate into elements like fines in the past, or fines in the industry that they can relate to.
But I think often in those cases, it's really important to kind of, again, just hone in on understanding the business model and outlining the risks. And when you can't get that quantified view of relating it back to the business, bringing in some of those quotes from across teams that you work with, as well as kind of what the business has historically seen.
- Thank you. So Julia, I got a question for you. Can you explain again why Forrester makes risk adjustments to benefits?
JULIA FADZEYEVA: Yeah. Good question. So I think we will all agree here, and everyone on this webinar will agree, that the risk is inherent in any technology project. And in an effort to present very reasonable, achievable, and also credible benefits the ROI totals, Forrester risk-adjusts the benefits downward. Because we want to make sure if we estimated a benefit up here, we want to make sure that if you try to do the same thing and your inputs were a little bit different, we account for that difference that could be more risky for you, right? And we bring benefits down.
But we also adjust then it costs up, right? So if you incurred slightly more implementation timeline, like a longer implementation timeline, that's already accounted for. So using this conservative approach and seeing a positive ROI still, helps us be very confident that this investment is likely to succeed.
- Thank you. That's [? fairly ?] helpful. All right. Louise, what are typically the highest return-use cases you see for master data management?
LOUISE BALDWIN: Oh, it's an interesting question. You know, there are so many needy business challenges that he can tackle from an MDM perspective. What we typically see and why we start there is customer data. We spent a lot of time today chatting through the business value of customer data. I don't think there's a business out there that will tell you that customers aren't important, right? They're really the center of every business. And so our guidance is often to start there from an MDM perspective.
I think the second element is just aligning to strategic priorities as well within the business, an it can vary from business to business depending on the year. So we spend a lot of time on customers. It is where we like to start, but we do have lots of customers with diverse use cases across supplier data, parts data, spend data. And again, often the use case is really just about understanding the strategic priorities for the business at that moment in time.
- Thank you. All right. So another question. In addition to ROI, what are some other good ways to convince my leadership team that we need the solution?
JULIA FADZEYEVA: Well if the 600-something percent ROI is not convincing enough, with that ROI I feel like the solution should sell itself, but if that's not enough, I would recommend going back to what Louise and I talked about in the unquantified benefits, right?
Because if saving the productivity or the efficiency in the time of your analysts and engineers that's already quantified is not what your leadership is focused on, then I would look at the other priorities that they have and try to see if those are addressed in the. Study that could be reducing the number of data errors, improving the data quality, improving the trust.
Or, as Louise was talking about, the compliance. Making sure that you make no errors in those customer data interactions where compliance is really important. So definitely look at the customer stories within the study and the unquantified section would be where I would start.
- Thank you. So here's the interesting question. Efficiency gains through [? account ?] savings is a sensitive subject. How do you approach the topic? Louise, you want to take that one?
LOUISE BALDWIN: Yeah. And it really is, it is such a sensitive subject at the end of the day. And you really have to think through, I guess, what those savings mean. I think in our best examples and when we think about the resources within the data org, it is about often allowing people to focus on the work that they want to do and often the core of the heart of of why they were hired in the first place.
Because often a lot of the sort of manual efforts that's being reduced are typically, sort of, end up being these side projects or often dedicated resources put to the issue. But really it's about thinking it through from how you're actually enabling the teams to actually focus on sort of the key priorities. So I think avoiding framing it as necessarily cost savings from the perspective of reduction, but cost savings from the perspective of how you're actually enabling resources to go and then prioritize and drive value for the business.
- That's great advice. All right. Looking at time, I think we have time for one last question. What advice do you have for engaging with the business as a data lead? Louise, you want to wrap up with that one?
LOUISE BALDWIN: Yeah. Well partnering with the business can be really hard. You know, we're short on time otherwise I would have loved to get Julia's view on this because I feel like she's a pro in having that experience of reaching out to so many across the business.
But I think really, at the end of the day, it's about avoiding sort of the jargon that goes with your own kind of team or outlook that we all have no matter which department you sit in. And trying to view it from the perspective of whatever business group that you're reaching out to. Often the trickiest thing as well is just, I think, reaching out in the first place. Figuring out who within the business is the right person to engage and then opening up the conversation.
Because I think at the end of the day, there are so many people within the business that can't wait for these problems to be solved, right? They're dealing with it day in, day out, and I'm sure they're very eager to talk about the problems that they're facing from a data perspective. And so I think opening up the conversation and allowing them to give their perspective on the problem is a great way to just build the partnership.
- Absolutely agree with that. All right. Well, we're about at time so I think we will wrap up there. Thank you, everyone, for joining us today. We will follow up with any questions we weren't able to answer live. And we really appreciate you all tuning in. Thank you, and have a great rest of your day.
JULIA FADZEYEVA: Bye.
LOUISE BALDWIN: Thanks very much.