Using Financial Statements

In Analytics, lenses and dashboards enable you to view, analyze, and perform actions on your data. Financial Statements provides pre-set data templates for you to use and modify as required. Refer to the following information about the datasets, lenses, and dashboards provided with Financial Statements.

For general information about datasets, lenses, and dashboards in Analytics, see the Salesforce Help for Analytics.

The Financial Statements app includes five datasets. Each dataset contains information from Accounting, Fixed Asset Management, or Planning that has been normalized and stored in Analytics.

When creating a dataset, Analytics applies one of the following types to each field:

  • Date: values such as days, months, years, and times. You can group, filter, and perform calculations on the data in Date type fields. If time zone support is enabled, Date/Time field values are converted to date values according to the time zone you specify as the default Analytics time zone in your org. For more information about enabling custom time zones, see the Salesforce Help.
  • Dimension: qualitative values such as regions, product names, and model numbers. You can group and filter the data in Dimension type fields. You cannot perform calculations. Dimension fields are indexed.
  • Measure: quantitative values such as revenue and exchange rates. You can perform calculations on the data in Measure type fields.

Similar to Salesforce reports and other reporting tools, you do not need to know the object model of the dataset. The dataset includes information from various objects which have been combined into one view. The naming convention remains as close as possible to the terminology that you are familiar with, for example, Trial Balance 1, General Ledger Account, Bank Account Name, Company Name. Additionally, all components are labeled to make it easy for you to recognize the content. Not all field names remain the same, however.

You can add your own fields to a dataset in the following ways:

  • Modify the dataflow.
  • Create your own expressions in dashboards.
  • Use the Recipes function in Analytics.

For more information, contact your implementation consultant.

The following datasets are available:

The Financial Balances dataset is equivalent to the Reporting Balances object in Accounting. The dataset includes fields from the Reporting Balances object such as Reporting Balance Type as well as additional fields that relate to a transaction. The Financial Balances dataset is not a copy of the Reporting Balances object. The transactions within the Transaction Line object are extracted from Accounting and are then aggregated within Analytics to create the balance equivalent.

As well as the standard fields available in the Reporting Balances object, the Financial Balances dataset includes other values which have been generated based on the transactions you have posted. The Financial Balances dataset includes summaries and additional transaction information at a summarized level. For instance, Transaction Name, Transaction Line Name, Transaction Document Number, and Transaction Document Description. These fields enable you to see transactional information without needing to drill into sources, such as Accounting.

Some of the members within the dataset contain the suffix (s). The (s) indicates that the value sign has been transformed in some way. For example, where the (s) suffixes a value, it indicates that the signage of that value has been reversed. For example, when you design an income statement, rather than showing your sales accounts as negatives, you can use the Home (s) field to reverse all of your sign values so that values stored as negative appear positive and vice versa. Similarly, (s) at the end of the name of a general ledger account indicates that the name has been trimmed.

Other examples of transformed values are the Year and Period values. These values have been transformed into numbers so that you can do ‘between’, ‘greater than’, and ‘less than’ calculations. For all transformed fields, see Transformed Fields.

In addition to the standard value based measure fields, there are fields for financial years and periods including the Financial Period, Financial Year, and the Financial Year and Period. These fields are typically held only as alphanumeric members within the dataset, allowing you to include them as 'group by' members only. However, by additionally cloning and transforming these members into numbers within the dataflow, they also appear in the Measures section of the lens and you can perform 'greater than' or 'between' calculations.

The Financial Balances dataset also includes the option for you to add your own custom code so that you can modify the order in which your data appears. For example, if the Trial Balance 1 picklist order is required for the report and it is not alphanumeric. Contact your implementation consultant or Customer Support for more information.

For information about which fields are included in the Financial Balances dataset, see Financial Balances Output Fields.

The Financial Transactions dataset stores fields associated with the Transaction and Transaction Line objects in Accounting, as well as some balance data from the Reporting Balance object, and ancillary objects such as Global Ledger Account, Periods, and Account. For information about which fields are included in the Financial Transactions dataset, see Financial Transactions Output Fields.

The Financial Periods dataset stores ancillary fields associated with a Accounting period. The dataset includes fields from the Period object in Accounting. For more information about which fields are included in the Financial Periods dataset, see Financial Periods Fields.

The Financial Matching dataset stores the matching history for all transactions that have been cash matched within Accounting. For more information, see the matching objects in Accounting.

The Financial Accounts dataset stores ancillary fields associated with Salesforce accounts. The dataset includes fields from the Accounts object in Salesforce. For more information, see the Accounts object.

After deploying the app template, the Financial Statements app contains over fifty different lenses. You can use these lenses as sources for your financial reports. You can edit a lens by selecting it and then editing the filters which are provided.

The standard lenses are based on all of the statutory reports that Certinia has previously included in its packages. The lenses include balance sheets, income statements, statements of cash flows, and variations of each of these.

You can use XL Plus to connect to the lenses and produce formatted outputs and statements.

Note: When launching an app, if some lenses are not displayed or an error is displayed, check the following items are correct in the dataset:
• All fields contain data
• All field references are valid
See the Salesforce Help for more information.

For a complete list of the lenses, see Lenses in Financial Statements.

Financial Statements includes the following dashboards:

Financial Statements Dashboards

Dashboard

Description
Transaction List Enables you to run transaction list reports in Financial Statements and view the resulting data within the dashboard. This dashboard uses the Financial Transactions dataset.
Trial Balance Enables you to run trial balance reports in Financial Statements and view the resulting data within the dashboard. This dashboard uses the Financial Transactions dataset.
Aged Analysis by Date Enables you to review the age of outstanding transactions for either accounts payable or accounts receivable, by analyzing whether transaction line items that occur before or on a selected date have been matched. This dashboard uses the Financial Matching dataset.
Aged Analysis by Period Enables you to review the age of outstanding transactions for either accounts payable or accounts receivable, analyzing whether transaction line items that occur in or before a financial period and year have been matched in that period or before. This dashboard uses the Financial Matching dataset.