データ マネージャーを使用する準備はどのようにすればよいですか?

This page contains tips and strategies to consider before you start using Data Manager.

You may need to coordinate with your SME (subject matter expert) to gather information and make some of the decisions required.

Data Manager allows you to bring in data from multiple data sources, ensure your data is complete, current, and correct, and then use this information to create a single source of truth.

Data Manager connects to tools which we refer to as data sources and these are frameworks that provide important details and specifications to make bringing in data easier.

There are two types of data sources:

• Product data sources – preconfigured data sources designed to work with a specific tool. They connect via API, SQL, ODBC, or PowerShell, passing authentication informatison and calls to the tool and receiving specific data in return.

• General data sources – general‑purpose data sources designed to work with any data that matches a specific format:

  • Data Source Aggregate – merge data from a group of other data sources.

  • Flat File data sources – bring in data from a flat file, such as a CSV or TXT.

  • Schema data sources – bring in data from an Assets schema.

  • SQL and ODBC data sources – bring in data from SQL or ODBC database sources.

Follow the workflow below to prepare to use Data Manager

1. Choose the tools

Select the tools you want to connect. When first learning how to use Data Manager, limit your initial selection to around five tools; you can add more later.

例

Jose works for a company that needs to manage ITAM (IT Asset Management) data. They want to use Data Manager to clean and reconcile information that is currently in four different places:

• Active Directory (product data source)

• SCCM (product data source)

• Lansweeper (product data source)

• A locally‑stored CSV catalogue (flat file)

Jose wants to integrate all these data sources in the best and easiest way possible.

2. Choose a data source

For each tool, decide how to connect it to Data Manager using a data source.

Some tools can only be used through a product data source with pre‑configured authentication and authorisation. Others can either be connected using a product data source or a general data source that pulls information from a CSV, TXT, or SQL source. Some data sources support different protocols (API, SQL, PowerShell).

You may need to coordinate with your SME to understand what information is available from each tool and the best way to connect it. Even where an API data source exists, the SME may choose not to enable the API in the environment.

ケーススタディ

Jose reviews the data source list and decides on the method for each tool:

• Active Directory – Active Directory (PowerShell) product data source

• SCCM – product data source with the out‑of‑the‑box SQL query

• Lansweeper – API data source

• CSV catalogue – Flat File data source exporting from Assets in Jira Service Management

Prepare a product data source

These steps apply to a product data source that connect to specific tools.

Review scope and filters

If you use an out‑of‑the‑box data source, you must define the data coverage of the tools before you bring the data into Data Manager.

次のことを考えてみてください。

• For Active Directory, how many AD domains exist, whether they align with your ITSM tool, and whether multiple AD data sources are needed for domains requiring different credentials.

• For ITAM solutions, what endpoints are relevant (laptops, servers, etc.) and whether they match across systems. If non‑computer assets (chairs, desks) are tracked, you may need cleansing rules in Data Manager to exclude these items.

• How each system defines “active” vs “stale” data. “Active” data sources are within the refresh gap; “stale” data sources are past their refresh gap. For example, AD may treat data older than 90 days as stale.

ケーススタディ

Jose’s scope and filters:

• AD – 6 domains exist; 4 contain data that must be reconciled. AD treats data older than 90 days as stale.

• Lansweeper – includes all assets, including desks and chairs, so Jose plans cleansing rules to remove these.

• Assets – records with Retired or Disposed status will also be filtered by cleansing rules, but will still be visible in Data Manager.

データと認証情報を確認する

Product data sources are pre‑configured to work with Data Manager. The data source documentation describes connection requirements and data attributes.

Coordinate with the SME to confirm:

• Authentication and authorisation are correctly configured.

• Network paths and firewalls allow access.

• Required databases grant read‑only access.

ケーススタディ

Jose reviews documentation for AD, SCCM, and Lansweeper data sources and confirms:

• Credentials and permissions are correct.

• Network paths and firewalls allow connectivity.

• Read‑only DB access is in place where needed.

Prepare general data sources

These steps apply to general data sources such as those that connect to TXT or CSV files.

ファイル形式とデータを確認する

Flat files may require extra formatting work. The most critical factor is consistency – if the format changes unexpectedly, fetches can fail.

What data should be in the flat file?

For compute/VM comparison across tools:

• One row per endpoint (computer, server, mobile device, etc.)

• Columns for attributes such as serial number, OS, RAM, disk space, and other core identifiers

Should filters be applied before sending data to Data Manager?

Prefer unfiltered extracts from the tool; then apply filters in Data Manager. For example, if a status is set incorrectly and would normally filter out a record, including it ensures Data Manager can show that the record exists with the wrong status.

Format requirements

• CSV or TXT (not Excel or Google Sheets)

• Encodings:

  • UTF‑8 (default)

  • UTF‑16

  • ISO‑8859‑1

  • ISO‑8859‑15

  • Windows‑1250

  • Windows‑1252

• A header row is recommended and can be used when creating attributes.

• Date fields must use a consistent format across the column; avoid mixing date‑only and date‑time.

• If values contain commas (for example, Microsoft Windows, XP, SP2), wrap them in a qualifier (typically ").

• No embedded line breaks within a single record.

• No blank rows before the end of the file.

ケーススタディ

Jose reviews his CSV export from Assets in Jira Service Management:

• Confirms a header row exists.

• Confirms the qualifier is consistent and correct.

• Ensures there are no blank rows or inconsistent date formats.

フィールドを確認

Because flat files can vary, merging them with other sources can produce duplicates or inconsistencies.

Key guidance:

• Include any field that you may want to compare, report on, or use to link data sources.

• Include potential primary keys for reconciliation across sources (for example, Instance_ID or Serial_No).

ケーススタディ

Jose reviews his flat file fields:

• He wants to compare devices and assets between the flat file and Lansweeper, so he ensures the file includes the primary key (Name).

• He ensures LastUpdatedDate is present to compare how fresh each asset record is across all tools.

Set up Data Manager

Once your tools and data sources are prepared, proceed to the setup documentation to configure Data Manager and run your first job.