Application Data Model

Transcription

1 Application Data Model Pega Marketing for Financial Services 7.31 October 2017 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 systems and how data are stored in the Customer Analytics Record in the Pega Marketing for Financial Services application. The extended Customer Analytics Record, also called xcar, in the Pega Marketing for Financial Services application is a flat structured record of the customer data used for customer analytics. Pega uses the Decision, or DDS, to store the records. The Decision is a Cassandra based big data repository developed on NoSQL technology. While Pega Marketing for Financial Services also supports relational tables, Pega recommends an xcar solution over using relational database tables for Pega Marketing for Financial Services for performance gains. 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 the Pega Foundation for Financial Services data model document 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 Pega-provided relational database Combined rational and decision data stores Data objects Rule types used Loading your data Data object details Customer Deposit accounts Tech Note 1

2 Loan accounts Investment accounts Credit card accounts Credit card transactions 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 this: System of Record A ETL Process Data Warehouse Batch Data Customer Analytical Data Access Data Update Pega Marketing Application System of Record B Real-Time Data Pega Integration Layer 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. Tech Note 2

3 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 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 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 7.31 release. Tech Note 3

4 Data objects The Pega Marketing for Financial Services application includes an extensible data model which consists of six data objects. Customer PegaMarketingFS-Data-Customer Deposit Accounts PegaMarketingFS-Data-Customer-Accounts-Deposit Loan Accounts PegaMarketingFS-Data-Customer-Accounts-Loan Investment Accounts PegaMarketingFS-Data-Customer-Accounts-Investment Credit Card Accounts PegaMarketingFS-Data-Customer-Accounts-CreditCard Credit Card Transactions PegaFS-Data-Transaction-Card Customer is the main data object and contains the customer details, including home address, telephone, and . A summary of assets and liabilities is also included in this data object. 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. 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 Tech Note 4

5 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. 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 DF_GetCreditCardAccountsData Credit Card Accounts Copies and saves credit card account information into the CreditCardAccountsDecision 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 DF_GetLoanAccountsData Loan Accounts Copies and saves loan account information into the LoanAccountsDecision Data Store Tech Note 5

6 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 through 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. Note that different data set rules will create different tables in the Decision. 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. 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 records After the decisioning data has been successfully stored in the Decision, it can be used for predictive and adaptive analytics as well as for decisioning logic. Two data flows pull these records from the Decision and populate them onto the clipboard. They are CustomerData and SingleCustomerData. CustomerData takes all you customers stored in the CustomerDecision and merges it with all the other pertinent records stored in all other Decision 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 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. Tech Note 6

7 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. 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 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. Create an Association rule in your customer class with the same name as the pagelist property created in Step 6. The class association defined on this association rule should be the class created in step 1. The class join prefix should be the same as the property name created in step 6 and the class name for class join prefix should your customer class. Define the foreign keys mapping (join criteria) by click Edit conditions. Add in the pages and class tab, the pagelist property defined in Step 6. Save your association rule. 8. Add this class as an Associated Customer Data in the Manage Data Relationships tab of the Application settings landing page on the Pega Marketing portal using the two rules that you created in step 6 and Optional. For customers who are also using these class to source their Decision, 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. Tech Note 7

8 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. Result 1 hr 52 min 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 Tech Note 8

9 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. Data model details Customer Data Object: Applies To Class: Relational Dataset: NoSQL Dataset: Customer PegaMarketingFS-Data-Customer Customer CustomerDecision Logical Data Model Data Source Property Type Description Table Column CustomerID Required. Primary Key for the Dataset [PegaDATA].FSF_SAMPLE_CUST C_CIFNBR CIFNBR Not used in this context. Use CustomerID instead. n/a n/a FirstName Customer first name [PegaDATA].FSF_SAMPLE_CUST C_FIRSTNAME LastName Customer last name [PegaDATA].FSF_SAMPLE_CUST C_LASTNAME pyfullname Customer full name [PegaDATA].FSF_SAMPLE_CUST C_FULLNAME Customer annual AnnualIncome Decimal income [PegaDATA].FSF_SAMPLE_CUST C_ANNUALINCOME CreditScore Customer credit score [PegaDATA].FSF_SAMPLE_CUST C_CREDITSCORE Gender Customer gender [PegaDATA].FSF_SAMPLE_CUST C_GENDER RelationshipStartDate DateTime Customer relationship start date with financial institution. Can take just date instead of full datetime. [PegaDATA].FSF_SAMPLE_CUST C_RELATIONSHIPSTARTDATE Tech Note 9

10 MaritalStatus OrganizationName BusinessSegment Territory Customer marital status [PegaDATA].FSF_SAMPLE_CUST C_MARITALSTATUS Organization name if customer is not an individual but an organization [PegaDATA].FSF_SAMPLE_CUST C_FULLNAME Organization business segment [PegaDATA].FSF_SAMPLE_CUST C_BUSINESSSEGMENT Organization territory (market region) [PegaDATA].FSF_SAMPLE_CUST C_TERRITORYID Industry Organization industry [PegaDATA].FSF_SAMPLE_CUST C_INDUSTRYCODE Required for outbound marketing. Customer [PegaDATA].FSF_SAMPLE_CUST_COMMS CS_CONTACTSTRING PrimaryFax fax [PegaDATA].FSF_SAMPLE_CUST_COMMS CS_CONTACTSTRING PrimaryHomePhone home phone [PegaDATA].FSF_SAMPLE_CUST_COMMS CS_CONTACTSTRING PrimaryMobilePhone mobile phone [PegaDATA].FSF_SAMPLE_CUST_COMMS CS_CONTACTSTRING PrimaryWorkPhone Required for outbound marketing. Customer primary work phone [PegaDATA].FSF_SAMPLE_CUST_COMMS CS_CONTACTSTRING Facebook Customer Facebook handle [PegaDATA].FSF_SAMPLE_CUST_COMMS CS_CONTACTSTRING Twitter Customer Twitter handle [PegaDATA].FSF_SAMPLE_CUST_COMMS CS_CONTACTSTRING PrimaryAddressLine1 address line 1 [PegaDATA].FSF_SAMPLE_CUST_ADDRESS CA_ADDLINE1 PrimaryAddressLine2 address line 2 [PegaDATA].FSF_SAMPLE_CUST_ADDRESS CA_ADDLINE2 City address city [PegaDATA].FSF_SAMPLE_CUST_ADDRESS CA_CITY State address state [PegaDATA].FSF_SAMPLE_CUST_ADDRESS CA_STATE Country address country [PegaDATA].FSF_SAMPLE_CUST_ADDRESS CA_COUNTRY PostalCode address postal code [PegaDATA].FSF_SAMPLE_CUST_ADDRESS CA_ZIPCODE TotalAssets Decimal Customer total assets [PegaDATA].FSF_SAMPLE_CUST C_TOTALASSETS TotalLiabilities Decimal Customer total liabilities [PegaDATA].FSF_SAMPLE_CUST C_TOTALLIABILITIES RiskCode Customer risk code (Representation of risk score) [PegaDATA].FSF_SAMPLE_CUST C_RISKCODE Tech Note 10

11 TaxIDNbr ResidentialStatus BalanceTransaction NetWealth LastReviewedDate Age Decimal Decimal DateTime Customer tax identification number [PegaDATA].FSF_SAMPLE_CUST C_TAXIDNBR Customer residential status for primary home [PegaDATA].FSF_SAMPLE_CUST_ADDRESS CA_RESIDENTIALSTATUS Customer balance transactions. This is a value used for calculating customer CLV in the simple model. Do not populate this column. NA Calculated value Customer net wealth. This value is calculated as part of CLV calculations. (Total assets - total liabilities) NA Calculated value Customer last reviewed date with financial advisor for investment accounts [PegaDATA].FSF_SAMPLE_CUST C_NEXTREVIEWDATE Customer age. This value is calculated based on customer date of birth. [PegaDATA].FSF_SAMPLE_CUST c_birthdate Credit card accounts Data Object: Applies To Class: Relational Dataset: NoSQL Dataset: Credit Card Accounts PegaFS-Int-FSF_SAMPLE_CCACCT CreditCardAccounts CreditCardAccountsDecision Deposit accounts Data Object: Applies To Class: Relational Dataset: NoSQL Dataset: Deposit Accounts PegaFS-Int-FSF_SAMPLE_ACCT DepositAccounts DepositAccountsDecision Tech Note 11

12 Logical Data Model Data Source Property Type Description Table Column CustomerID Primary key of data set. Customer Identifier. FSF_SAMPLE_ACCT A_CIFNBR AccountNumber Account number FSF_SAMPLE_ACCT A_ACCTNBR AccountType Account type FSF_SAMPLE_ACCT A_ACCOUNTTYPE AccountDescription Account product name FSF_SAMPLE_ACCT A_ACCTDESC Active Whether account is active FSF_SAMPLE_ACCT A_ACTIVE Status 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 Numerical representation of the type of account FSF_SAMPLE_ACCT A_APPL AccountBalance Decimal Account balance FSF_SAMPLE_ACCT A_YTDDISPUTES TotalDisputes MarketSegmentID Number of times transactions have been disputed on this account since January 1st FSF_SAMPLE_ACCT A_ACCOUNTBALANCE Numerical representation of the market segment. (1 = Retail, 2 = Commercial, 3 = Private wealth, 4 = Small medium business) FSF_SAMPLE_ACCT A_MARKETSEGMENTID Investment accounts Data Object: Applies To Class: Relational Dataset: NoSQL Dataset: Investment Accounts PegaFS-Int-FSF_SAMPLE_CMACCT InvestmentAccounts InvestmentAccountsDecision Logical Data Model Data Source Property Type Description Table Column CustomerID Primary key of data set. Customer Identifier. FSF_SAMPLE_CMACCT CMA_CIFNBR AccountNumber Account number FSF_SAMPLE_CMACCT CMA_ACCTNBR Tech Note 12

13 AccountType Account type (E.g. Brokerage, retirement, college funds) FSF_SAMPLE_CMACCT CMA_TYPE AccountSubType Account sub-type (E.g. Traditional individual retirement account, pension, 529 plan, ROTH IRA) FSF_SAMPLE_CMACCT CMA_SUBTYPE Status 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 Numerical representation of the type of account FSF_SAMPLE_CMACCT CMA_APPL OwnershipType 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 Numerical representation of the market segment. (1 = Retail, 2 = Commercial, 3 = Private wealth, 4 = Small medium business) FSF_SAMPLE_CMACCT CMA_MARKETSEGMENTID Loan accounts Data Object: Applies To Class: Relational Dataset: NoSQL Dataset: Loan Accounts PegaFS-Int-FSF_SAMPLE_LOANACCT LoanAccounts LoanAccountsDecision Logical Data Model Data Source Property Type Description Table Column CustomerID Primary key of data set. Customer Identifier. FSF_SAMPLE_LOANACCT LA_CIFNBR AccountNumber Account number FSF_SAMPLE_LOANACCT LA_ACCTNBR Status Account status FSF_SAMPLE_LOANACCT LA_STATUS InArrears Boolean Whether account is in arrears FSF_SAMPLE_LOANACCT LA_INCOLLECTIONS Appl Numerical representation of the type of account FSF_SAMPLE_LOANACCT LA_APPL DelinquencyAmount Decimal Account delinquency amount FSF_SAMPLE_LOANACCT LA_DELINQUINCYAMOUNT MaturityDate Date Date loan matures FSF_SAMPLE_LOANACCT LA_MATURITYDATE LoanToValue Decimal Loan to value ratio FSF_SAMPLE_LOANACCT LA_LOANTOVALUE UnpaidPrincipal Decimal Remaining principal FSF_SAMPLE_LOANACCT LA_UNPAIDPRINCIPAL Tech Note 13

14 RiskScore Decimal Risk score on loan FSF_SAMPLE_LOANACCT LA_RISKSCORE Rate Decimal Rate percent FSF_SAMPLE_LOANACCT LA_RATE RateType Rate type (E.g. HELOC, 3/1 ARM, FHA, conventional) FSF_SAMPLE_LOANACCT LA_RATETYPE YTDBrokenPromises Number of times customer has broken promise on a scheduled late payment or payment plan since January 1st FSF_SAMPLE_LOANACCT LA_YTDBROKENPROMISES YTDInArrears Number of times account has been in arrears since January 1st FSF_SAMPLE_LOANACCT LA_YTDINARREARS MarketSegmentID Numerical representation of the market segment. (1 = Retail, 2 = Commercial, 3 = Private wealth, 4 = Small medium business) FSF_SAMPLE_LOANACCT LA_MARKETSEGMENTID CyclesPastDue Decimal Credit card payment cycles past due FSF_SAMPLE_LOANACCT LA_CYCLESPASTDUE DaysDeliquent Total number of days payment is past due FSF_SAMPLE_LOANACCT LA_DAYSDELINQUENT NoOfSuccessfulPayments Total no of successful payments FSF_SAMPLE_LOANACCT LA_NOOFSUCCESSFULPAYMENTS NoOfTimesinCollections Total number of times account has been in collections FSF_SAMPLE_LOANACCT LA_NOOFTIMESINCOLLECTIONS Tech Note 14

15 2017 Pegasystems Inc., Cambridge, MA All rights reserved. Trademarks For Pegasystems Inc. trademarks and registered trademarks, all rights reserved. All other trademarks or service marks are property of their respective holders. For information about the third-party software that is delivered with the product, refer to the third-party license file on your installation media that is specific to your release. Notices This publication describes and/or represents products and services of Pegasystems Inc. It may contain trade secrets and proprietary information that are protected by various federal, state, and international laws, and distributed under licenses restricting their use, copying, modification, distribution, or transmittal in any form without prior written authorization of Pegasystems Inc. This publication is current as of the date of publication only. Changes to the publication may be made from time to time at the discretion of Pegasystems Inc. This publication remains the property of Pegasystems Inc. and must be returned to it upon request. This publication does not imply any commitment to offer or deliver the products or services described herein. This publication may include references to Pegasystems Inc. product features that have not been licensed by you or your company. If you have questions about whether a particular capability is included in your installation, please consult your Pegasystems Inc. services consultant. Although Pegasystems Inc. strives for accuracy in its publications, any publication may contain inaccuracies or typographical errors, as well as technical inaccuracies. Pegasystems Inc. shall not be liable for technical or editorial errors or omissions contained herein. Pegasystems Inc. may make improvements and/or changes to the publication at any time without notice. Any references in this publication to non-pegasystems websites are provided for convenience only and do not serve as an endorsement of these websites. The materials at these websites are not part of the material for Pegasystems products, and use of those websites is at your own risk. Information concerning non-pegasystems products was obtained from the suppliers of those products, their publications, or other publicly available sources. Address questions about non-pegasystems products to the suppliers of those products. This publication may contain examples used in daily business operations that include the names of people, companies, products, and other third-party publications. Such examples are fictitious and any similarity to the names or other data used by an actual business enterprise or individual is coincidental. This information is the property of: Pegasystems Inc. One Rogers Street Cambridge, MA USA Phone: (617) Fax: (617) Pega Marketing for Financial Services Document: Application Data Model Software Version: 7.31 Updated: October 2017 Tech Note 15