Release Overview: April 2021¶
Aunalytics® is excited to announce the April 2021 Daybreak™ release to our clients. This release will provide clients with model and site enhancement information along with any fixes to existing functionality we have included.
Daybreak¶
Daybreak™ for Financial Services 3.0¶
Daybreak for Financial Services 3.0 (DFS 3.0) represents a new generation of Daybreak data model for clients in the financial services industry. Based on insights gleaned from the experiences of numerous client banks and credit unions, Aunalytics has identified numerous improvements that will now be delivered to future and existing clients as Daybreak for Financial Services 3.0.
At its core, DFS 3.0 starts with a next-generation datamart core to support a more streamlined datamart structure and delivery pipeline. Whereas previous versions of Daybreak for Financial Services relied on a monolithically structured datamart, 3.0 has streamlined the logic used to build derived fields into a tiered structure so that datamarts can be implemented faster and customizations handled more dynamically for clients.
In addition to the data model, DFS 3.0 takes advantage of next-generation developments in the Aunsight™ Data Platform to deliver faster and more resilient datamarts. Improvements such as the microprocessing of client data and delivery via storage infrastructure built for ultra-fast query response times adds efficiency and performance to our next-generation data model.
Finally, Aunalytics operations and product documentation teams are working behind the scenes to provide documented standard processes for client implementation that will help educate existing clients on the steps needed to migrate to this version and enable our teams to deliver these upgrades faster and more reliably.
Datamart Restructuring¶
An improved structure for datamarts enables us to deliver the core analytical database tables to new clients faster while enabling clients to build out additional custom fields as desired.
Tier Levels¶
One of the major changes to Daybreak for Financial Services 3.0 is the introduction of a tiered system for organizing fields and tables in the datamart:
| Tier | Purpose | Requirement | Example fields |
|---|---|---|---|
| Tier 0 | Required System fields | Required | CustomerID |
| Tier 1 | Foundational fields | Required | DateofBirth, TaxpayerID, AddressKey (foreign key) |
| Tier 2 | Enhancing Fields | Recommended | Gender, Email, CurrentCreditScore |
| Tier 3 | Customer selected fields | Elective | HasBillPay, HasEStatements, CheckingAccountBalance |
The purpose of this tiering system is to decrease the time to implementation of a standard set of tables common across all clients. Once the initial two tiers are complete, customers can immediately access a basic datamart featuring additional data from tier 2 as that becomes available. Tier 3 enables clients to request custom fields for ad-hoc or client-specific use cases or to add Smart Features on a subscription basis.
By decoupling the layers of the data model, Daybreak for Financial services offers faster time delivery to clients and enhanced ability to provide custom services without complicating the implementation of the foundational datamart structure.
New Tables¶
As part of the restructuring of datamarts into a tiered system, Daybreak for Financial Services will be adding new tables that enhance the relational structure of datamarts:
- Products: Information on the products offered by the client
- Services: Information about the different services a customer has subscribed to
-
Milestones: Information about the stages of the loan approval process
-
Addresses: Customer address and geolocation data
Many of these new tables serve to normalize relationships that had not always been clearly reflected in the 2.0 datamart versions. For example, customers in the previous model were limited to one address, which created various problems when the same customer had accounts listed under different addresses. The new structure makes it easier to accomodate customers who may have a business account listed under one address, and personal accounts under a home address.
Datamart Microprocessing¶
Daybreak for Financial Services 3.0 enables an evolutionary leap in the processing of data for Daybreak: datamart micro-processing. In past versions of the Daybreak for Financial Services model, data is refreshed nightly by processing complete datasets, including fields whose values change infrequently. Datamart microprocessing enables much faster and more frequent datamart refreshes by only building where source data has changed.
Datamart microprocessing leverages transactional workflows in Aunsight™ Golden Record to transfer new or updated data from a source in the form of a dataset log. These small chunks can then be processed on their own and the results integrated into the datamart, greatly reducing unnecessary resource load since only new data is processed in every batch.
In addition to greatly reduced resource footprint for cost and energy savings, microprocessing opens up the possibility of refreshing Daybreak datamarts multiple times in a day. If implemented for clients, this approach would offer a more dynamic experience as datamarts would be accurate to within hours of when their source data has changed.
Point-in-Time Snapshots¶
Daybreak for Financial Services 3.0 also includes datamart snapshots, a new database dimension providing point-in-time information about datamart fields. Snapshots provide an extra dimension to the Daybreak for Financial Services experience, allowing users to retrieve trend data about a field, such as credit score or account balances. In addition to exposing historical data to users in search of trends, the Innovation Lab data science team will have access to historical data for exploration and use in developing new SmartFeatures built on data trends over time.
Dynamic Subscription to Smart Features™¶
Clients on the 3.0 data model will now be able to dynamically subscribe to machine learning generated Smart Features as a part of their tiered datamart. Previously, delivery of new Smart Features required an extensive overhaul of the data pipeline used to generate that datamart. Thanks to its tiered data model, Daybreak for Financial Services 3.0 enables the activation of Smart Features by simply changing the configuration objects for a datamart pipeline.
Dynamic subscription enables new business models for delivering industry intelligent data points as a service. Clients gain the ability to personalize their Daybreak experience by subscribing to the individual machine learning scores and insights they want in their datamart.
Daybreak for Financial Services 2.2 Patch¶
Daybreak for Financial Services version 2 clients have a new update to their data model available to correct a change in the way the transaction summary table is built. Based on changes to Accounts table, the DFS 2.2 patch will change the foreign key for the Transactions summary to point to a uniquely identifying field, AccountPrimaryKey, rather than the previous AccountID which was a non-unique identifier in many client data sources. This change will resolve certain issues for some banking clients whose core systems did not provide unique account IDs (account numbers) for all sub-accounts (e.g. checking versus savings).
Download Insights¶
Last month Aunalytics released Insights, graphical representations of query results in the Daybreak Data Builder. At that time, users could create Insights in one of four types, save them to their query's Insights dashboard and share the query and its Insights with other Daybreak users in the app. This month, users can download the visualizations as PNG or JPG files for inclusion in other applications, or data (CSV) files to create additional visualizations manually. This allows users to import Daybreak Insights into documents and presentations using other tools directly from the app.
Read more about how to download Insights here.
is one of/is not one of Operator for Structured Queries¶
Many Daybreak datamart fields hold enumerated values; in other words, a field can only have one of a limited set of possible values such as ProductType being one of "Mortgage", "Checking," "Savings," etc. To support new query capabilities on such fields, Daybreak now offers is one of and is not one of operators which allow a user to select a subset of values that they would like to find.
For example, if a user wants to select mortgage or HELOC accounts, they can now use the is one of operator on the ProductType field and check the "Mortgage" and "HELOC" boxes from the operands field.
is not one of works similar to is one of but in reverse: it will exclude fields that are checked and return all other results.
Direct Notifications¶
The April release features in-app notifications as a new Aunsight platform component. Daybreak users will experience this as an in-app notification area that provides updates about changes to queries that are shared with them. By default, users will be automatically subscribed to notifications for all queries shared with them. Notifications will enable users to be updated when these queries are changed by their creators.
Notifications appear as an extension of the webapp in the lower left corner of the screen above the account/log out information. When a user visits this part of the app, they will see the last thirty days of activity in a sorted stream of notifications about changes to queries shared with them. Clicking on a message will mark it as read, or users can mark all as read.
Insights for Natural Language Answers¶
Last month Aunalytics released Insights, graphical representations of query results in the Daybreak Data Builder. At that time, users could create Insights in one of four types, save them to their query's Insights dashboard and share the query and its Insights with other Daybreak users in the app. This month, Daybreak Natural Language Answers will feature automatic Insights, graphical representations of the results generated by a natural language question. At present, Daybreak Natural Language Answers will show a summary Insight displaying the number of records in the result set returned by the query. In the future, Insights for Natural Language Answers could be trained to display different visualizations, such as a breakdown of customers by generation (“Show me a list of customers with checking accounts by generation”) or zip code (“Show me mortgage accounts by ZIP code”). Insights for Natural Language Answers lays the groundwork for applications of machine learning to provide answers to clients using a simple, natural language interface.
Aunsight Golden Record¶
Snowflake Plugin for Transactional Workflows¶
Last month, Aunsight Golden Record released Transactional Workflows, a new capability that can move large datasets from a source database to the datastore of their choice while bypassing the Mapping, Matching, and Merging configuration steps. Initially, this feature came with plugins for performing bulk transfers of data with Amazon S3 and the Aunsight platform. This month, Aunsight Golden record has released another plugin for the Snowflake cloud analytics platform. This feature adds additional capabilities for transactional workflows that will enable Aunsight Golden Record to provide enhanced support for customers using that platform.