Software License Usage Tracking

[Natalie Omahony]

Asan IT manager, you also want to reduce expense overhead from unwanted software renewals and eliminate unnecessary programs. All that is only possible when you capture and analyze software usage tracking data.

Software usage tracking is the process of gathering, monitoring, and analyzing what software applications employees access and how frequently. Organizations typically collect this data from user activity records, application logs, and SSO or OAuth platforms.

As companies keep adding new SaaS tools to their tech stack, it may not always be possible to stay on top of renewal deadlines. Missed renewals not only negatively impact employee productivity but also prevent you from accurately budgeting IT spending. Knowing upcoming renewals also helps you optimize allotted seat numbers and negotiate better contract terms with the software vendor.

When you make a note of metrics like how long someone has used a certain tool and when their last login was, you can better access if this application is necessary for your team, if you can find its features elsewhere, or if you can get rid of it altogether.

For example, you may need to buy additional licenses when more people use a tool than the number of licenses you paid for. Similarly, you may need to find alternative solutions when teams don't use a tool as much.

Next, take a look at your tech stack, also known as a software stack. Your tech stack is the combined software systems that are needed to complete your entire platform of tools so that no additional software is needed to support any of your applications.

G2 Track integrates with SSO and OAuth platforms to help you with software usage monitoring and see whether employees are using assigned applications. This real-time software usage tracking data goes a long way in helping you during license reclamation or contract rightsizing activities.

Mara Calvello is a Content Marketing Manager at G2. She received her Bachelor of Arts degree from Elmhurst College (now Elmhurst University). Mara works on our G2 Tea newsletter, while also writing content to support categories on artificial intelligence, natural language understanding (NLU), AI code generation, synthetic data, and more. In her spare time, she's out exploring with her rescue dog Zeke or enjoying a good book.

This chapter describes how to manage usage tracking for Oracle Business Intelligence. The Oracle BI Server supports the collection of usage tracking data. When usage tracking is enabled, the Oracle BI Server collects usage tracking data for each query and inserts it directly to a database table.

Oracle BI Summary Advisor is only available when you are running Oracle Business Intelligence on the Oracle Exalytics Machine. See "Using Oracle BI Summary Advisor to Identify Query Candidates for Aggregation" in Oracle Fusion Middleware Metadata Repository Builder's Guide for Oracle Business Intelligence Enterprise Edition for more information about the Summary Advisor feature.

The Oracle BI Server supports the accumulation of usage tracking statistics that can be used in a variety of ways such as database optimization, aggregation strategies, or billing users or departments based on the resources that they consume. The BI Server tracks usage at the detailed query level.

When you enable usage tracking, statistics for every query are inserted into a database table or are written to a usage tracking log file. If you use direct insertion, then the BI Server directly inserts the usage tracking data into a relational database table. It is recommended that you use direct insertion to write statistics to a database table.

Before you can use direct insertion usage tracking, you must set up a database to store the usage tracking statistics. You must run the Repository Creation Utility (RCU) on the target database to create the required statistics schemas.

Typically, you use the database you installed for use with Oracle Business Intelligence as the statistics database because this database already has the RCU-created schemas. The RCU-created table names for usage tracking are S_NQ_ACCT and S_NQ_DB_ACCT. See Section 9.3, "Description of the Usage Tracking Data" for more information about these tables.

Run the Repository Creation Utility on an external database of your choice. You can skip this step if you choose to use the database you installed for use with Oracle Business Intelligence for usage tracking statistics, because this database has the RCU-created tables already.

Open the Administration Tool and import the database into the Physical layer. See Oracle Fusion Middleware Metadata Repository Builder's Guide for Oracle Business Intelligence Enterprise Edition for more information.

Use Fusion Middleware Control to upload the repository and make it available for queries. See Section 10.2, "Using Fusion Middleware Control to Upload a Repository and Set the Oracle BI Presentation Catalog Location" for more information.

Ensure that the UsageTrackingCentrallyManaged attribute is set to true. When UsageTrackingCentrallyManaged is set to false, the following parameters are managed using the NQSConfig.INI file on each Oracle BI Server computer rather than the System MBean Browser:

Set the UsageTrackingPhysicalTableName attribute to the name of the fully-qualified database table for collecting query statistic information, as it appears in the Physical layer of the Oracle BI repository. For example:

Set the UsageTrackingConnectionPool attribute to the name of the fully-qualified connection pool for the query statistics database, as it appears in the Physical layer of the Oracle BI repository. For example:

For Usage Tracking insertions to succeed, the connection pool must be configured with a user ID that has write access to the back-end database. Also, it is recommended that the connectivity type supports international data.

For upgrading customers, the usage tracking parameters are not centrally managed by default. You can set UsageTrackingCentrallyManaged to true as described in the previous procedure, and use the System MBean Browser to update the parameters, or you can manage the usage tracking parameters using NQSConfig.INI.

BUFFER_SIZE. This parameter indicates how much memory the BI Server allocates for buffering the insert statements. Such a buffer lets the BI Server submit multiple insert statements as part of a single transaction, improving Usage Tracking insert throughput. It also means that ordinary analyses do not have to wait on Usage Tracking insertions, which improves average query response time. You might want to adjust this value based on available memory and memory utilization on the server computer.

BUFFER_TIME_LIMIT_SECONDS. This parameter indicates the maximum amount of time that an insert statement remains in the buffer before the Usage Tracking subsystem attempts to issue it. This time limit ensures that the BI Server issues the insert statements quickly, even during periods of extended quiescence.

NUM_INSERT_THREADS. This parameter indicates the number of threads that remove insert statements from the buffer and issue them to the Usage Tracking database. Assuming separate connection pools for readers and inserters, the number of insert threads typically equals the Maximum Connections setting in the connection pool.

MAX_INSERTS_PER_TRANSACTION. This parameter indicates the maximum number of insert statements that the Usage Tracking subsystem attempts to issue as part of a single transaction. The larger this number, the greater potential throughput for UsageMarathon Tracking inserts. However, a larger number also increases the likelihood of transactions failing due to deadlocks. A small value for BUFFER_TIME_LIMIT_SECONDS can limit the number of inserts per transaction.

As you review the descriptions in Table 9-1, you might assume that certain of the time-related columns can be added or subtracted to equal exact values. For example, you might assume that TOTAL_TIME_SEC is equal to END_TS minus START_TS. The following list explains why the columns do not provide such exact values:

Note: If the user submitting the query navigates away from the page before the query finishes, then the final fetch never happens, and a timeout value of 3600 is recorded. However, if the user navigates back to the page before the timeout, then the fetch completes at that time, and this is recorded as the end_ts time.

Error message from the back-end database. This column is only applicable if the SUCCESS_FLG (for more information, see entry later in this table) is set to a value other than 0 (zero). Multiple messages are concatenated and are not parsed by the BI Server.

You can change the length of this column (using the ALTER TABLE command), but note that the text that is written into this column is always truncated to the size that is defined in the physical layer. It is the responsibility of the repository administrator not to set the length of this column to a value greater than the maximum query length that is supported by the back-end physical database.

For example, Oracle Databases enable a maximum Varchar of 4000, but Oracle Databases truncate to 4000 bytes, not 4000 characters. Hence, if you use a multibyte character set, the actual maximum string size has a varying number of characters, depending on the character set and characters used.

Table 9-2 describes the S_NQ_DB_ACCT table, which supplements the usage tracking table by providing the physical SQL information for the logical queries stored in S_NQ_ACCT. S_NQ_DB_ACCT has a foreign key relationship back to S_NQ_ACCT.

Note that the usage tracking measure "Number of Queries" does not distinguish between reports run from a link within Alma (an analytics object) and reports created manually in the Oracle analytics interface. Therefore, you cannot create reports that display usage from just one of those sources.

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