Use Filtering
Decide When to Use Filtering
It is not likely that you will want to filter data in your Fund Summary or detail models, though you may do so if necessary. The most likely use case for filtering is if you have certain types of securities, trades or positions that you must always exclude from reporting, and which you can identify by a simple filter.
However, it often makes sense to filter group-level data. Group models are made for delivering information in an analytical way, and a filter can omit information that is not pertinent to the analysis provided by the model. An example is a grouping on ranges of equity beta, in which it is natural to filter out all but equity securities.
Filter for Specific Versus General Purposes
Any filter in a Data Mart model is applied to just one type of data at a time, such as positions, cash, trades, or security. This means that in a group model you may filter for example, trades for purchases only, but leave your position data unfiltered. It also means that an across-the-board filter such as “supervised assets only” must be applied to each type of data separately and identically. So, to create a group model for fixed-income only featuring position, performance and transaction fields you would need to set up a filter for “investment type = fixed income” three times in the Filters and Mappings tab.
When you filter data in a group model, map details to the group, and use the map table “pointers” in your report SQL query to retrieve data, detail rows are only retrieved for detail rows that pass the filter. This means that a group model filtered for fixed income reports only bond positions under a set of bond groups. However, this does not mean that the Position Detail table loses any non-bond positions; only the map table from groups to positions is missing rows for non-bonds as a result of the filter.
Filter Performance Data with Dynamic Performance
Regular Performance Analysis OLAP reports based upon performance dictionaries do not support filtering because returns and other performance data stored for a dictionary are pre-computed based upon an unfiltered portfolio.
Performance Analysis reports may be generated without a performance dictionary. This is the Dynamic Performance approach that is supported by Data Mart. Since Dynamic Performance computes security-level returns first and then aggregates and links them, filtering can take place during the Mart build process to eliminate any investment category from the analysis that may be required.
Simplify Entity Hierarchies
Entity Hierarchy field attributes allow you to report data for groups of entities that have parent-child relationships such as members of a composite, where composite members can themselves be composites, etc. You can maintain more than one such hierarchy, if necessary, to express all of the composite relationships among your entities.
Best practice for reporting Entity Hierarchies in Data Mart is to set up and regularly submit, for each distinct entity hierarchy structure supported, a dedicated group-level model whose groups are the Entity Hierarchy field attributes of the hierarchy. You must select some field for any model, but you can simply choose a single position field like market value just to enable the model to populate its table. The model should be built for every entity at the topmost level of the hierarchy, which might be a single entity if the top level is “total firm”.
Your SQL queries in reports and other downstream applications will use the hierarchy table as a directory to enable you to find the up- and down-hierarchy entities for any given entity. You must also populate the Fund Summary model for all entities.
Support Portfolio Look-through
Look-through reporting at the detail level is supported in a Position Details look-through model and table. The granularity of a row in this table is the look-though position piece. The piece is the position in the portfolio or in any child find at whatever level of nesting. This is the same granularity as in a position OLAP report having look-through fields. The data is returned flattened for look-through in Data Mart.
Look-through in Data Mart Group Level Models
Group level models support look-through by enabling look-through processing. By default, the Enable Look thru Processing checkbox on the Model dialog box is blank. When you select it, it conditions the model to support only look-through processing.
Support Long-Term Summaries of Trade Activity
You may need to report summaries of trade or cash activity over long periods of time, such as the sum of buys year-to-date. Any generic trade field attribute can build this type of data. While it is possible to rebuild these field attributes over a long time period every day in the mart, this is an inefficient practice. A more efficient strategy is to build that summary field on a month-to-date basis daily, then sum over a set of monthly fields at the report level, perhaps with the use of a general-purpose stored procedure.
Support General Ledger Data
Data Mart supports General Ledger data. Although Eagle Accounting was enhanced to facilitate building ledger data in Data Mart, other accounting systems may send ledger data to Data Mart if they load the gl_detail_posting table of the ledger database in the same way as Eagle Accounting does.
Eagle Accounting sends ledger data under different sources for different accounting bases and for daily versus monthly balances. If you want to support more than one basis or periodicity in your reporting, it is best practice to use a separate snapshot for each.
Support NAV Data
Data Mart does not directly support fields from the Net Asset Value (NAV) table. However, it does support Dynamic Mutual Fund field attributes. A series of field categories and about 20 different effects were added to the Dynamic Mutual Fund field attribute type to allow a wide range of NAV fields to be supported in Data Mart.
Limitations of Selective Fields
The Build Selective Fields option has a number of significant benefits, such as selective back-filling. For example, when you add one or more new fields to a model that you have been populating for a while you can use this option to populate values for the new fields only. Otherwise, you have to submit the model to populate every field of each row. When you re-populate fields that already have data values, the values are not deleted, but re-generated. This can lead to an unintended restatement of reported data values.