Welcome to the Webinar
Please join and post your questions in this chatter group
https://sfdc.co/ea-ed-cert-fp
Einstein Analytics & Discovery Consultant
Certification Fast Path
Ayhan Sahin
Senior Manager , Partner Field Enablement
Community Cloud & Einstein
Divya Alavarthi
Senior Manager, Partner Field Enablement
Platform
Vikas Roy
Director, Partner Field Enablement
Community Cloud & Einstein
Agenda
Webinar 3
● Objective
● Certification breakdown
○
○
Section 5 : Einstein Analytics - Dashboard Implementation
Section 6 : Einstein Discovery - Story Design
● Q&A
Objective
Give you some guidance on where to find the content, the
knowledge, share tip and tricks to start your journey with
Einstein Analytics & Einstein Discovery and successfully pass
the exam in a month.
Certification
Breakdown
Certification Exam Sections
Section Title
Weighting %
Nbr of Q
1
Data Layer
24%
15
2
Security
11%
7
3
Admin
9%
6
4
Dashboard Design
19%
11
5
Dashboard Implementation
18%
10
6
Discovery Story Design
19%
11
Section 5 - Analytics Dashboard Implementation
Sections and Objectives
Weighting %
Section 5 Analytics Dashboard Implementation
18%
5.1
Given business requirements, define lens visualizations such as
charts to use and dimensions and measures to display.
23%
5.2
Given customer business requirements, develop selection and results
bindings with static steps.
22%
5.3
Given business expectations, create time series analysis.
10%
5.4
Given customer requirements, develop dynamic calculations using
compare tables.
22%
5.5
Given business requirements that are beyond the standard UI, use
SAQL to build lenses, configure joins, or connect data sources.
23%
Bindings
In a dashboard you usually have several charts (widgets). How do you link them to each other?
You control the interactions by binding queries to each other. There are two types of interactions: selection
binding and results binding. The selection or results of one query triggers updates in other queries in the
dashboard.
Use cases
Bindings - Examples
●
Binding by selection of a grouping
●
Select Consumer and Fin Svcs
Bindings - Examples
●
Binding by selection of a grouping
●
Select Consumer and Fin Svcs
Bindings - Examples
●
Binding by selection of a grouping
●
Binding by selection of a measure
Bindings - Examples
●
Binding by multiple selection
Bindings - Syntax
"{{}}"
"{{cell(Amount_3.result,\"avg_Amount\"
"{{cell(Amount_3.result).asString}}"
"{{cell(Amount_3.result)}}"
"{{Amount_3.result}}"
"{{.result}}"
"{{cell(Amount_3.result,
0, \"avg_Amount\"
).asString}}"
).asString()}}"
"{{column(Amount_3.result, ["avg_Amount\"] ).asObject()}}"
"{{cell(Step_1.selection, 0, \"value\").asString()}}" or "{{column(Step_1.selection, [\"value\"]).asObject()}}"
- A parameter that compiles/becomes a value at run time
- This value can be a number (ex. 5000), a color (ex. #E6ECF2 ), a dimension name (ex.
AccountId.Type), a measure (ex. Amount), etc… and is read/received from another query on the
dashboard
- It also has a specific format so that it “fits” correctly within the dashboard code (json) ex. “#E6ECF2”
or [“AccountId.Type”] or [“sum”,”Amount”]
All about bindings by Rikke Hovgaard
Time Series
●
Using existing data to predict the future.
1
result = timeseries resultSet generate (measure1 as fmeasure1 [, measure2 as fmeasure2 ...]) with (parameters);
○
Example : how many tourists will visit next year? Suppose that you run a chain of retail stores, and the
number of tourists in your city affect your sales. Use timeseries to predict how many tourists will come to your city
next year
11
q = timeseries q generate 'sum_NumTourist' as Tourists with (length=12, dateCols=('Visit_Year','Visit_Month', "Y-M"));
All about Time Series by Rikke Hovgaard
Compare Table
●
View measures side by side, and perform math across the table’s columns and rows. Use
string values to create labels, concatenate dimension values, provide simple buckets, or add
image URLs.
All about Compare Table by Rikke Hovgaard
What is SAQL?
What is SAQL?
The Salesforce Analytics Query Language (SAQL) is a run-time query language that enables ad-hoc analysis of
datasets. A SAQL query consists of a sequence of statements that are made up of keywords (such as filter, group, and
order), identifiers, literals, or special characters. It’s JSON based and PIGQL familiar. A SAQL query loads an input
dataset, operates on it, and outputs the results.
When to use SAQL?
Use SAQL when you need to do custom calculations across multiple datasets, advanced data
manipulations/transformations on the fly, cogrouping (joining) data from different datasets, different aggregate levels on
one chart, joining multiple streams of data, complex filters, and in conjunction with bindings for dynamic linking on
charts and related, etc...
Simple Use Case: a formula based on 2 metrics or more from different datasets on the SAME chart
q = load ”My_Dataset";
q = group q by 'Division_Name';
q = foreach q generate 'Division_Name' as 'Division_Name', sum(’Amount’)/sum(’Quantity’) as 'sum_Amt/Qty';
q = order q by 'sum_Amt/Qty' desc;
All about SAQL by Rikke Hovgaard
Tips / FAQ
●
If data are missing in a dataset, you can still use SAQL with
the “fill” statement
○
●
https://developer.salesforce.com/docs/atlas.en-us.bi_dev_guide_saql.meta/bi_dev_guide_saql/bi_saql_statement_fill.htm
Change null value with the coalesce()function
○
https://developer.salesforce.com/docs/atlas.en-us.bi_dev_guide_saql.meta/bi_dev_guide_saql/bi_saql_functions_coalesc
e.htm#!
Einstein Analytics/Discovery Consultant Path
Section 5 – Dashboard Implementation – Helpful Links
5.1 – Visualizations
https://help.salesforce.com/articleView?id=bi_visualize.htm&type=5
https://help.salesforce.com/articleView?id=bi_chart_reference_properties.htm&type=5
5.2 – Bindings
https://developer.salesforce.com/docs/atlas.en-us.bi_dev_guide_bindings.meta/bi_dev_guide_bindings/bi_dbjson_bindings.htm
https://help.salesforce.com/articleView?id=bi_dashboard_data_source_connections.htm&type=0
5.3 – Time Series
https://developer.salesforce.com/docs/atlas.en-us.218.0.bi_dev_guide_saql.meta/bi_dev_guide_saql
/bi_saql_statement_timeseries.htm
https://help.salesforce.com/articleView?id=bi_limitations.htm&type=5
5.4 – Compare Tables
https://help.salesforce.com/articleView?id=bi_explorer_compare_table_nav.htm&type=5
https://help.salesforce.com/articleView?id=bi_explorer_compare_table.htm&type=5
5.5 – SAQL
https://developer.salesforce.com/docs/atlas.en-us.bi_dev_guide_saql.meta/bi_dev_guide_saql/bi_saql_functions_coalesce.htm?search_text
=coal
https://developer.salesforce.com/docs/atlas.en-us.bi_dev_guide_saql.meta/bi_dev_guide_saql/bi_saql_statement_group.htm#
https://developer.salesforce.com/docs/atlas.en-us.bi_dev_guide_saql.meta/bi_dev_guide_saql/bi_saql_statement_fill.htm?search_text=fill
Einstein Analytics/Discovery Consultant Path
Section 5 – Dashboard Implementation – Links from the Exam guide
●
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●
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Explore and Visualize Your Data in Einstein Analytics
Build Einstein Analytics Dashboards
Progressive Disclosure (Loading)
Embed and Customize Einstein Analytics
Analytics Bindings Developer Guide
Analytics REST Query Resource
Analytics SAQL Reference
Wave Funnel Powered by Custom SAQL
Timeseries SAQL Statement
Analytics Extended Metadata (XMD) Reference
Run Your Dashboards Faster with the Dashboard Inspector
Section 6 - Einstein Discovery Story Design
Sections and Objectives
Section 6
Weighting %
Einstein Discovery Story Design
19%
6.1
Given a dataset, use Einstein Discovery to prepare data for story output
by assessing data and adjusting outputs.
35%
6.2
Given initial customer expectations, analyze the story results and
determine suggested improvements that can be presented to the
customer.
35%
6.3
Given derived results and insights, adjust data parameters,
add/remove data, and rerun story as needed.
20%
6.4
Describe the process to perform writebacks to Salesforce objects.
10%
Einstein Discovery
Einstein Discovery is AI-Powered analytics that enables business users to
automatically discover relevant patterns based on their data - without having to build
sophisticated data models.
Einstein Analytics is a visualisation tool to uncover details. Einstein Analytics allows
you to explore all your data quickly and easily by providing AI-powered advanced
analytics.
Einstein Discovery
Automated Analytics - Analyze millions of data combinations
in minutes
Unbiased Insights - Understand what happened, why it
happened, what could happen, and what to do about it
Narrative Explanations - Natural language-based insights
and stories exported to Salesforce or Microsoft Office
Recommended Actions - Take action, stay on top of
changes, and iterate
Einstein Discovery
Automated
Insights
Narrative
Explanations
Data Prep
Recommendat
ions
Einstein Discovery
1000s of combinations
1000s of charts
1000s of statistic runs
Feedback
Loop
Operationalizi
ng Results
Einstein Discovery - Creating Stories
● Create Stories directly from the dataset
● Manage Stories
● Deploy to Salesforce
Consider datasets loaded from native and/or external data sources, csv, enriched
using dataflows and recipes.
Einstein Discovery - Integration Examples
Einstein Discovery - Integration Examples
Einstein Discovery - Integration Examples
Embedded Analytics
Embedded Predictive, Explanatory and
Prescriptive
Write back
1 - Install the Managed Package
Your Salesforce admin must install the Salesforce managed package to enable the Writeback feature.
2 - Create Custom Fields in Salesforce to Display Recommendations
You create custom fields in Salesforce to display the outcome, explanation, and prescription information imported from
Einstein Discovery.
3 - Add an Einstein Discovery Section to the Object’s Page Layout
Add the custom fields to the object’s page layout.
4 - Enable Salesforce Writeback
Before you can use the Writeback feature, you must enable it.
5 - Connect Einstein Discovery to Your Custom Fields
To import the recommendations, you connect your custom fields with Einstein Discovery.
6 - Create an Apex Trigger
The Apex trigger fetches the Einstein Discovery predictions when a record is inserted or updated. The Apex trigger is stored
under the object with which it is associated.
7 - Troubleshoot the Integration
To troubleshoot the integration, you can use the Developer Console. The Developer Console is an integrated development
environment with a collection of tools for creating, debugging, and testing applications in your Salesforce org.
https://appexchange.salesforce.com/appxListingDetail?listingId=a0N3A00000FOm9nUAD
Write Back
Write Back
Tips / FAQ
●
Two variables that behave the same can give similar outcome
but they might be differences from the business perspective
●
As a best practice, for more accurate model or outcome
always remove data bias
●
If you have 3 stories, segment your data first before deploying
the appropriate model for each segment in Einstein Analytics
Einstein Analytics/Discovery Consultant Path
Section 6 – Discovery Story Design – Helpful Links
6.1 – Data Prep
https://help.salesforce.com/articleView?id=bi_edd_prep.htm&type=0
https://help.salesforce.com/articleView?id=bi_edd_model_metrics.htm&type=5
https://help.salesforce.com/articleView?id=bi_edd_create.htm&type=0
https://help.salesforce.com/articleView?id=bi_edd_prep.htm&type=5
6.2 – Improvements
https://help.salesforce.com/articleView?id=bi_edd_story_explore.htm&type=5
https://help.salesforce.com/articleView?id=bi_edd_story_graph_whatcould.htm&type=5
https://help.salesforce.com/articleView?id=bi_edd_story_interface.htm&type=5
6.3 – Adjust Parameters
https://help.salesforce.com/articleView?id=bi_edd_prep.htm&type=5
https://releasenotes.docs.salesforce.com/en-us/winter19/release-notes/rn_bi_edd_model_metrics.htm
6.4 – Writeback
https://help.salesforce.com/articleView?id=bi_edd_wb_native.htm&type=5
Einstein Analytics/Discovery Consultant Path
Section 6 – Discovery Story Design – Links from the Exam guide
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Explore Stories
View Model Metrics
Einstein Discovery Limits
Optimize Data for Predictive Analytics
Display Einstein Discovery Predictions in a Salesforce Object
Improve Your Einstein Discovery Models by Investigating Their Metrics and Performance
Q&A
GOOD LUCK!