2025 is the year of multi-tenant analytics

let's get started welcome everyone welcome to the Qrvey webinar 2025 is the year of multi-tenant analytics uh we're so glad that you could join us today and we look forward to getting this show on the road today we're going to talk about first of all why SAS companies have different needs for analytics uh our presenter David Abramson our chief technology officer at Qrvey will also share the blueprint for multitenant analytics success and this is based on the work that we've been doing with our clients and lastly we also talk about the path to monetizing your multi-tenant analytics so let me first introduce David Abramson uh he is the CTO here at Qrvey uh he drives a technical direction of the Qrvey

platform he plays a leading role working with our customers and helping SAS providers expand their products and create value from all of their data with embedded analytics Automation and self-service data collection um David has over 20 years experience in full life cycle product development and management from product Inception all the way through General availability and he works with clients to help shepherd data-driven custom applications analytics and business intelligence products to Market so without further Ado welcome David take it over perfect thanks and welcome everybody thanks for joining us on today's webinar I'm really excited um to start talking about your analytics Journey for

2025 and I think like everybody in in sort of the SAS space with a product um it all kind of starts with data right and and analytics allows us to generate value um from that data whether it's creating a more complete version of our products uh or even just satisfying all of the key requirements that customers keep coming to you with um to help them better get more uh value out of the data through the Analytics that's what we're going to be focusing on and talking about today how you can achieve that uh particularly um through this year and of course as a SAS company there's a lot of pressures to delivering product and uh

in order to stay competitive and to keep customers happy um you need to be able to do things quickly and you need to be able to react very very fast when it comes to market trends uh or particularly when it comes to customer demands and customer needs and requirements uh on the analytic side of things the you know obviously the data picture isn't slowing down anytime soon uh data is growing even more at an exponential rate than it ever has in the past uh and therefore all of the ways that we can leverage that data whether it's customer requirements ways that we can deliver a better experience around data and analytics or our competitors that are putting pressure on us to deliver a more Complete product or maybe

we just want to build new revenue streams um we have to do that quickly and we have to be able to get these capabilities and new features out to the market really as fast as possible to stay competitive and to grow as a SAS business so I don't think um you know saying why we want to why we need to invest in analytics is anything new um but obviously there's a lot of opportunities that analytics can create for us as SAS companies um you know we've we've already mentioned Revenue a couple of times but the ability to take data and create um whether it's you know new product tiers new modules Premium capabilities or even expanding Services Professional Services and other types of

work gives us some great opportunities to uh leverage a key investment in analytics to improve our our product overall experiences uh and of course we've talked about things like the competitive pressures from the outside whether it's customer competition or other products or Market forces driving us being able to take data and invest in an analytics use case that allows us to differentiate our product um not only helps us to generate Revenue but it gives us a way to certainly improve our products in a meaningful way uh and and overall satisfy the needs of our customers um whose needs obviously continue to change and evolve uh over time now with analytics a lot of times you think hey

this is this is about getting reports in the hands of users or data in the hands of users but it goes well beyond that when it comes to satisfying the needs of complicated SAS multi-tenant application environments um so multi-tenancy is kind of an overarching theme that we're going to be talking a lot about and why it's so different from just kind of basic reporting tools or other tools that exist in the market and that's because you know tenants or customers have their own unique requirements and needs whether it's security related or data related uh being able to take into account all of their their different data requirements and data structures and data needs um or maybe it's really just about scaling and security and

performance and getting the data and the analysis in the hands of the right types of users with the right amount of capabilities and the right amount of performance and the right uh growth and and sort of control over that experience that you want to offer so as soon as you introduce kind of these multi-tenant concepts everything becomes a little bit more robust and a little bit more uh difficult to to to match and to um to manage when it comes to a successful implementation the other thing that's really important about analytics the analytics journey is thinking about how you can use that to differentiate your products um you know we talk about use cases a lot when it comes to kind of data and analytics and of course

you know a lot of people get focused on the the sort of Baseline use cases of reports and dashboards but it's not just about getting reports and dashboards in front of customers it's about providing self-service capabilities so that customers can really do a lot on their own it's about creating meaningful value out of data that's not just in the form of a dashboard or visualization but actually providing data as a service so that users can uh better uh understand and interact and maybe take that data into other tools or other Integrations it's about uh modernization uh introducing new capabilities whether those be um updating environments or uh introducing you know AI features or or

similar types of capabilities but really taking uh you know older Technologies and bringing them forward into the future and then of course satisfying all the requirements around uh whether it's Gathering additional data or automating around that data um the variety of use cases that you can tackle through a successful analytic strategy um are really very diverse and offer a lot of interesting opportunities moving forward into the future but one thing I will also kind of highlight is in this multi-tenant space um you might hear a lot about embedded analytics um I'm going to draw a distinct line between multi-tenant and embedded because they really aren't the same thing um when you think about business intelligence um as a whole in the use cases around business

intelligence um you're probably familiar with a lot of more traditional um business analytics or bi tools the likes of a tableau or a powerbi or or similar types of tools these are great tools for that more traditional internal traditional bi use case where you you're an organization and you want to be able to understand how data and is impacting your organization through analysis and those tools are great at doing that um some of those tools have extended their capabilities with some embedded options which allow you to sort of plug those outcomes into some other capabilities and some other tools and some other

Integrations but for a SAS business it really has to go well well beyond that um SAS companies have to meet the needs of their customers it's not an internal use case it's about satisfying the needs of the external customer uh and so multi-tenant analytics is really designed with that singular focus of helping SAS companies meet the needs of those external customers by offering those users uh the right types of analytical capabilities fully integrated and built in as part of your application ecosystem and architecture and so it's really about um taking what

used to be historically a very internal um use case and extending that to a variety of your external customers fully integrated as part of your overall product um and experience for those customers and as a SAS provider you have a lot of different requirements that can't be satisfied by those traditional business intelligence tools um those tools were largely again designed for internal use managed by kind of the it teams within those organizations um supporting you know maybe some different users and different roles within that organization But ultimately for a single tenant um that business um and it came with a cost and

Licensing and all these different things with multi-tenant analytics we're not even looking looking at it from the sort of internal business perspective this is all about product how do you make your products better how do you create more value for your external customers and how do you support all of your different tenants and all of those different external and potentially unlimited numbers of users as you grow more and more users using your products every day you have to have tools that can not only scale to support that but also have meaningful capabilities to support that and then ultimately the key goal here is using your products to generate Revenue um we're not just spending money on analysis tools we're actually using these tools to generate meaningful

revenue for your SAS product and for your SAS business and so these are very different use cases very different types of requirements um that we're that we're talking about and last just key Point here is it's a totally different audience right multi-tenant analytics we're talking about product people um like yourselves you you own a product You're Building product features you're not working for an internal uh analysis or as a data analyst internally inside your own organization this is about uh giving data and Analysis tools to the customers of your applications who of course are going to be different organizations different businesses of their own uh and different companies so again different use case different

audience different needs and of course different pains and different challenges that come with all of that as a as a product team you have to be worried about how's it going to scale I've got a lot of users I've got a lot of different types of customers I've got a lot of different tenants I need to make sure that whatever I do from an analytics perspective is going to scale effectively to satisfy those data volumes and the use cases that we're covering from a security standpoint it's not just about internal security controls it's about external security controls and I need each tenant potentially to have their own different types of users and different data point access and different requirements around their data um and that creates a lot of complexity right it's it's not just about one type of data and one type of

user and one type of customer it's about all of these different customers coming into your product working with their data uh maybe they want to augment that data with something else maybe you need to leverage other types of Technologies and tools such as AI machine learning to get more value out of that data uh and so as a SAS company understanding that this is a different use case a different Pro uh a different type of of pain a different type of requirement um all of that comes into play when it comes to mapping out and planning a successful analytic strategy um which is what we're going to talk about now so what is a successful multi-tenant analytic strategy actually look like and then how

do we get there in 2025 so of course the key is building the right type of plan um and with a plan obviously timing is really critical um so the earlier you can start obviously the better um and then of course building upon that to deliver the right types of capabilities and features to your customers so again the earlier you get started um if you can start planning that 2025 go to market now obviously the faster and sooner you're going to be able to start generating some meaningful revenue from the analytics that you're deploying inside your product but Thinking Beyond just the revenue goals what about satisfying customer

requirements and customer needs um so self-service now plays a huge role in making customers happy keeping them engaged and giving them the right types of capabilities and this is all about again the multi-tenant use case whether it's you know security or data um everybody's going to have slightly different requirements um and in order to satisfy those requirements you could try to address those one by one but you're going to go out of business trying to satisfy every single customer requirement one at a time this is where self-service comes into play putting the capabilities in the hands of the customers to give them the power to sort of do their own things uh build their own content collaborate and share and

develop their own outcomes really really important last thing I'll also mention is we're in a we're in a time starting now where technology is rapidly rapidly evolving particularly around how users engage with data um more and more people are starting to work with whether it's gen services or tools um to to wrap their heads around how the data makes sense and is Meaningful and as the data to grow and evolve the demands coming from end users for those same types of capabilities are going to continue to to build uh and be part of what you need to do to deliver a successful set of outcomes to your customers so all of

this keeping in mind starts your plan to delivering success in 2025 now this is just kind of an example of a path to that timeline and how you can deliver a successful analytics project uh over the next several months obviously everything starts with kind of the the proof of concept so what do you need to do to sort of build consensus internal stakeholders to Bringing uh you know an onboard model that makes sense for not only your team internally but for your external customers to launching that mvp now what you'll notice about this timeline is we're talking about weeks not months or years and so in order to be very successful in 2025 um you can actually get started really

really quickly and be generating meaningful Revenue you know as early as you know at this point we could say as early as q1 in 2025 given that in a lot of cases you can get to launch at least of an MVP with maybe a limited tenant rollout in just a few weeks and so the idea here is where do you want to get to and then how do you build the right steps to get there successfully uh for your 2025 analytics roadmap plan well one of the keys that we found to be incredibly important is what we like to refer to as a phase delivery so how do you figure out what features get wrapped into which phases and then and then get launched out to customers so you know

there's a POC phase which which I mentioned which is all about deciding how to create and build the initial consensus around uh the the Baseline capabilities and and what you're trying to achieve when it comes to successful analytic outputs for your customers and then what's kind of the minimally viable set of outcomes that you need to deliver that first version that mvp um and as part of this obviously feedback is going to be really really important uh and so being able to create meaningful demo assets and then generating meaningful feedback both from internal stakeholders and external customer stakeholders is going to help you map those correct phases for that

delivery and ultimately you know one of the goals that we like to shoot for is getting to that full self-service experience so those future phases beyond that POC and MVP has to have those defining features whether it's a self-service feature or whether it's an automation feature or whether it's an AI or gen feature they should be incrementally adding those defining components and modules and value and should be significantly less time to develop than your original MVP phase um and is actually going to give you new opportunities to get better capabilities in front of your customers and maybe even add additional new revenue streams as part of those future steps and future

phases that you're going to be building out as part of your analytics offering so you know what are some of those features um you know I already mentioned a handful but obviously full self-service is going to be really important and that can mean a lot of different things and we'll talk about some of the concepts around monetizing that in a little bit here uh but other features that can be part of that successful phase roll out would be more of the sharing and collaboration automation capabilities and then of course more of the sort of generative AI types of capabilities to support not just the customer use cases but around the data and the developers who need to take advantage of those those key capabilities in your analytic strategy that's part of your overall um product

offering but with those phases one of the most important aspects of delivering success is the iteration and the opportunities to innovate through that iteration um so everything starts with creating kind of that original POC MVP phase and then getting in front of customers so that you can analyze and get that feedback to understand what's working what isn't working create engagement create value and then build on top of that um add new features uh create the right user experiences and then iterate through the cycle again and all of this helps you deliver not just quickly but also new modules and new opportunities to potentially generate additional revenue

on top of your existing initial offerings or MVP offerings for your analytics stack so there is a lot of benefit in creating not just the right phased plan but then iterating through that to create kind of that Loop of feedback and engagement with uh the right stakeholders within your organization so let's talk a little bit now about what it means to monetize those phases and that multi-tenant analytic approach obviously it starts with the um sort of the demo asset um one of the things we like to encourage as part of this process is to start with kind of a fully integrated core set of demo assets that

you can use to generate interest and excitement within the customer base um a lot of that allows you to not only put sort of ideas and features in front of customers but it also helps you figure out how this is going to sell and how this is going to be easily monetized into the product so that demo asset has multiple functions it's to essentially get people interested and excited about the features and capabilities that are coming but it's also to help better understand how we're going to ultimately monetize these capabilities uh within the product because we can break it down into the key functional areas that are meaningful for us the next phase can can

go a couple different ways um you can go uh down the path of you know kind of that full sort of dashboard reporting experience or you can start iterating around the data more directly um one of the things that we like to approach um sort of an analytics experience with is just getting the data in the hands of the user through what we like to refer to as interactive data views these can be fully integrated um views across all the different data outputs and data assets that you would have uh and by giving users the interactions that they want um basically you're allowing them to explore the data on their own um creating maybe customize data fields customize data buckets

filters exports you know all the things that they need to explore and work with the data gives you a great opportunity to start getting data in the hands of the users extremely quickly and so as a phase this can be a crucial step in figuring out how you get data in the hands of users fast and then you can iterate from there to understand okay what data is more meaningful what data can I further add value through monetization what data can I add value through things like dashboard and visual output use cases which can then be more of the phase three process right take the data that's most valuable and create that Baseline set of visual outcomes that users can also further customize uh

but it gives them the right types of functionality you know drilling filtering going from dashboard to dashboard connecting the dots creating exports um adding maybe some of their own visuals on top of the the dashboard uis all of these add incremental value to the data and to the analytics experience that you can offer to your customers and make a great opportunity for an additional phase of of of module that you can offer to your customers as part of the uh the analytics experience Within your product system um phase four here um I've talked about this concept of full self service so what we're talking about here is is really giving the power to the customer

um to basically do what they need to do uh within the environment so again um when it comes to the overall picture of your analytics value within your product you want to make sure users stay engaged you want to make sure that they're getting access to the data that they they want and you want to make sure that they're not just taking that data and going somewhere else uh and using it in some other types of tools because the longer they stay your product obviously the more value you're bringing to them and and the more opportunities now exist for additional modules and additional capabilities and features so full what does full self-service enable well it allows customers to create their own content it allows them to build their own custom dashboards and data visualizations it maybe even allows them

to build their own data assets Maybe even connect some of their own data into the mix and so there's a lot of opportunities here through that full self-service experience to give customers the right types of value and monetize that value in a highly highly meaningful way as part of the overall analytics experience within your product uh and the last phase I'm just going to quickly touch on here is um going Beyond just kind of the standard analytics outcomes of dashboards reports and data views and creating more more customizable data alerts and workflows so how do you go from users having to log in to see what what's changed you know how their metrics are tracking if something's going down or going up um

right the traditional way of doing that was log in and see what's happening in the dashboard that's been created with customizable alerts and workflows you can now create business rules that even take action on the insights that are gleaned from the analytics without the user having to log in and actually view those insights directly if my metric is dropping set up an alert or workflow that does something about that uh if my um the amount of data has changed in some meaningful way all of a sudden now I can set up alerts and workflows to do that and with a full self-service experience giving this power to the End customer externally is also something that's possible so now not just creating those alerts and workflows for

internal um design and assumption it's all about how do you provide these to the customer so they can take advantage of them and then build real value around um what it means to create that full self-service experience within your product so it's all about those phases we've talked about the phased approach it's all about creating the right types of value within each phase um so now what I'm going to do is I'm going to talk about how Qrvey helps um so Qrvey as an organization we built our entire company around helping software product teams deliver the right types of analytic value within their software product offering um so the problem that we're solving is not about internal data

and analytics use case it's about how do you solve the problem of secure multi-tenant analytics for your external customer um and so 100% focused on solving this problem and helping our customers achieve the right types of outcomes uh when it comes to analytics inside of their SAS product now we talked about the time timing is everything and obviously one of the most important advantages that working with tools like Qrvey offers is that extremely fast time to Market um you saw the timeline that I shared when it comes to those phase approaches we're talking about getting in front of customers and even monetizing the the capabilities those different phases that you're putting in

front of customers in weeks we're not talking about months or years and so the time it takes to integrate obviously saves you a ton of time now why does it save you a ton of time um well one it also um reduces costs so with Qrvey you know again we we look at the the whole picture it's not just about licensing specific tools it's about the infrastructure required to match the scale and the data requirements and all of the different use case requirements and what we found is the reduction in costs is not just about cost savings it's also about Time Savings as well because the amount of different utilities and tools that go into creating a successful SAS analytic implementation um it's pretty big um

this is actually a a good illustration of what it would take to do this 100% without Qrvey versus what it looks like when you introduce something like Qrvey into the picture um so with any analytics project you're obviously going to need your visualizations uh whether that's a charting Library reporting library or some other set of tools you're going to need the the integration so you're going to need the apis to integrate to the front end tools and the web application technology stack that you're working with but you're also going to need the data the data warehouse the analytic optimized data structures the entitlements in the semantic layer to map to all of those unique tenant requirements that you have across all of your different customers so this is a

pretty big investment not just in terms of infrastructure resources but also in terms of time and um overall um you know number of developers and you know other folks that are going to be working on this initiative contrast that with with what Qrvey has and it's an entire layer as a package so you're going to get that that analytics-optimized data Lake built in you're going to get the semantic layer and the multi-tenant entitlements layer baked in into that structure and you're going to get all of the front-end integrated tools and apis for integration into your comprehensive uis so all of that comes together and really that's the reason why you can get to

Market with those different phases uh we're talking weeks instead of months or years when it comes to launching the capabilities around Qrvey the other really kind of um driver for success with a true multi-tenant solution is the fact that the technology stack around Qrvey is a fully deployed solution um we don't Host this for customers you're not sending data externally you're not sending all of your tenant logic and security and all the user information somewhere else it's all fully integrated it's deployed into your stack it matches your release cycle it matches your software development life cycle and so the fact that it is deployed and running in your own environment gives you not just the flexibility but the comfort of

knowing that it's completely integrated and scalable inside of your existing product Solutions and becomes part of your overall product offering that you can then decide how you're going to to roll that out update it manage it uh and deliver it seamlessly to um your customers and that analytics layer is complete like I said it does contain all of those tools for managing data coming from any different types of data structures and sources it has all the built-in Logic for handling the tenant rules the semantic Logic for managing you know different tenant information and different tenant security um layers it has all the apis and JavaScript and and frontend tools to integrate directly into your application Frameworks and so

all that's baked in into what you get with the package uh for delivering a successful analytics project through Qrvey so all of this packaged up together helps you achieve all of those different phased rollouts and outputs when it comes to successful analytics story and the way it works is it's fully modular right so all of the technology in Qrvey offers the ability to modularize and put it in front of customers in a meaningful way so it's all designed to be fully external facing fully modular so you can craft the right types of phase rollouts for your customers that makes sense not just for customer use cases and feature

sets but for creating the right types of monetization strategies around the data the dashboards the UI the visualizations the automation capabilities the actionable uh tools in the analytics stack all of that gives you that ability to craft the exact right types of phased approach and the right types of modules that you can easily monetize for your customers uh in the external internal facing analytics experience and I'm just going to kind of wrap it up here with self-service being kind of the key so when you create the right type of self-service experience it helps you deploy not just a stickier product but um to create the right types of tenant specific value um for your customers

within their application experience of your overall product UI so it's all about getting to that end goal of kind of the full self-service experience for analytics for end users within your application uis all right so I'm just going to end here now um talk a little bit about uh what we feel are some great next steps for um launching your analytics Journey um to get started for 2025 um so if you uh want to learn a little bit more about working with Qrvey we encourage you to sign up for a custom demo um as part of that we're offering some really great opportunities one is uh a

free analytics assessment where our experts will actually go in and review um what sorts of capabilities you might have today and provide um a set of recommendations for where we see you can add value uh and build additional capabilities into your analytic offering inside your product um or the other opportunity is to engage with us on a free roadmap guide for analytics um this is all about uh understanding what are the right phased modules that you can roll out how do you build out the right plan for future features and how do you then map that to the appropriate types of timelines and launch planning um so two great opportunities to engage with

the Qrvey team and uh we encourage you to uh to get started uh as quickly as possible because the sooner you can start engaging with your customers on the analytics front the sooner not only you can continue to satisfy their their requirements but also start understanding opportunities for monetization around analytics within your overall product offering all right so thanks again everyone for participating in today's webinar reach out uh find Us online and let us know how we can assist you in your analytic Journey