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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