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Data Mart stores data in various tables in a Data Mart schema according to the aggregation level of the data. All data at the fully aggregated or fund level resides in the Fund Summary table. Data relating to nodes of a grouping of a portfolio, such as group subtotals or group returns, resides in group level tables. Disaggregated data relating to individual trades, positions, or security level returns resides in a series of detail level tables based on the type of data.

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Analyze Your Reporting Requirements

A well-built Data Mart starts with an analysis of your reporting requirements. This means taking a complete inventory of the data you want in your Data Mart at the group, fund, and detail portfolio levels.

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Create Models

Group, Fund Summary, and Detail models define how to populate the group, fund, and detail level tables in Data Mart for reporting purposes. The following figure illustrates how Data Mart displays data stored at the group, fund, and detail portfolio levels.

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Group Models

Group models define group level data, such as the Technology and Energy sectors and the partially aggregated fund totals for each sector as shown in the previous figure.
Group models are user defined according to your unique reporting requirements. You can create one or more Group models.
Data Mart stores group level data in the group tables in the Data Mart schema. Table names are user assigned.

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Fund Summary Models

The Fund Summary model defines fund level (entity) data, such as the fund name, manager name, and fully aggregated fund totals as shown in previous figure.
The Fund Summary model is pre-defined in terms of its basic structure. However, it requires configuration according to your unique reporting requirements.
Data Mart stores fund level data in the Fund Summary table in a mart schema.

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Detail Models

Detail models define detail level data, such as the name of each security, book value, and market value shown in the previous figure. Data Mart provides the following Detail models:

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Detail models are pre-defined in terms of their basic structure. However, they require configuration according to your unique reporting requirements.
Data Mart stores detail level data in the Position Details, Lot Level Position Details, Trade Details, Cash Flow Details, Performance Details, Ledger Activity Details, Security Details, and Issuer Details tables in a Mart schema.
Data Mart has the flexibility to differentiate reporting at the group, fund, and detail levels. For example, you can report on market values at the fund, group, and detail levels. Additionally, if you forget to add a field during the initial set up of your Data Mart, you can add it later.
A Data Mart schema also contains the Fund Master table, which defines the Entity and Effective Date of each table row. The system identifies the unique combination of Entity ID and Effective Date as the Data Mart Fund ID.

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Submit Models

When you submit a model, the Data Mart engines launch one or more OLAP (online analytical processing) processes based on the type of fields in the model. For example, if you submit a model that contains performance fields, the Data Mart engines would generate a performance analysis OLAP process that provided a number of field values, such as the following:

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The end date of the performance records that the report must return. This date is combined with the time span of the longest linked return of a model or extension.
After selecting data from the database based on the types of fields in the model, additional processing may be done, such as return linking. The Data Mart engines would then save the results of the performance analysis report in the specified tables of a Data Mart schema for reporting purposes.
Since OLAP reports are specialized by field type, such as position, performance, or transaction, it may take four or more OLAP reports to provide the necessary fields for a fund level or group level table. You have the option of defining table extensions for the Fund Summary and Detail models. A table extension stores a subset of the model's fields in a separate physical table that has a key structure identical to the main table, but is populated by its own OLAP reports.

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Create Snapshots of Your Data

By default, Data Mart builds one daily version of your business information according to a single data source hierarchy known as a source rule in PACE. With Data Mart's snapshot feature, you can build and save your information more than once per day, and use more than once source rule in the build process. For example, suppose your organization runs a pension fund, and you maintain both a custodian source and a manager source of the same accounting data. You can create one snapshot that looks at fund activity from a custodian's view point and another snapshot that looks at fund activity from a manager's view point. You can also use snapshots to report on the same portfolios in different accounting bases. Separate snapshots can also save separate builds done at different times of the day, allowing you to make intra-day comparisons of data values.

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View Process Logs

Execution logs provide information on scheduled and ad hoc (manual) model submissions.
You can view Execution Logs at the following levels:

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Displays the OLAP process contents within the model.
The PACE Application Server Log provides information about the application server on which Data Mart processes are executed.

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View Data in the Data Mart

You can also view the contents of Data Mart tables, refresh data changes, explode group levels, and export, print, and configure data columns on a number of windows.
Using the Query Expert feature, you can create and copy SQL code that retrieves Data Mart data. You can then paste the SQL code into an external reporting application for reporting purposes.
The remaining sections of this guide provide details on navigating the Data Mart user interface, configuring the Data Mart environment, creating and editing models, submitting and managing models, working with processing logs, and viewing data in the Data Mart.
The appendices provide high level details on configuring models and using Mart data in third party reports.