OpenText Analytics Suite 16: Technical Summary of New Features

Analytics Studio Redesign

Analytics Studio is a web-based, self-service reporting tool deployed on OpenText Information Hub (iHub) that empowers non-technical business users to create and share interactive reports and BI content without IT support. Reports can be as simple as a single table or as complex as interactive tabular or crosstab reports with data drill-down and visualizations.

Ad-hoc reporting is especially helpful for letting business users explore their organization’s data spontaneously, whenever new situations come up. Instead of being locked into pre-set queries and reports or having to seek expert help to build new queries, non-technical users can speedily ask whatever questions they want.

Moreover, they can upload their own data to enrich the overall scheme. And non-technical users don’t need an in-depth understanding of the data sources because Analytics Studio draws on iHub Data Objects and Data Models that shield users from too much complexity by exposing table and field names in user-friendly terms. Whenever managers think of a new query to make against company-wide sales data, Analytics Studio gives them the freedom to do so, without delays or increasing the IT workload.

Web-based Ad-hoc reporting provides many benefits:

• Business users get answers to their questions in a timely manner.
• Users can easily adapt to a changing business environment.
• An ad hoc reporting tool such as Analytics Studio does not require extensive training. Users simply drag and drop report elements into the layout pane to create a report.
• No installation is required because the ad hoc reporting tool is web-based. The tool is quickly deployed to users via a browser.
• Business users can build their own reports, greatly reducing the burden on IT.
• Users can collaborate and share reports on the web.

For example, in a company continuously rolling out new products, individual product managers can build their own reports focusing on the parameters they are most interested in at a given time, whether it’s profit margins, sales performance by territory or season, or comparing sales of a new product to an existing one (e.g. to see if it is cannibalizing the older product’s revenue stream).

Analytics Studio has a redesigned graphical interface that gives users more flexibility and convenience. They are no longer tied to the formats of pre-set templates. Now, they can create reports in any format by dragging and dropping data and visual elements such as tables, crosstabs, and charts into the layout pane. Users can format and rearrange visual elements, sort, group, and filter data, and create computed columns and aggregate data. They can also run, view, and modify reports in OpenText™ Interactive Viewer and perform ad hoc analysis.

Smart Web Layout

By using Smart Web Layout, Analytics Studio now allows the user to easily create dashboard-style layouts directly in the browser. Users drag and drop data and visualizations to create reports. Visualizations can be moved anywhere in the layout and resized, while adhering to a grid. Users can specify borders, margins, and other properties using a property sheet. Visualizations include table, crosstab, text, image, and many types of charts.

Analytics Studio automatically controls the page size. If the report will be printed, users can specify standard page sizes, such as A4, US Legal, and US Letter, as well as custom sizes, so that converting the report to PDF or any other document format produces WYSIWYG output.

Figure 3: Sample Report created in Analytics Studio shows three steps of report creation: 1. Select the data, 2. Choose the properties, and 3. See the final layout.

Hyperlinks

Dynamic hyperlinks can easily be added to a report in Analytics Studio. Hyperlink URLs can be static or generated using data elements from the report. Hyperlinks can also jump to a bookmark within the same report or drill through to another detail report.

Figure 4: Analytics Studio Hyperlink Builder illustrates the interface for creating hyperlinks, allowing selection of type (URL, Bookmark, Drill-Through), location, target, and tooltip.

Data Preview

To make sure it is the data they really want before they start processing it, users can preview the data in a data set, data model, or column and filter the data to meet the requirements of a particular report.

Figure 5: Data Preview in Analytics Studio displays a screenshot of the data preview functionality within Analytics Studio, showing a table of data with options to navigate rows.

Parameters

Analytics Studio continues to support filters that limit the data displayed in a report so that users can define a filter condition at report design time – for example, COUNTRY LIKE 'USA%,' to display data only for the USA.

Now, instead of locking in a filter value via the query that provides data for a report, users who want to customize their reports on a self-service basis can do so via setting parameters – that is, specifying one or more values at report run time. The data that appears in the report depends on the parameter values specified when generating the report.

Another new feature allows a parameter created in one visualization to be easily linked to the filter in any other visualization – to any object. With one parameter, users can filter the data in all the visualizations in a report.

Also new: The ability to manage parameters so the user can more easily track or change the labels of a parameter and in which visualizations it shows up.

For example, if the user has just gotten a question about Eastern Europe, he or she can quickly set a parameter for this on-demand query, e.g. COUNTRY LIKE 'Hungary%,' or with multiple values, e.g. COUNTRY IN (Hungary, Slovakia, Romania, Poland).

Aggregate Measures

OpenText™ Analytics Studio now displays aggregate measures included in a data object. Aggregate measures perform a calculation such as a sum or average over a set of values – for example, total revenue or average rainfall. Aggregate measures can be used in tables, crosstabs, and chart visualizations.

Aggregate measures in a data object can be used in a summary table without further modification. Users can also create their own aggregate measures.

Analytics Designer

Analytics Designer is a powerful IDE (integrated development environment) based on the Eclipse™ Workbench platform that developers use to create fully featured reports, gadgets, dashboards, web applications, and iHub Data Objects.

Analytics Designer offers the following features:

• Direct access to multiple types of data sources
• Rule-based secure access to data
• Support for even the most complex analytic requirements
• Scripting for customization of any report component
• Libraries for sharing standard report components and style definitions
• Publishing reports, gadgets, dashboards, and web applications to Information Hub

Data Model Enhancements

iHub Data Models continue to provide flexible, optimized access to multiple data sources and data sets, as they have since Version 3.0. In Release 16, OpenText boosts the linking power of iHub Data Models, including their support of outer joins and predefined measures.

For some background, an iHub Data Model consists of several data sets linked by joins. Each data set uses a simple query on a single table. When a report developer uses the data model to provide data to a report, the model joins the required tables and sends the optimized query to the data source. The developer controls the fields available to the report developer and sets security rules to further refine access to data by the end users of the report. The fields in the data sets are visible in Analytics Studio, Interactive Crosstabs, and in Dashboards when the data model is used as a data source.

Outer Join in Data Model

The Analytics Suite 16 offers improved techniques of data merging, such as faster outer joins. When linking multiple data sets, Data Models now directly support the Left Outer Join, the Right Outer Join, and the Full Outer Join. For example, the data model shown below contains Customers, Orders, and Order Details data sets. The Customers and Orders data sets are joined on the CUSTOMERNUMBER column, and the Orders and Order Details data sets are joined on the ORDERNUMBER column. The developer has set the join type between Customers and Orders to a Left Outer Join. The generated query using both of these tables returns all customers even if they have no orders. The join type between the Orders and Order Details tables is an inner join. A generated query using these two tables does not return orders that have no details.

Figure 6: Data Model in Analytics Designer showing join conditions depicts a diagram of data model joins between Customers, Orders, and Order Details tables.

Analytics Designer provides additional functionality to the report developer using the data model to change the type of join used when the report runs. In this example, if the report should not show customers who do not have orders, the developer can change the join type between the two tables to an inner join.

Predefined Measures in Data Models

As a more convenient way to provide aggregated data for ad-hoc reporting, complex measures can now be created in iHub, predefined within the Data Model, and then made available to Analytics Designer and Analytics Studio. A predefined measure in an iHub Data Model uses an aggregation function to create a predefined summary of an expression. For example, a measure named “Sales Total” is the expression “Price * Quantity” and the aggregation function SUM. Predefining “Sales Total” in a data model makes it available to the users of Analytics Designer, Dashboards, and Analytics Studio without each needing to recreate or even understand the complexity of the measure.

Figure 7: Predefined measures in Analytics Designer shows an example of predefined measures within the Analytics Designer interface.

REST API Data Access

Analytics Designer 16 provides a new REST Open Data Access (ODA) driver. This lets the developer authenticate and connect to JSON structured data through REST directly in the design tool. The developer can then specify the URIs and methods to access the API and the authentication information returned by the API.

Figure 8: Analytics Designer REST ODA Data Source displays the configuration screen for setting up a REST Data Source profile.

MongoDB and Apache Spark JDBC Access

MongoDB is an open source NoSQL database that uses its own query syntax to access data. Apache Spark is an open source cluster computing framework commonly used for big data file systems; it is centered on a read-only multiset of data items distributed over a cluster of machines that is maintained in a fault-tolerant way. Analytics Designer 16 includes drivers that provide direct relational JDBC access to both the MongoDB data structure and the Spark SQL data.

Secure JDBC Access using SSL

Analytics Designer 16 supports encrypted Secure Sockets Layer (SSL) access to any supported JDBC database. The default access does not use encryption. To use encryption, provide the standard SSL parameters to the data source.

New Fiscal Year Functions

Many companies use a fiscal year that does not align to calendar year boundaries. Instead of using complex expressions to calculate the values of fiscal quarters, months, weeks, and so on, Analytics Designer 16 provides a full set of fiscal year functions. Based on the day and month on which the fiscal year begins, a call to a single function can provide the number of the quarter, month, week, or day of a date in the fiscal year, or the start date of a fiscal year, quarter, month, or week.

Improved Cloud Readiness for iHub

iHub Release 16 includes underlying mechanisms, including support for SSL (Secure Socket Layer), for operating in the cloud, as part of any cloud-enabled configuration into which a customer may deploy. For example, users can upload any desired files, report designs, gadgets, dashboards, or applications from outside the firewall into a cloud application based on iHub.

You can access the cloud deployment using secure messaging transfer or SSL.

User Experience Enhancements

Process enhancements were introduced to Big Data Analytics to reduce the learning curve of the application and provide a better experience during the data preparation process. The major enhancements are related to the way Big Data Analytics manipulates the loaded data in the columnar database engine: more flexible access to discrete values and more tips to help in the loading process. Changing right-clicks to drop-down menus (making the application more tablet-friendly and cleaner) and providing smart charts when exploring or charting improves the user experience.

Data Tree Enhancements

A drop-down menu now replaces the tabs in the data tree space with three options: My Data, Discrete Values, and My Folder.

When browsing the data tree, discrete values are also accessible behind the columns in the tables. It is possible to navigate through those values, sort ascending or descending by percentage of appearance or by value (numeric or alphanumeric sorting), and search for particular values.

Figure 9: Big Data Analytics Data Tree View shows the interface for navigating data trees, including options like 'My Data', 'My Folders', and data fields.

New Distribution of Tabs Toolset

There are two main tab enhancements from our previous release:

• Selections has moved to the root of the tabs toolset (from Analytics).
• Gallery is now under the tab Analytics.

Figure 10: New tab locations in Big Data Analytics illustrates the updated layout of tabs within the Big Data Analytics interface.

Useful URLs in the Help Menu

The useful URLs that were in the Start page are now in the Help menu.

Figure 11: Big Data Analytics useful URLs menu displays the Help menu with links to the Big Data Analytics website, documentation, webcasts, blog, and professional services.

Drop-down Menus Replace Right-Click

All elements that previously had a context menu accessed through right-click now have a drop-down menu icon highlighted in blue when it is selected. Those elements are databases, tables, columns, filters, and data sources.

Figure 12: Big Data Analytics drop-down menus shows examples of these new drop-down menu interfaces.

Clearer Columns and Bar Diagrams

Charts with columns or bars with a unique measure are displayed with a single color in Explorer or crosstabs visualizations.

Loading Projects Enhancements

The previous release allowed business users to load their own data in the front end; in this new release, Big Data Analytics adds more enhancements to that self-service load module.

More Help Panels and Tooltips in the Load Module: The whole process of loading data from the front end has more guidance with help panels.

Figure 13: Loading data in Big Data Analytics illustrates the interface for loading data, showing a drag-and-drop area and project management options.

Save Loading Projects

First, the user can save the loading project and recover it whenever it needs to be run or edited. This functionality is available under the option Projects in the drop-down menu in the loading menu.

Figure 14: Saving a loading project in Big Data Analytics shows the interface for saving and managing loading projects.

Schedule Loading Projects and Manage Related Events

Additionally, this release has added new tasks and events related to loading projects in the Frontend Task Manager, in order to schedule load processes (manage Tasks) and make actions after loading processes (manage Events).

Figure 15: Scheduled Tasks in Big Data Analytics displays the interface for scheduling tasks and managing events related to data loading.