Corios was hired by a rapidly growing bank to build the newest release of their prospect acquisition scorecard model; not once, but twice: once in SAS 9.4 (their production environment), and the second time in a hybrid SAS Viya / open source approach that leveraged Python, Dask and Spark. The reason for the second modeling effort was to explore what an innovative, modern cloud-focused analytic environment could and should look like to support predictive model lifecycle management: authoring, champion/challenger experimentation, validation, version management, cloud deployment, drift analysis and ongoing refresh.
Building the first, traditional model pipeline was familiar territory for us because we had built several models for this client, that had been put into production over the past few years. The greatest challenge was that the bar was set very high for the mathematical performance of the model, since we had to beat the performance of the current version, which was constructed effectively and exhibited strong performance.
The second model broke a lot of new ground for the client. Major elements included: Amazon Web Services clustered compute, storage, code development and management; Python, Dask and Spark as open source frameworks for analytic pipeline development; side-by-side comparisons for analytics assets built in familiar territory (SAS) and unfamiliar territory (open source frameworks on cloud services); and novel analytics techniques (and their potential performance contributions) that the open source frameworks made available to the bank for the first time in a native, business-critical context.
Corios was hired by a prominent insurance carrier to modernize their analytics and data practices for all things analytical: underwriting, pricing, claims, repairs, coverage, compliance, and regulatory support. They wanted to reduce the cost of data storage, to align all their analysts on a consolidated set of tools and environments, and to modernize the enterprise so they could react to climate events and other large-scale adverse events faster and more efficiently.
The Corios solution we use in these engagements is Corios Rosetta, which includes Corios software and service methodology to inventory, score, prioritize and modernize our clients’ SAS data and analytics assets. After inventorying their workloads, data and teams, and interviewing leadership and subject matter experts, we recommended to centralize their workloads that relied on their primary atomic-level data warehouse (in Oracle), and to move their non-warehouse workloads and analysts to the use of Python on Domino Data Labs for virtual analytic environment provisioning and archiving. Then we invested the next 6 months modernizing the work of their 800+ analysts along this roadmap.
Corios has helped several SAS clients modernize their data and analytics assets, practices and workloads, most commonly to move them from on-premise-only to AWS, or hybrid on-premise and AWS. Our solution suite for modernizing SAS analytics is Corios Rosetta. One of the initial steps in helping the client qualify whether they should entertain this step is to calculate the change in Total Cost of Ownership (TCO). As Certified AWS Solutions Architects and a Select Tier AWS Consulting Partner, we perform this task for our SAS clients on a routine basis. Here is a summary of what we’ve learned from conducting the pricing estimates and TCO savings for these clients.
First, clients tend to fall into one of three “t-shirt” sizes in terms of their SAS data and workload needs, which tends to be driven more by storage than by number of users or number of compute hours.
The “small” t-shirt size is characterized by storage requirements between 50-400TB (average of 275TB), with AWS annual compute costs averaging $250,000, and supporting between 25 and 200 SAS users.
The “medium” t-shirt size is characterized by storage requirements between 500-1000TB (average of 750TB), with AWS annual compute costs averaging $400,000, and supporting between 20 and 500 SAS users.
The “large” t-shirt size is characterized by storage requirements above 1000TB (average of 1.5PB), with AWS annual compute costs averaging $625,000, and supporting more than 500 SAS users.
Corios specializes in modernizing an enterprise’s legacy SAS data and analytics assets, by migrating them into modern cloud platforms like AWS, and integrating these workloads with open-source frameworks. The Corios Rosetta Scanner is a software and consulting offering that gets this process started by inventorying, scoring and prioritizing all your legacy data and analytics assets, at a very detailed level.
What we’ve learned through these analytics asset inventory projects over the past 18 months is that you can sort and prioritize them along four dimensions: value, cost, risk and transformation potential. Once that’s been accomplished, we’ve identified markers of which strategies are best for each asset: migration, modernization, or even, leaving the existing asset in place (because sometimes, when something’s not broken, it might be preferable to leave it alone).
Corios is a Salesforce Einstein Analytics & Discovery partner, and Salesforce Pardot partner, focused on driving business impact from analytics. We have worked with retail and commercial banks in the past on customer interaction optimization, and on how to move corporate-centric marketing to customer-centric marketing via personalization & optimization.
When Salesforce and one of their retail banking clients shared with us their desire for an action-oriented sales dashboard in Einstein Analytics, we understood immediately how this fits in the organizational evolution to customer-centric optimization and we know the process the organization needs to go through to migrate from ‘gut-level’ marketing to something more guided, analytic and most importantly effective.
What we will show you is how Corios has seen these action dashboards integrated into the branch operations, using roll-up to the enterprise level and down to the banker level.