datamaster summit 2020

Product Roadmap: Mastering for Every Business

 

Anthony Deighton, Chief Product Officer, Tamr & Katie Porter, Sales Engineering Lead, Tamr

Join our Chief Product Officer, Anthony Deighton for an overview of key product updates. The session will discuss the vision for Product at Tamr, recap major releases over the last 12 months and preview product development plans for the year ahead.

Transcript

Speaker 1: DataMasters Summit 2021, presented to you by Tamr.

Anthony Dayton: Hello. I’m Anthony Dayton, chief product officer here at Tamr. I am incredibly excited to share with you today our product strategy and direction. Broadly, there are three topics we’re going to come over today. First, I’ll go over our overall product strategy and direction. Second, I’m going to take a deep dive into some of the product features and capabilities we’ve been working on most recently, and that are available today, including a couple of really exciting product announcements. And third, we’re going to share two focus areas for Tamr over the next year. So let’s get into it. Let’s talk about Tamr’s broad product strategy.
I firmly believe that we are in the midst of the fourth industrial revolution. Now, if we’re going to be in the midst of the fourth industrial revolution, it begs the question, what are the first three? In my view, the first industrial revolution is the mechanized production revolution, where we really think about augmenting human labor. By adding machine capabilities to humans, we could augment the work that a single person was able to do, and they could simply do more work more quickly. The second industrial revolution is the factory system revolution, where we took mechanized production, organized in systems, and now we are not no longer constrained by what the machine could do and the system could even, in fact, do even more. Of course, the prototypical example of this is Henry Ford’s car factory.
The third industrial revolution is the digital content revolution. Now we free the constraint of actual physical goods and we got an infinite copies of a given good. We have the ability to scale to infinite copies of digital, digital goods. Now, before I reveal the fourth industrial revolution, I do want to make a quick side note here. These revolutions, of course, overlap, they build on each other, and as with every revolution., there’s a dark side to any revolution. And I’m not going to cover the details of some of the dark and difficult parts of these revolutions in this presentation. The fourth industrial revolution is the digital insight revolution, and this builds on everything we’ve done before, in particular, on this idea of digitization.
With the fourth industrial revolution, we recognize that every business generates a huge amount of data, huge volumes of data. And in some ways, the data that the business generates is more valuable than the core business itself. And the foundation of success in the fourth industrial revolution is the ability to mine or generate insights from this data, and this is what creates competitive advantage. So if you think about it with the fourth industrial revolution, as I like to say, every business is a data business. And this is really building on this idea that Andy presented in our kickoff presentation, this idea that every business at its core is a data business. Now, what is a data business? As a business, you generate data, that’s your raw material, and your business is transforming that data into unique insights, and then delivering those insights to those who most benefit from it.
So you might think you are a retailer or an oil refinery or a hospital, but you’re not. You’re a data business. And the challenge of data businesses is that while we finally solve the challenge of storing and managing this huge volume of data that we’re generating, it’s still stuck in silos., it’s overlapping, it’s messy, and the problems just getting worse. As we add more data volume, it comes at us faster for more and different sources. It’s growing exponentially. So every business is a data business and every business is drowning in data. And that’s why we believe that there’s a new category of software that joins all the software out there for helping manage your data. Software for managing data movement, its analytics, governance into this, we believe the key for managing the data driven enterprise is data mastering, a new category of software, which helps you create clean, curated data around the key business topics that matter to you.
So our vision is that every business should be a data-driven business, and we believe that the key to becoming a data-driven business is clean, curated, and comprehensive data. And so our mission is to allow every organization and enable every organization on their journey to become a data-driven business. So to achieve this, we need a plan. In building this plan, we look to another master plan that was published actually in 2006, by Elon Musk. You may or may not be aware, in 2006, Elon Musk published a blog post with the Tesla Master Plan. So he actually published this publicly. He told every auto manufacturer how he intended to launch the electric car. The plan was very simple. First, he was going to build a sports car, then he was going to use the money from the sports car to deliver an affordable car, and use the money from that to deliver an even more affordable car.
For those of you who are interested, you can Google this. This is in fact, a cut and paste of the actual master plan. And the sports car was the original Roadster, then was the Model S and then the Model 3. I am glossing over a little bit of details here for those Tesla fans, there are other cars, of course. But at a high level, the core idea’s right, which is we want to create a premium offering, then establish that in the marketplace, where then you deliver high performance offering, and then a mass adoption offering. And the same is true for Tamr. We start with our high performance validating point with the original academic work with Mike Stonebraker. We’ve been delivering to date a high performance solution with our Tamr cloud scaleout offering, and where we’re going is to deliver to the masses through a SaaS offering.
So how this looks from a product perspective, if we start from where we started, we have the world’s leading cloud native data mastering solution. It’s really the foundation for all that Tamr does. It has market leading machine learning, open APIs. It’s built natively on the three major clouds. To this, and we’ll talk about this a little later, we are adding our data enrichment capabilities, and we’re packaging that together as the Tamr cloud platform, adding data capture and data publishing capabilities so that we can bring that together in the Tamr cloud platform. And on that platform, we’re delivering a series of prebuilt templates to help you get started quickly. And so this is our product strategy in a nutshell. Data mastering for everyone, for the largest organizations of the world, with the most complex data challenges, we have table core, the world’s lead cloud native open extensible data mastering solution.
And for those more focused use cases, and we’re starting with B2B customer mastering first, we have Tamr cloud. So from large to small, from complex to simple, Tamr has a solution for everyone. So let me shift gears a little bit. We’ve talked about Tamr’s broad product strategy. Now let’s get into a little bit of the details around what we’ve been working on over the last 12 months, and what’s available today, how you can get value from Tamr today. And we’ll start with Tamr Core. When we look at Tamr Core, we actually went out to you, our customers, and asked you about the benefits and value that you’re getting from Tamr Core. We contracted with Forrester Research and they did independent research on Tamr customers, and asked them what kind of value that they were getting through the use of Tamr software. And the results were astounding. What they showed us is that you had an over 600% return on investment through the use of Tamr software. This is an incredibly significant ROI. In fact, it is among the highest return on investment for a software purchase that Forrester had ever seen.
And that ROI came across three really important vectors. The first is increased productivity, because when you use Tamr, getting a handle on your data is significantly easier. You need less people. You need less resources in order to get a handle on that data. Mastering that data produces operational efficiency. So now you can have better and more efficient operations because you’re working off clean, curated data. And you can build to the upside. So you can generate additional profits because when you get a handle on your customer data, you can upsell and cross-sell more effectively, your sales reps are more efficient. So what you told us is there are significant bottom line benefits from using Tam’s data mastering. And you haven’t just told Forrester in a survey, but you’ve also done it through Gartner’s Peer Insights website.
So you may be aware that you can go to Gartner Peer Insights and you can post reviews. And these reviews are astounding. But what I particularly appreciate about these reviews is not only do they talk about the great user interface and the impressive data matching using machine learning, but it talks about this idea of impossible to possible. In my view, there are two kinds of software in the world. There are software that just works better, so something that makes it an incremental improvement in a capability, and a second cost of software that helps go from impossible to possible, to things which were previously not achievable to finally achieving an outcome. And that’s what a lot of the reviews on the Gartner Peer Insights website said. We tried this before with competing solutions, it had failed. We used Tamr and we finally achieved the possible.
Tamr is achieving these great outcomes today at scale with some of the largest leading organizations in the world, organizations like GSK, which uses Tamr for clinical trial conversions on Azure, Microsoft Azure, cloud native over 380 projects, 600 million plus records. They’ve been doing this with us in partnership for over five years, and we’ve been working with some really complex, detailed quality control standards, because this is, of course, drug manufacturing and development, and this is a really complex industry. And we’re in production today, cloud native on Microsoft Azure in a scaleout deployment. Or Maersk, which is doing large scale Master Data management across hundreds of mastering projects, many, many entities that are looking to replace legacy solutions like Informatica because they know they can get faster time to value with Tamr. And they have a long list of Tamr mastering use cases across many key entities.
One particular thing of note for Maersk is that the first use case was customer mastering, where we mastered a three-level hierarchy, including geospatial mastering, a unique capability for Tamr. Or Western Union, which is working towards mastering over a billion records on a scaleout AWS implementation across their digital customer applications. And there’s a really tight integration here between their digital business and their retail business globally. They have over 300 million records today and they’ll be approaching a billion records in the future, and running scaleout on AWS. Interestingly, in addition, Western Union is using Tamr’s enrichment services. So these are three leading organizations doing data mastering at scale. And data mastering at scale is incredibly difficult.
A simple example here, if you start with a billion records, and you want to master a billion records across a number of different sources, if you were to try to compare each of the values in that billion record corpus, that would be 10 quintillion comparisons. And if you were able to do each of those comparisons in 10 microseconds, it would take you 100 million years to compare all that data and rationalize it. And if you could magically stand up a cloud scaleout implementation, a million node cluster on one of the three major clouds, it would still take you a year and cost you $10 billion. Tamr does that on a standard eight node cluster in 10 hours for under $100. And more impressively, we’ve cut the cost and time to do that this year by over half. So we have absolutely hacked the scale problem. This is absolutely a case of what used to be impossible is now finally possible.
And we’re continuing our investment in these cloud native data mastering solutions. Our view is that the cloud is a disruptive platform change, which finally makes machine learning based cloud native approach to data mastering possible. Some of the investments we’ve made over the last year in Tamr Core, in our cloud native capabilities, are the ability to make it available or purchasable rather on the cloud marketplaces. So today, across each of three major clouds, you can go to their marketplace of choice and you can actually purchase Tamr through that marketplace. The cloud native architecture on each of the three major clouds allows you to scale to effectively infinite volumes of data. And we do this by fully leveraging each of the cloud native services from each of the three major clouds. So for example, on AWS, we use EMR, for data processing Azure, we use Azure data bricks, on Google, we use Google Dataproc.
And by leveraging those cloud native capabilities, we can use the elasticity and ephemeral nature of those services. And our testing shows that using those capabilities can lead to savings of over 80% because you can stop using services when you’re no longer running the pipeline. In addition, our work across the three major clouds works with an open platform architecture so that you can take advantage of the amazing capabilities that each of these clouds makes available. So for example, Amazon Glue or Azure Data Factory or Google Data Fusion. And we’ve worked hard over the last year across these three major clouds to implement significant cost optimizations. For example, our testing shows that using pre group pry on these pipelines can bring the run time down by nearly 70% and costs down by 50%.
Our relationships with the three major clouds are not simply about technology, but also a go to market partnership. For example, with AWS, we’ve been working with a major life sciences organization to move their on-premise Tamr deployment to AWS. And we fully leverage AWS’s EMR capability, a massive EMR cluster with over 5,000 parallel spark tasks across 200 billion, yes, billion, 200 billion records. And on AWS cloud, this ran five times faster and at a lower cost than what they were doing on-premises. Or Google Cloud, where we’ve partnered deeply with Google Cloud, including a direct relationship with the product management organization. Tamr is a premier partner and we’re building initial versions of Tamr cloud on GCP. And Microsoft Azure, where Tamr was selected as a launch partner for Azure Synapse Analytics, as well as Azure Purview, because Microsoft realizes exactly what you realize, which is that feeding Synaps and Purview with clean Master Data allows you to unlock the full analytical benefits of those tools.
And in more recent months, we’ve invested a significant amount energy in our relationship with Snowflake. And this helps customers join customers between Tamr and Snowflake. Customers like Blackstone generate the clean data that’s necessary to better leverage their significant investment in Snowflake. And one of the benefits here is speed and time to market, because we were able to deliver Master Data for Blackstone in only 50 days, giving them clean, curated customer data in Snowflake quickly. And so that they were able to generate the operational efficiency and analytical outcomes that they desired more rapidly. And we’re continuing this drumbeat of significant investments in Tamr Core. Investments like our work in collaborative data mastering where we’ve added roles into Tamr Core to allow you to better distribute the work, a verifying data and clusters inside Tamr, which helps make data mastering a team sport.
It builds confidence in the results because everyone in your organization was a part of creating those results. Or investments we’ve made in high performance cloud scale data synchronization, because we know that when you’re using large volumes of data in Tamr Core, you want to store that data in cloud native file stores in formats, common formats like Parquet. And we want to make it really fast to be able to ingest that data into a Tamr pipeline. We want to do that in a multi-threaded way with a clean user interface in Tamr to make it accessible with the security and authentication that you expect from those cloud native cloud file storage systems. We’ve also invested heavily in simplifying dev test and prod implementations because in these enterprise scale deployments of Tamr Core, we know you want to be able to segment content between environments.
We want to simplify the upgrade and change management process. We want to make sure you have version to APIs so that you can have dev test and prod working efficiently and easily as you move content between them. We’ve also invested significantly in having a world class security implementation in Tamr Core, which means we have a secure by default installation model. We scan all of our code on every build, even builds we don’t ship, so that we can find and remediate any security violations, and we do white hat penetration testing quarterly so we can make sure that your implementations of Tamr Core are as secure as possible. And lastly and arguably most importantly, we continue to invest in the core machine learning that’s the foundation of Tamr’s market leading data mastering. We have currently 17 patents, and the most recent of which was issued just in June.
A lot of the work of the last 12 months in the machine learning is focused on learning from clusters. And in fact, the patent that was most recently issued was exactly on cluster-based learning in the Core product. So today, as you verify clusters, as you provide cluster feedback, and that improves the model, those are now patented capabilities available only from Tamr. We’ve also worked very recently on making active machine learning from clusters so that we can surface high value clusters for feed back. And really the point here is that we’re not resting on our laurels. We know that the value of Tamr comes from the unique machine learning capabilities, and we continue to double down on that investment.
Okay. So we’ve talked about Tamr Core, all of the amazing investments, and success that we’ve had in the last year with Tamr Core. I want to shift gears a little bit and talk about something new, Tamr enrichment. I talked about this at last year’s data master, or I should say that I announced that we would be working on this. And I’m pleased to share with you that we’ve made significant progress on Tamr enrichment. And the idea here, in a way, is really simple. 100% of Tamr customers use third party data as part of data mastering. And it just makes sense, right? If you can add high quality data into a data mastering pipeline, it just improves the efficiency and effectiveness of that pipeline. We’re just making that way easier because it’s tightly bundled with Tamr.
We’ve fully integrated enrichment into the Tamr UI, and it allows you to enrich your data directly in Tamr with just one click. And we do this because we recognize that the best source of complete and accurate data often is not inside your firewall, but data that comes from a third party. And by making this a fully-managed service available from Tamr, we reduce complexity, we make it easy, and it’s natively integrated into your Tamr mastering solution. There are two really important capabilities that this provides, first is data standardization and validation. So we can look at data like email addresses, physical addresses, phone numbers, and validate that these data are correct and formatted correctly. Secondly, we can improve company data by enriching it with firmographic details.
Now these are important capabilities, but what they provide is a huge benefit for your mastering pipelines. One of the customers that we’ve been working with most recently was looking to master new inbound leads into their CRM system to drive their lead follow-up process. Without changing anything in their machine learning, in their model, in Tamr, simply by using Tamr enrichment, they drove the match rate up for these new leads from 22% to 58%, almost 60%. So a significant improvement in lead matching without changing a thing in their machine learning, their model, their training. So how does this work? Imagine we start with two disparate siloed systems in this case, Salesforce and Marketo, and two customer records in each of those siloed systems, in this case, Lloyds. This company is in two places, it’s in Marketo, it’s in Salesforce, and we want to bring it together.
The first thing we can do is standard mastering. So recognizing that this is, in fact, the same customer, we can bring those records together into a cluster, and then show a golden record. Then using Tamr enrichment, we can standardize, in this case, the address and phone number field to make sure that they’re formatted correctly and that they’re consistent, and validating that in these are valid phone numbers and valid addresses in this case for a UK company. And lastly, we enrich it. So we add four fields in this case. A parent company, the ultimate parent company, the industry, and many others, we add four fields of information that we are not part of the original source system. And the result here is a better record for this customer, Lloyd’s Limited, than existed in either of the two previous systems, in this case, Salesforce and Marketo. So you’re getting better data, the best data possible, even though the source systems don’t even provide all of the details necessary.
I began this presentation with a call to action. I said, “Every business is a data business.” Well, what’s good for the goose? We’re taking that advice as well. Tamr is doing this as well. Tamr is a data business. We provide amazing software and machine learning for insights and do data mastering, but we also provide the data and enrichment that powers that, and the result is exactly what you need, which is better cleaner data more quickly. So we talked about the investments we’ve made in Tamr Core, we’ve shown you Tamr enrichment, something we had talked about last year and I’m making available today. It is with great pleasure that I get to share with you and announce Tamr Cloud. With Tamr Cloud, we’ve been working for the last two years building on the foundation that we’ve built over the last eight years of technology and experience in building machine learning-based data mastering to develop a full SaaS offering for Tamr, which we call Tamr Cloud.
Importantly, at the core of what we’re doing for Tamr Cloud is security and cloud ops. We want this to be a fully-managed and highly secure offering. We’ve been working closely with early alpha and beta customers to validate not only the feature set, but also the platform itself, and it’s going to be available next month. So simply put, Tamr Cloud is a fully-managed, packaged SaaS solution for B2B customer mastering built on Tamr Core with enrichment built in. So the focus for Tamr Cloud is on B2B customer mastering. It’s a focused solution around that important business problem. And why we focus on B2B customer mastering? Because it is an unsolved problem. Over the last decade, we’ve seen almost every organization, every software company in the world announce some kind of customer 360 application, from Seibold to Salesforce to Microsoft. Everybody, it seems, has tried to build a customer 360 solution.
And it’s our view that this is a true, because it is a really important problem, bad customer data, inflicts significant pain on your organization, all around the world. That pain comes through wasting time, manually curating customer lists, especially when they change. So think about examples like I described earlier with inbound leads or doing an acquisition, every time that customer has changes, huge manual effort to curate it. Or challenges around inefficient operations. As your sales reps work to engage customers and do the difficult work of selling, they struggle through duplicates and multiple systems and entry across different systems and incomplete data. And of course, for your organization as a whole, if you’re trying to understand your customer base analytically, that’s a really important challenge, but often your analytics are wildly off when they’re built across data silos or across data that you’ve tried to manually curate.
And we believe that with Tamr Cloud, by leveraging the best of Tamr, these core capabilities, we can finally deliver on this vision of customer 360. And we can do this because we uniquely have this fundamental missing keystone part of the problem, data mastering. Only with data mastering, can you get a persistent Tamr ID across systems, then you can understand the customer hierarchy at multiple levels. And only with Tamr, can you have enrichment out of the box. So it’s really very simple. Tamr cloud is the fastest way to clean, curated customer data. And it is with great pleasure that I’d like to bring to the stage, Katie Porter, who will now do a live demonstration of Tamr Cloud.

Katie Porter: Thank you, Anthony. Today, I’m going to be walking you through the Tamr Cloud workflow as a sales ops lead analyst. In my role, my bonus is directly aligned to our revenue performance. That means it’s critical for me to be able to provide our sales team with accurate, clean lead data. When I joined the company, I also inherited two Salesforce instances, our current accounts and a legacy acquisition. Well, there had been past attempts to consolidate these two sources, ultimately, they’re kind of a mess. So when I realized that I was spending more time on data quality issues than on lead generation, I realized that it was time to call Tamr. The first thing that Tamr Cloud provides is a location to search and browse all of the data entities within my organization. Today, I’m focused on the customer. If I enter into the catalog, I can see all of the sources that are contributing to our customer 360 view and what those sources are. I can also see our total number of accounts and customers globally.
If I enter in to one of our golden records, I can see all of the individual touch points that we’ve had with that particular customer, all of the information that we have related to them, as well as when that information was last updated. This visibility into the entire relationship that we have with each of our customers has greatly improved the customer experience, as well as decrease the amount of frustration that our sales team has had with incomplete data duplicates while they’re trying to do account reviews, as well as identify opportunities for expansion. I can also directly see the impact that Tamr has had here in linking previously disconnected accounts with thousands of accounts consolidated into a more complete view. Today, like many days, I’ve started with a data challenge. The CRO has asked for the number of existing customers that have replied to our most recent marketing campaign. To do so, I need to integrate that new lead list into our existing data.
So with Tamr Cloud, I need to add that to my data flow. In designer, I can see all of the different steps that my data goes through to prepare it, master it, and then publish it to my entity catalog as well as downstream systems, like my analytics platform. Sources that are already a part of this flow are automatically updated, so I can trust that they’re always clean and fresh. The lead list is a new source, and so I need to add it here. It’s very easy for me to select an existing connection, navigate to the directory where all of my data is related to sales ops, select the source that I want to add, name it, and add it. Now that it’s part of my flow, like the other sources, it will be continuously refreshed and updated every time my pipeline is run.
And going back to the remaining steps, that’s really all I have to do. And that’s because one of the most exciting things about Tamr Cloud is that Tamr has years and years of experience mastering customer data. And they’ve taken that experience and wrapped it up into templatized solutions and pre-trained models that were out of the box, and gracefully handle the addition of new sources like this lead list today. So all I have to do is add it to my flow, run update, go back to my dashboard and refresh it, and then voila, I can see my new leads already integrated with my existing account data. So now I can move forward with my campaign analysis. I can immediately report to my CRO how many new prospects engaged with the campaign, as well as that key number she wanted, the existing number of customers who responded.
I also have a list of the top customers who engaged with the campaign. So now I can give a targeted list to my sales team so they know who to immediately reach out to before another competitor gains foothold in the account, and they can enter those conversations, knowing that they have the full view of our entire relationship. If the CRO or a sales rep wants to interrogate the data, then I know that they’ll find they can trust it. And that’s because you can directly see the work of Tamr Cloud in this Tamr ID column right here. This is a truly persistent ID that links together all the different touch points that we’ve had with our customers. So now, not only have I rebuilt trust and our data assets across my entire team, I can also spend less time on things that are annoying, like data quality issues, and more time on things that actually matter to me like lead generation, supporting contract negotiations, really anything that’s more directly impacting our ARR and my bonus. Thanks very much. Anthony, back to you.

Anthony Dayton: Thank you, Katie. That was a remarkable and a great demo and really exciting to see. And I can’t wait to get this in the hands of everyone who’s interested. Now, I started with this idea that a core feature for Tamr Cloud is this security first mindset. Job one is that Tamr Cloud is a fully secure environment. So that starts with running as a fully multitenant environment, and that allows us to create significant cost savings because we have one infrastructure shared across all customers, but we build into a data isolation to keep all data or rather all tenants data separated. We also support, we have native support for data encryption, both at rest and at transit, and it’s a fully-monitored environment. So we use machine learning, in fact, to look for on detect anomalies in the environment so we can detect any challenges in the environment or any ingress into the environment before it has an effect.
And it’s fully integrated into your IDP so that you can control user login and authentication. So not only is security important, but so is operations. With Tamr Cloud, it’s a fully-managed service. There’s no operating system, there’s no servers, no firewalls, no upgrades, no patches, nothing for you to do, but log in and use it to generate clean, curated customer data. And as we’ve talked about, we’ve chosen to start our journey with Tamr Cloud with a specific solution on B2B customer mastering, but nothing in what we’ve built with Tamr Cloud is locked or built to only support B2B customer mastering. We’ve built a robust SaaS platform with a solution’s model driven by templates. But what is a template? A template has, at its core, a whole series of pre-built capabilities that get you started faster, things like a pre-trained model or industry specific schemas, or source connections and mappings, and pre-built transformations. In short, 90% of the work is done leaving just 10% necessary work to sort of customize it to your specific needs.
The time to value with Tamr Cloud is better because it’s a SaaS solution. As we discussed, we do all of the heavy lifting, all of the infrastructure, upgrade, support, but it’s also faster time to value because it’s pre-configured to solve a specific problem. Much of the work is already done, but B2B customer mastering is just the beginning. We expect to build out many templates into the future. In fact, we expect in the future that you, a customer, might build a template or you, a partner, could be building templates. The model we’re building here with Tamr Cloud is something not unlike the Apple App Store. We create the framework, we build a series of reference applications, or in this case, a customer mastering templates, and we allow others to build those templates on our platform. So customer mastering is just the beginning with Tamr Cloud and a fantastic beginning it is, but over the next year, you should expect us to make available other templates and eventually make available the ability for you to build your own templates.
Next, I want to share an exciting initiative that we’re working on around Tamr Core. Again, looking at this graphic we use to describe Tamr, messy source systems on the left, clean, curated end points on the right, Tamr Core and Tamr Cloud in the middle. You may notice on the lower right, we talk a lot about how we deliver the data out to both analytic end points and operational end points. And analytic use cases traditionally are a strength of Tamr. Delivering to analytics stores, you can finally answer those business critical questions quickly on top of clean, correct complete data. And that has tremendous value. But what we hear from you is that there’s a need to make delivering data in real time to operational systems even easier. This allows you to drive operational efficiency because all of your systems are working from a single source of mastered truth.
And that’s exactly what we’re working on. We want to block bad data as it’s being entered, and we want to share good, clean, curated master data as it’s resolved. And to do that, we’re working on improving the real time nature of Tamr and providing an operational MDM design pattern and a service that’s at the core of Tamr. And we’ve already been working on this with a number of alpha customers to do some testing and validation of the design pattern. And I thought it’d be helpful if I shared with you some of the use cases that we’ve seen in case it’s relevant inside your Tamr deployments. So we’ll start with an example like we’re entering a new customer into SAP, Mary Smith from 1, 2, 3 Fern Avenue. And so our users come into SAP, they’re entering Mary into SAP.
In real time, Tamr’s matched service shows a number of possible duplicates, people or records that look like Mary Smith, but are subtly different. For example, Mary J. Smith who lives at the same address or Frank Smith, presumably a husband or brother or child. And then there’s also a May Smith, which maybe isn’t even a match, but is still showed. And these are rank ordered as the most likely match to the least likely match. And of course, the user immediately sees what everybody sees, which is that Mary Smith at 1, 2, 3 Fern Street is the same as Mary J. Smith. And so in finding that match, Tamr auto completes that record in SAP. And you’ll notice that Tamr also provides the Tamr ID, so that when the record is entered into SAP, it’s not entered as a new record, but in fact is the same record that exists across the other three operational systems, in this example, and the data remains the same synchronized and consistent. There’s no dirty data.
Let’s walk through another scenario. Imagine that a user wants to enter another customer into SAP and this time, Jane Lewis. Unlike the first example, there’s no obvious match for Jane in Tamr’s operational MDM service. And so we want to enter a new customer. So the user adds Jane, in this case, into SAP, but Tamr intelligently enriches this data, for example, adding the correct zip code, but it could be many or a variety of different things. And critically, Tamr reserves a Tamr ID for this new record, so that when it comes back and is synchronized into the three operational systems, again, it’s synchronized correctly and it’s consistent across the three operational systems. And once again, there’s no dirty data.
Lastly, now, imagine if immediately after we entered Jane into the system through SAP, another system tries to add Jane Lewis and this time into Oracle, and this could occur for many different reasons. Perhaps there’s an automated process that having entered it into SAP, it’s going to now enter it into Oracle. Maybe it’s an RPA bot or there’s a new customer, and everyone in the organization’s rushing to get her system or rather get her information into then to the various systems. In this case, Tamr’s auto-complete capability through the operational MDM service will properly catch the duplicate. And again, the result is that the data across these operational systems remains the same, synchronized and consistent, and there’s no dirty data. So with this operational MDM capability, the value is no dirty data. A real time engine gives instant results across all users and systems. Auto-complete blocks errors at the point of entry, the Tamr ID becomes a global identification for key data, and enrichment improves the data as you use it.
In summary, we started with Tamr’s product strategy and vision. We talked about data mastering for everyone. I shared with you our investments in Tamr Core, the market-leading data mastering solution at enterprise scale, delivering real-world results for customers today. And I shared two new product introductions, Tamr enrichment, which makes enrichment simple, accessible, and turnkey, and Tamr Cloud, simply the fastest way to clean, curated customer data. And lastly, I shared with you our bold direction, investments we’re making in Tamr Cloud templates to dramatically increase the scope of Tamr Cloud as a powerful new platform, and our investments in Tamr Core around operational MDM, stopping bad data in operational systems and improving operational efficiency. Thank you, and enjoy the rest of the conference.