CbC Reporting Made Easy


A recent mandate from the OECD calls on large, multi-national companies to report financials annually for each country in which they do business to their local tax authority. The OECD requires that this detailed Country by Country (CbC) Report be filed in XML document according to their reporting schema. But for tax departments that work largely in Excel, this provides a significant stumbling block – and companies are scrambling to meet the requirements by the end of 2017.

The new CbC Reporting Solution from Altova takes the pain out of meeting the mandate by automatically generating valid, properly formatted CbC XML reports based on data either entered manually – or imported directly from Excel. Let’s take a look at how it works.

 

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MapForce Server Accelerator Edition Achieves a New Level of Data Transformation Performance


MapForce Server automates recurring execution of data mappings and transformations designed and tested using Altova MapForce. Every day, MapForce Server is employed in business communication, financial reporting, database ETL, and many other applications to transform critical data between any of XML, JSON, database, EDI, XBRL, flat file, CSV, Excel, and/or Web service formats.

Now, MapForce Server Accelerator Edition offers even faster throughput for high-performance server platforms.

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Use Join to Integrate Data in Any Format


Join is a powerful SQL operation implemented across most database types and familiar to database users. Join is typically used to select and combine information from multiple database tables.

Altova MapForce includes a join component for data mapping that works like a SQL join for database tables and extends data integration functionality by empowering users to join data trees of any data format. Anyone familiar with join operations for database tables will find the MapForce join component especially intuitive. A join operation in MapForce can even combine two different data formats and produce output in a new format altogether.

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A Typical MapForce Server Use Case


Envision a manufacturing company that controls costs by exploiting a just-in-time assembly process with a very low supply of parts inventory on hand. New customer orders are logged in a sales database and at the end of every day the components needed to assemble that day’s sales are tabulated.

The IT department runs a SQL query to identify the required parts and transforms the list into a purchase order in JSON format to be transmitted to the supply chain.

Sound familiar? Our recent blog series on JSON tools and JSON data mapping were based on this real-life scenario. In this post we describe a MapForce Server use case that automates the repetitive task of generating each day’s purchase order.

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JSON Data Mapping and Transformation with MapForce


JSON is a popular format for transferring data between systems thanks to its simple markup, small footprint, and heritage based on the JavaScript programming language. MapForce supports JSON as both an input and output format for JSON data mapping and transformation. For instance, MapForce can extract information from any popular database and produce a JSON file ready for transfer.
The Requirement: Here is an example of a typical need for JSON data mapping: A manufacturing company controls costs by exploiting a just-in-time assembly process with very little parts inventory on hand. New customer orders are logged in a sales database, and at the end of every day the components needed to assemble that day’s sales are tabulated via a query into the database. The required parts will be ordered from suppliers via a purchase order transferred in JSON format.

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EDI Data Mapping with MapForce


Any computer industry standard that promotes reliability and clear communication between independent enterprises will have a long life. EDI (Electronic Data Interchange) originated in the 1960s to enable automated transactions between corporate computer systems. EDI remains in widespread use today and continues to evolve to meet modern requirements, under the administration of the UN/EDIFACT and ANSI standards bodies.

Altova MapForce supports EDI data mapping between EDI messages and XML, JSON, relational databases, flat files, Excel, or other data formats to bridge between commonly used information interchange and in-house technologies.

MapForce includes support for the latest EDIFACT versions 2015B and 2016A including the new VERMAS message. Mapping and translating EDIFACT messages to other usable data types for transfer, storage, and management is a common business requirement solved by MapForce.

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MapForce Supports SQL Merge When It’s the Right Tool for the Job


Large database tables can easily contain a million, even hundreds of millions of rows of data. Database administrators and others charged with maintaining such large datasets are always concerned about execution time for ETL (Extract, Transform, and Load) operations, updates, and other SQL queries. To make these operations more efficient, some — but not all — database vendors implemented a SQL merge statement to insert or update rows of an existing table as a single bulk-insert statement rather than requiring individual statements for each row.

Altova MapForce automatically supports SQL merge when it is available for the target database. Let’s look at an example.

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