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TRANSCRIPT
R oughly 2.5 quintillion bytes of data is produced every
single day. In an ideal world, organizations would
leverage that data to build better products, provide better
services, and even create new revenue streams. Yet, there’s a
fundamental problem holding organizations back from leveraging
the treasure trove of data that’s available to them. Despite the fact
that 62% of organizations say that self-service business intelligence
is “critical,” most only analyze 12% of their data.
Embedded analytics play a key role in democratizing data access
and driving adoption outside of data and BI teams. It also can
accelerate time to value for data applications by allowing teams to
work together to build applications faster with better user
experiences – and reduced development costs.
This comprehensive guide will look at precisely what embedded
analytics is, the bene�ts organizations can expect, best practices
and essentials for success, popular use cases, and �ve must-have
features for embedded analytics platforms.
Table of Contents
What Is Embedded Analytics? How Embedded Analytics IsDierent From Traditional BI:Access and Participation
Why Embedded AnalyticsPlatforms Accelerate Time ToValue For Data Applications
5 Immediate Benets ofEmbedded Analytics For Data-driven Organizations
4 Popular Examples of EmbeddedAnalytics
5 Keys To Success In EmbeddedAnalytics
10 Must-have Features ForEmbedded Analytics Platforms
Experience the Benets ofEmbedded Analytics
What Is Embedded Analytics?
Embedded analytics tames cumbersome workows and increases the speed and
ease of data discovery by adding dashboards and visualizations directly into
internal and external applications. Most commonly, organizations take the reports,
visualizations, and dashboards that they build in BI and analytics tools and embed
them into:
CUSTOMER OR PARTNER-FACING APPLICATIONS: Many companies are
leveraging embedded analytics solutions to monetize their data and quickly
deliver high-quality data products that drive customer and partner
satisfaction.
INTERNAL BUSINESS APPLICATIONS: Embedding relevant analyses directly
into business workows increases the ease and frequency of data-driven
decision-making across the organization.
PUBLIC-FACING WEB PAGES: Companies may choose to share research they’ve
done around particular industries or global events to garner press coverage
and brand awareness.
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How Embedded Analytics IsDierent From Traditional BI:
Access and Participation
Access
Traditional BI requires that real-time reports and visualizations be viewed natively
within analytics tools, which are most often unfriendly to business users.
Embedded analytics makes reports and visualizations available within existing
workows, software, and systems — allowing non-technical users to work with and
benet from data insights easily.
Participation
With traditional BI, only those with technical expertise and SQL skills can
participate in modeling, querying, and creating visualizations. With embedded
analytics, on the other hand, users of all types can leverage their company’s data to
reach their goals. For example:
PRODUCT AND DEVELOPMENT
TEAMS can monetize their data
and provide customers with
business insights by
embedding a third-party
solution, saving signicant
time and resources.
Traditionally, this process
would be a heavy lift with many
infrastructure, compliance,
and security considerations.
DATA AND BI TEAMS can embed
specic reports and
dashboards directly in the
context of relevant business
workows, which encourages
data-driven thinking within an
organization — and can also
reduce the volume of ad hoc
requests.
MARKETING AND PUBLIC
RELATIONS TEAMS can leverage
rst-party research by
embedding survey results or
research analyses across public
web pages or publications.
Why Embedded AnalyticsPlatforms Accelerate Time ToValue For Data Applications
Success and an organization’s ability to stay competitive depends on enabling agile
analytics processes — without sacricing security and compliance. Collaboration
is compromised when static tools limit organizations with limited functionality.
Embedded analytics solves three signicant challenges to a collaborative state of
ow: slow time to insights, slow production times, and sloppy governance.
With the right embedded analytics solution, users can ask follow-up questions of
their data for themselves, and they aren’t required to toggle back and forth
between separate systems. They can quickly and easily get the answers they need
using familiar tools. Additionally, embedding ensures that users are more likely to
see and act upon analytic insights. When reports and visualizations are easily
accessible and at the forefront of their workows, users of all kinds will take
advantage of them.
An added benet of embedded dashboards and applications is that it allows
organizations to monetize their data while making products faster to build and
deploy. This is because it enables application builders to focus on creating great
user experiences and get products out the door quickly instead of worrying about
infrastructure or new programming languages.
Finally, embedded analytics also helps with data security and compliance.
Collaboration without an analytics tool that’s designed to be community-driven will
result in governance nightmares. When permissions remain intact and users work
within the parameters of the analytics tool’s security features, data governance
becomes much more manageable.
5 Immediate Benets ofEmbedded Analytics For Data-
driven Organizations
Flexible data sharingEmbedded analytics gives companies complete exibility in sharing insights: it’s
possible to keep reports and visualizations in-house or share them more widely.
The specics will vary by platform but, in Sigma, exible embedding works by
generating and embedding a unique and secure URL of the dashboard or
dashboard visualization teams want to share, then placing that generated URL into
an iframe in an application. This secure URL contains elds to dene what viewers
will see, areas to ensure that the URL is unique, and a signature created by
encrypting the URL. In other words, embedded analytics provides a completely
secure solution with access restricted to whom is deemed necessary.
What is agile analytics?
Agile analytics is based on agile development methodology, which facilitates
speed, adaptability, and collaboration. Agile analytics is designed to be exible
so that business teams can move quickly to explore data, nd answers to
pressing questions, and iterate on analyses to answer follow-up questions. With
agile analytics frameworks, the emphasis is on the outcome, not a rigid process.
It allows business teams to leverage their unique perspectives and expertise for
broader organizational initiatives and to streamline and inform daily decision-
making.
Encourage data-driven decision-makingThanks to the fact that users of all types can be involved in embedded analytics,
business users in marketing, sales, nance, or operations can generate real-time
insights quickly. This means more insights covering a more comprehensive range of
questions. As a result of this abundance, data-driven insights can inform all types
of decision-making — strategic, tactical, and operational. There’s no longer a limit
on what kinds of decisions get data or BI team resources allocated to them.
Increase productivityWhen business teams can run their analyses, they don’t need to wait around for the
BI team to deliver dashboards or reports. They can handle their own information
needs, making them much more ecient. At the same time, data and BI teams
become more productive because they no longer have to spend most of their time
running reports — instead, they can focus on work more aligned with their
specialized skills.
High adoption boosts ROIThe combination of more decisions informed by data — and greater productivity
— translates to a better return on a company’s analytics investment. As people
experience the benets of using insights in their day-to-day work, they’ll put the
tool to even greater use. According to a survey by Dresner Advisory Services,
embedded analytics tools have, on average, a 59% adoption rate compared to the
27% average adoption rate of traditional BI tools.
Transform data into a productMany organizations are generating unique, valuable data that customers and
partners would happily pay to access. Embedded analytics makes it easy to
transform data, reports, and visualizations into a product that generates revenue or
that can be used as a value-add for existing products or services, allowing
companies to be more competitive or even raise pricing.
4 Popular Examples ofEmbedded Analytics
Public web pagesWhen companies want to make the public aware of data that continually changes,
creating an embedded analytics visualization is the ideal solution. Using a simple
HTML embed code, it is easy to share visualizations, dashboards, and reports that
automatically update as data changes.
A timely example is COVID-19 pandemic data. Metrikus, an IoT integration platform
for smart buildings, created the Occupancy Index to track occupancy during COVID
throughout the UK and was subsequently approached by Bloomberg to use it in
their ticker.
Internal web portalsInsights intended only for specic teams can be embedded into internal web
portals. The advantage of web portal pages is that several data reports and
visualizations can be grouped together for ease of interpretation or for diving in
deeper while looking at context. These pages can also be used to feature important
dashboards that teams want their colleagues to be able to easily nd and use.
Third-party applicationsEmbedding analytics capabilities directly into third-party applications allows
teams to streamline existing workows without having to exit the software they nd
familiar. People can query and visualize data directly in the software they use every
day. This capability is especially benecial for sales and marketing teams. Being
able to produce relevant analyses within Salesforce and other applications leads to
faster and more informed decision-making by eliminating the need to switch
between applications to access the data that users need.
Customer products: How Payloadused embedded analytics tomonetize its dataIn a recent webinar, Chris Lambert, CTO of Payload, explains the benet of making
data available to customers: “With Sigma’s application embedding capability, we
were able to create data-rich and interactive dashboards that show our customers
all of the key metrics they need for daily decision-making and embed them directly
into our proprietary products without any interruption to the service we provide to
our customers.”
The results speak for themselves. Payload was able to leverage embedded
dashboards to create a new revenue channel while experiencing 50% BI resource
savings and 600% cost savings. “Adding Sigma dashboards and insights to the
Payload application has had a huge impact on the perceived value of our product,”
says Chris. “It’s not only helping us retain current customers, but it’s also enabling
us to expand these accounts as well.”
5 Keys To Success In EmbeddedAnalytics
Provide a strong user experienceThere are two important aspects to UX. First, the user experience should be
seamless. Can embedded dashboards be customized according to each user’s
needs? Ideally, each user’s dashboard contains the functionality that the user
needs, no more and no less. Another thing to think about is implementation. How
easy is the embed process? A simple implementation process will preserve
resources and increase adoption.
Understand the needs of each usertypeA good embedded solution should match the unique needs of an organization’s
end users. To accomplish this, seek to thoroughly understand the needs of internal
and external stakeholders like employees, partners, vendors, and customers.
What are their roles and responsibilities? What problems are they aiming to solve?
What are their skills and experience with technology? What do their workows look
like?
Identify the functionality each usertype will needAnother important set of details that will inuence the choice of a tool is the
functionality each user type will need. What types of data do they need to be able
to work with? What specically will they need to be able to do with the data?
(Modeling? Querying? Creating visualizations?) Match the capability needs with the
required functionality.
Consider where and how analyticswill be embeddedWhere do stakeholders want to embed analytics, and how integrated do they need
the analytics to be? Embedded analytics may be “bolted on,” providing security
but little else in the way of integration, or may provide a seamless user experience
with full integration (and a variety of in-betweens). The most robust type of
integration oers data discovery and complete, real-time analytics functionality
within the external platform interface.
Quantify the value of analytics foreach user typeUser adoption will be greatly improved if they understand exactly how the tool will
help them reach their goals, save them time, and improve their work. Identify the
value that participating in analytics will bring to each user type. In order to help
users understand the value and get the most from an embedded analytics BI tool,
teams should invest in analytics training and data literacy. This training isn’t
designed to turn non-technical users into technical ones, but to prepare them to
participate in the data conversation, discover meaningful insights, and drive
business growth.
10 Must-have Features ForEmbedded Analytics Platforms
To fully benet from what embedded analytics has to oer, you’ll need specic
features in your analytics software. Here’s what to look for in an embedded
analytics platform to ensure your people can use it eectively.
Unconstrained drill paths. The insights revealed by high-level dashboards
almost always trigger additional questions. Why is this pattern showing up?
Why is that trend happening? What would happen if we changed this
variable? For scalable business intelligence that doesn’t depend on BI
teams that are overwhelmed with endless to-do lists, business teams must
have the ability to drill down into the live data underlying their dashboards
to nd answers to questions in a timely manner.
Spreadsheet-like, intuitive interface. To be truly data-driven, business
users must be able to nd answers quickly on their own — and they
shouldn’t have to learn SQL or a proprietary coding language to do so. A
good embedded analytics platform will have an intuitive user interface,
such as a spreadsheet, that’s simple for even non-technical users to engage
with.
Row-level detail. With a spreadsheet-based interface, users can dive down
into row-level detail using formulas, functions, pivot tables, and so on.
Without this level of detail, users will be limited in the value they can get
from their data.
Direct connection to the CDW. For most forms of data analytics, the
freshness of data impacts the accuracy of the insights derived from it. Users
must have the ability to connect directly with the cloud data warehouse or
data platform to access live data and take advantage of the power of the
cloud for fast and scalable ad hoc data exploration.
SaaS, not on-premises. A SaaS-based embedded analytics solution
requires minimal deployment and maintenance resources. There’s no need
to procure hardware or deal with conguring, maintaining, or backing up
software.
Lightweight modeling and conguration. To maximize adoption and ease
of use for all stakeholders, dashboards should not require code to build and
should provide fast data-modeling capabilities. Additionally, they should be
easy to embed in both private and public websites and applications with a
single URL.
Robust access permissions. For privacy and security purposes, an
embedded analytics platform should oer granular control over what
viewers can see and do, including seeing and exploring only their data.
Must-have security features include access permissions, object and row-
level security options, and one-time signed URLs.
Authentication options. Another crucial security feature is providing
multiple authentication options, including through external applications.
Users need to work quickly and adoption will increase as friction is reduced.
Flexible dashboard builder with pre-built content. Dashboards should be
customizable and interactive so viewers quickly get the data they need, but
they shouldn’t take a lot of time to build. A good embedded analytics
solution will oer pre-built or custom themes and layouts, chart types, data
tables, colors, and fonts. Additionally, users should have the option to
display either a full dashboard or a single visualization or table within a
dashboard.
Multiple ltering options. For eciency, an embedded analytics solution
should also oer ltering capabilities via drop-down menus, input
parameters, or visual ltering via a single click on a chart element. It should
not take users more than a moment to lter.
Experience the Benets ofEmbedded Analytics
Without a good embedded analytics tool, collaboration is certainly possible, but
it’s limited. Organizations need embedded analytics to ensure adoption so they
can benet from the knowledge and perspectives of people in a variety of roles as
well as generate insights to inform a wide range of decisions in a timely manner.
Additionally, the ability to share dashboards and visualizations and build upon one
another’s work is foundational to collaboration. All of these capabilities are found in
embedded analytics.
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