The title of this brief presentation is Profisee, the best MDM for Azure. Now that’s a pretty big statement, but it’s a very important one because in many situations, master data management or MDM is really the key to unlocking the power and advantage of Azure. To walk through this, let’s break it down a little bit. Let’s look at, first of all, who is Prophsi and what is Adaptive MDM?
Then we will get into why are MDM and Azure better together? And then we’ll get to a demo where we’ll show you all these things working together, Proficy MDM and Azure, which will include Fabric, PowerView, Power BI, Data Factory adaptive cards, OpenAI, amongst other things. So let’s get started with, Proficy. First of all, who is Proficy?
We are a leading master data management vendor. We’re a fast growing vendor. Why are we fast growing? Hopefully, we’re doing good things as a company, but most importantly, MDM is recognized as being a key to using your data, leveraging your data within your business to accomplish business goals.
We’re gonna talk a lot about that in a second. As a company, we’re very invested in our customer success. We measure that through Gartner Peer Insights, where we have a very high score and also through measuring our Net Promoter Score or NPS. Without getting into the details of NPS, it’s really the number of people who like you as a minus the number of people who don’t like you.
For enterprise software, an average score is forty one, a high score is fifty five, and Profy has a score of seventy eight, which is, pretty far out there in terms of high customer satisfaction. Why do we mention that and measure it?
Because we think it’s part of our value proposition to our customers.
In enterprise software, you’re buying into not just the software, you’re buying into relationship with the vendor, longer term relationship typically, and, we think that you should be able to to, have a relationship with someone you can trust, part of our value prop. So we are a, well known as being a Microsoft centric organization with a Microsoft centric architecture. If you ask anyone in Microsoft, any, industry analyst like Gartner or Forrester or any of our customers, most importantly, they would tell you that if you have a strategy to be Microsoft centric yourself and you want MDM, then adding, proxy MDM would be the obvious no brainer thing to do.
How we, approach MDM is through an adaptive methodology. Traditional MDM tends to come with prebuilt models that are kind of hard to implement because they don’t really fit to your exact requirements, and you go one domain at a time, which is kinda slow and expensive and not really that helpful. We believe that the way to implement MDM is through an adaptive methodology, one that adapts to your organization, your data, your requirements, and your people, which makes it easier to deploy. And, also, you can ultimately get to full coverage because you can implement as many master data domains and as much reference data as you need to get full coverage of your data estate or your fabric.
Tremendously important to, to do that. Our relationship with Microsoft goes back a long way. We’ve been well aligned with Microsoft and worked with them from a high level for a long time. We work with Microsoft dev teams to make sure that we are integrated with our products on day one.
We were integrated with Fabric and Purview, the days that they were first, introduced.
We have certified connectors with Data Factory and Power BI that are certified by Microsoft, in fact, shipped as part of their products.
We just released an integration with Microsoft Adaptive Cards, which is a brand new Microsoft product that allows our data stewards to to work with, data and answer data questions without even signing in to Profisee. They can do it just directly within Teams or, Outlook. We work with Synapse. We’re cloud native.
You can implement as platform as a service, or we can run the whole system turnkey hosted in Azure for you, software as a service. And if you choose that option, it’s SOC two certified and ISO twenty seven thousand and one certified as well. We do a lot of work with Microsoft. We have a lot of traction.
We’re a global prioritized marketplace partner. The most MDM implementations on Azure, the most joint sales with PureView, the most deals on marketplace, the most reference architectures for MDM on the Microsoft support site, and all of that led to, in this last year, being awarded twenty twenty three partner of the year finalist in the rising Azure technology category, out of four thousand companies that submitted for our Partner of the Year. And all of this is really kind of dependent on or enabled by the fact that Microsoft themselves recognize that MDM can be a very important part of the ecosystem, integrated with data governance, integrated with fabric.
A good MDM solution is, that’s well integrated is a tremendous benefit for Microsoft customers. Okay. So let’s talk a little bit about adaptive MDM that I mentioned earlier. So a full understanding of of how we do this is, the subject of a longer video than this one.
But very briefly, it starts with domain coverage, master data domains. You should have adaptive coverage there so that you can cover, all of the domains you’d want in exactly the way with the configuration and definitions and standards that you would want. You should be able to implement all your domains as well as reference data to get full coverage because full coverage is how you’re going to get maximum ROI, and cover the most number of, use cases across your data estate. Also, your business rules should be adaptive.
Your match merge rules, the data quality rules, and the workflow rules that govern how the system operates configured in a no code environment should be exactly what you need for your data, and not what someone else thinks you may need. Data stewardship, we don’t want to touch and and we don’t wanna actually touch and interact with the data, but sometimes you can’t avoid it. And where you can’t avoid it, you need to have a human being, interact with the software, and, then we have this concept of fast apps. You can create as many of them as you would like, and they are constrained by the role, the task, and the domain that the the data steward is is working in to allows them to to maximize their engagement with the software with minimum effort.
In fact, we’ve just, released an AI based stewardship copilot to go along with this, stewardship fast app capability as well as integration with adaptive cards. You’re gonna see that in the demo in just one moment. So how does all of this, come together with Fabric? Well, Fabric, with the integrated tools and, most importantly, the one place to put the data, we think will be a tremendous, tremendously impactful, and important piece of software and capability as people adopt it.
With all of these tools and this data in place, do you still need MDM?
Yes, you absolutely do still need MDM, and here’s why. You’re going to have data from lots of different disparate systems from all over the place. You’re going to be able to load or represent at least that data within fabric. But does that mean that you’ve got the data that you need?
Well, the data coming from all these different sources is gonna be inconsistent, incomplete, and duplicated. So you can certainly produce charts and graphs and what’s and metrics, but you probably can’t rely on them because the data is not, really, that good just yet. So what do we do about that? Well, we can put, MDM in place.
This little footprint that we just mentioned here earlier, whenever we create a master data entity, it gets published into Purview, the governance platform of Microsoft. Governance stewards here can append governance standards and policy information, which then gets represented back within Profisee. So now we know what rules we’re trying to enforce. We’re able to then pull data from Fabric straight into Proficy, put it through our little data car wash here, and then publish that master data or golden deck record data back into Fabric.
So now we have high quality trusted data that’s complete, consistent, accurate, and ready to use. And so now the insights that we produce from that are trustworthy enough that we can use it to drive our whole business. So there’s our better together picture. Let’s show you what that looks like now in real live software.
Okay. So what we’re gonna do is we’re gonna be going through a scenario here where I’m a data analyst, and I’ve been asked to put together an analysis of our top customers. I’m gonna be begin my work here in Microsoft Fabric. This is Microsoft’s new SaaS analytics platform that really brings together Power BI, data factories, synapse, and a whole another other suite of tools into a single SAS experience. And so to build out my analysis, I’m gonna go into a fabric workspace.
You know, this is where we can leverage things to load data, prepare data, etcetera. Notably, I’ve used that to build out a new Power BI report, which is often the case. The first thing I found out as I build this report is that I’ve got a lot of data quality issues and and challenges that I need to clean up. So a couple examples here that jumped out at me is I’ve got a number of customers that don’t have any address data.
So if I try to do an analysis by geography, by state, I’ve got a lot of blank states. And then when I look at the actual customers themselves, I can see here that, well, I’ve got a ranking of customers. As I look through this list and sort by name, I see a lot of obvious duplicates where, you know, I’ve got four clear duplicate customers here. And so it’s really hard for me to get a picture of, you know, who my top customers are.
And and, obviously, I could go do some sort of manual data cleanup exercise here. But what I like to do is find a permanent solution to this problem. Luckily for my organization, we’ve adopted Microsoft Purview, and Purview really helps me catalog everything across my data state. And one of the big benefits of Purview is the ability to discover information on my enterprise.
So in this scenario, I’m looking for some b to b customer data that represents the distributors that we sell to. And when I do my search, I find here a certified data set, that ultimately lives inside of Prophsi.
And here I can understand the description of this data. I can look at the schema. I can see the lineage here, and I can see that this information I can also, link I can also, link here and navigate from this metadata asset inside of Microsoft Purview to the actual asset as it as it exists in Proficy, where I can actually see and start to work with the data. Okay, so now I’ve gone from Purview to Proficy. And here now I can see the the actual underlying customer data that’s under managed within Proficy. Now our integration with Purview is bi directional. So in addition to the ability to see data assets from processing inside of Purview, we’re also bringing that same governance information from Purview back into Procy where it’s easily exposed to data stewards.
Now, in this scenario, I’m trying to identify who my distinct customers are. And if we search for full support supply, we can see the same four duplicate customer records here within Profisee. And if we open one of these records up, we can jump into, our matching experience. We can see here that these four individual customers from a few different applications operationally are all tied together into a group and are are are sitting underneath a golden record.
So this golden record now represents the best information that we have about full sports supply, and there’s transparency in the process. So we can always understand why these record match, what the score was, and there’s transparency in that process. So matching is a one of the core capabilities of an MDM platform, but there’s actually quite a bit more to it. So if we go back over here to the homepage and we can see here that within Procy, we are a multi domain MDM platform, and this means we can really manage any demand of data within your enterprise.
So, you know, customers, products, you know, assets or locations, you know, business partners, reference data, etcetera. And what we can see here actually fast apps that have been created that create a curated experience for different organizations and groups within your enterprise. So we can organize fast apps by domain. We could organize organize them by business function.
What this allows us to do is provide an experience that’s purpose built for the task at hand or your different user communities. In addition to that, we also have the ability to generate tasks as a part of workflows. This allows us to allocate work to individuals so that we can orchestrate those cross functional business processes within the context of data stewardship. So now what we’d like to do is actually show a few other capabilities of an MDM solution in the context of product data.
So data quality is one of the things most people think of when they think of MDM. And Prostny has a a a an always on data quality engine that allows us to define data quality rules and for proxy to always be evaluating those rules as data is created or updated over time. So here we can see an example of a data quality issue that’s been highlighted, making it easy for users to come and address those issues.
Another key capability of the data management platform is the management of relationships. Our data across the enterprise, really a set of interconnected data and those connections are all represented by relationships. So this scenario can open up this individual road bike product and I can see, you know, its relationship to its manufacturer, where it’s manufactured itself, which countries we we we take we go to market within. I can navigate around this. I can steward the data. I can associate disassociate relationships and manage the records themselves.
In addition to managing relationships, we also have the ability to manage hierarchies. It’s pretty common for organizations want to manage multiple different analytical rollups of their master data so they can display that information in different ways within their analytics platform. So here we can see we can visually navigate up and down this hierarchy, opening up different nodes within the tree. Or if we need to, we can search for records within the hierarchy and find, potential, records and navigate to those locations within the hierarchy.
Now when it comes to automation, Proficy, like many vendors out there, are adding open AI powered experiences to to our platform. And we can show a couple examples of of this. One of them is the ability for to help users find the information they need without necessarily having to understand how the data has been modeled and loaded into proceed. So in this example, we’re able to actually just describe the the records that we’re looking for and it will actually generate the filter and ultimately makes it easy for us to find.
In this case, the fourteen mountain bikes that we’re looking for. We also have the ability to leverage OpenAI to help with the creation of master data. In this example, we have a set of unstructured data that are coming from Walmart’s website and, you know, we wanna begin redistributing this product. And, historically, this has required users to go in and mainly read and and and and parse that information and then, key that rekey that information into Profisee.
By leveraging OpenAI, we can simply paste that unstructured data into Profisee. We can take that information and leverage OpenAI to then parse out, you know, the name, the description, the color, the dimensions, etcetera, all aligned to the reference data that has been configured within the proxy platform. Now when there’s actually work to be done, what we’ve added is the ability now to enable users to collaborate on master data, but not necessarily within the Procy platform, but instead within the tools that they’re using every day, namely Microsoft Outlook and Microsoft Teams. This is using something from Microsoft called adaptive an individual to an individual within within our organization, actually begin collaborating on the stewardship of data there.
And so now that I’ve shared this record with within teams, I can actually navigate over to Microsoft Teams. We’ve taken that record with us dynamically in tow and began a conversation with my coworker within my organization that allows me to actually that allows us to go in here and and message back and forth and actually interactively edit this data within Microsoft Teams without my peer having actually go log into Profisee in swivel chair. So now back to the original task at hand, which is building out that better analysis of our top customers. So pivoting back over here to Microsoft Fabric, we can now bring that improved golden record data from Profisee into my fabric workspace and then update my Power BI dashboard.
And here I can see the results of that. So First off, you’ll notice that I have much better geographic data about my customers and a different view of, you know, where I’m doing business and what regions of the country.
But more importantly, I also have a much better picture of who my top customers are. So this case, we can now see that weekend tours is in fact my top customer and actually the lineage here to be able to see all the various permutations that we can tours across my different operational applications that collectively make up my two hundred and eighty one thousand dollars worth of revenue from that customer. And this is all done using Proficy’s certified connector for Power BI, which allows users to easily pull data from Proficy into a Microsoft fabric environment into the one link, then use that information to build out their analytics here within Power BI.
This will also allows us to build these sort of mashup experiences that’s bringing together master data and transactional data. Then we can ultimately actually expose that information back within the proxy platform. What this allows us to do is actually bring together master data and transactional data and bring it together within Microsoft Fabric. We can also then take that augmented information and bring it back to our proxy experience.
So going back to proxy, looking at our customer fast app, we can see here that we’ve actually able to take that power BI content with that additional transactional context sales data for our customers. We can now embed that with back within our proxy experiences. So now as a data steward inside of Profisee, I also have access to that same information available natively within Power BI delivered inside of Proficy.
So we just covered Proficy and adaptive MDM. We covered why MDM and Azure are better together. And we showed you a pretty comprehensive demo of MDM and Azure working together with Fabric, Purview, Power BI, etcetera. Just a final thought and some additional resources if you would like them.
You can check out more information at Profisee dot com. You can register for live weekly demos where you can get live Q and A along with the demo if you would like it, as well as you can search on, Microsoft reference go to docs. Microsoft dot com and search Profisee to find the integration reference architectures that I mentioned before. Also, I think you get a lot of benefit of checking in with us, the CDO Matters podcast with Malcolm Hawker.
Malcolm was the co author of, the last three Gartner MDM Magic Quadrants. He’s an expert on all things MDM and data strategy. You get a lot out of listening to his podcasts, as well as you he’s very active on LinkedIn. You can engage with him directly there and get, respectively, a free consultation.
Also, I would point you to the Data Hero Summit. That’s, was an all day online conference hosted by, Profisee.
We talked a lot about, MDM strategy, technologies, and a lot of experience from the Profisee customers who have implemented MDM on how that’s gone for them. With that, thank you very much for your time.