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New Delhi Salesforce Developer Group
#ImpactSalesforceSaturday
Discover deep insights with Salesforce Einstein
Analytics and Discovery
(Einstein Learning Series – Session 2)
By: Jayant Joshi
LEARN . SHARE . CELEBRATE . SALESFORCE
60 Minutes
About New Delhi Salesforce DG
• First Revival Meetup in February 2016
• Twitter: https://twitter.com/newdelhisfdcdug
• New Delhi Salesforce DG Trailblazer Community Group:
http://bit.ly/NewDelhiCommunity
• Facebook: https://www.facebook.com/newdelhisfdcdug
#ImpactSalesforceSaturday
What is #SalesforceSaturday
• Started by Stephanie Herrera in Austin, Texas
• Meetup every Saturday in a Coffee Shop or anywhere to share and
learn about Salesforce
• It’s now a global phenomena with more than 25 SalesforceSaturday
Group over 5 Continents
• For India, it comprises of Online Knowledge Sharing sessions and
Trailhead Challenges
Meet New Delhi Leaders
Atul Gupta
• 8X Certified
• Salesforce MVP
• Founder, CEO
of CloudVandana Solutions
• Community Manager
at MentorshipCentral
• More than 8 years of
experience working in
Salesforce domain
• Author of “Salesforce
Platform Developer I (Apex &
Visualforce)
Certification Training” live on
Simplilearn
Nitin Gupta
• 6X Salesforce Certified
• Sales Cloud Consultant |
Pardot Consultant |
Marketing Cloud Consultant
• Speaker | Blogger
• New Delhi Salesforce
Developer Group Leader
TIME Topic
‘05 Introduction & Background
AGENDA
‘10 What is Einstein Discovery plus ED Demo
’05 Q & A and Wrap-Up
‘40 What is Einstein Analytics plus EA Demo
About Me: Jayant Joshi
Professional:
- SFDC Managing Delivery Architect
- Around 14 Years of overall experience and 9+ years in SFDC
- Currently Working in Capgemini and have worked in Accenture,
Deloitte Consulting and IBM earlier.
- Have worked in India, US, Canada, and Germany.
- Passionate about SFDC
- Among Top SFDC Certified People in World (19X)
- Regularly contribute to Salesforce related articles on Social
Media
- Upcoming Public Sessions on SFDC Topics in India and
Germany
- Mentoring around SFDC Topics
- Enterprise Architecture/TOGAF
- Additional Skills: SFDC Commerce Cloud, Machine Learning,
IOT, TOGAF etc.
Hobbies include Travelling (Have visited 24 countries so
far), Sky Diving and Astronomy.
1-2 Min
Recap - Session 1
 What is AI and Machine
Learning?
 CRM Specific AI Use Cases
 Einstein Use Case
 How Salesforce Einstein use
machine learning? Einstein
Products
 Demo on few Good Einstein
features
2 Min
Recording available at:
https://www.youtube.com/watch?v=kk3KR5LPV
WA
29.06.2019
1 Min
What is Einstein Analytics? 10.08.2019
10-15 Min
A brief History of Analytics
2-3 Min
We will revisit this slide...
What is Einstein Analytics (EA)?
 Easily import the data to Einstein Analytics
 Get great visualizations using charts and
dashboards
 Explore your organization’s data and get
interesting insights.
 Export data from EA
 Query data with Salesforce Analytics
Query Language (SAQL)
 Use Apex to pull data in real time to your
Einstein Analytics dashboard.
2-3 Min
• Native
• Embedded
• Fast
• Mobile
Einstein Analytics – How it work?
2-3 Min
Einstein Analytics – Use Cases
2-3 Min
• Combine various data sources (e.g. your Salesforce org’s, your cloud data
warehouse (AWS Redshift) etc.)
• Create various types of visualizations (charts, dashboards) on your combined
data
• Create interesting insights on your Opportunities by combining data across
different Orgs using Einstein Discovery
• Share the results with your Team using chatter
• Use Einstein Analytics Mobile App for viewing results on the go
EA - Layers
1 Min
Einstein Analytics – Data Layer
2-3 Min
Data Recipes, Dataflows, Data Sync
A dataset is a collection of related data that is
stored in a denormalized, yet highly compressed
form that is optimized for interactive exploration.
A dataflow is a set of instructions that specifies what
data to extract from Salesforce objects or datasets,
how to transform the datasets, and which datasets to
make available for querying.
Einstein Analytics – Designer Layer
2-3 Min
App, Lens, Dashboards
Lens Dashboard
Einstein Analytics – Intelligent Layer
2-3 Min
Stories
A story is the output of Einstein Discovery's comprehensive statistical analysis of your EA dataset.
- Represents a collection of insights around a metric (outcome) that highlights any of the following:
important trends, explanations on what may have influenced those trends, comparisons between
factors, predictions on future outcomes, and suggested actions that may improve outcomes.
Dataflow
2 Min
A dataflow is a set of
instructions that specifies:
- What data to extract
from Salesforce objects
or datasets
- How to transform the
datasets, and
- Which datasets to
make available for
querying.
Design Load Data Configure Start &
Monitor
Schedule
Flow of SFDC
and external
Data based on
data mapping
Load external
data into
Datasets
Download
existing
definition file
Start
Monitor
Schedule the
dataflows
based on the
suitable
frequency
Identify the
Transformations
needed
Configure new
definition file via
Dataset builder,
JSON, Manual
upload
Troubleshoot
Errors
It is all about Visualization (well, almost)
1 Min
Charts and Reports
1 Min
It is all about Visualization (well, almost)…
Time for a DEMO?
 Einstein Analytics –
Case Study
20 Min
Let’s talk about SAQL
2 Min
Developers can write SAQL to directly access Analytics data via:
• Analytics REST API
Build your own app to access and analyse Analytics data or integrate data with existing
apps.
• Dashboard JSON
Create advanced dashboards. A dashboard is a curated set of charts, metrics, and
tables.
• Compare Table
Use SAQL to perform calculations on data in your tables and add the results to a new
column.
• Transformations During Data Flow
Use SAQL to perform manipulations or calculations on data when bringing it in to
Analytics.
I had heard
about SQL,
SOQL, SASL
but What is
SAQL?
1 q = load "Opportunity_Dataset1";
2 q = group q by all;
3 q = foreach q generate count() as
'count‘;
4 q = limit q 2000;
Line
No.
Description
1 This loads the dataset that you chose when you created the chart widget. You can use the variable q to access the
dataset in the rest of your SAQL statements.
2 In some queries, you want to group by a certain field, for example Account ID. In our case we didn’t specify a
grouping when we created the chart. Use group by all when you don’t want to group data.
3 This generates the output for our query. In this simple example, we just count the number of lines in the DTC
Opportunity dataset.
4 This limits the number of results that are returned to 2000. Limiting the number of results can improve performance.
However if you want q to contain more than 2000 results, you can increase this number.
1 Min
SAQL Example
q = load "Opportunity_DS1";
q = foreach q generate
day_in_week(toDate(Mail_sent_sec_epoc
h)) as 'Day in Week‘;
q = group q by 'Day in Week‘;
q = foreach q generate 'Day in Week',
count() as 'count';
Calculate the Average Amount of an
Opportunity Grouped by Type
Use avg() to compare the average size of opportunities for
each account type.
q = load "Opportunity_DataSet1";
q = group q by 'Account_Type‘;
q = foreach q generate 'Account_Type' as
'Account_Type', avg('Amount') as 'avg_Amount';
1 Min
How the components fit together?
2-3 Min
JSON – Yeah, We LOVE it…
- The JSON defines the components of the dashboard
and how they interact.
- Modify a dashboard’s JSON file to perform advanced
customization tasks that can’t be accomplished in the
designer’s user interface, like:
1. Set query limits.
2. Specify columns for a values table.
3. Specify a SAQL query.
4. Populate a filter selector with a specified list of static
values instead of from a query.
5. Set up layouts for mobile devices for a dashboard.
2-3 Min
Sample JSON File
AlphaRobotiks Dashboard JSON File
Time for another DEMO?
 Einstein Analytics
20 Min
Hell Yeah!!!
What is Einstein Discovery? 10.08.2019
10 min
Brief Background
1 Min
Source:
principa.co.za
What is Einstein Discovery?
Einstein Discovery provides answers to key
business questions:
• What happened? What was significant or
unusual?
• Why did it happen? What are the factors that
possibly contributed to the observed outcome?
• How do some factors compare with other
factors?
• What might happen in the future, based on a
statistical analysis of the data? Is there a trend,
or does this data represent an isolated
incident?
• What are some possible actions that could
improve the outcome?
2 Min
Story
Model
Metrics
Outcome
Variable
Let us revisit “A brief History of Analytics”…
2-3 Min
Another DEMO?
Einstein Discovery
05 Min
Let’s do it!!!
Next Sessions 10.08.2019
Einstein Sessions
 Einstein – Use Cases and Products,
29.June > COMPLETED
 Einstein Analytics and Discovery, 10.08
> Scheduled
 Einstein Platform > To be Scheduled
1 Min
Einstein Platform:
 What is Einstein Platform?
 Einstein API‘s
 Einstein Voice
 Einstein Prediction Builder
 Demos
How to Learn Einstein? 10.08.2019
Einstein Resources
 Salesforce Trailhead
 Einstein Trailmixes
 Salesforce Einstein Help
1 Min
Einstein Resources
1 Min
Sign-Up for Einstein Analytics Org
1 Min
Q & A 10.08.2019
Total Time: 05
Min
Follow & Join New Delhi Salesforce DG
• Join to know about future events and to RSVP:
https://trailblazercommunitygroups.com/delhi-in-developers-group/
• Let’s start conversations on Success Community:
http://bit.ly/NewDelhiCommunity
• Follow us on Twitter: https://twitter.com/newdelhisfdcdug
• Follow us on Facebook: https://www.facebook.com/newdelhisfdcdug
• For all the content: https://newdelhisfdcdug.com
#ImpactSalesforceSaturday
#ImpactSalesforceSaturday

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Discover deep insights with Salesforce Einstein Analytics and Discovery

  • 1. New Delhi Salesforce Developer Group #ImpactSalesforceSaturday Discover deep insights with Salesforce Einstein Analytics and Discovery (Einstein Learning Series – Session 2) By: Jayant Joshi LEARN . SHARE . CELEBRATE . SALESFORCE 60 Minutes
  • 2. About New Delhi Salesforce DG • First Revival Meetup in February 2016 • Twitter: https://twitter.com/newdelhisfdcdug • New Delhi Salesforce DG Trailblazer Community Group: http://bit.ly/NewDelhiCommunity • Facebook: https://www.facebook.com/newdelhisfdcdug #ImpactSalesforceSaturday
  • 3. What is #SalesforceSaturday • Started by Stephanie Herrera in Austin, Texas • Meetup every Saturday in a Coffee Shop or anywhere to share and learn about Salesforce • It’s now a global phenomena with more than 25 SalesforceSaturday Group over 5 Continents • For India, it comprises of Online Knowledge Sharing sessions and Trailhead Challenges
  • 4. Meet New Delhi Leaders Atul Gupta • 8X Certified • Salesforce MVP • Founder, CEO of CloudVandana Solutions • Community Manager at MentorshipCentral • More than 8 years of experience working in Salesforce domain • Author of “Salesforce Platform Developer I (Apex & Visualforce) Certification Training” live on Simplilearn Nitin Gupta • 6X Salesforce Certified • Sales Cloud Consultant | Pardot Consultant | Marketing Cloud Consultant • Speaker | Blogger • New Delhi Salesforce Developer Group Leader
  • 5. TIME Topic ‘05 Introduction & Background AGENDA ‘10 What is Einstein Discovery plus ED Demo ’05 Q & A and Wrap-Up ‘40 What is Einstein Analytics plus EA Demo
  • 6. About Me: Jayant Joshi Professional: - SFDC Managing Delivery Architect - Around 14 Years of overall experience and 9+ years in SFDC - Currently Working in Capgemini and have worked in Accenture, Deloitte Consulting and IBM earlier. - Have worked in India, US, Canada, and Germany. - Passionate about SFDC - Among Top SFDC Certified People in World (19X) - Regularly contribute to Salesforce related articles on Social Media - Upcoming Public Sessions on SFDC Topics in India and Germany - Mentoring around SFDC Topics - Enterprise Architecture/TOGAF - Additional Skills: SFDC Commerce Cloud, Machine Learning, IOT, TOGAF etc. Hobbies include Travelling (Have visited 24 countries so far), Sky Diving and Astronomy. 1-2 Min
  • 7. Recap - Session 1  What is AI and Machine Learning?  CRM Specific AI Use Cases  Einstein Use Case  How Salesforce Einstein use machine learning? Einstein Products  Demo on few Good Einstein features 2 Min Recording available at: https://www.youtube.com/watch?v=kk3KR5LPV WA 29.06.2019 1 Min
  • 8. What is Einstein Analytics? 10.08.2019 10-15 Min
  • 9. A brief History of Analytics 2-3 Min We will revisit this slide...
  • 10. What is Einstein Analytics (EA)?  Easily import the data to Einstein Analytics  Get great visualizations using charts and dashboards  Explore your organization’s data and get interesting insights.  Export data from EA  Query data with Salesforce Analytics Query Language (SAQL)  Use Apex to pull data in real time to your Einstein Analytics dashboard. 2-3 Min • Native • Embedded • Fast • Mobile
  • 11. Einstein Analytics – How it work? 2-3 Min
  • 12. Einstein Analytics – Use Cases 2-3 Min • Combine various data sources (e.g. your Salesforce org’s, your cloud data warehouse (AWS Redshift) etc.) • Create various types of visualizations (charts, dashboards) on your combined data • Create interesting insights on your Opportunities by combining data across different Orgs using Einstein Discovery • Share the results with your Team using chatter • Use Einstein Analytics Mobile App for viewing results on the go
  • 14. Einstein Analytics – Data Layer 2-3 Min Data Recipes, Dataflows, Data Sync A dataset is a collection of related data that is stored in a denormalized, yet highly compressed form that is optimized for interactive exploration. A dataflow is a set of instructions that specifies what data to extract from Salesforce objects or datasets, how to transform the datasets, and which datasets to make available for querying.
  • 15. Einstein Analytics – Designer Layer 2-3 Min App, Lens, Dashboards Lens Dashboard
  • 16. Einstein Analytics – Intelligent Layer 2-3 Min Stories A story is the output of Einstein Discovery's comprehensive statistical analysis of your EA dataset. - Represents a collection of insights around a metric (outcome) that highlights any of the following: important trends, explanations on what may have influenced those trends, comparisons between factors, predictions on future outcomes, and suggested actions that may improve outcomes.
  • 17. Dataflow 2 Min A dataflow is a set of instructions that specifies: - What data to extract from Salesforce objects or datasets - How to transform the datasets, and - Which datasets to make available for querying. Design Load Data Configure Start & Monitor Schedule Flow of SFDC and external Data based on data mapping Load external data into Datasets Download existing definition file Start Monitor Schedule the dataflows based on the suitable frequency Identify the Transformations needed Configure new definition file via Dataset builder, JSON, Manual upload Troubleshoot Errors
  • 18. It is all about Visualization (well, almost) 1 Min
  • 19. Charts and Reports 1 Min It is all about Visualization (well, almost)…
  • 20. Time for a DEMO?  Einstein Analytics – Case Study 20 Min
  • 21. Let’s talk about SAQL 2 Min Developers can write SAQL to directly access Analytics data via: • Analytics REST API Build your own app to access and analyse Analytics data or integrate data with existing apps. • Dashboard JSON Create advanced dashboards. A dashboard is a curated set of charts, metrics, and tables. • Compare Table Use SAQL to perform calculations on data in your tables and add the results to a new column. • Transformations During Data Flow Use SAQL to perform manipulations or calculations on data when bringing it in to Analytics. I had heard about SQL, SOQL, SASL but What is SAQL? 1 q = load "Opportunity_Dataset1"; 2 q = group q by all; 3 q = foreach q generate count() as 'count‘; 4 q = limit q 2000; Line No. Description 1 This loads the dataset that you chose when you created the chart widget. You can use the variable q to access the dataset in the rest of your SAQL statements. 2 In some queries, you want to group by a certain field, for example Account ID. In our case we didn’t specify a grouping when we created the chart. Use group by all when you don’t want to group data. 3 This generates the output for our query. In this simple example, we just count the number of lines in the DTC Opportunity dataset. 4 This limits the number of results that are returned to 2000. Limiting the number of results can improve performance. However if you want q to contain more than 2000 results, you can increase this number.
  • 22. 1 Min SAQL Example q = load "Opportunity_DS1"; q = foreach q generate day_in_week(toDate(Mail_sent_sec_epoc h)) as 'Day in Week‘; q = group q by 'Day in Week‘; q = foreach q generate 'Day in Week', count() as 'count'; Calculate the Average Amount of an Opportunity Grouped by Type Use avg() to compare the average size of opportunities for each account type. q = load "Opportunity_DataSet1"; q = group q by 'Account_Type‘; q = foreach q generate 'Account_Type' as 'Account_Type', avg('Amount') as 'avg_Amount';
  • 23. 1 Min How the components fit together?
  • 24. 2-3 Min JSON – Yeah, We LOVE it… - The JSON defines the components of the dashboard and how they interact. - Modify a dashboard’s JSON file to perform advanced customization tasks that can’t be accomplished in the designer’s user interface, like: 1. Set query limits. 2. Specify columns for a values table. 3. Specify a SAQL query. 4. Populate a filter selector with a specified list of static values instead of from a query. 5. Set up layouts for mobile devices for a dashboard.
  • 25. 2-3 Min Sample JSON File AlphaRobotiks Dashboard JSON File
  • 26. Time for another DEMO?  Einstein Analytics 20 Min Hell Yeah!!!
  • 27. What is Einstein Discovery? 10.08.2019 10 min
  • 29. What is Einstein Discovery? Einstein Discovery provides answers to key business questions: • What happened? What was significant or unusual? • Why did it happen? What are the factors that possibly contributed to the observed outcome? • How do some factors compare with other factors? • What might happen in the future, based on a statistical analysis of the data? Is there a trend, or does this data represent an isolated incident? • What are some possible actions that could improve the outcome? 2 Min Story Model Metrics Outcome Variable
  • 30. Let us revisit “A brief History of Analytics”… 2-3 Min
  • 31. Another DEMO? Einstein Discovery 05 Min Let’s do it!!!
  • 33. Einstein Sessions  Einstein – Use Cases and Products, 29.June > COMPLETED  Einstein Analytics and Discovery, 10.08 > Scheduled  Einstein Platform > To be Scheduled 1 Min Einstein Platform:  What is Einstein Platform?  Einstein API‘s  Einstein Voice  Einstein Prediction Builder  Demos
  • 34. How to Learn Einstein? 10.08.2019
  • 35. Einstein Resources  Salesforce Trailhead  Einstein Trailmixes  Salesforce Einstein Help 1 Min
  • 37. Sign-Up for Einstein Analytics Org 1 Min
  • 38. Q & A 10.08.2019 Total Time: 05 Min
  • 39. Follow & Join New Delhi Salesforce DG • Join to know about future events and to RSVP: https://trailblazercommunitygroups.com/delhi-in-developers-group/ • Let’s start conversations on Success Community: http://bit.ly/NewDelhiCommunity • Follow us on Twitter: https://twitter.com/newdelhisfdcdug • Follow us on Facebook: https://www.facebook.com/newdelhisfdcdug • For all the content: https://newdelhisfdcdug.com #ImpactSalesforceSaturday