Collaborate on Format for Billing Client for Life Sciences with Ease Using airSlate SignNow

Format for billing client for Life Sciences

hello everyone welcome to today's webinar how top life sciences companies are leveraging analytics to gain a competitive advantage we're pleased you could join us as we have an in-depth discussion on how an integrated analytics platform can empower life sciences teams from commercial to R&D and drive better business outcomes my name is Megan Billingsley and I will be the moderator for today's webinar but before we get started I have just a few housekeeping items please note the slides will advance automatically throughout the presentation to enlarge the slides click the enlarge slides button located in the top right hand corner of your presentation window should you deem technical assistance click on the help widget located in the bottom left corner of your console we encourage you to submit questions at any time throughout the presentation using the Q&A widget at the bottom of your console will answer as many questions as we can at the end of this presentation following the presentation you'll also be able to access the on-demand recording of the webinar using the same link from today's live event now I'd like to introduce you to today's speakers with us today are Natasha Baron wall product line manager rivo analytics at CES associates and Kapil Nair business technology partner rivo analytics at CES associates Natasha and Camille it is wonderful to have you both with us today in today's webinar we will go over the need and challenges of an integrated analytics platform the critical elements of an integrated analytics platform and how zs associates built an analytics platform and its industry impact appeal I'll pass this over to you Thank You Megan um now we've been working with many life science companies over the years and and many of them are looking to modernize their analytics ecosystem due to several drivers one arm or client teams are facing significant pressure to do a lot more with less PLC's are great but how do you make these analytics and rest life capabilities that can scale and adopt to the changing business needs um then the way analytics are being done is rapidly-changing there is explosion of data in terms of velocity and any variety of data along with the traditional data sets like sales activity shipment analytic team wants to explore larger data set if our type of data sets are digital data patient data like claims EMR social media and they want to do it fast waiting for a server set up for like two months it is not an option anymore acceleration of these capabilities is a strategic imperatives for many of our companies now with so much data AI ml is not a buzzword anymore and rather than a necessity in life science industry especially when it comes to R&D and even commercial having said that um AI ml required complete different set of tools that are that and capabilities that we traditionally used to up and lastly companies are looking to optimize their spend we always hear that how can we reduce our cost but at the same time have agility and elasticity of cloud and they don't want to solve problems that are already solved you know many of the clients are now looking to buy certain solutions which are already available and proven versus custom building on their own now life sciences companies are looking to for an integrated platform to meet these needs however they are facing many challenges as they start establishing an analytic ecosystem to start with there are so many tools and technology that emerge everyday just to evaluate figure out the benefits and trade off it's just very time-consuming effort takes a lot of energy and time on top of that there is no single tool that is a silver bullet and that can do it all you need the right set a tool that support ever-changing analytics needs and AI and data science is here but you know so ad all the dashboards and reports that we all love to look at those are not done yet so you need something that can provide you both capability we are also seeing companies struggling to migrate given the investment they have made in the traditional analytics infrastructure on top of that the people who are trained in using those components so that's another major challenge and many of you must have experienced that how lines between analytics and technology are starting to blur exactly the thing of the past analysts want to use our Python along with the visualization tools data find this and emerging ml engineers are writing code for AI models the analytics we used to know it is drastically changing and really really looking technology to partner together and don't forget about our favorite topic with the security and governance you know we all love that and it's enormous data out there which makes the security and go in and even more important in this country especially when it comes to cloud life sciences companies are facing unique challenges from a compliance perspective and and which makes the cloud adoption even harder solid now I see how this would look like mission impossible but there is hope we will strongly believe complete who can who leverage and invigorate analytics platform can overcome these challenges and as a matter of fact many of them already done it I'll talk to you about one of them in few minutes but let's take a look at it what it's what is required in an in a platform like this so first you need a unified cloud-based platform that can provide flexibility and continue to evolve over time because you need going to change not only it will provide a you know a smart data ingestion it should also provide you processing data quality capability and it should also scale linearly because the data is going to continue to grow as we see in future you should also be able to process variety of data small data big data scaling should be part of life and it should be very much seamless and automated number two you need proven tools that can interoperate provide traditional dashboards and fourth but along with sophisticated ad hoc and analysis analysis capabilities people are still looking for this they still looking for dashboards and reports so you need that you also need the tool that cater to your analysts and data science community their needs aware unique and this will include a data catalog so they can actually find the data a workbench with tools and and sandboxes so they can explore data prove certain hypotheses and also you know leverage many of these tools for the analytics use cases a last but not least a very secure and cost effective environment this is Table six security is ingrained for many of the life sciences company and compliance needs are paramount but at the same time this platform needs to provide an agility so you should be able to set up on a data science lab in minutes not in weeks you should be able to evaluate a large data source and it should be doable within a day and should not wait for weeks and weeks of our current road infrastructure approvals that you required date now let me walk you through a journey and for client who on some some I know them very well and the client was planning to launch their first specialty product and they were looking to establish analytics capability infrastructure pretty much greenfield their vision was to establish a centralized data repository for enterprise with goals to drive performance excellence with data analytics this required an agile and scalable platform which they were hoping that can meet their needs you know for now but it could scale to support R&D finance and other radius and climb choose a real analytics or platform and they started their journey just you know few months before their launch was scheduled and then established a daily and a commercial data warehouse to support their product launch post launch of the platform they expanded the platform to support medical supply chain and Finance to enable a truly integrated analytics capability and actually right now they are working on bringing in a lot of the real world evidence data and then enabling some of the other team members in our league um so that's that's one of the solution that we put in and helping with this client now fast forward a few months our client had a very successful launch they were up and running in less than four months with capability but even some of the larger competitors they were looking at they even they were lacking those capabilities commercial teams were able to closely monitor their launch and it's just their strategy but leveraging almost real-time insight and by building a truly integrated analytics ecosystem climb team was able to collaborate effectively I mean they had these launch meetings together where they would look at it for marketing data market access data a lot of the sales data together and and it was highly productive sessions now a year into their launch several advanced analytic use cases were enabled what while analytics this included patient level data call center nodes voice and and many of the use cases like adherence etc were were labeled using their capability now they're in the process of launching even event prediction and necklace action so their foundation continue to grow as they may evolve and build highly intuitive and inside driven reports the user adoption was consistently over 90% and this was enabled by highly automated processes and in quality data which was done by design so there is a lot of trust in the data that they are providing to their customers now this established the foundation where now they're also thinking about launching the next potential blockbuster product so it was a great story for them from start wind and it didn't took them that long now I've been talking about rebuild quite a bit now I like my colleague the pace to walk you through it and tell you a little bit about Revo and maybe show you a few of those components on the pace all right thanks couple for providing us all that rich industry experience in perspective a lot of good stuff there now that we look at the case study let me introduce our rebo analytics platform to you guys and in fact before that let me also describe zs for those of you who might not be very familiar with our brand so zs is a premier sales and marketing consulting company primarily focused on the life sciences industry with over 35 years of experience we work with almost all the top pharma and meant devices companies along with few clients in the travel transportation financial services and even high-tech oh and by the way CS is always had its roots in analytics and over the years we have consistently built several products solutions and services' which are built on these core pillars of data management and analytics now with the advent and adoption of cloud and Big Data technologies and life sciences which was a little delayed by the way we felt that it was time we shared our expertise and packaged it into an analytics platform built for the future this is essentially how Revo was born it is built on top of AWS from ground up and has several integrated components to provide a unified analytics experience across different internal teams ranging from clinicals to commercial operations and whether it's smart data management or reporting or analytics we serve all different kinds of users by providing an ecosystem of the right tools and applications for the job all integrated and exposed behind a very secure layer of axis we have also built several accelerators within the platform which drastically reduced time to completion for projects and provide a high degree of quality and confidence for our users and the real beauty of this platform is that it is agile fast and fully transparent giving its users full control over data both processed and raw as well as processes and insights within the governance model they want in summary rivo analytics addresses many of the industry challenges we discussed earlier by providing a unified and ready to use analytics platform without having the life sciences companies developed years of internal capability themselves so with that quick introduction let me dive into the five layers of our platform and introduce many of the competence in our stack as you can see starting on the leftmost layer at number one we have our Revo business insights layer and this layer provides data use advanced applications designed to solve business specific life sciences problems and accelerate analytics it combines all of the backend power of Revo with very sleek visualizations and end-user optimized apps that can provide very actionable - our business users including HQ and field and these business applications are available for a wide array of focus areas ranging from launch to detail sales performance band marketing to customer centric marketing specialty Pharma to manage market and even real-world evidence and there's a there's a few more that you can see on that list number two is a Revo Analytics workbench layer and this layer enables ad hoc analysis through use a variety of tools and applications this tool this layer is aimed at our more advanced analytics users so we you know we expect the users to be able to interact with these systems and use the tools in the right way and within this layer we have a few different components going on so the first one is data hub which lets our users interact with a data warehouse setting in the cloud and this has been a little bit of a challenge you know with the with a new way that we are architecting these things so with the data hub the users can perform ad hoc ingestion and extraction and they can also manage the configuration data and in fact also monitored the data processes they are running in the backend which are constantly generating these insights we've also launched a new algorithm service framework which can be used to industrialize and integrate zs build AI ml algorithms and even if you have some algorithms that you are bringing to the table we can also look to you know sort of industrialized and make them more integrated with the different client applications applying systems using very secure REST API is along with these we also provide access to variety of data science tools such as our studio Jupiter h2o etc in our workbench and these are the more open source tools that you've seen grow very popular in the industry in fact if you if you're working with some other tools that you might want to bring into the workbench we can always work with you and get those tools available to the users as well and finally to cover all of these days is a management console for the workbench admins which lets them provision the right AWS resource these preferred tools and applications using a very simple user interface so that was about the analytics workbench the middle layer is our intelligence engine and this is where a lot of our secret sauce lives so we have a bunch of stencils which are basically collection of data connectors data model quality checks and business rules and these are all built on top of our data management product and tailored for specific business areas the ones that I was mentioning in layer one so the beauty of these stencils is that they reduce the implementation times and cost significantly for projects especially if they are used without a lot of customization and we also have a lot of reporting templates that go with these stencils which you know plug and and form the basis of the business application that I mentioned earlier beyond these we also have a very powerful data search that's built on top of our dere-lique and expose through a data catalog product and and you know we also have a rich repository of algorithms in our library for marketing mix launch analytics etc which can be exposed to using our framework or del X directly used within the workbench by the with the data science tools that I mentioned number four is the data services layer and this layer provides end-to-end data services from ingestion of data connecting to a wide variety of sources to processing and storage of data sets which are ready for analysis and within this layer you know we can expose raw as well as processed data like I mentioned earlier so the users have a complete view of the entire data journey through this through this layer it houses the Data Manager product which is the backbone of format service for data management and there's also the data catalog product which is also very popular amongst our users and this data catalog product can be used by business users directly to search and discover the data sets which can you know which can grow very very many fold in a big organization along with these products the layer also enables best practices in frameworks for data warehousing and data leak using the right AWS services and then finally on the rightmost layer we have a Revo cloud services layer this layer is fully abstracted from our end users so they will never interact with it directly and our very mature cloud engineering team is primarily responsible for this layer and takes care of the critical infrastructure level considerations such as security disaster recovery high availability all while monitoring all of our services and tracking their billing on AWS so it's really nice to have someone watching our backs that's that's the five layers that I want to cover I did want to point out that while there are some relations between companies in different layers these layers themselves are not totally interdependent for example you can just use Data Manager along with a stencil in a business app or you can just use the data catalog of workbench by itself so with that let me show you all the different data sources there are platform supports in us of course this is not a final list it continues to grow as we onboard more clients as a matter of fact we're not restricted to any specific set of vendors on the data side and can process pretty much any of the syndicate data they exist in the market now let me take you through a small demo to show all the Revo layers in action hello and welcome to a quick overview of a Revo analytics platform in this video we will navigate our entire analytics journey for specialty pharma let's begin with three-body imagine our data management product here I will quickly create a new project for data management using our specialty stencil the stencil expedites my project implementation as it provides relevant pre-configured data sources like patient hum and beaver data associated business rules metrics and even data quality checks I will import all these objects into my project let's take a look at our project we can begin by adding some adapters using the many bi-directional connectors available from there we can configure injection schemas data quality checks business rules and exports business rules can be configured using a graphical canvas and drag-and-drop transformations once our project is implemented in Data Manager we can run all the flows to process all data and load into our data warehouse while simultaneously cataloguing it on that note let's view the repo data catalog our simple yet powerful cataloging solution we can use categories and subcategories to discover datasets or search using keywords we can also explore a dataset in detail for example specialty pharmacy shipments data and even look at its sample data and metadata further we can add it to our basket and export to our workbench for some ad hoc analysis now let's look at our pre-configured Revo specialty business application for the home office which informs me about every aspect of my specialty product including customer reach geography sales patient status top panel reasons and other aspects we can even take a deeper look for example at our channel partners to analyze how our shipments match market demands these and many other reports and insights are available to our users out-of-the-box let's switch gears and go to Revo data hub our self-service portal to quickly ingest a data set say shipment data from my local machine into our data warehouse I can quickly configure settings and get a preview of the data update schema add some data quality checks name the ingestion and run it I can even choose to schedule it for later beyond this data hub can be used to extract data match configuration data and morter data cross seas in real time and finally I can blend our ingested shipment data from the previous step with hub data stored in the data warehouse using areevo analytics workbench for some quick ad hoc analysis I choose to query these datasets using hue or visualize them using tableau other tools such as our studio jupiter Zeppelin and h2o are also available within the workbench to perform data analytics and science using code these tools are installed using the workbench management console this console lets me create projects and assign users as part of my team I can also provision and manage the AWS resources and tools I need right from the simple user interface I can then access them using my workspace and carry out my analysis as I showed earlier this brings us to the end of our quick demo for more information on Revo Analytics please contact Z s alright hopefully the demo gave you some more idea about the different moving parts in our platform and really Revo has been very successful and currently used by many of our clients across several established use cases and you can see them at the bottom we have a lot of experience with a commercial data and analytics solution with more than 15 plus clients the ending from emerging to even a few the medium to large clients who are using Revo today and really our greatest value prop has been very quick time-to-market at reduced costs and we still deliver all four projects with very high quality which is achieved through using more than a dozen odd stencils that we've built over time and a similar number of turnkey business applications and again this is only possible through our deep knowledge of the life sciences industry which we have encoded starting from our pre-built connectors targeting over 100 data sources we also know the right data quality checks and the business rules to apply to these sources and we've also come with the most relevant set of KPIs and metrics so you know that's the real power of the platform and going you know going forward we've also enabled our route bench and data catalog products for some of our clients whose needs of evolved beyond traditional data management and reporting over time so you know always that's that's an option for all four clients and I didn't want to call out that all of this is being enabled by a very strong team of over 80 members across onshore and offshore many of whom have AWS certifications and in fact zs is a premier partner for AWS and is a member of a very small club of partners accredited with AWS life sciences competency and you know so that's really a testament to our commitment to the platform and the value that we bring to our clients and we really hope we can bring that same value to you so with that we come to the end of our presentation and we'll be happy to take some questions from the audience now Thank You Natasha Kapil that was a great discussion a little bit Thank You Megan okay we have a few minutes left and several questions from the audience for the first one that we have is what types of youth cases is Revo handling in the advanced analytics capabilities so I can I can take that and let me begin by saying that Evo has many business apps with embedded advanced analytics for marketing mix performance analytics attribution modeling and many of those advanced analytics use cases these and many other algorithms are also available as part of our library which can be accessed by data scientists directly working within a sandbox they can build on top of these algorithms they can end it with their own AI ml models or they can also use the algorithm service to integrate and and you know we also have a lot of these analytics transformations in our Data Manager product which can enable the users to build their own AI ml processes a couple is you want to add anything is yeah and then many of our clients are also working with us so put the processes together where we help them industrialize many of these analytics models that they create so some of them want to create a event prediction which is then embedded in delivering some of those insights to the users directly so all of that industrialization part is also codified within the real fact great thank you okay we have another question here does a client need to license the entire platform or can parts of the platform do you subscribe to based on use cases okay let me let me try that one couple so real is built in a very modular way so no clients do not have to license the entire platform at once in fact in our experience each each client situation is very different right so some clients license the entire platform right from the get-go and then some clients start with one or two key components based on their current needs and as the needs of all they consider other other components what you've seen typically is a lot of the clients license the data management module along with a few select business applications depending on where they are with their products and you know they also leverage our stencils for that that time to market in quality and then they move on to the other modules in repo so we have another question coming in from the audience where have you seen the most widespread usage of Revo which team and any specific use cases um I can take that up so we have seen on emerging pharma lines they are using Revo to accelerate their analytics capability so for example you know launching their products or you know and they using full stack from everything from data management to applications to analytics some even some of the actually some of the larger clients are also you know leveraging this model to transform their legacy capability we've also seen our analytics teams and data science teams using workbench and sandboxes to do data exploration and Vance analytics in some cases they're using a larger volume of data where they need to run one off hypothesis or perhaps evaluate these data sets so they're using rebo in order to do that in many cases of clients are also used the apps business insights apps where they already have their data infrastructure underneath and those apps are sort of providing the usability to users some of the recent clients are also using our algorithm as a service in which case our package algorithms are being used on their own science data in providing the analytics capability there great thank you okay we have another question here how does it compare with other platforms from software vendors hmm okay um so a lot of the other platforms in the market many of them are pure software play and they very focus on certain areas so for example some of them have data catalogues some of them have this analytic sandbox capability you'll also find some reporting and dashboard offerings for Life Sciences however we will provide them end to analytic solutions so from starting from data ingestion to data processing inside generation and inside interpretation it does it all and and with the agility of cloud and ongoing innovation you're constantly getting the innovation that's happening in the industry and best practices for many many clients coming to you so that's the main one of the major difference and client can simply jump from you know creating insights from data without really worrying about why the landscape is changing or how it's changing and and you know minimize their upfront investment so you're not looking to buy many of the servers or any infrastructure any large software purchase up front from our domain-specific knowledge it's baked into our sensor and that provides you a very life science of centric solutions versus just a plain vanilla box that you receive which is the empty shell we format nology but those are some of the key differences great thank you so much this next question is actually sort of two-in-one is this platform one that can be accessed by global teams and are you able to scale it to different geographies hmm so if they'll be mean again and the answer is absolutely one of the key benefit of cloud is ability to rapidly scale and and of course global availability currently Revo is you know many many clients in us are using we are also happy available in South America we have a science there and B are planning to launch in EU region in you know the first half or towards the q2 2019 hf pact in Japan we are targeting 2020 so our role is by end of 2020 early 21 we will have a very regional eyes you know models for free available where we were providing tailored use cases by region leveraging our stencil so this will provide more localized data sources all of them the model the reports very very tailored to the mean there need awesome thank you can you share some benchmarks on the quantum of data Revo is able to process yeah let me take that one so we have patient level analytics processing about 10 terabytes from way sources like optim claims practice Fusion Records health records and Symphony sales and many more and you know some of our clients are also pushing that limit so we will see you know how far we can go and just to add that in terms of scale of we have clients as we mentioned for emerging pharma which are from 50 60 reps to a large pharma where there are 3,000 reps in us and they're managing their entire commercial data onto the system and so that also tells you the scale from data that is being used awesome thank you both does your platform cater to all life sciences companies yeah it definitely does to all the different companies that we've seen majority of our clients are coming from the pharma industry but we also have clients from med devices who are using many different products from the platform great ok looks like we have one more question coming in how do we know more about Revo and who do we get a touch with oh that's that's a nice last question so we have it on the screen you can visit our website the Escom slash people for more information or you can reach out to us directly i'm notation i have my colleague couple here directly for detailed demos wonderful thank you both so much that is all the time that we have today we want to thank our audience again for the great questions for those who are not able for those questions that we weren't able to address zs will follow up with responses in the coming days a big thanks to today's speakers nitesh farewell and 50 linear and again to you our audience for joining us as a reminder today's webinar will be available on demand and the link will be sent to you after the webinar have a great day everyone