Connecting Systems

Objective

After completing this lesson, you will be able to discuss the standards and methods of transferring data to and from SAP Fieldglass.

Data Connectors

When manually sharing data between SAP Fieldglass and other ERP systems, you’ll use what’s called an integration "Connector"— essentially, a file which is used to format and process data between the systems. The connector file contains data that aligns with the Access, Master, and/or Transactional data that resides in a client’s instance of SAP Fieldglass.

Play the video to learn how data connector files are used to share data between SAP Fieldglass and other ERP systems.

Anatomy of a CSV File

A completed user upload connector displayed in Microsoft Excel, showing the header row, data rows, and file header.

The CSV file illustrated here represents a completed user upload connector.

File Header

The File Header of a completed user upload connector in a spreadsheet application.
Rows 1-8The File Header provides instructions on how Fieldglass should handle the data. These seven rows represent typical file header information. Individual connector files may have additional rows to indicate specific requirements for that connector.
Row 1Type indicates the type of integration connector; in this case, a User Upload. The names of the SAP Fieldglass default connectors would be reflected here. If a custom connector is used, that connector must appear here as named in the application.
Row 2

Transaction defines how the data will be handled by SAP Fieldglass.

  • True = If any record results in an error during upload, the entire upload fails.
  • False = Only records that are in error fail. Remaining records are committed successfully.
Row 3

Send Notification? determines whether notification of this upload should be sent to other users.

  • True = If the value set to True, email will be sent.
  • False = If the value is set to False, email will not be sent.
Row 4

Language indicates the language to be used in the file. This should reflect what is appropriate for the company.

For example, if the company is based in the U.S., the language should reflect English (United States). If the company is based in Germany, the language should be German (Germany).

Row 5

Number Format determines the number format to be used in the file. The valid number format values are =

"#,##9.99 (Example = 1,234,567.99)"

"#.##9,99 (Example = 1.234.567,99)"

"#'##9.99 (Example = 1'234'567.99)"

"# ##9,99 (Example = 1 234 567,99)"

Row 6

Date Format determines the date format to be used in the file. This should reflect what is appropriate for the company.

For example, if the company is based in the U.S., the date format should reflect month/day/year.

Conversely, in the company is based in Germany, the date should reflect Day/Month/Year.

Valid formats are =

MM/DD/YYYY

DD/MM/YYYY

YYYY-MM-DD

Row 7Update Profile Worker BU when Owner is Closed determines if profile worker's business unit should be changed to new owner's business unit when previous owner is closed.
Row 8Comments is an optional field where you can add notes about the upload file.

Header & Data Rows

The Header Row and Data Rows of a completed user upload connector in a spreadsheet application. The columns headers include Modification Type, Username, Prefix, First Name, Last Name, Display Name, Email, Phone Number, Fax Number, and many more that appear off the page.
Row 10The Header Row identifies the type of information that is contained in the respective columns. The system that the data is being uploaded into then reads and processes the information in the data rows accordingly.

Data Rows

Row 11 and downData Rows, which are basically every populated row below the header row, contain the actual data that is being uploaded. This is the information that is processed according to the type of data identified in the header row.

Connection Methods

There is a little more to integrations than just downloading and uploading connector files. There are, in fact, different methods of transferring data to and from SAP Fieldglass depending on what activity is taking place.

Two commons methods available for transferring data are Batch and Single Transaction.

Batch

A Batch integration allows multiple transactions or records to be moved at a time, at specified intervals.

For example, if Mavis and other hiring managers at WorkingNet complete twenty-five invoices in a given day, the data associated with all twenty-five invoices might be downloaded from SAP Fieldglass and uploaded into WorkingNet’s accounts payable system at the end of each work day.

And there are actually two ways that data can be Batch loaded into or out of SAP Fieldglass: through what’s called a Delta batch or a Full batch.

Delta Batch

With a Delta batch, only new data is transferred.

The CSV file that is downloaded from SAP Fieldglass will contain records of items that are new or have changed since the last time the data was downloaded.

So for Nelson, the CSV contains the data of the six new temporary employees that were procured since the last time data was uploaded into WorkingNet’s human resource system.

Full Batch

On the other hand, the data can also be transferred as a Full batch.

This means that SAP Fieldglass would download data for all of WorkingNet’s temporary workers, regardless of when they were added to SAP Fieldglass.

WorkingNet’s HR Information System, then, is responsible for parsing out all of the data that is new since the last upload.

Dependencies

One thing to consider about Batch processing is that the processing behaviors are different for uploads and downloads.

Delta Batch Download

A Delta batch download is considered dependent.

That is, SAP Fieldglass compares the download against the previous download to determine what’s changed.

Thus, the download is dependent upon the last download for a comparison basis.

Full Batch Download

A Full batch download, on the other hand, is independent.

Since SAP Fieldglass downloads all records, it doesn’t process any changes.

The client system—like Patricia’s ID system—compares and processes the data.

Delta Batch Upload

Conversely, with uploads, the opposite is true. The Delta batch upload is independent, and SAP Fieldglass assumes that the data from the client system is all new and processes it accordingly.

Full Batch Upload

A Full batch upload is dependent, so SAP Fieldglass compares the file against the previous upload to determine what has changed.

Single Transaction

A Single Transaction integration is triggered by an event or action and transfers the data immediately from SAP Fieldglass to the other system.

It’s a "just-in-time" integration and it still uses a Connector file to move the information, but the download is triggered by a particular action and contains only the information for that change.

So if Mavis approves a time sheet submitted by Joe resulting in a new invoice, the information contained in that invoice can be downloaded from SAP Fieldglass as soon as Mavis approves the time sheet (with auto-invoice enabled), which can then be uploaded into WorkingNet’s AP system.

Summary

Connector files format and process data between systems and users can edit and upload them to create corresponding records. The data format depends on the requirements, such as invoices, worker data, or temporary worker characteristics.

Key Points:

  • Connector Files: Used to format and process data between SAP Fieldglass and other ERP systems. Contains Access, Master, and/or Transactional data.
  • Data Transfer Process: Involves downloading a connector file from SAP Fieldglass, editing it, and uploading it to another system. Common file type is CSV.
  • Connector Logic: Depends on the required data. Connectors for different systems (e.g., accounts payable, ID system) contain relevant data types.
  • CSV File Structure: Includes a Header Row identifying data types and Data Rows containing actual data. Optionally includes a File Header for instructions.
  • Batch and Single Transaction Integrations: Batch moves multiple records at specified intervals. Delta Batch transfers only new or changed data. Full Batch transfers all data. Single Transactions are triggered by an event and transfers data immediately.