Aura – aura-analytics

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Mon, 01 Jan 0001 00:00:00 +0000

Aura Analytics 2.0.0

Description of Aura Analytics 2.0.0, the monitoring tool designed and managed by Aura Global Team that allows active listening in Aura

What is Aura Analytics 2.0.0?

Active listening is defined as a key process that involves a continuous monitoring of Aura performance based on real logs from the users to analyze them and gather insights on the efficiency and effectiveness of Aura as a system and also to track the interaction of our users with Aura.

In this framework, Aura Analytics 2.0.0 is a tool used by Aura Global Team that uses active listening with the ultimate goal of improving Aura quality, as it generates accurate information to carry out both corrective and predictive actions and to decide how Aura should evolve in the future.

How does Aura Analytics 2.0.0 work?

The process is built upon Aura users logs generated in production environment
From these logs, Aura Analytics 2.0.0 create PPDs (Privacy-Preserving Datasets)
Datasets are processed, enabling the visualization through dashboards and the extraction of statistical insights
The Aura Global Team consumes this data to support decision-making processes

Target users

The Aura Global Team is the target user of the Aura Analytics 2.0.0 tool, responsible for its design and management as well as for the interpretation of results for decision-making.
OBs should allow the generation of datasets from their Aura users logs in their local environment just by installing and executing a single process, as shown in the document Guidelines for OBs.

Index of documents

Aura Analytics 2.0.0 includes the following documents:

Version history
Architecture overview and main processes and components
Internal operation
Guidelines for OBs willing to consume data provided by Aura Analytics 2.0.0
Aura Analytics dashboard
Annex: Dataset fields

Aura Analytics versions

Release 1.0.0

The first release 1.0.0. sets up the basic paths, deploys the PPD infrastructure and produce:

Version 1.0.0. of the OB Analytics system, which includes the OB Dashboard.
The first version of pre-processed datasets (clean PPDs) for training and analytics at Aura Global.

As mentioned, this version enables going further by:

Enhancing the analytics dashboard with new visualizations.
Processing the PPD files as desired (they are standard CSV files, which can be ingested in alternative platforms if desired). Restrictions on them are softer than on the original logs due to the anonymization process they have been subjected to, although they are still subjected to management precautions (a code of conduct is being prepared for that).

Release 1.1.0

Version 1.1.0. introduces the following changes:

The table of data has been enlarged with these new fields: AURA_ID, STATUS_CD, sesId, sesSize, sesDuration.
An expanded list of test users is used, so that the userType column contains more identifications.
The code for data ingestion into a local Kibana, which previously consisted on a single Python script, has been turned into a full Python package to be installed, due to its increasing complexity.

Release 2.0.0

Version 2.0.0 introduces the following changes:

In 2.0.0 version, Aura Analytics has undergone a refactor to improve its structure and make it easier to understand, maintain and extend in the future.
Aura Analytics 2.0.0 simplifies the deployment and execution process.
But one of the most significant enhancements in Aura Analytics 2.0.0 is its capability to manage both processed and to-process files centrally in one place (database).

Prerequisites and recommended tools

The prerequisites for the use of Aura Analytics 2.0.0 are set below:

Recommended tool for data visualization: ELK stack

Docs:

Mon, 01 Jan 0001 00:00:00 +0000

Aura Analytics 2.0.0. architecture

Technical architecture of Aura Analytics 2.0.0 and description of main processes and components

Architecture overview

Aura Analytics 2.0.0 contains two different environments:

OB local environment: Processes in this side are managed by the OB, that should install and execute certain processes related to the PPD-Creator for the creation of raw datasets.
Global environment: Processes here are managed by Aura Global Team for data recovery, processing and generation of dashboards and statistics. The output includes data and metrics to be consumed by Aura Global Team for decision-making.

Aura Analytics 2.0.0 architecture flowchart

The following diagram shows an overview of Aura Analytics 2.0.0 architecture, including the environments involved and the main components and processes, which are fully described in succeeding sections.

Figure 1. Aura Analytics 2.0.0 Architecture flowchart

Aura Analytics 2.0.0 processes

PPD-Creator process

The PPD-Creator is a Python module for the creation of PPD-Raw datasets.

It is the only component that belongs to the OB environment. The OB should install it and is responsible for its execution. The PPD-Raw datasets will be stored in the destination blob PPD-RAW.

This process reads the files included in OB MANAGED INSTANCES columns of the tables in Annex: Dataset fields. The result of this process is the PPD RAW columns of the tables in the above-mentioned annex.

The main tasks executed by the PPD-Creator are summarized below:

Reads the Aura log files in a Kernel Blob.
Anonymize the sensible fields (AuraID, AuraGlobalID, and personal information of user sentence such as DNI, phone numbers, etc).
Save the anonymized files to another directory of blob (PPD-Raw).

Figure 2. PPD-Creator process

The PPD-Creator anonymizes the following data, in the different OBs:

ES	UK
dni	creditcard
nie	insurance
phone	postcode
email	imei
	phone
	imsi
	email
	twitter
	passport

Manage PPD-Raw process

The Manage PPD-Raw process inserts the PPD-Raw path files (output from PPD-Creator) to PostgreSQL table for files management data centric:

It reads the output of PPD-Creator JSON file
Afterwards, it saves the paths to PostgreSQL server

Figure 3. Manage PPD-Raw process

PPD-Clean process

The PPD-Clean is a Python package used to clean PPD-Raw datasets.

Firstly, this process locates the directory where the PPD-Raw files are located, reads the corresponding files and processes them.

Once the process is finished, it writes to the files_processed table in the database and saves them in the PPD-Clean directory.

The main tasks executed by the PPD-Clean are summarized below:

Apply transformations to columns
Extract the explicit frustration
Calculates the Nones n-grams
Save the data in Directory or blob, PostgreSQL server and ElasticSearch for visualization

Figure 4. PPD-Clean process

User Dynamics process

User dynamics is a script used to measure the user’s behavior through metrics. It extracts statistics on the recurrence of users in Aura in a monthly basis.

The processes executed are summarized below:

User dynamics reads the file_processed table of the database and the all PPD-Clean files stored for 1 month.
It extracts metrics regarding new users, recurrent users, lost users and recovered users.
Afterwards, it saves these metrics in the User_dynamics schema, in a PostgreSQL database, within the tables connections, daycount, user and channel.
Data is also saved in ElasticSearch.

Figure 5. User Dynamics process

Components

Active Listening Database

The Active Listening Database is a PostgreSQL database that stores the processed and to-process files centrally in one place. It is used by the PPD-Clean and User Dynamics processes to store the processed data and metrics.

Schema files management

Currently, in the Active Listening project, we have input and output files for each of the processes and files that are processed. With the proposed database solution through the files management database, a more efficient management of raw files is achieved:

The PPD-Creator process transfers files from the OB to a shared blob.
The transferred files are written to a file in that blob called aura-sync-cache-dst.json.
The manage_ppd_raw process will read the aura-sync-cache-dst.json file from the PPD-Raw folder and ingest the records into the FILE_PPD_RAW table of the database.
It will also insert into the EMPTY_DATA_FILES table the days that are not found in the JSON file. This table is necessary for logging metrics in Prometheus. This process will run daily.

Figure 6. Files management database

Schema user dynamics

The User Dynamics process generates the statistics of Aura users, number of daily active users and types of users, with 4 categories: new, recurring, lost and recovered.

The Channel table contains all the channels in Aura that have been processed by the User Dynamics process.
The User table contains the unique Aura users in each environment and country.
The Daycount table contains the number of total users for each day, indicating how many of them are new, recurring, recovered or lost users, the number of weekly unique recurring users and the number of monthly unique recurring users.
The Connection table has the status of the user for each day (whether it is new, recurring, lost or recovered).

Figure 7. User dynamics database

Aura Analytics Dashboard

Aura Analytics 2.0.0 produces as a result, among other elements, an analytics component named Aura Analytics Dashboard that is the one used by Aura Global Team to gather statistics on the production system and to analyze user’s behavior.

This Analytics Dashboard is based on the ELK stack that contains:

ElasticSearch: distributed search and analytics engine at the heart of the Elastic Stack. It allows the storage of data and its subsequent indexing, search and analysis.
Kibana: provides a visualization tool that includes dashboards and panels created over the ElasticSearch data. Users interactively explore, visualize and share insights into data and manage and monitor the stack.

Once installed:

An ElasticSearch index is created. It is called aura-ppd-ENTITY-COUNTRY-YEAR, and its index schema contains a cleaned version of the AURA MESSAGE, RECOGNIZER or API tables (which registers input and output messages).
A Kibana index pattern is created, matching the uploaded ElasticSearch index.
A pre-defined set of visualizations are installed in Kibana over that index pattern, as a means to get a default peek on the index data. See the section pre-installed analytics dashboard.
The system automatically ingests any new clean PPD being produced in the ElasticSearch database, so that the index and dashboards remain up to date.

In principle, the PPD creation process specifies daily production, since Aura logs are sent to Kernel once a day. This means that information about Aura behavior and user actions on one given day will be available in the dashboards of the following day.

As mentioned above, the Aura Analytics Dashboard is conceived to be used by Aura Global Team. However, OBs can install locally the ELK stack or any other visualization tool for data consumption. Access to the document Local data visualization for further details.

Docs:

Mon, 01 Jan 0001 00:00:00 +0000

Aura Analytics 2.0.0 operation

Discover Aura Analytics 2.0.0 operation at a glance

Introduction

Based on Aura Analytics 2.0.0 architecture, the current documents provides an overview of its global operation.

Take the Aura Analytics 2.0.0 architecture flowchart as a reference to follow each step of the dataflow described below:

Aura logs generated in local instance are converted to datasets and transferred to local Kernel via the standard procedure and with the established frequency (typically, daily). Once there, the Active listening process flow fires up daily.
PPD-Creator: This is the first process that runs, and it is the only one executed in the OBs’ environment. It retrieves Kernel data, anonymizes all sensitive data that could identify users, and then transfers this data to an environment shared with the Aura Global team.
Manage-PPD-Raw: This is the first process executed from the global environment. It solely stores the paths of the data transferred by the PPD-Creator into a PostgreSQL database to keep a record of which data has been transferred.
PPD-Clean: This process runs from the global environment. Once the data is anonymized, it is processed to extract additional features (such as user frustration or the extraction of n-grams from user phrases about iterations that do not have an intent).
Once the data is processed, a path is saved in the environment and also in ElasticSearch to create dashboards that tracks Aura usage by its customers.
User-Dynamics: This is the last process, also executed in global environment. It is responsible for extracting statistics about users’ recurrence and the number of users per day. Among that, it identifies new users, recurring users (those making iterations every day), recovered users (those who have stopped using Aura at some point and have returned to the system) and lost users (those who have stopped using Aura in 3 months).

Examples of different dashboards are included below:

Figure 1. Users dashboard

Figure 2. Daily users dashboard

Figure 3. Weekly users dashboard

Figure 4. Trends dashboards

Docs:

Mon, 01 Jan 0001 00:00:00 +0000

Guidelines for OBs

Guidelines for OBs to allow the generation of datasets from their Aura users logs in local environment

Introduction

As seen in the Aura Analytics 2.0.0 architecture flowchart, Aura Analytics 2.0.0 contains two different environments: the OB local environment, managed by the OB and the Global one, managed by Aura Global Team.

Within this framework, the current guidelines are tailored towards OBs, indicating how to install and execute the PPD-Creator, for the creation and processing of PPD RAW datasets.

Once it is carried out, the subsequent processes of Aura Analytics 2.0.0 are executed in global environment by Aura Global Team.

Installation of PPD-Creator

The OB must install and store the PPD-Creator in a specific destination blob PPD-RAW and is responsible for its execution.

Guidelines are included in installer Aurak8s documentation: Active listening deployment.

Execution of PPD-Creator

The execution of the PPD-Creator must be done by the OBs, previous to its installation.

Parameters to launch the PPD-Creator

These are the parameters that the PPD-Creator takes from Kernel:

Mandatory parameters:

Parameter	Description	Example
`--src-type`	Source type	‘aws’, ’local’, ‘azure’
`--src-name`	Source folder name (raw files)	`/directory/data/`
`--dst-type`	Destination type	‘aws’, ’local’, ‘azure’
`--dst-name`	Destination folder name (raw files processed)	`/directory/data/output`
`--country`	Customize for a lang_country pair	es
`--table`	Table to process {message, grootmessage, recognizer, api}	MESSAGE
`--environment`	Environment to process (ap-one, ap-two, prod)	ap-one
OPERATIONS
`--transfer`	File processing: transfer, anonymize and group files
`--copy`	Raw file copy – no processing
`--copy-fix`	File copy + apply small fixes
`--show`	Show available files; no action performed

Optional parameters:

Parameter	Description
`--src-user`	Access key for the source folder
`--src-pass`	Source access secret
`--src-encryption`	Encryption key for data in source
`--dst-user`	Access key for the destination folder
`--dst-pass`	Destination access secret
`--dst-encryption`	Encryption key for data in destination
`--cloud-type`	Cloud type (‘aws’,’azure’)
`--dry-run`	Process dry-run: no action performed
`--reraise`	Re-raise exceptions on errors
`--options-file`	Read additional options from a file in dest repo
`--verbose`	Verbose level
`--console`	Act as a console app (format logs as console messages, raise on errors)
`--dest-log-skip`	Do not write the logfile at the destination folder
`--dest-log-dir`	Logging subdirectory at destination
`--show-input`	Print out input arguments
`--anon-key/ encryption-key`	Encryption key for anonymization of sensitive columns
`--max-files`	Maximum number of files to process
`--folders/month`	Restrict transfer to certain folders (i.e. months)
`--tables`	Restrict transfer to certain tables
`--reset`	Reset the index and re-process
`--encrypt-index`	Save the index files encrypted or unencrypted (default depends on cloud type)
`--raw`	Copy as raw data instead of text file
`--fix-header`	Fix file header
`--fix-anon`	Fix anonymized fields in API columns

Launching PPD-Creator

Execute the following command:

docker run aura/ppd-creator --country <country-code> --anon-key <KEY> <source-params> <dest-params>

Example:

docker run aura/ppd-creator \
   --country ar \
   --anon-key as34-dre23-4127 \
   --src-name 4P-bucket-name-for-uk \
   --src-user EF45IHWD34DE4FGA \
   --src-pass k/Erf/6DSWWPjhdde1/abc123def-2331ldf \
   --dst-name aura-ppd-ar \
   --dst-user EF4341sdf3EFGUA1 \
   --dst-pass J/DQW/Sdde5k12ldsf/1abcde12dd1d-123c11 \
   --dst-encryption 1234ab56-12a3-45eb-8e06-8c522cdbb668-75f1b00f-6ca6-4a13-a741-64514cce728b \
   --table message \
   --environment prod \
   --transfer

Output from PPD-Creator

The output includes the following items:

BOT_XXXXXX.txt.bz2: raw files (processed).
log folder: if the logging options have been configured.

aura-sync-cache-dst.json: table/month: processed files (automatically generated in destination). For example:

{
  "AURA_DATA/ES/API/202212/": [
    "BOT_04095750-724e-11ed-9565-53054255c842_ES_API_20221202T150000Z.txt.bz2",
    "BOT_d2e93fc0-7656-11ed-a8eb-49a811568ab3_ES_API_20221207T170000Z.txt.bz2",
    "BOT_987780e0-7660-11ed-ba4a-2dac114c5321_ES_API_20221207T180000Z.txt.bz2"
  ],
  ...
}

aura-sync-cache-src.json: table/month: raw files_to_process (source). For example:

{
"AURA_DATA/ES/API/202212/": [
  "BOT_04095750-724e-11ed-9565-53054255c842_ES_API_20221202T150000Z.txt",
  "BOT_05a5b860-7663-11ed-bbf7-cb8fd9eb3c25_ES_API_20221207T190000Z.txt",
  "BOT_05ae43e0-7663-11ed-a0aa-8b7e0e134809_ES_API_20221207T190000Z.txt",
  "BOT_0d69fb10-7492-11ed-a1fc-95dce7e56901_ES_API_20221205T110000Z.txt"
],
  ... 
}

aura-sync-key-dst.json: key used to encrypt sensitive fields. For example:
```
{
  "sample": "abcd1234-ab12-12ab-ab12-1abc234e56fg"
}
```

Local data visualization (optional)

As explained before, Aura Global Team will be in charge of the analysis of the generated data through the global tool Aura Analytics Dashboard.

Nevertheless, just in case the OB wants to visualize certain data locally:

This will be done following a prior agreement with the OB on privacy-related matters.
Aura Global Team will provide access to the clean data stored in the corresponding PPD-clean blob container.
The OB can install locally the ELK stack or other alternative tool for data visualization.
No support will be offered by Aura Global team for this task.

Docs:

Mon, 01 Jan 0001 00:00:00 +0000

Annex: Dataset fields detail

The current annex describes the process that each field of Aura Analytics 2.0.0 data model is going through towards a clean PPD

Introduction

The objective of the following tables is to explain the process that each field is going through within this flow:

Aura datasets

>>>

PPD_RAW

>>>

PPD_CLEAN

Each cell of the table explains the process that the data field is undergoing in this specific moment before it gets to the concrete stage (table column).
For example, the field GLOBAL_AURA_ID is undergoing a “hashing” before it gets stored in PPD_RAW. After this, the “hashed data” is progressed without any further processing to PPD_CLEAN.

Tables used in the Active Listening process are described in the following sections. They belong to the Aura Entities database.

Columns FIELD and DESCRIPTION: instances managed by the OB
Columns PPD RAW and PPD CLEAN: instances managed by Aura Global Team

MESSAGE dataset

Message dataset (stored in local Kernel).

#	FIELD	DESCRIPTION	PPD RAW	PPD CLEAN
1	USER_ID	Unique user ID in the OB systems	NOT transferred	NOT transferred
2	MSG_DT	Timestamp of the data
3	MSG_ID	Unique ID of the message		NOT transferred
4	ACTION_CD	Code of the action that produces the data		NOT transferred
5	AURA_ID	User logging ID in Aura. The user will have a new `Aura_id` each time she logs in Aura.	Hashed
6	PHONE_ID	Phone number of the user	NOT transferred	NOT transferred
7	CHANNEL_CD	Code of the channel where the action happened
8	SUBSCRIPTION_CD	Code of the subscription type of the user in the OB		NOT transferred
9	DOMAIN_CD	Code of the domain where the action happened		NOT transferred
10	CATEGORY_CD	Code of the category where the action happened		NOT transferred
11	COUNTRY_CD	Code of the country		NOT transferred
12	CORR_ID	Correlator ID of the request that produces this data
13	IS_CACHED	Shows if the entity content was already cached or not		NOT transferred
14	STATUS_CD	Status code of the action, if meaningful
15	REASON	Result of the action in error case, code of the error		NOT transferred
16	VERSION_ID	Aura version that produces this data
17	LANG_CD	Language configured by the user for communication		NOT transferred
18	TZ_CD	Timezone where the communication happened		NOT transferred
19	DURATION_NU	Duration in milliseconds of the action
20	MESSAGE	Content of the message	Anonymized
21	DIALOG_ID	Id of the dialog where the message happens
22	CONVERSATION_ID	Id of the conversation where the message happens		NOT transferred
23	WIN_RECOGNIZER_CD	Code of the recognizer that wins for this message		NOT transferred
24	WIN_RECOGNIZER_SCORE_NU	Score of the recognizer that wins for this message		NOT transferred
25	INTENT	Selected intent
26	ENTITIES	List of entities determined by the recognizer
27	MODALITY_CD	How does the user communicate with Aura
28	AURA_ID_GLOBAL	Identifies the same `user_id` logged with the same authentication method	Hashed
29	ACCOUNT_NUMBER	Unique account number of the user	NOT transferred	NOT transferred

GROOTMESSAGE dataset

Groot Message dataset (stored in local Kernel).

#	FIELD	DESCRIPTION	PPD RAW	PPD CLEAN
1	USER_ID	Unique user ID in the OB systems	NOT transferred	NOT transferred
2	MSG_DT	Timestamp of the data
3	MSG_ID	Unique ID of the message		NOT transferred
4	ACTION_CD	Code of the action that produces the data		NOT transferred
5	AURA_ID	User logging ID in Aura. The user will have a new `Aura_id` each time she logs in Aura.	Hashed
6	PHONE_ID	Phone number of the user	NOT transferred	NOT transferred
7	CHANNEL_CD	Code of the channel where the action happened
8	DOMAIN_CD	Code of the domain where the action happened		NOT transferred
9	CATEGORY_CD	Code of the category where the action happened		NOT transferred
10	COUNTRY_CD	Code of the country		NOT transferred
11	CORR_ID	Correlator ID of the request that produces this data
12	IS_CACHED	Shows if the entity content was already cached or not		NOT transferred
13	STATUS_CD	Status code of the action, if meaningful
14	REASON	Result of the action in error case, code of the error		NOT transferred
15	VERSION_ID	Aura version that produces this data
16	LANG_CD	Language configured by the user for communication		NOT transferred
17	TZ_CD	Timezone where the communication happened		NOT transferred
18	DURATION_NU	Duration in milliseconds of the action
19	MESSAGE	Content of the message	Anonymized
20	CHANNEL_CONVERSATION_CD	Id of the channel conversation where the message happens		NOT transferred
21	SKILL_CONVERSATION_CD	Id of the skill conversation		NOT transferred
22	WIN_RECOGNIZER_CD	Code of the recognizer that wins for this message		NOT transferred
23	WIN_RECOGNIZER_SCORE_NU	Score of the recognizer that wins for this message		NOT transferred
24	AURA_ID_GLOBAL	Identifies the same `user_id` logged with the same authentication method	Hashed
25	ACCOUNT_NUMBER	Unique account number of the user	NOT transferred	NOT transferred
26	SKILL_CD	Unique id of skill

RECOGNIZER dataset

Recognizer dataset stored in local Kernel.

#	FIELD	DESCRIPTION	PPD RAW	PPD CLEAN
1	USER_ID	Unique user ID in the OB systems	NOT transferred	NOT transferred
2	RECOGNIZER_DT	Timestamp of the data
3	RECOGNIZER_ID	Unique ID of the recognizer
4	ACTION_CD	Code of the action that produces the data		NOT transferred
5	AURA_ID	User logging ID in Aura. The user will have a new `Aura_id` each time she logs in Aura.	Hashed
6	PHONE_ID	Phone number of the user	NOT transferred	NOT transferred
7	CHANNEL_CD	Code of the channel where the action happened
8	DOMAIN_CD	Code of the domain where the action happened		NOT transferred
9	CATEGORY_CD	Code of the category where the action happened		NOT transferred
10	COUNTRY_CD	Code of the country		NOT transferred
11	CORR_ID	Correlator ID of the request that produces this data
12	IS_CACHED	Shows if the entity content was already cached or not		NOT transferred
13	STATUS_CD	Status code of the action, if meaningful
14	REASON	Result of the action in error case, code of the error
15	VERSION_ID	Aura version that produces this data
16	LANG_CD	Language configured by the user for communication		NOT transferred
17	TZ_CD	Timezone where the communication happened		NOT transferred
18	DURATION_NU	Duration in milliseconds of the action
19	SCORE_NU	Score returned by the recognizer
20	INPUT	User input sent to the recognizer. Null if incoming message is an AuraCommand	Anonymized
21	OUTPUT	Complete output generated by the recognizer
22	INTENT	Intent returned by the recognizer
23	ENTITIES	Entities returned by the recognizer due to the intent
24	COMMON_THRESHOLD_NU	Common threshold used to determine the best answer of all recognizers		NOT transferred
25	THRESHOLD	Specific threshold of the specific recognizer being executed		NOT transferred
26	EXPECTED_INTENT	Intent expected to be returned by the recognizer		NOT transferred
27	EXPECTED_ENTITIES	Entities expected to be returned by the recognizer due to the intent		NOT transferred
28	AURA_ID_GLOBAL	Identifies the same `user_id` logged with the same authentication method	Hashed
29	ACCOUNT_NUMBER	Unique account number of the user	NOT transferred	NOT transferred

This Markdown table can be directly used in your GitHub Markdown files.

API dataset

API request dataset (stored in local Kernel).

#	FIELD	DESCRIPTION	PPD RAW	PPD CLEAN
1	USER_ID	Unique user ID in the OB systems	NOT transferred	NOT transferred
2	REQUEST_DT	Timestamp of the data
3	REQUEST_ID	Unique ID of the request
4	ACTION_CD	Code of the action that produces the data		NOT transferred
5	AURA_ID	User logging ID in Aura. The user will have a new Aura_id each time she logs in Aura	Hashed	NOT transferred
6	PHONE_ID	Phone number of the user	NOT transferred	NOT transferred
7	CHANNEL_CD	Code of the channel where the action happened		NOT transferred
8	DOMAIN_CD	Code of the domain where the action happened		NOT transferred
9	CATEGORY_CD	Code of the category where the action happened		NOT transferred
10	COUNTRY_CD	Code of the country		NOT transferred
11	CORR_ID	Correlator ID of the request that produces this data
12	IS_CACHED	Shows if the entity content was already cached or not	NOT transferred	NOT transferred
13	STATUS_CD	Status code of the API request
14	REASON	Result of the action in error case, code of the error
15	VERSION_ID	Aura version that produces this data		NOT transferred
16	LANG_CD	Language configured by the user for communication		NOT transferred
17	TZ_CD	Timezone where the communication happened		NOT transferred
18	DURATION_NU	Duration in milliseconds of the action
19	HOST	Host of the API
20	PATH	Specific path of the API being called		NOT transferred
21	HTTP_STATUS	HTTP status of the server response		NOT transferred
22	RESPONSE	Response body	Anonymized
23	AURA_ID_GLOBAL	Identifies the same user_id logged with the same authentication method	Hashed	NOT transferred
24	ACCOUNT_NUMBER	Unique account number of the user	NOT transferred	NOT transferred
25	REQUEST	Request body