Data3Sixty® DQ+ enhancements in this release include:
Support for Text Analytics in the cloud
There are four new nodes which enable you to analyze text to identify key words and phrases, and
to gauge the sentiment of the language:
- Identify Key Phrases - Identifies specific words and phrases in an input string field.
- Sentiment Analysis - Inspects the text in an input string field to gauge the sentiment.
- Detect Entities - Identifies text entities in an input string field. An entity is a textual reference to
the unique name of an object, such as people, places, dates and quantities.
- Detect Entities in Medical Text - Inspects an input string field to identify words that are medical
entities, for example, Protected Health Information (PHI) and medical condition
These nodes use the Amazon Comprehend API to analyze text. In the Analysis Designer, the nodes
are located in a new Text Analytics category.
The following new system fields have been added to case stores to enable case managers to
quickly identify when a user last worked on a case, and to identify the most recently added note:
- LastUserModified - The date and time of the most recent user-initiated change to a case. This is
a system datetime field and cannot be edited.
- LastNote - The most recently added note at the case level. This is a system string field and
cannot be edited.
Data3Sixty DQ+ Community
You can now access the Data3Sixty DQ+ Community page from the product Help menu in the top
right corner of the screen.
From the Community page, you can post or vote on feature requests, ask a question, post a
comment, or find shared content in the discussion forum.
Semantic type support
The list of available semantic types that can be applied to fields has been expanded. The system
contains a set of pre-defined semantic types which can be automatically detected. You can also
create custom semantic types and define data quality rules that are tagged with these types to
enable executions to automatically match up fields and rules based on semantic type.
You can use semantic types to give additional detail about the kind of information that the data
represents. For example, a field with a string data type may semantically represent a city or a
Value conformity exclusion rule
You can now specify a list of values, or a range of values, to create an 'exclusion rule' which you
can use to check that specific values are excluded from a data set, or to check that the values in a
data set fall outside a given range. This expands on the existing value conformity rule functionality
that allows you to check whether a data set contains specific values, or a range of values.
For example, you could create an 'exclusion list' of invalid email domains and have the system
check against this list to ensure that the invalid domains are not included in a data set.
The system now allows Application users to be included in groups and super groups. This can be
useful if you want to give Application users access to secure objects within the system.
Data view string fields
You can now change the maximum length for string fields in data views to a value lower than the
default of 65,000 characters. It can be useful to reduce this value to avoid errors if your data
contains a large number of very wide string fields.
Note that for your changes to take effect, you must save your data view before changing the
Return to Dashboard from Visualizer
When viewing data in the Visualizer screen, you can now easily return to the dashboard view
without navigating via a pipeline view.
Dashlet computed field editor
Support has been added for conditional expressions in computed fields within a dashboard.
The following new functions have been added:
- CASE - Handles divide by zero cases.
- COALESCE - Returns the first non-null value in a parameter.
This extends the existing support for calculations that you can perform within a dashlet.
When working with cascading filter dashlets, there is now always an option to select the 'Latest'
value in one filter, based on the selected value in another filter. A 'cascading filter dashlet' is one
where the values that are displayed are determined by a previously selected value in another filter
dashlet within the dashboard hierarchy.
For example, you have a filter dashlet for year, and a filter dashlet for month. December is the
'Latest' month in the data set, but there is no data for December for the 'Latest' year. If you select
'Latest' in the year filter, the 'Latest' value in the month filter would show the 'Latest' month
according to the selected year.
Execution record counts
Improved the accuracy of the record counts shown in an analysis when the following settings are
- Cache Output of Nodes
- Collect Accurate Record Counts
It can be useful to disable these settings to improve the performance of your application.
However, note that an accurate record count cannot be guaranteed unless these settings are
The system no longer uses iframe for SSO authentication by default. This change ensures
successful integration with Active Directory.
If you want to change the default setting, you can do this by executing the updateAuthType
command line script.
Note that the updateAuthType command line script will now fail with a '-1' error code if the
specified domain cannot be found in the system.
See the attached Release Notes for additional details and information on Bug Fixes.