Did you know that the lack of in-context data prevents you from making smarter business decisions - and as a result, missing out on key revenue opportunities?
2. Your Hosts Today
Jason Beres
Senior VP
, Developer Tools
jasonb@Infragistics.com
Casey McGuigan
Product Manager, Reveal
cmcguigan@Infragistics.com
revealbi.io
3. Today’s Agenda
1. Understand Embedded Analytics Use Cases
2. Embedded Analytics vs Traditional BI
3. Benefits of Embedded Analytics
4. Buy vs Build
5. Embedded Analytics Features
6. Wrap Up
House Keeping
• Recording and slides will be
available after the webinar. We’ll
send a follow-up email
• Please ask questions in the
Questions window
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5. What is Embedded Analytics
Embedded analytics takes
business intelligence
capabilities and integrates
within existing applications
– bringing insights directly
in-context.
6. How Does This Differ From Traditional BI
Business Intelligence is
brining together a range of
data sources to analyze
through data visualizations
in a standalone application.
7. • Enabling real-time reporting, interactive
data visualization and/or advanced
analytics, including machine learning,
directly into an enterprise business
application is critical to app success.
• Embedded existing, robust solutions
eliminates time & cost of your internal dev
resources.
revealbi.io
87% of application
providers say that
embedded analytics is
important to their users
Why Care Embedded Analytics?
8. User Experience Is …
The overall feeling or experience
that a person has when using a
product, including:
• How easy it is to use
• How pleasant and satisfying it is
to use
• How useful it is
• How well it meets the person’s
needs
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9. What is Customer Experience?
Customer experience is the
impression received when the
customer interacts with your
brand throughout all touchpoints
of the buyer’s journey.
There are two main touchpoints
during the customer lifecycle
People Product
Your
Brand
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11. 4 Advantages to Offering Embedded Analytics
Increased Productivity Competitive
Advantage
Data-Driven Culture and
Decision Making
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Increased Revenue
12. Eliminating the need for your users to seek
alternative methods to gain insights
When your analytics, insights and data are
all within your application you eliminate
the need for users to leave your
application for other services
Increase Productivity & Decreased App
Switching
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13. Giving users relevant and timely insights right
within their workflow promotes a data-driven
culture and encourages more analytical
thinking. In-context analytics enables your
users to make better, faster decisions that are
based on the information available at that
moment or visible on the specific screen they
are viewing.
Data-Driven Culture and Decision Making
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14. • Modern app experience when your users experience
in-application access to data or dashboard creation.
• Deeper understanding of markets and customers
when you can spot trends in your data, staying a step
ahead of your competitors.
• Users spend more time in your app because
embedded analytics provides more data points to
your users without the need for them to go to
another source.
• App becomes stickier as you collect more data about
your users. Users become less likely to switch to
another app because your app contains information
collected over time that is useful for them. This in
turn increases customer satisfaction for your users.
Competitive Advantage
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15. When you increase customer satisfaction, application usage, and
provide your customers the ability to make better, faster decisions
based on data – you will increase your revenue streams.
Embedded analytics can help improve customer retention and
grow your business by:
• Increasing word of mouth through customer satisfaction
• Expanding your potential customer reach to appeal to more
users
• Collecting more insights and data to analyze and reduce
customer churn
Increased Revenue
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20. When choosing a business intelligence
vendor – don’t settle for also needing to
get a certification that comes with it.
Chose a tool with superior customer
experience to get to market faster
• Intuitive drag-and-drop interface
• Easily View, Edit & Share Dashboards
Simple & Beautiful
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21. All your users are different. Each has a
different use case for using analytics. Each has a
different level of sophistication when it comes to
analytics. Don’t limit experienced users – and
don’t overwhelm beginners.
• Offer easy-to-use dashboards and visualizations
for your basic users
• Enable more editing features &
customization for your power users.
2 Provide Self-Service Features
revealbi.io
22. Dashboard Linking
Take drill-down to a new level
when you are able to connect
visualizations to URLs or other
dashboards while passing
through filters
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23. Rich Data Analysis
4
Don’t settle for just
visualizing data. Gain
deeper insights without
needed to be a data
scientist!
- Statistical Functions
- Drill Down
- AI Features
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24. Match the Look & Feel
It is disruptive to the end users when the
analytics they are being provided within
an application do not look like they
belong. Like they were slapped together
without caring how it looks.
• Deliver a “Built-in” experience, not a
“Bolt-on” experience
• Making sure that all UX elements
(theming & styling, roundness vs.
squareness, dialogs) match your brand
experience
5
revealbi.io
25. API-Driven vs. URL Parameters
Reveal is a real SDK - It’s made for developers.
You aren’t forces to simply embed iFrame’s in
your application, and you aren’t forced to
configure dashboards with a parametrized
URL. Use real code, with real object and real
properties.
• API-Driven approach to configuring
dashboards
• Docs, Samples for .NET, React, jQuery, and
more
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6
https://reportserver/reports/powerbi/Store
Sales?rs:Embed=true&filter= Store/Territory eq 'NC' and
Store/Chain eq 'Fashions Direct’
vs.
26. Modern Architecture
The developer experience can make or break an embedded
analytics implementation. Your BI vendor needs to keep
pace with modern technology to make it simple for you.
• Native SDKs that utilize the specific features of each
platform and provide a superior user experience.
• Robust APIs for dashboard rendering, dashboard creation,
deep linking in dashboards and custom UI for data source
acquisition
• Modern API design with multi-channel distribution
capabilities
revealbi.io
7
Small, fast projects: Creating analytics features in-house can be the best option when working on smaller projects with limited sets of requirements — especially if the development team in question has a relevant skill set and previous experience of developing embedded analytics and data visualizations.
Total control: One of the most convincing arguments for building is that it lets product managers remain fully in control over every aspect of their application: not just its functionality but the look and feel as well. By keeping all aspects of development in-house, product teams can control branding, user experience, and functionality. The loss of this control is one of the main disadvantages of buying.
Limitations of support services: Although most providers offer support services, these are not normally included in the price of the package, and are instead an add-on. However, in the majority of cases, such support services are in fact effectively a requirement to ensure the visualization is tailored to the use case and look and feel of the app.
Cost predictability: Standard price entry points for embedded analytics start at anywhere from $30K to $75K per year. However, behind the upfront pricing structure, there are often multiple levels of service, as well as limits on usage and number of applications the embedded analytics can be used in. This can make pricing far less predictable.
Focus on core product: The main disadvantage of the “build” approach is that developers have to switch their focus away from working on the core product to create complex embedded analytics features. Buying saves time and money over-training a development team that may lack previous embedded analytics experience and eliminates the need for training where internal resources are simply not available.
High cost to build: There is a significant cost associated with building embedded analytics, which on average takes seven months to complete. The estimated average cost is as much as $350k (based on average U.S. salaries).
This includes:
4 software developers for 7 months
1 QA professional for 7 months
2 UX/UI designers for 6 months
1 data scientist for 1 month
In-house support: Anything built in-house will have to be supported in-house. With the buy option, support will be provided by the third party, via the cloud, and ISVs will not have to allocate resources to fixing issues if and when they occur. As much as 90%1 of the cost of software during its lifetime is tied to keeping it up and running. Maintenance costs can be significant.
Faster time to market: With average build-it-yourself times taking seven months or more, many product teams decide to buy a bolt-on analytics solution due to the need to release a product as quickly as possible. In a fiercely competitive SaaS market, and with CEOs demanding quick turnaround, buying a pre-built, off-the-shelf solution drastically improves time to market.