Highlights of Significant Changes in Einstein Analytics Winter '18 Release

Published on
December 21, 2017

There is excitement in the world of analytics over the newest features of Einstein Analytics Winter '18. In this release, the UI has been updated again. The changes mainly involve the import wizard and the addition of two more connectors. I have tried to summarize the release notes with all the new and improved features of Einstein Analytics in this blog post.

Capability Review with Dashboard Inspector

The dashboard performance and the steps can be improved by smart suggestions provided by the Dashboard Inspector. While building multiple dashboards with SAQL and bindings, there are performance concerns during querying. The Dashboard Inspector tool recognizes the possible changes to load fast and aids in developing the steps and dashboard (Figure 1).

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Einstein Analytics Dashboard Inspector

Figure 1. Performance enhancement using Dashboard Inspector[/caption]

Dataflow Editor Providing Simple Adaptability

In the previous version of Analytics, datasets could be efficiently designed in Einstein Analytics but reconciling was a significant challenge. Now, with the help of the Data Manager UI, you can customize the dataflow. It allows the data to be transformed by processes such as filtering, append, etc.

Automated Security Configuration

Einstein Analytics has the advantage of being able to continually implement row level security to the dataset. It is time-consuming to replicate a complex security model followed by Salesforce. The new feature of Einstein Analytics now translates the security model by Salesforce by only providing the source object (API name) of the allotment legacy.

User-Specific Refined Dashboards

Einstein Analytics is inclined to do bindings to widgets. Managing different dashboards according to individual user filter selections was indeed challenging. Nevertheless, with a SOQL step, you can instantly demand information from the Salesforce User object and more easily connect the value to action.

Explore Additional Compare-Table Formulas and Functions (Beta)

You can refer to measure and group (dimension) columns by name rather than by reference letter when writing a formula in a compare table. SAQL percentile functions can be passed down in the formula editor. Referencing measure and group columns by name allows you to write more direct and effortlessly justifiable formulas.

Customized Information with Focus

Assign the data according to your requirements and correspond with the drill down button followed by Focus. Your customized information is presented by Focus as a chart or table based on your set parameters.

Configuration of Dataset Fields

Configure the fields you want to hide from the others who are working with your dataset using the new feature of Einstein Analytics.

Customized Configuration for Tables Style

With the support of advanced table properties, you can manage the configuration of cells, borders, themes, and so on. You can locate table styles in table properties. You can decide on a theme from the group theme choices available, and the table style can be altered via a themes panel. You can execute the arrangement of cells, columns, header, borders and color panels.

Smarter Approach for Data Visualization

By applying an advanced algorithm that analyzes the composition of your lens, explorer recommends a variety of charts communicating the most important information in an improved manner (Figure 2).

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Einstein Analytics Dataflow

Figure 2. Smart Chart Suggestions by Einstein Analytics[/caption]

Stream Values with Sankey Charts

Sankey charts can anticipate values streaming between two groupings. These chart also can present negative numbers and measure the comprehensive response.

Display Values with Origami Charts

The Origami chart is a robust technique to showcase pop-out values in data with a distinct portion and grouping. The graph can efficiently analyze products that are performing better in relation to other products.

Modify Tooltips for Charts

Now it is possible to modify chart tooltips. In Chart properties, select scope and groupings to illustrate the data. For grouping, you can also showcase a proportionate data point value of the aggregate.

Collective Markup for Charts

Chart markup can be improved by using multiple reference lines. This is useful when, for example, you want to display reference points before or after scheduling.

Innovative Reporting in Lightning Experience

Users can now search Salesforce data with Lightning Report Builder's automatic tool, which is an upgrade in Lightning Experience.

Dashboards Include Extra Storage

A new dashboard table element can showcase 200 records and 10 columns from the fields usable in the original report. Just add a Lightning table to a supplement chart and metric based analysis with proceeding details – this is an upgrade in Lightning Experience.

Lightning tables are convenient in the dashboard builder but not in Salesforce Classic. Lightning tables cannot be seen when you view a dashboard in Salesforce Classic, and they can't be joined from the Classic dashboard builder.

Enhance Dashboard Performance by Segregating

Dividing the dashboard into various pages enhances the performance of the dashboard. Analytics considers outcome for the widgets for a single page only. To access different pages for a dashboard, you can edit the dashboard by selecting Add Page (Beta) from the Layouts menu.

Pages assign to the dashboard layout as per their design. The challenge is that once pages are disabled they cannot be enabled for that layout again.

Rearrange Steps for Swift Deliverables

You can alter any part of a step's query, but not the dataset. For instance, you can change the order of columns in a values table or alter measures and groupings, and so on. Such alterations collide with all widgets that are using it.

Active Creation by Grouping the Steps

The panel now arranges steps into sections with a search bar to quickly locate your steps. The steps panel has sections such as dataset name and step type, and offers an alphabetical pattern of indexing. You can break down the sections, and also view a section for inaccurate steps.

Manage Filters of Dashboard with New Faceting Behavior

Faceting with an exceptional command is achievable. You can command which steps will refine their outcome based on options that are broadcast as facets. Actual steps do not collide with this variation – they enjoy their faceting behavior.

Upgrading of Global Filters

By choosing Apply Filters and Selections in the widget properties of a link widget, Analytics passes preferences in the prevailing dashboard as options in the linked dashboard. It also passes global filters as filters to a combined dashboard as far as the filters are useful.

Analytics overlooks a global filter if the linked dashboard doesn't use the dataset on which the global filter is prescribed. If the origin and destination (connected) dashboards both have a global filter defined on the identical field, Analytics overlooks the source global filter if the destination filter is secured. But if it is not secured, the global origin filter dominates the destination filter.

Synchronize into Analytics

Enhancements to the dataset edit page, dataflow editor, and replication setup page make it comparatively easy to integrate Upload Wizard, New Connectors, Data Preparation and Data integration into Analytics. With the state-of-the-art Upload Wizard, you can represent and alter file properties before uploading the data. In the dataset builder, choose dataflow and invent advanced data.

In the past, to run one replication the admin had to run them all. But with this new release, the admin can simply use the replication Action menu and run a complete abstract of additional replication at the touch of a button.

MS Dynamics CRM Connector Supporting Data Replication

Perfect data was not handy in Salesforce while migrating from MS Dynamics 365. But thanks to the enhanced MS Dynamics CRM Connector, data can now be cloned in Analytics for the building of lenses and dashboards.

Data Import Using Google BigQuery Connector

Online traffic is tracked using Google Analytics. Now, all the tools from Google Analytics have been made available in Google BigQuery, which can be cloned in Analytics and analyzed separately in lenses and dashboards.

Advanced Wizard for .csv Data

The .csv transmitted data can be irregular. Analytics pinpoints the confidential specifics of your files, allowing you to prospect and rearrange the properties. The advanced wizard assists leads you through the process smoothly.

Add Datasets You Need in the Dataflow

Dataflow editor and dataset builder have been improved – streamlining Salesforce dataset development. Now you can edit and execute the dataflow. The dataset builder is a visual tool to develop datasets from connected Salesforce objects.

It achieves the JSON complete changeover to concentrate and improve Salesforce object data and record the outcome as an advanced dataset. Presently, when you open the dataset builder from Analytics, you can choose which dataflow to combine.

Unique Features of Dataflow Editor

The dataflow editor (a visual tool) for setting up and rearranging dataflows has enhanced buttons and additional features. For preferred changeover, the node buttons are updated. Communication with nodes on the canvas has also been updated.

Customized Configuration of the Dataflow Editor

It is a slow process to add nodes in the dataflow editor, but now there is a button to accelerate the dataset builder inside the dataflow editor. By customizing the objects and fields, Analytics computes essential nodes to develop in the dataflow.

Functionality Upgrade in Compute Relative Node

Compute Relative node in the dataflow editor now has a feature comparable to JSON: When you include a field in a Compute Relative Node, you can choose the SAQL expression type and enter a calculation utilizing SAQL

Dataflow Replication is Now Self-Regulating

Edited dataflow often failed because necessary replication was not executed. In this new version of Analytics, upon starting the dataflow, the program looks ahead, inspects for any replication that must be implemented, and carries out the operation.

Advanced Techniques to Export Data to Einstein Discovery

Data in Analytics is handy for Einstein Discovery by utilizing the advanced export transformation in dataflow. Upon execution of dataflow via public API, these files are ready for use by Einstein Discovery users (Figure 3).

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Einstein Analytics transformed dataset

Figure 3. Export transformed dataset from Einstein Analytics to Discovery using dataflow[/caption]

Datasets Revival Attainable

Due to false moves, data gets rearranged in the dataflow, dashboards are disintegrated, and lenses display inaccurate data. In a jiffy, Analytics can reconstruct the dataset to an earlier version.

Intelligently Build and Cleanse Data with the Aid of Smart Suggestions and Column Profiles

In Analytics you can figure out the features of your data with help from column profiles and practical recommendations to keep uncluttered data (Figure 4).

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Einstein Analytics smart suggestions

Figure 4. Smart suggestions on column profile in dataset recipes[/caption]

Competent Approach to Developing Data

Upon making a preference for dimension, Analytics examines the column values in the earlier data for deciding which transformation to put forward in the suggestions bar and which transformation settings to use. For example, Einstein Analytics gives the smart suggestion on split transformation, delimiters, etc.

Security Predicate Merging with Sharing Inheritance

With the up-to-date launch of sharing inheritance in Analytics, many organizations have enforced their Salesforce settings to Analytics datasets. Now, you can define a security predicate to approve users who are part of sharing the inheritance. You also can specify the predicate in the dataflow editor and in the edit dataset page. Setting a predicate is mandatory for utilizing sharing inheritance.

Data Analysis with Unique Features Customized for iOS

Stunning new charts are available in Analytics for iOS showcasing robust data analysis. Additional features contributing to the advanced appearance are Standard Bar, Line charts, Matrix, Treemap, Pyramid, and Radar Chart. For more detail, please check the Salesforce release notes.

Analyze the Up-To-Date Dashboards for Android

Prospect your preferred dashboards and lenses from Analytics. In new release, Analytics for Android allows its users to use Analytics on the go. Here are some of the new features for Android users in the Winter 18 release: improved version of the line, donut and bar charts, the ability to get insights into Salesforce objects, etc. For more details, please check the release note.

Operational Apex Functionality in a Dashboard

Developers can now accommodate operational Apex functionality in a dashboard for bringing in Salesforce platform features that are not naturally promoted in Analytics.

End-to-End Customization of Recipes

While developing a package, you can add an Analytics recipe by change set. The primary drawback of Dataset Recipes over Dataflow JSON was the limitation in developing a JSON file for additional editing, as it will act as a sandbox. That issue has been eliminated in this release. The recipe can be packaged and moved from sandbox to production (Figure 5).

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Einstein Analytics transferring to production

Figure 5. Screenshot of transferring dataflow, recipes, app, lenses and dashboards from sandbox to production org[/caption]

I liked the improved, as well as the totally new, features of Einstein Analytics. I'm sure you will all have positive hands-on experiences with these amazing features. However, stay tuned for our next blog post that highlights the outcome of Dreamforce 2017 on Einstein products.

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