The document discusses embedded analytics and five critical success factors for embedded analytics projects. It provides an overview of embedded analytics compared to traditional BI, discusses how embedded analytics can help overcome barriers to analytics adoption, and outlines use cases. It then details five critical success factors for embedded analytics: 1) tying analytics to strategic goals and planning, 2) focusing on information visibility, 3) ensuring better data access, 4) providing real-time value, and 5) maintaining constant visibility. The presentation concludes with a discussion of Information Builders' approach to embedded analytics.

1. Lyndsay Wise
Research Director
Enterprise Management Associates
Twitter: @wiseanalytics
Five Critical Success Factors
for Embedded Analytics
Jake Freivald
VP of Product Marketing
Information Builders
Twitter: @jdfreivald

2. Today’s Speakers
Slide 2 © 2017 Enterprise Management Associates, Inc.
Lyndsay Wise, Research Director
Lyndsay Wise joined EMA in 2015 as Research Director for Business
Intelligence (BI) and Data Warehousing, focusing on data integration, data
governance, cloud technologies, data visualization, analytics, and
collaboration. She has over 10 years experience in software research, BI
consulting, and strategy development, specializing in software evaluation and
best-fit solution selection.
Jake Freivald, VP of Product Marketing
In his position as Vice President of Product Marketing for Information Builders,
Jake is responsible for strategic and product-level messaging, content creation
for digital marketing, and related requirements for press and analyst relations.
He graduated from Cornell University with a Bachelor of Science in Electrical
Engineering in 1991.

3. Logistics for Today’s Webinar
Slide 3 © 2017 Enterprise Management Associates, Inc.
An archived version of the event recording will be
available at www.enterprisemanagement.com
• Log questions in the chat panel located on the lower
left-hand corner of your screen
• Questions will be addressed during the Q&A session
of the event
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EVENT RECORDING
A PDF of the speaker slides will be distributed
to all attendees
PDF SLIDES

4. Lyndsay Wise
Research Director
Enterprise Management Associates
Twitter: @wiseanalytics
Five Critical Success Factors
for Embedded Analytics

5. Agenda
A look at embedded analytics
• How user expectations differ between traditional and embedded analytics
• The barriers to adoption that embedded analytics can help overcome
• How organizations can gain buy-in and sell the value to project stakeholders
• Use cases and examples of operational and customer-focused embedded analytics
• Technical requirements and differences of both operational and customer-focused
embedded analytics
5 critical success factors
• Tying embedded analytics to strategic business goals and planning
• Focus on information visibility across the organization
• Ensure better data access
• Real-time value
• Constant visibility
Slide 5

6. Why Embedded Analytics?
Slide 6

7. Traditional Business Intelligence vs. Embedded Analytics
Embedded Analytics
Ease of use
Broad deployment
Intuitive self-service
Supports agility
Easy to reach customers
Real-time operational access
Slide 7 © 2017 Enterprise Management Associates, Inc.
Traditional Analytics
Limited reach within organization
Pre-defined analytics
Information silos
Longer times to delivery

8. Barriers To Analytics – Solving Them Through Embedded
Analytics Adoption
Wide adoption remains hard – depending on
solution, self-service does not truly exist for
most business users
Data access is limited with data silos and
lack of information sharing
Difficult to create real-time data access
Requires an understanding of where data
comes from and how it interrelates
Lack of trust – can create invalid analytics or
access to wrong data
No barrier to access as analytics become a
part of operational/transactional applications
Predefined access takes out the guesswork
Ability to stream analytics within applications
Users don’t have to think to get information
applicable to their line of business
Assumption of trust – developed to take into
account business scenarios, rules, and
processes
Slide 8 © 2017 Enterprise Management Associates, Inc.

9. Gaining Buy-In And Selling The Value To Project
Stakeholders
Slide 9 © 2017 Enterprise Management Associates, Inc.

10. Use Cases Of Operational And Customer-Focused
Embedded Analytics
Slide 10 © 2017 Enterprise Management Associates, Inc.

11. Technical requirements for embedded analytics
• Level of integration – portal versus white
labeled within application
• Security and privacy of data – need to
ensure that operational intelligence data
reflects the access level of those who view
it; customer-focused needs to provide
customers with trust that their data is
secure
• Potential need for multi-tenancy for
customer-focused embedded analytics
• Level of embedded access – URL to white
labeling – what type works best may differ
based on use case
Slide 11 © 2017 Enterprise Management Associates, Inc.

12. 5 Critical Success Factors
Slide 12

13. 1. Tying embedded analytics to strategic business goals
and planning
Slide 13 © 2017 Enterprise Management Associates, Inc.
Identify
Strategic
Goals
Understand
How Data
Fits
Develop A
Data
Driven
Approach
Tie Data To
Business
Initiatives
Leverage
Analytics
For
Constant
Visibility

14. 1. Tying embedded analytics to strategic business goals
and planning
Slide 14 © 2017 Enterprise Management Associates, Inc.
What information and metrics are required to
manage the process?
How are data and business processes
interrelated?
What information is needed to manage
business processes?
What actions need to take place for deviations
or missed targets?
What information is tied to strategic business
goals?

15. 2. Focus on information visibility across the organization
Slide 15 © 2017 Enterprise Management Associates, Inc.

16. 2. Address data silos
Slide 16 © 2017 Enterprise Management Associates, Inc.

17. 3. Ensure better data access
• Making sure data is consolidated only
provides value if it is available to users in
the way they require
• Looking at self-service in a way that
meets needs of user groups
• Information needs to be easy to get to –
both in terms of self-service and
visibility/transparency based on who
needs what information
• Embedded provides this type of capability
providing embedded is delivered with
these considerations in mind
Slide 17 © 2017 Enterprise Management Associates, Inc.

18. 3. A little about self-service
Slide 18 © 2017 Enterprise Management Associates, Inc.

19. 4. Real-time value
• Latency has always been an issue for companies
• Embedded analytics provide real-time access and give users
instant access to data/analytics
• Historic reports only provide part of the picture for trends or
planning but not for operational analytics or efficiencies
• Operational BI enables constant business visibility to what is
happening within specific processes
• Organizations cannot wait until after the fact to get the
visibility into operations they need
Slide 19 © 2017 Enterprise Management Associates, Inc.

20. 5. Constant visibility
• Real time access leads to
constant visibility
• Organizations understand what is
happening as it is happening and
can become more proactive.
• Identify potential risks more
proactively
• Take action when issues occur
• Evaluate factors of performance
Slide 20 © 2017 Enterprise Management Associates, Inc.

21. 5. Real-time for embedded BI
Slide 21 © 2017 Enterprise Management Associates, Inc.

22. Takeaways
Slide 22 © 2014 Enterprise Management Associates, Inc.
• Embedded BI can enhance a broader analytics program and
provide operational insights
• Organizations need to align their business strategy with their data
access
• Evaluate the type of embedded analytics that work best by targeting
the level of self-service access
• Identify the latency required to meet business needs
• Leverage embedded analytics to increase overall information
visibility

23. Embedded BI and Analytics
The Information Builders Approach
23
Jake Freivald
VP, Product Marketing

24. Better Data. Better Analytics.
Information Builders

25. 25
Data
Quality
Data
Governance
Master Data
Management
Integrity
Integration
Batch ETL Real-Time ESB
ApplicationsLegacy Systems Relational/Cubes Big Data Columnar/In Memory Unstructured Social Media Web Services Trading PartnersIoT
Hadoop-Based
BI Portal Embedded InfoApps™ Mobile
Natural Language/
Narrative BI
Data Discovery Reporting Dashboards
High-Performance
Data Store
Intelligence
Location
Analytics
Casting
and Archiving
In-Document
Analytics
SearchPredictive
Analytics
Sentiment and
Word Analytics
Performance
Management
Social
Hot
Bad
Feedback
Write-Back

26. 26
Data
Quality
Data
Governance
Master Data
Management
Integrity
Integration
Batch ETL Real-Time ESB
ApplicationsLegacy Systems Relational/Cubes Big Data Columnar/In Memory Unstructured Social Media Web Services Trading PartnersIoT
Hadoop-Based
BI Portal Embedded InfoApps™ Mobile
Natural Language/
Narrative BI
Data Discovery Reporting Dashboards
High-Performance
Data Store
Intelligence
Location
Analytics
Casting
and Archiving
In-Document
Analytics
SearchPredictive
Analytics
Sentiment and
Word Analytics
Performance
Management
Social
Hot
Bad
Feedback
Write-Back

27. IT Developers Management Operational Workers Business Partners Customers
Tools for business users & IT
Self-Service for Everyone
Business Analysts
App Studio InfoAssist+
Data
Discovery
Predictive
Analytics
Social
Intelligence
Search
Location
Intelligence
Reporting
Dashboards
Generate & Deploy Insights
Social
Hot
Bad
Feedback

28. IT Developers Management Operational Workers Business Partners Customers
Tools for business users & IT – InfoApps™ for non-technical users
Self-Service for Everyone
InfoApps™
Dashboards &
Scorecards
Governed Self-
service Reporting
Mobile InfoApps
E-Statements
Data Discovery
InfoApps
Predictive InfoApps Search & Social
Analytic InfoApps
Operationalize Insights & Monetize Data
Business Analysts
App Studio InfoAssist+
Data
Discovery
Predictive
Analytics
Social
Intelligence
Search
Location
Intelligence
Reporting
Dashboards
Generate & Deploy Insights
Social
Hot
Bad
Feedback

29. Embedded BI
For Commercial Portals
168/286 JSR-compliant portlets/webparts
For Custom Portals and SaaS-style Apps
a. iFrame “Mashup” Style – simplest
b. RESTful Web Service API – maximum control and flexibility
For Resellers, Partners, and On-Premises Apps
Deliver a fully customized and styled BI service within or alongside
an on-premises application
29
Some technical details

30. Information Builders understands
the economics of the SaaS business.
They were able to come up with
a pricing structure that works
for both of us.
Alan Rich
Chief Executive Officer
WebFOCUS at Chrome River
30
Software Provider Makes SaaS Offering Shine With BI
Challenge: Integrate a robust reporting solution
that supports RIAs in a lightweight, web-based
deployment model. Establish flexible pricing to
complement a subscription-based SaaS model.
Strategy: Create parameterized reports that leverage a
multitenant security model, and augment these reports
with a flexible ad hoc environment.
Results: Expense reporting now includes flexible
inquiry and analysis tools that provide visibility into
spending patterns by office, department, practice
group, user, client, and other variables.
Computer Services, Customer Since 2009

31. WebFOCUS at Chrome River
31
SaaS Application
Create
Expense
Report
Chrome
River
Servers
Review/
Approve
Reports
Reimbursement
Deposit
Accounts
Payable
Client
Billing
Client
Invoices
Data exportsData imports
Clients /
Matters
Daily
FX Rates
Cash
Advances
Credit card
Transactions
Scanned
Receipts
Phone
Entries
Computer Services, Customer Since 2009

32. Don’t Take Our Word For It
32

33. Additional Resources
33
Videos
What is Embedded BI
and its requirements?
Other information
Fact sheet
Recent blog post
Customer story
Paycor embeds WebFOCUS
into their HR application
White paper
Making a Case for Embedded
Business Intelligence