This article takes you through the process of running an analysis in MindBridge, from initial system setup to completing the analysis.
- Step 1: Set up a new account grouping
- Step 2: Create a library
- Step 3: Create an organization
- Step 4: Create an engagement
- Step 5: Import data
- Step 6: Run the analysis
The image below is a visual representation of the MindBridge workflow.
The image below is an example of the "folder" hierarchy that MindBridge uses to organize your engagements and analysis data.
Step 1: Set up a new account grouping
An account grouping is a hierarchical set of categories that MindBridge uses to define accounts in the dataset and the relationships between them.
Creating your own account groupings allows you to align your unique account hierarchy with the MindBridge Account Classification (MAC) code system.
Account groupings are required in order to map the structure of the chart of accounts into MindBridge. This ensures accounts are structured and presented in MindBridge within the context of the accounts present in your imported dataset.
Step 2: Create a library
A library is a standardized configuration used to manage and maintain you settings, including account grouping structures, financial ratios, populations, control points, and risk scores for each type of analysis. Setting up a unique library saves you from having to re-create a configuration for each engagement that you initiate.
There are several categories of libraries available to you by default:
- For-profit (you can set up libraries for particular industries)
- Not-for-profit with funds
- Review engagement
Overview of library settings
Each analysis type has library settings that can be configured. When you update the library settings of one analysis type, it will impact all analyses of that type that are using the library.
For example, if you create a new revenue filter in the Accounts payable tab, any of your accounts payable analyses that use this library will have access to the new filter.
The following settings can be configured within the library:
- *Analysis configuration — Toggle features off and on in analysis results
- *Ratios — Default and custom financial ratios available in analysis results dashboards. These are a group of metrics used to measure the efficiency and profitability based on the results of the analysis. You can build specific ratios that you would typically want to see for a particular industry.
- **Filters — Default and custom dashboard filters available in analysis results dashboards. Filters are used when reviewing the results of an analysis — you have the ability to visualize the data displayed in the MindBridge analytics dashboards. These enable you to glean insights from the data. For example, you can filter the analysis to look at a particular account, a transaction that occurred on a certain date, transactions of a particular amount range, etc.
- **Populations — Determines custom populations of data available in analysis results dashboards. Populations can be created using a range of conditions that reflect the complexity of your organization. For example, you can create conditions to include or exclude specific account groupings, control points, keywords and/or risk scores.
- *Segments — Determines custom segments of data available in analysis results dashboards. Risk segments allow you to see various fields of data laid out in a structure that is meaningful to you and aligns with your firm's methodology.
- **Risk scores — Default and custom risk scores (and associated control points) to be run against the data. Risk scores are constructed by different combinations of control points, which are the algorithms and analytics that power MindBridge's analytics capabilities. Each control point provides unique information about an entry/transaction in a ledger that could be of interest during an audit – think of it as a trigger raised by MindBridge about a particular transaction that may need to be reviewed. The Risk Score is an aggregate score that combines multiple control point results together to establish a numerical score between 0 and 100. The more control points that are triggered, the higher the risk score.
- *Risk ranges — Unique configurations of low, medium, and high risk percent ranges, and how each unique range should be applied to risk scoring in the analysis results. Each risk range can be applied to your custom risk scores to help you set clear guidelines around how to handle entries within each risk level.
- *Custom control points — Create new control points based on your firm's methodology and organizational goals.
* Accessible through the General ledger tab only.
** Accessible through the General ledger, Accounts payable, and Accounts receivable tabs.
Learn how to create a library.
Step 3: Create an organization
The organization represents each business/client/company/entity that you want to analyze. The organization houses all of your client's engagements (audit analysis), account mappings, and any other data you have imported into MindBridge for a particular client.
Learn how to create an organization.
Step 4: Create an engagement
Housed within an organization, the engagement represents a service or activity you performed for the client. For example, if you are doing an audit for FY20 for Company ABC, the engagement would be ABC-1 FY20.
Enter an engagement name and select the library you just created. This is critical — once you select the library you cannot change it.
If you wish, you can import settings from another engagement. This can be useful in a situation where you are creating a new engagement for a new year and you would like to use information from a prior year's engagement. The following information and settings will be copied over:
- Account mapping
- Custom control point settings
- Prior period data (if it was in the engagement)
- Fill in the details regarding your engagement from a planning perspective
Learn how to create an engagement.
Step 5: Import data
The analysis process involves the following key steps:
- Create an analysis.
- Import a chart of accounts (optional)
- Import the general ledger.
- Import the opening balance and closing balance.
- Import additional data — this could be additional data columns that can be used to filter analysis results
- Verify the accounts.
Import chart of accounts (optional)
Typically the chart of accounts is built from the Trial Balance. The following columns are required in a chart of accounts:
- Account number / Account ID: Contains codes used to identify your Client's accounts. This column helps MindBridge understand your client's account types
- Account description
- Account code: this Account Code should line up to the Account Code in your account grouping
Learn how to import a chart of accounts.
Import the general ledger for the current period
The following columns are required when importing a general ledger into MindBridge:
- Account ID
- Effective Date: Contains the date at which the transaction actually occurred. Note that this is different from the entered date which is the date that the entry was entered into the General Ledger
Learn how to import a general ledger.
Import the opening balance for the current period
The following columns are required:
- Account ID
- Balance/Amount: Contains the monetary amount for each account at the beginning of the designated fiscal period
Learn how to import an opening balance.
Import the closing balance for the current period
The following columns are required:
- Account ID: Contains codes used to identify your Client's accounts. This column helps MindBridge understand your client's account types
- Balance: Contains the monetary amount for each account at the beginning of the designated fiscal period
Learn how to to import a closing balance.
Import prior period(s) general ledger and opening balance files
Follow the steps to import a General Ledger and Opening Balance above for prior periods. To maximize the value of the Analysis in MindBridge, we recommend importing data from four prior periods.
Import additional data
Learn how to import additional data.
Verify the accounts
Select Verify accounts to see the account balances and hierarchy before running an analysis, and better assess the accuracy of the account grouping and account mappings
Learn how to verify the accounts in MindBridge.
Step 6: Run the analysis
Once all the data above has been imported, select Run Analysis to run the dataset through MindBridge's analytics.