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2FA, or Two-Factor Authentication, is a security mechanism that requires users to provide two separate forms of identification before granting access to an account or system. It adds an extra layer of protection beyond just a password, making unauthorized access more difficult for potential attackers.
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Financial and Profitability activity refers to the various transactions that involve the movement of money within a business over the course of a period of a time, such as operations, raising funds, investing, sales, and costs.
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Activity involves the movement of money and resources within a business to achieve financial goals, while ending balances are the final amounts in various accounts at the end of a specific period.
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Allows users creating new scenarios to prepopulate the new scenario with data from an existing scenario as a starting point through a specific period. Data from those periods are locked. Actuals Through is a setting that determines the range of actual data to be included in a scenario. It plays a role in defining the starting point for certain scenario types, such as forecast and budget scenarios, capturing actual data up to a specified period.
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A Catalyst planning tool that allows the user to decide, on a granular level, how changes are spread across periods.
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An attribute table, also known as a dimension table, is a data structure within a relational or multidimensional database that stores descriptive information related to specific attributes or characteristics of data. It is a fundamental component in data warehousing and is used to provide context and details about the data stored in fact tables.
Attribute tables work in tandem with fact tables to provide a comprehensive data model for business intelligence and analysis. The combination of fact and attribute tables allows analysts to perform complex queries, aggregations, and calculations while maintaining the context and descriptive information necessary for informed decision-making.
Attribute Table Traits:
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Descriptive Data: Offers context like product names, locations.
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Foreign Key: Links via primary key, enabling cross-dimension analysis.
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Hierarchies: Often hierarchically structured, e.g., year, quarter.
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Categorization: Groups data, enhancing analysis organization.
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Slowly Changing Dimensions: Manages evolving attribute values for historical accuracy.
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Metadata: Serves as metadata source, detailing attributes and definitions.
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Characteristic unique to specific transaction. A transaction attribute refers to a specific characteristic, detail, or piece of information associated with a financial or profitability transaction. In the context of business and finance, a transaction attribute provides additional context and information about a transaction, helping to categorize, analyze, and understand the nature of the transaction, such as ID, Date and Time, Payment Method, Description, Category, Location, Type, Ref or Invoice Number, Discounts or Promos, Shipping or Delivery Info, Status, etc.
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Automatic data extraction from the client ERP system to our EBM data lake. Data is pulled from the source and drawn into the cube and Catalyst website for easy use, reporting, and analysis. See Nightly Job.
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Microsoft Azure, commonly referred to as Azure, is a comprehensive cloud computing platform and infrastructure offered by Microsoft. It provides a wide range of services that enable organizations to build, deploy, and manage various applications and services through Microsoft's global network of data centers. Azure encompasses a vast array of tools, frameworks, and services that cater to computing, storage, databases, networking, analytics, artificial intelligence, and more.
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Azure AD, or Azure Active Directory, is Microsoft's cloud-based identity and access management service. It provides authentication and authorization services for users, devices, and applications across cloud and on-premises environments. Azure AD enables organizations to manage user identities, control access to resources, and enhance security through features like Multi-Factor Authentication (MFA) and Single Sign-On (SSO). It's a fundamental component in modern IT systems, facilitating secure access to various Microsoft and third-party services.
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Azure Analysis Services is a cloud-based analytical data engine provided by Microsoft Azure. It enables users to build and deploy interactive, tabular data models that facilitate fast querying and analysis of large datasets. Azure Analysis Services supports data modeling, data transformation, and the creation of calculations using the Data Analysis Expressions (DAX) language. It's commonly used for business intelligence and data analytics, allowing organizations to gain insights from their data through interactive reports, dashboards, and visualizations.
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A type of scenario or plan for a fiscal year.
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Allows the Administrative User to assign management of a selected hierarchy to a specific user. Business Owners can automatically receive email notifications when unassigned accounts are created so that they can be mapped to a given hierarchy.
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Allows for the definition of Standard Calculated Accounts, as well as the creation of User Defined Calculated Accounts. (i.e. Gross Profit, EBITDA, etc.). Allows addition or subtraction of accounts.
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Allows users to create percent or numeric measures to compare a selected group/element across hierarchy levels (i.e. % of Gross Sales). Allows the division of an account. (e.g. per unit per gross sales, per something etc.).
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A standard report presenting data by period.
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The menu tabs along the left hand sidebar. Applications within Catalyst, such as Planning, Profitability, etc. May also refer to Cube types, such as AR Cube, Inventory Cube, Smartload Cube, Payroll Cube, POS Cube, AP Cube, etc.
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A SQL job queue or table that’s running on the database server. It holds tasks in queue that need to be executed. It is a way for the site to perform actions without locking up the user interface while it waits for these tasks to complete. Tasks include things like hierarchy rebuilds, permissions changes, etc. The Core Server Process job runs every three minutes in the background, and it processes the contents of the tasks in queue. The CSPQ job can also be manually run by clicking “run job” on the System Status Page.
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A tool used in Catalyst to quickly pull and organize data. Cubes leverage Pivot Table functionality within Excel and utilize macros to bring data into Excel from the database.
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Tool allowing the EBM connection to be reinstated into an Excel file. Commonly fixes Excel issues.
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The user can define the appropriate currency and exchange rate for their organization by
period and by company.
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The defined rates applied to multi-currencies.
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Also known as Harness. Repository for custom documents (Excel, Word, PDF, etc.) and commonly used files. Similar to a Cloud-based file storage system, like Dropbox, OneDrive, or SharePoint.
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Dimension within profitability data. Often its own primary hierarchy.
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A SQL job that performs the same actions as the Hourly Job except for one distinction -- when it's manually run it will not refresh the scenarios assigned to the hourly list, only those assigned to the daily list found in System Status. Can be manually run by a user by clicking the "Refresh Financial" or "Refresh Profitability" buttons in the System Status page above the Scheduled Daily section of scenarios. If the Daily Job is set to run on a schedule it will do everything the same but will also process the hourly scheduled scenarios as well. Note: Daily Job is not set to a schedule when automation and the Nightly Job are enabled. This avoids job overlap.
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Visualizations module and Power BI reporting synonym.
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Allows users to create standard data views that can be utilized via the Office Bridge tool.
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A dependent scenario type in Catalyst refers to a scenario that continuously synchronizes with a source scenario during its creation and beyond, allowing selective control over specific data types while excluding ongoing updates. This type is valuable when users want to model financial situations based on existing data without being affected by subsequent changes in the source scenario.
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Terminology used in database management. Refers to a state where data is now out of date. When data, an object, file, or scenario is dirty or stale, it means that changes have been made to that dataset that have yet to be processed.
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DIY, DWY, DFY
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Denotes service models and their relevant product offerings provided by EBM and Blue Ops:
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DIY: Do It Yourself; Catalyst; Diligent 3.0; Smartload 3.0; Ontario
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DWY: Do it With You; Diligent 3.0; Smartload 3.0; Ontario
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DFY: Do it For You; Compass; Diligent
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Ending Balances refer to the financial balances of various accounts at the end of a specific period, typically at the end of a month, quarter, or fiscal year, providing a snapshot of a company's financial position at a particular moment in time, such as cash, accounts receivable, accounts payable, inventory, and equity balances.
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A fact table is a central component in a relational or multidimensional database that stores quantitative data or facts related to a business process or event. It is a fundamental concept in data warehousing and business intelligence.
Fact tables play a crucial role in decision-making processes by providing a repository of quantifiable data that can be analyzed and interpreted using various business intelligence tools and techniques. They are typically used in conjunction with dimension tables, which provide context and additional information about the data stored in the fact table. Together, fact and dimension tables form the basis for designing effective data models in data warehousing environments.
Fact Table Highlights:
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Numeric Data: Stores quantifiable measurements like sales revenue, quantities.
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Foreign Keys: Links with dimension tables using foreign keys for context.
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Granularity: Holds data at specific levels, e.g., daily, monthly.
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Aggregated Data: Can include summarized results for faster complex analysis.
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Measures: Numeric values subject to calculations, like sum, average.
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Time Dimension: Often connected to time dimension for varied period analysis.
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Links to the most used Planning and Report pages that will then appear on the user’s homepage. Favorites are unique to each user.
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Also known as Smartload. Tool allowing large files to be loaded into the backend. Allows user to navigate UI while files are being loaded. Displays history.
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Database for General Ledger based data (anything that comes from the P&L and BS).
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Characteristic unique to a GL transaction.
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A Pivot Table containing the financial database.
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Tool used to override an automated financial record. Amount entered replaces the original amount. Nightly ERP automation does not overwrite overrides. Overrides will always win.
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A 12-month period for each predetermined fiscal year.
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A type of scenario combining Actuals with Budget data.
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When uploading data to Catalyst, a full reload allows you to replace all existing data with what you're uploading. Usually not recommended.
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Allows the individuals in an organization to forecast sales based on volume and rate assumptions.
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Defines navigation and hierarchy permissions within a specified group. Allows individual users to be added to groups. (Preferred and more efficient than inidivudal user permissions).
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In the context of MDX cubes, a hierarchy refers to a structured arrangement of related attributes within a dimension. It provides a way to organize and navigate data across different levels of granularity. Hierarchies are a fundamental component of multidimensional data modeling and are used to facilitate various types of analyses and querying within MDX cubes.
Data aggregation in Catalyst involves hierarchies built upon foundational data. These hierarchies encompass categories like account, company, item, and customer. Catalyst allows the creation of unlimited hierarchies to organize data effectively.
Hierarchy Characteristics in MDX Cubes:
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Levels: Granularity varies; e.g., time hierarchy—year, quarter, month, day.
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Attributes: Each level links with specific elements, e.g., "Month" level has attributes like "January."
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Parent-Child Links: Often attribute relationships, e.g., "Year" as parent to "Quarter," "Quarter" to "Month."
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Drill-Down and Roll-Up: Users dive deep or rise high in data, adjusting perspective.
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Navigation: Systematic data exploration across dimensions.
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Aggregations: Efficient pre-aggregation, enhancing query speed.
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A SQL job that runs automatically in the background of each Catalyst instance which performs various actions each hour of the day. The Hourly Job or Process_Schedule job is a processing job that will run the CSPQ and any task that a user has queued up in the user interface, then it will process any dirty or stale Scenarios in the Hourly Scheduled list of Scenarios, then it will process any and all cubes that need to be rebuilt.
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Process of creating and onboarding a new client onto the Catalyst platform.
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Process of automating client data from one or many ERP systems so that it feeds into Catalyst directly and automatically each night.
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Profitability attribute.
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Dimension within profitability data.
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Financial attribute.
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A Pivot Table containing the Journal Entry database.
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The lowest level attribute within a record and hierarchy.
In the context of data structures, particularly trees and hierarchical databases, a "leaf" refers to a node that does not have any child nodes. It's the endpoint or bottommost element in the structure.
Here's a breakdown of the concept:
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Tree Structure: Imagine a hierarchical structure resembling a tree, where each node can have child nodes. The top node is called the "root," and the nodes that stem from it are branches. Nodes that have child nodes are called "internal nodes," and nodes without children are referred to as "leaves."
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Leaf Node: A leaf node (i.e. leaf) is a node in this structure that does not have any children. It's the final point in a specific branch of the tree, indicating the end of that particular path.
In the context of data and databases, the concept of "leaf" is often used in applications such as:
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File Systems: In a file directory structure, a leaf node represents an individual file rather than a directory.
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Data Modeling: In hierarchically organized data, leaf nodes store the actual data values, while parent nodes represent categories or groups.
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Decision Trees: In machine learning, decision trees use nodes to make decisions. Leaf nodes in a decision tree correspond to final outcomes or classifications.
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In the realm of data organization, particularly in accounting and data management, a hierarchy is a systematic arrangement of categories or elements that are structured in a cascading manner, forming a multi-level structure. Each level of the hierarchy represents a distinct layer of detail, from more general categories at the top to more specific subcategories at lower levels. This hierarchical structure helps organize and categorize data in a way that reflects the relationships between different elements.
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Hierarchy Overview:
A hierarchy consists of several levels, with each subsequent level delving deeper into the specifics of the categories. Think of it as a layered structure, resembling a tree with a root node at the top and branches extending downward. The root node represents the highest-level category, and as you move down the branches, you reach increasingly specific subcategories.
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Increasing Granularity:
Granularity refers to the level of detail or specificity of data. In a hierarchy, as you move down the levels, you're increasing the granularity, getting closer to the specific details. This is crucial in accounting and data analysis as it allows for a more nuanced understanding of the data.
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Accounting Example:
In accounting, hierarchies are often used to organize financial data, such as accounts on the balance sheet and income statement. The example you provided illustrates this:
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Level 1: This is the top-level classification in the hierarchy. It often signifies major categories, such as "Balance Sheet" and "Income Statement." These are fundamental divisions of financial information.
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Level 2: This represents a more detailed classification under level 1. For instance, under "Balance Sheet," you might find categories like "Assets," "Liabilities," and "Equity." Under "Income Statement," you might find categories like "Net Sales" and "Operating Expenses." These categories provide more specificity within the major divisions.
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Allows for the closing (locking) and opening of period(s) within a given scenario. Found in the Scenario Management screen.
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The ability to structure the User Interface of Catalyst.
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This is the process of categorizing and assigning hierarchy levels to financial accounts that are unassigned or lack proper classification. It ensures that uploaded financial data aligns with predefined account categories, making reporting and analysis accurate and structured. This mapping process is crucial for organized financial management and informed decision-making.
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An MDX cube, also known as a Multidimensional Expressions cube, is a data structure used in multidimensional databases for efficient and complex querying and analysis of data. MDX is a query language specifically designed for interacting with multidimensional databases, and it's commonly associated with Microsoft SQL Server Analysis Services (SSAS) and other OLAP (Online Analytical Processing) systems.
MDX Cube Highlights:
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Multidimensional Data Model: Allows versatile analysis across dimensions like time, geography.
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Hierarchies: Shows granularity levels, e.g., year, quarter.
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Measures: Numeric data for analysis, such as sales.
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Aggregation: Pre-calculated data boosts query speed.
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Query Language: MDX for complex queries, custom sets.
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OLAP Capabilities: Supports tasks like drilling, rolling up.
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Business Intelligence: Used in BI for interactive analysis.
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MFA, or Multi-Factor Authentication, is a security mechanism that enhances account protection by requiring users to provide multiple forms of verification before gaining access to a system, application, or account. It adds an extra layer of security beyond traditional single-factor authentication, such as a password.
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Standard report preinstalled in Catalyst presenting data based on defined periods.
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A SQL job specific to automation or automated data pulls or data syncs from client-side ERP systems. The Nightly Job is a custom job that's set up for each automated Catalyst website. It runs automatically each night at a time designated by the client during implementation. The main function of the Nightly Job is to extract data from the previous day's activity in the client-side ERP system and draw it into Catalyst so it can be used in the cubes. The amount of data that's pulled is defined during implementation, but will include all history from the past year and sometimes the prior year. Once the Nightly Job completes it will then call the Daily Job in order to process any changes through to Catalyst.
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In the context of financial data analysis and reporting, the terms "node" and "leaves" are often used to refer to elements within a hierarchical structure used for organizing and categorizing financial data. This structure is commonly known as a chart of accounts.
Node in Financial Data: In financial data management, a "node" refers to a specific account or category within a chart of accounts. A chart of accounts is a structured list of financial accounts used to classify and categorize financial transactions. Each account represents a unique element, such as an asset, liability, revenue, or expense. Nodes in this context can represent any level of categorization within the chart of accounts hierarchy.
Leaves in Financial Data: The "leaves" of a financial data hierarchy, often referred to as "leaf nodes," represent the individual accounts or categories that do not have any further subcategories within the chart of accounts. In other words, they are the lowest level of accounts in the hierarchy and do not have any child accounts. These leaf nodes are where the actual financial data is recorded and stored. They include specific accounts like "Cash," "Accounts Payable," "Rent Expense," and so on.
Hierarchical Structure: The chart of accounts is typically organized hierarchically, with broader categories at higher levels and more specific subcategories at lower levels. The hierarchical structure allows for better organization, analysis, and reporting of financial data. Nodes at higher levels represent summary categories, while nodes at lower levels (leaf nodes) contain the detailed data.
For example, consider a simplified chart of accounts for a company:
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Assets
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Current Assets
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Fixed Assets
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Liabilities
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Current Liabilities
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Accounts Payable
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Short-Term Loans
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Expenses
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Operating Expenses
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Rent Expense
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Salaries Expense
In this chart of accounts, each level represents a node, and the leaf nodes are the specific accounts where financial data is recorded. For instance, "Rent Expense" is a leaf node under the "Expenses" category.
In summary, in the context of financial data, a "node" refers to an account or category within a chart of accounts, and "leaves" refer to the individual accounts at the lowest level of the hierarchy, where actual financial data is stored.
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Allows users to access and format Datasets within PowerPoint, Word, or Excel.
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Accounts utilized in the Profitability side of Catalyst. Operational Accounts generally include, but are not limited to, Gross Sales, Material Costs, Labor, and Overhead.
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Allows users to make overriding adjustments to actual data.
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Preferred. When uploading data to Catalyst, it replaces existing data with only records in upload sheet, rather than full replacement.
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A standard report preinstalled in Catalyst presenting data with a flexible structure.
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Allows users to populate planning data via a defined calculation based on related accounts or other fixed values.
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A Primary hierarchy is included in all Business Intelligence and Planning tools, used for Planning, and cannot be deleted from the system. A Secondary hierarchy includes options for displaying in the cube, as well as reporting options to add to existing reports or to create a new set of reports based on this hierarchy.
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A scenario type allowing data to be loaded in parallel with actuals data, but remains separate. "Pro forma" refers to financial statements or projections created based on assumptions or hypothetical scenarios. These statements show how financials might appear under specific conditions, like mergers or changes. They help evaluate potential outcomes but should be interpreted cautiously as they're not based on actual historical data.
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A database for invoice level data. Also known as Operational data.
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In MDX Cubes: Profitability accounts in MDX cubes refer to financial metrics and measures associated with evaluating the financial performance and profitability of a business. These measures could include metrics like sales revenue, costs, expenses, and profits. MDX cubes store these measures within a multidimensional data structure, allowing for intricate analysis based on various dimensions, such as time, geography, products, and customers.
In Tabular Cubes: In tabular cubes, profitability accounts similarly represent financial indicators, but they are stored within a tabular data model. This format resembles traditional relational databases, making it easier for users familiar with spreadsheets and databases. Tabular cubes use the Data Analysis Expressions (DAX) language to define and compute profitability measures. This model provides fast query performance, making it suitable for efficient analysis of profitability data.
In summary, both MDX cubes and tabular cubes can store and analyze profitability accounts, but they differ in their data structures (multidimensional vs. tabular) and query languages (MDX vs. DAX).
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Ability to allocate amounts across multiple customers/items. (e.g. when unsure where to apply the revenue amounts, spread across all)
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Characteristic unique to an Invoice transaction.
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A Pivot Table containing the profitability database.
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Tool used to override an automated profitability record.
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Calculation used to create profitability record based on existing data (e.g. % of Gross Sales, Amount Per Selling Unit, Fixed, etc.).
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A tool allowing queries to be generated over Journal Entry or Profitability databases.
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Defined amount per quantity (e.g. Selling Price Per Unit, Materials Per Unit, etc.).
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A sales bridge (or price volume mix analysis) is a report which shows the gap between budgeted and actual sales, and the explanation for that variation. Price effect: deviation due to apply higher or lower selling prices. Volume effect: variation in the turnover due to the total units sold.
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The process of rolling the year end balances into the beginning of the following year. Example: If you have $10 in your account at the end of 2021 Actuals (on 12.31.21), you should also have an opening balance of $10 at the start of your 2022 Actuals (on 1.1.22). The balance of an account at the end of one year should always appear as the opening balance for the following year (e.g. Net Income from 2021 to Retained Earnings in 2022).
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12-month block of data. (i.e. 2017 Actuals, 2018 Budget, etc.).
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Scenario source periods are specific time intervals selected during the setup of a new scenario, determining which data from the source scenario will be captured during the scenario creation process.
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Allows the user to invert the sign an account is displayed in from a positive value to a negative value and vice versa. (e.g. Revenue appears in the data as a negative (credit) amount the user wants it to appear in the reports as a positive amount (debit).) Sign Flip only applies to Actuals Scenarios, it will have no effect on Forecast or Budget Scenarios.
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SmartLoad is a component of Diligent 2.0 and a user interface that was rolled out for use by Blue Ops in October of 2022 that allows the finance consulting team to quickly create standard and custom measures with a smaller data footprint required. This will allow us to process datasets significantly quicker.
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See Tabular Cube.
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Different exchange rates used to translate the balance sheet and income statement activity. Income statement accounts should be defined to use monthly average exchange rates (i.e. the Average Rate) and Balance sheet account should be defined to use month end exchange rates (i.e. the Spot Rate).
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SQL is a domain-specific language used in programming and designed for managing data held in a relational database management system, or for stream processing in a relational data stream management system.
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Preinstalled or canned reports that come standard with each Catalyst instance installation.
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Star Schema is a type of data modeling technique used in data warehousing and business intelligence. It involves structuring data with a central fact table surrounded by dimension (attribute) tables. The fact table contains quantitative data, while dimension (attribute) tables hold descriptive attributes. This schema simplifies querying and analysis by creating clear relationships between data elements, aiding in efficient and comprehensive reporting.
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The ability to receive standard reports via email at a specified frequency (daily, weekly, monthly, etc.).
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Catalyst will automatically populate exchange rates each month and these rates will be used as the System Rate if User Defined Rates are not loaded. Within the Currency Exchange Rate tab, a user can load User Defined Rates to ensure the exchange rates being applied exactly match their organization’s specific rates.
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Administrative page displaying the current status of financial and profitability data.
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A tabular cube is a type of data model used in business intelligence and analytics, designed to provide efficient and flexible querying and analysis of data. Tabular cubes are commonly associated with Microsoft Power BI and Microsoft SQL Server Analysis Services (SSAS) Tabular mode. Tabular cubes are favored for scenarios where data needs to be rapidly queried, analyzed, and visualized, making them a popular choice for organizations seeking to empower business users with data-driven insights. The familiarity of the tabular structure and the power of DAX calculations make tabular cubes a valuable tool in modern business intelligence environments.
Tabular Cube Key Points:
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Tabular Data Format: Resembles traditional tables, like spreadsheets or databases.
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Columns and Rows: Familiar structure for easy comprehension.
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DAX Language: Employs DAX for calculations and transformations.
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Measures and Calculated Columns: DAX-defined metrics and values.
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In-Memory Storage: Quick analysis due to in-memory data storage.
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Columnar Storage: Enhances compression and query speed.
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Self-Service Analytics: User-friendly interface for non-technical users.
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Business Intelligence Tools: Used with Power BI, Excel, and more.
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Relationships: Allows complex analysis with table connections.
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A standard report that comes preinstalled which compares data across three separate scenarios.
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Allows for an easy-to-read view of the current hierarchy structure. Can be utilized to adjust the order of the hierarchy. Tree View can be found with Manage Hierarchy or Hierarchy Configuration.
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A standard report that comes preinstalled which compares data across actual scenarios.
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A scenario type allowing data to be grouped based on the previous 12 months from a given point in time.
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Defines navigation and hierarchy permissions for a specific user. (Less preferred and less efficient than group user permissions).
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Catalyst provides user and group security settings for access management. User accounts are designed for individual access. Groups allow security settings to be applied to multiple users at the same time.
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Visualizations module and Power BI reporting and dashboards synonym.
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Planning based on number of units. Focuses on estimating and forecasting the expected sales or production volumes of products or services over a specified period.
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The whole collection of standard reports that come preinstalled into Catalyst.
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