Application Data Model 1 Application Data Model Pega Marketing for Financial Services 7.4 April 2018 Introduction This document provides an overview of the Pega Marketing for Financial Services customer data model. It is intended for system architects and data architects. It provides an overview about how the application retrieves and uses data from your internal and external systems and how data is stored and transformed in the extended Customer Analytics Record (xCAR) in the Pega Marketing for Financial Services application. The Pega Marketing for Financial Services xCAR is a “flat” structured record of the customer data in a format optimized for performance and real-time analytics and 1-to-1 marketing. The result is a solution that provides more accurate and faster business decisions and present customers with more attractive and tailored offers. Pega Marketing for Financial Services also supports the integration to data from Pega Customer Relation Management for Financial Services. This includes contact, opportunity, lead, and organization data from Sales Automation for Financial Services. This document also covers the data objects used in Pega-provided use cases of Pega Marketing for Financial Services. For a complete list of all data objects and properties available on this and other Pega Financial Services applications, see Pega Foundation for Financial Services database changes on the Pega Discovery Network (PDN). For additional details on how Pega integrates with your specific applications and databases, consult your Delivery Team. Content • How Pega integrates to your systems • Customer analytics data store deployment options − Pega-provided Decision Data Store − Pega-provided relational database − Combined rational and decision data stores • Data objects − Rule types used • Loading your data − Marketing with Decision Data Store records − Implementing Segmentation
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Application Data Model 1
Application Data Model Pega Marketing for Financial Services 7.4
April 2018
Introduction This document provides an overview of the Pega Marketing for Financial Services customer data model. It is intended for system architects and data architects. It provides an overview about how the application retrieves and uses data from your internal and external systems and how data is stored and transformed in the extended Customer Analytics Record (xCAR) in the Pega Marketing for Financial Services application.
The Pega Marketing for Financial Services xCAR is a “flat” structured record of the customer data in a format optimized for performance and real-time analytics and 1-to-1 marketing. The result is a solution that provides more accurate and faster business decisions and present customers with more attractive and tailored offers.
Pega Marketing for Financial Services also supports the integration to data from Pega Customer Relation Management for Financial Services. This includes contact, opportunity, lead, and organization data from Sales Automation for Financial Services.
This document also covers the data objects used in Pega-provided use cases of Pega Marketing for Financial Services. For a complete list of all data objects and properties available on this and other Pega Financial Services applications, see Pega Foundation for Financial Services database changes on the Pega Discovery Network (PDN).
For additional details on how Pega integrates with your specific applications and databases, consult your Delivery Team.
Content • How Pega integrates to your systems
• Customer analytics data store deployment options
How Pega integrates to your systems The Pega Marketing for Financial Services application integrates to your customer data to provide Next Best Actions, execute marketing campaigns and learn customer behavior using predictive and adaptive analytics. Pega Platform is a highly flexible software development platform that provides integration to your data using widely available industry protocols.
A typical Pega Marketing for Financial Services implementation looks like the following diagram:
Application Data Model 3
System of Record A
Data Warehouse
Customer Analytical Data Store
Pega Marketing Application
ETL Process BatchData
Data Access
Real-Time Data
Pega Integration Layer
Data Update
System of Record B
The data in your systems of record is usually extracted to a data warehouse or data lake for internal purposes. Typically, only a portion of this data will be required for this application and Pega accesses this batch data using our Pega Marketing for Financial Services data integration components. Real-time data can be accessed directly from systems of record using web services technology. Your project objectives and data latency requirements will determine your data access implementation.
After the source data is identified, data is loaded to the Pega-provided customer analytical data store. This process restructures the customer data as “flattened” data objects, which are designed to maximize performance and scalability while ensuring data integrity. Designing the customer analytical data store is an important part of project initiation and should be done in conjunction with your business and implementation partners.
The customer analytical data store is also referred to as the “spine table” or the “Customer Analytics Record.”
Customer analytical data store deployment options To maximize reuse and reduce implementation effort, Pega recommends that you select one of the following deployment options for your customer analytic data store when implementing Pega Marketing for Financial Services.
Pega-provided Decision Data Store In this option your customer analytic data store is contained in Pega using NoSQL technology. Use this option if you are planning to deploy your application using inbound Pega Customer Decision Hub or in analytics-driven decision processes.
Pega-provided relational database In this option your customer analytic data store is contained in a defined external database schema using standard SQL technology. Use this option if you are planning to deploy your application for outbound marketing or if you plan to use audience-driven marketing
Application Data Model 4
campaigns. The creation of Audiences requires customer data to be stored in relational tables.
Combined relational and decision data stores In this option your customer analytic data store is contained in both SQL and NoSQL data stores and is automatically replicated and maintained within Pega for maximum flexibility. Use this option if you plan to deploy your application in both inbound and outbound marketing use cases, or when using Pega Customer Decision Hub with outbound treatment capabilities.
Combined relational and decision data store is the recommended deployment option in the 7.4 release.
Data objects The Pega Marketing for Financial Services application includes an extensible data model which consists of six data objects.
Customer is the main data object and contains the customer details, including home address, telephone, and email. A summary of assets and liabilities is also included in this data object.
Accounts is the list of all customer banking accounts. This is a flattened list.
Application Data Model 5
Deposit Accounts, Loan Accounts, Investment Accounts, and Credit Card Accounts define the details of all Customer owned accounts including balances, description, account status, and important account dates.
Credit Card Transactions defines the transaction details, including amount, currency, merchant category code, description, important transaction dates, and merchant locations of credit card account transactions.
Opportunity defines the details related to a Sales Automation opportunity such as opportunity owner, Sales Automation product type, status of an opportunity. This is available only if you have included in your implementation application the ruleset PegaMarketingFS-CRM.
Lead defines the lead related details like email, phone number, and stage. This is available only if you have included in your implementation application the ruleset PegaMarketingFS-CRM.
Rule types used The application data objects are implemented via Dataset rules. A Dataset represents a data object and can be logical, physical or both. The dataset and its properties are defined by the class it applies to. The Dataset can also define the physical location of data such as relational database table, a NoSQL object, or even the application clipboard. Datasets are highly flexible and are accessed by the application at run-time to retrieve the data needed for AI and marketing purposes.
To learn more about Datasets, see About Data Set rules.
Datasets can be loaded and retrieved through Data Flow rules. Data Flows orchestrate the movement of data from source to destination within Pega. Data Flows also have the ability to transform your data. This is the primary mechanism used to “flatten” data in Pega Marketing for Financial Services.
To learn more about Data Flows, see About Data Flow rules.
The Data Flows rely on Report Definition rules such as LoadCustomerSummary to query data from your systems. Use report definitions to identify the columns and filters in your data model to pull against. Customers are expected to create their own report definitions for their data models. For more information about creating a report definition rule, see Report Definition rule form.
Loading your data to the customer analytics data store Pega ships with data flows that demonstrate how to import data from a relational database record into the Decision Data Store. These data flows act like an ETL process. They pull
from a number of defined data sources and save all records into a data set that has been defined, when the data set rule was created, as a decision data store.
Customers should start by identifying the data objects your use cases will require. At a minimum, the Customer data object must be loaded. Extend the Data Flow listed below that corresponds to the data object required for your decisioning. Create your own where necessary.
Data Flow Data Object Usage DF_GetCustomerData Customer Copies and saves customer information into the
CustomerDecision Data Store DF_GetCreditCardAccountsData Credit Card
Accounts Copies and saves credit card account information into the CreditCardAccountsDecision Data Store
DF_GetDepositAccountsData Deposit Accounts
Copies and saves deposit account information into the DepositAccountsDecision Data Store
DF_GetInvestmentAccountsData Investment Accounts
Copies and saves investment account information into the InvestmentAccountsDecision Data Store
DF_GetLoanAccountsData Loan Accounts
Copies and saves loan account information into the LoanAccountsDecision Data Store
DF_GetAccountDetails Account Calls DF_GetCreditCardAccountsData, DF_GetDepositAccountsData, DF_GetInvestmentAccountsData, and DF_GetLoanAccountsData and merges and flattens all the account details into one table.
DF_GetSalesOpportunityData Opportunity Copies and saves the Sales Automation Opportunity details into the table PMFS_OPPORTUNITY_REPOSITORY. This is meant to be used for segmentation only.
DF_GetSalesLeadData Lead Copies and saves the Sales Automation Opportunity details into the table PMFS_LEAD_REPOSITORY. This is meant to be used for segmentation only.
In order to pull the records from the source tables, class definition rules need to be created and mapped to each relevant database table. A report definition is then needed to pull the records. For example, in DF_GetCustomerData, the Pega Foundation for Financial Services database tables FSF_SAMPLE_CUST, FSF_SAMPLE_CUST_ADDRESS and FSF_SAMPLE_CUST_COMMS are mapped to via the respective class definitions and pulled from the referenced report definitions. The records of these three tables are merged in the compose shape based on a common key.
Customers are expected to create a class for each of their source database table and define properties that map to the columns in those tables relevant to their intended decisioning logic. Create a report definition to pull those properties and then create or update the existing data flows to do the actual storing.
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Note that different data set rules will create different tables in the Decision Data Store. Pega handles the structure of these tables based on the definition of the class defined on the data set rule. A database administrator is not needed to define a table structure when using the Decision Data Store.
Customers can use an agent to run these data flows to load the values into the Decision Data Store. This way, it can be scheduled as a reoccurring process. Pega Marketing for Financial Services provides the agent PegaMarketingFS that runs the activity StoreCustomerRecordsinDDS as a reference. This functionality is provided disabled. To enable, open the agent rule PegaMarketingFS and check the checkbox Enable, and then restart your system. Check that the agent schedule rule for PegaMarketingFS has been updated to include an enabled StoreCustomerRecordsinDDS.
Marketing with Decision Data Store records After the decisioning data has been successfully stored in the Decision Data Store, it can be used for predictive and adaptive analytics as well as for decisioning logic. Two data flows pull these records from the Decision Data Store and populate them onto the clipboard. They are CustomerData and SingleCustomerData.
CustomerData takes all you customers stored in the CustomerDecision Data Store and merges it with all the other pertinent records stored in all other Decision Data Store tables that have been created. In Pega Marketing for Financial Services, this is all of the accounts associated to the customer. It then takes that bulk set of data and pushes it into a clipboard through the use of an abstract shape. This data flow is used when marketing for against a large subset of customers, for example for campaigns and for field marketing.
SingleCustomerData is designed to pull from the Decision Data Store for a single customer record. It does this by calling the CustomerData finding the intersection between the customer defined on your customer class at run time and the set of data generated by the CustomerData data flow. This occurs in the merge shape in the data flow. This data flow is leveraged whenever information needs to be load for a single customer or decisioning is done on a single customer. Examples of this is for pulling customer information in the Marketing Profile and providing offers for a specific customer through real time containers.
When creating an implementation marketing solution, save as both CustomerData and SingleCustomer data to the implementation customer class and update the shapes to refer to the new data flows and data sets in the implementation customer class. Do not change or alter the MasterSegmentTable data set. This is used as part of segmentation. Unfortunately segmentation does not support the Decision Data Store.
Implementing segmentation Segmentation is dependent on Associated Customer Data defined in the Application Settings of your Pega Marketing portal. The Associated Customer Data has to be stored in
Application Data Model 8
relational databases. It is recommend to use the source relational database tables that you mapped to for your data flow pulls as the associated databases.
This chapter provides step by step instructions on integrating your associated customer tables. Repeat the below steps for each associated table.
1. Create a new class.
2. Ensure that your tables contains a foreign key to match and join the contents of this table with your customer data. This typically is represented by CustomerID.
3. Create a Database Table rule that maps your class to the associated database table. The database operator used on this database table rule only needs to have read access.
4. Define properties in this class. While these properties do not have to have the same name as the database columns from which they are sourced, they should represent the properties on the tables from which you plan to use for marketing decisions.
5. Update the external mappings tab of your class so that the properties in the class map to the columns in your database table. While many databases are not case-sensitive, some such as MS SQL are and so it is safer to be case-sensitive.
6. Create a page list property in your customer class that applies to your newly created class in step 1. This will be used as the Entity Name when defining your Associated Customer Data. Entity Names cannot be inherited. They must be on your top most customer class.
7. Add the associations in your context dictionary. Save and regenerate your context dictionary after updating it with your new associations.
8. Optional. For customers who are also using these class to source their Decision Data Store, create a report definition in this new class to pull from all the columns and use this report definition in a data flow to pull and store the data into the Decision Data Store.
Real-time data The usage of real-time data can be critical in providing the most accurate and targeted offers or next-best actions to customers. Pega provides for multiple real-time data integration solutions to empower the customer and provide flexibility in their design and implementation solution.
Data steams
Data streams are the recommended solution for implementing real-time data. Pega supports Kafka and Kinesis options for data stream. For more details on how to implement a Kafka or Kinesis data steam in Pega Marketing for Financial Services, see Creating a Kafka data set and Creating a Kinesis data set.
If you have an existing data repository or decisioning data store in a relational data base, you can directly connect to it by defining a database rule in Pega. Create one class for each table that you need to pull data from and map the class to the table with database table rules inside Pega. You can then pull and load the data through report definitions or data flows. While data flows also support this functionality, it is not recommended to use data pages due to performance concerns.
Connecting via services
Pega Marketing for Financial Services can read real-time data directly through services. The recommended solution is to push your data in though a regular data steam using a REST service or a web socket connection. For more information on this configuration, see Detecting Patterns in Streaming Data.
Alternatively, you can extend the real-time containers to pass additional real-time data every time the REST service for the container is invoked.
It is not recommended for you to invoke REST or SOAP services to fetch data during the processing of your decisioning as this can lead to performance challenges. Consider instead updating your decision records each time a change occurs so that your decision records are always maintained up to date.
Performance test results The following are performance test results conducted on loading data into the DDS via a simple data flow based off of DF_GetCustomerData. Each test was run 3 times and an average of the resulting times were taken. The environment details are as follows:
• Test 1 – Load 100 million customer records with 45 columns of varchar(32) on 1 database table in PostgreSQL with no partitioning and 3 nodes and 1 thread. Partition key defined as a random number between 1 and 100.
Result – 5 h 3 min
• Test 2 – Load 100 million customer records with 45 columns of varchar(32) on one database table in PostgreSQL with partitioning and 3 nodes and 1 thread. Partition key defined as a random number between 1 and 100.
• Test 3 – Load 100 million customer records with 45 columns of varchar(32) on one database table in PostgreSQL with partitioning and 3 nodes and 5 threads. Partition key defined as a random number between 1 and 100.
Result – 27 min 26 sec
• Test 4 – Load 100 million customer records with 45 columns of varchar(32) on one database table in PostgreSQL with partitioning and 3 nodes and 10 threads. Partition key defined as a random number between 1 and 100.
Result – 21 min 31 sec
• Test 5 – Load 100 million customer records with 45 columns of varchar(32) on one database table in PostgreSQL with partitioning and 3 nodes and 15 threads. Partition key defined as a random number between 1 and 100.
Result – 20 min 32 sec
• Test 6 – Repeated run REST services to invoke DF_GetCustomerData through real-time container with 100 million customer records, 200 million account records with 45 columns of varchar(32) on two database table in PostgreSQL with partitioning on both tables and 3 nodes and 10 threads. Partition key defined as a random number between 1 and 100.
Result – 99% of REST call response under 100 ms
Recommendations to optimize performance 1. Define a partition key. Make sure that this is numerical. Customers may need to
perform further tests to identify the number of partitions that is optimal for their data records.
2. Increase thread count to improve performance. It is not recommended to increase the thread count past 10 as it does not increase performance significantly.
3. Increase the node count to improve performance. Multiple nodes are only useful after setting up partitioning. The performance tests were run under 3 nodes but this can be increased further. 3 is the recommended default. Customers may need to perform further tests to identify the number of partitions that is optimal for their data records.
4. Limit the number of database tables that you pull from. Similarly limit the number of fields that you pull so that your only pull records that you need for decisioning.
5. Add indexes to your tables.
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Data model details Customer
Logical Data Model Data Source Property Type Description Table Column
Age Integer
Customer age. This value is calculated based on customer date of birth.
NA – Calculated value
AnnualIncome Decimal Customer annual income
[PEGADATA].FSF_SAMPLE_CUST C_ANNUALINCOME
BalanceTransaction Decimal
Customer balance transactions. This is a value used for calculating customer CLV in the simple model. Do not populate this column.
NA – Calculated value
BirthDate DateTime Date of birth [PEGADATA].FSF_SAMPLE_CUST
[PEGADATA].SAFS_WORK_CONTACT
C_BIRTHDATE
BIRTHDATE
BusinessSegment Text Organization business segment
[PEGADATA].FSF_SAMPLE_CUST C_BUSINESSSEGMENT
CIFNBR Text
Used only in the context of CRM to hold the PFFS customer identifier
[PEGADATA].SAFS_DATA_CONTACT_XREF EXTERNALID
City Text Customer primary address city
[PEGADATA].FSF_SAMPLE_CUST_ADDRESS CA_CITY
Country Text Customer primary address country
[PEGADATA].FSF_SAMPLE_CUST_ADDRESS CA_COUNTRY
CreditScore Integer Customer credit score
[PEGADATA].FSF_SAMPLE_CUST C_CREDITSCORE
Data Object: Customer
Applies To Class: PegaMarketingFS-Data-Customer
Relational Dataset: Customer
NoSQL Dataset: CustomerDDS
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CLV Text Text description of customer lifetime value
MAPPED FROM CLV NA
CustomerLifetimeValue Decimal
Numerical value that represents the customer lifetime value
NA – Calculated value
[PEGADATA].SAFS_WORK_CONTACT
NA
CLV_VALUE
CustomerID Text
Required. Primary Key for the Dataset. Holds SAFS pzInsKey if customer data is pulled from SAFS.
[PEGADATA].FSF_SAMPLE_CUST
[PEGADATA].SAFS_WORK_CONTACT
C_CIFNBR
PZINSKEY
Email Text Required for outbound marketing. Customer email
[PEGADATA].FSF_SAMPLE_CUST_COMMS CS_CONTACTSTRING
Facebook Text Customer Facebook handle
[PEGADATA].FSF_SAMPLE_CUST_COMMS CS_CONTACTSTRING
FirstName Text Customer first name [PEGADATA].FSF_SAMPLE_CUST
[PEGADATA].SAFS_WORK_CONTACT
C_FIRSTNAME
FIRSTNAME
Gender Text Customer gender [PEGADATA].FSF_SAMPLE_CUST
[PEGADATA].SAFS_WORK_CONTACT
C_GENDER
GENDER
InArrears Boolean
Flag to determine if the customer is in arrears. True if any account owned by the customer is in arrears
NA NA
Industry Text Organization industry
[PEGADATA].FSF_SAMPLE_CUST C_INDUSTRYCODE
IsActive Boolean Represents if customer still valid
LastName Text Customer last name [PEGADATA].FSF_SAMPLE_CUST
[PEGADATA].SAFS_WORK_CONTACT
C_LASTNAME
LASTNAME
LastReviewedDate DateTime
Customer last reviewed date with financial advisor for investment accounts
[PEGADATA].FSF_SAMPLE_CUST C_NEXTREVIEWDATE
MaritalStatus Text Customer marital status
[PEGADATA].FSF_SAMPLE_CUST
[PEGADATA].SAFS_WORK_CONTACT
C_MARITALSTATUS
MARITALSTATUS
NetWealth Decimal Customer net wealth. This value is calculated as part of
NA – Calculated value
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CLV calculations. (Total assets - total liabilities)
NextReviewDate DateTime
Approximate date of when the next financial review with a financial advisor should take place. Retrieved from CSFS integration for schedule appointment functionality.
NA NA
Org_Key Text Organization key. Links to the Sales lead.
[PEGADATA].SAFS_WORK_CONTACT ORGANIZATION_ID
OrganizationName Text
Organization name if customer is not an individual but an organization
[PEGADATA].FSF_SAMPLE_CUST C_FULLNAME
PostalCode Text Customer primary address postal code
[PEGADATA].FSF_SAMPLE_CUST_ADDRESS CA_ZIPCODE
PrimaryAddressLine1 Text Customer primary address line 1
[PEGADATA].FSF_SAMPLE_CUST_ADDRESS CA_ADDLINE1
PrimaryAddressLine2 Text Customer primary address line 2
[PEGADATA].FSF_SAMPLE_CUST_ADDRESS CA_ADDLINE2
PrimaryFax Text Customer primary fax
[PEGADATA].FSF_SAMPLE_CUST_COMMS CS_CONTACTSTRING
PrimaryHomePhone Text Customer primary home phone
[PEGADATA].FSF_SAMPLE_CUST_COMMS CS_CONTACTSTRING
PrimaryMobilePhone Text Customer primary mobile phone
[PEGADATA].FSF_SAMPLE_CUST_COMMS CS_CONTACTSTRING
PrimaryWorkPhone Text
Required for outbound marketing. Customer primary work phone
[PEGADATA].FSF_SAMPLE_CUST_COMMS CS_CONTACTSTRING
pyEmail1 Text
Required for marketing. Contains the email of the customer
[PEGADATA].FSF_SAMPLE_CUST_COMMS
[PEGADATA].SAFS_WORK_CONTACT
CS_CONTACTSTRING
WORKEMAIL
pyFullName Text Customer full name [PEGADATA].FSF_SAMPLE_CUST C_FULLNAME
pyHomeFax Text Customer fax number
[PEGADATA].SAFS_WORK_CONTACT FAX
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pyMobilePhone Text Customer mobile phone number
[PEGADATA].FSF_SAMPLE_CUST_COMMS
[PEGADATA].SAFS_WORK_CONTACT CS_CONTACTSTRING
RelationshipStartDate DateTime
Customer relationship start date with financial institution. Can use date instead of the full date and time.
[PEGADATA].FSF_SAMPLE_CUST C_RELATIONSHIPSTAR
ResidentialStatus Text Customer residential status for primary home
Holds the date time of next accounts review with financial advisor. Retrieved from CSFS integration for schedule appointment functionality.
NA NA
RiskCode Text Customer risk code (Representation of risk score)
[PEGADATA].FSF_SAMPLE_CUST C_RISKCODE
RiskScore Decimal
Numerical value that represents the risk associated with the customer.
NA – Meant to be calculated. Not currently implemented in PMFS 7.4
NA
State Text Customer primary address state
[PEGADATA].FSF_SAMPLE_CUST_ADDRESS CA_STATE
TaxIDNbr Text Customer tax identification number
[PEGADATA].FSF_SAMPLE_CUST C_TAXIDNBR
Territory Text Organization territory (market region)
[PEGADATA].FSF_SAMPLE_CUST
[PEGADATA].SAFS_WORK_CONTACT
C_TERRITORYID
TERRITORYID
TotalAssets Decimal Customer total assets
[PEGADATA].FSF_SAMPLE_CUST C_TOTALASSETS
TotalLiabilities Decimal Customer total liabilities
[PEGADATA].FSF_SAMPLE_CUST C_TOTALLIABILITIES
Twitter Text Customer Twitter handle
[PEGADATA].FSF_SAMPLE_CUST_COMMS CS_CONTACTSTRING
WinScore Decimal Represents the value of winning a sale with this customer
[PEGADATA].SAFS_WORK_CONTACT WINSCORE
Application Data Model 15
Credit card accounts Data Object: Credit Card Accounts
Applies To Class: PegaFS-Int-FSF_SAMPLE_CCACCT
Relational Dataset: Accounts
NoSQL Dataset: AccountsDDS
Logical Data Model Data Source Property Type Description Required Table Column
CustomerID Text Primary key of data set. Customer Identifier.
Yes if you use this table.Primary key FSF_SAMPLE_CCACCT CC_CIFNBR
AccountNumber Text Account number No FSF_SAMPLE_CCACCT CC_ACCTNBR CardNumber Text Card number No FSF_SAMPLE_CCACCT CC_CARDNUMBER AccountType Text Account type No FSF_SAMPLE_CCACCT CC_ACCOUNTTYPE Role Text Customer role on account No FSF_SAMPLE_CCACCT CC_ROLE Active Text Whether account is active No FSF_SAMPLE_CCACCT CC_ACTIVE
ProductType Text Card type. Eg., Platinum Checkings, Gold Savings No FSF_SAMPLE_CCACCT CC_PRODUCTTYPE
RewardType Text Reward type associated to account No FSF_SAMPLE_CCACCT CC_REWARDTYPE
Status Text Account status No FSF_SAMPLE_CCACCT CC_STATUS OpenDate Date Account open date No FSF_SAMPLE_CCACCT CC_OPENDATE
BehaviorScore Decimal Behavior score of based on account usage No FSF_SAMPLE_CCACCT CC_BEHAVIORSCORE
AvgMonthlyBalance Decimal Account average monthly balance No FSF_SAMPLE_CCACCT
CC_AVGMONTHLYBALANCE
AvgYearlyBalance Decimal Account average yearly balance No FSF_SAMPLE_CCACCT CC_AVGYEARLYBALANCE
YtdDisputes Integer
Number of time account has been in disputes since January 1st No FSF_SAMPLE_CCACCT CC_YTDDISPUTES
InArrears Boolean Whether account is in arrears No FSF_SAMPLE_CCACCT CC_INARREARS
Appl Integer Numerical representation of the type of account No FSF_SAMPLE_CCACCT CC_APPL
CreditLine Decimal Account credit line No FSF_SAMPLE_CCACCT CC_CREDITLINE
CreditLineAvailable Decimal Remaining available account credit line No FSF_SAMPLE_CCACCT CC_CREDITLINEAVAILABLE
Application Data Model 16
DelinquencyAmount Decimal Account delinquency amount No FSF_SAMPLE_CCACCT
CC_DELINQUINCYAMOUNT
YTDOverLimit Integer
Number of times account has been over limit since January 1st No FSF_SAMPLE_CCACCT CC_YTDOVERLIMIT
YTDLatePayment Integer
Number of times late payments have been made on the account since January 1st No FSF_SAMPLE_CCACCT CC_YTDLATEPAYMENT
YTDBrokenPromises Integer
Number of times customer has broken promise on a scheduled late payment or payment plan since January 1st No FSF_SAMPLE_CCACCT CC_YTDBROKENPROMISES
YTDInArrears Integer
Number of times account has been in arrears since January 1st No FSF_SAMPLE_CCACCT CC_YTDINARREARS
YTDInterestPaid Decimal Total interest paid since January 1st No FSF_SAMPLE_CCACCT CC_YTDINTERESTPAID
YTDPayments Decimal Total payments since January 1st No FSF_SAMPLE_CCACCT CC_YTDPAYMENTS
MarketSegmentID Integer
Numerical representation of the market segment. (1 = Retail, 2 = Commercial, 3 = Private wealth, 4 = Small medium business) No FSF_SAMPLE_CCACCT CC_MARKETSEGMENTID
CyclesPastDue Decimal Credit card payment cycles past due No FSF_SAMPLE_CCACCT CC_CYCLESPASTDUE
NoOfSuccessfulPayments Integer
Total no of successful payments No FSF_SAMPLE_CCACCT
CC_NOOFSUCCESSFULPAYMENTS
NoOfTimesinCollections Integer
Total number of times account has been in collections No FSF_SAMPLE_CCACCT
Logical Data Model Data Source Property Type Description Table Column
CustomerID Text Primary key of data set. Customer Identifier. FSF_SAMPLE_ACCT A_CIFNBR
AccountNumber Text Account number FSF_SAMPLE_ACCT A_ACCTNBR AccountType Text Account type FSF_SAMPLE_ACCT A_ACCOUNTTYPE AccountDescription Text Account product name FSF_SAMPLE_ACCT A_ACCTDESC
Active Text Whether account is active FSF_SAMPLE_ACCT A_ACTIVE
Status Text Account status FSF_SAMPLE_ACCT A_STATUS OpenDate Date Account open date FSF_SAMPLE_ACCT A_OPENDATE
BehaviorScore Decimal Behavior score of based on account usage FSF_SAMPLE_ACCT A_BEHAVIORSCORE
AvgMonthlyBalance Decimal Account average monthly balance FSF_SAMPLE_ACCT A_AVGMONTHLYBALANCE
AvgYearlyBalance Decimal Account average balance this year FSF_SAMPLE_ACCT A_YEARTODATEAVERAGEBALANCE
Appl Integer
Numerical representation of the type of account FSF_SAMPLE_ACCT A_APPL
Status Text Account status FSF_SAMPLE_CMACCT CMA_STATUS OpenDate Date Account open date FSF_SAMPLE_CMACCT CMA_OPENDATE
AvgMonthlyBalance Decimal Account average monthly value FSF_SAMPLE_CMACCT CMA_AVGMONTHLYVAL
Appl Integer Numerical representation of the type of account FSF_SAMPLE_CMACCT CMA_APPL
OwnershipType Text
Ownership type of customer on account (E.g. Joint owner with right of survivorship, individual, community property) FSF_SAMPLE_CMACCT CMA_OWNERSHIPTYPE
CurrentValue Decimal Current value of account FSF_SAMPLE_CMACCT CMA_CURRENTVALUE
MarketSegmentID Integer
Numerical representation of the market segment. (1 = Retail, 2 = Commercial, 3 = Private wealth, 4 = Small medium business) FSF_SAMPLE_CMACCT CMA_MARKETSEGMENTID
Application Data Model 19
Loan accounts Data Object: Loan Accounts
Applies To Class: PegaFS-Int-FSF_SAMPLE_LOANACCT
Relational Dataset: Accounts
NoSQL Dataset: AccountsDDS
Logical Data Model Data Source Property Type Description Table Column
CustomerID Text
Primary key of data set. Customer Identifier. FSF_SAMPLE_LOANACCT LA_CIFNBR
AccountNumber Text Account number FSF_SAMPLE_LOANACCT LA_ACCTNBR
Status Text Account status FSF_SAMPLE_LOANACCT LA_STATUS
InArrears Boolean
Whether account is in arrears FSF_SAMPLE_LOANACCT LA_INCOLLECTIONS
Appl Integer
Numerical representation of the type of account FSF_SAMPLE_LOANACCT LA_APPL
