Embedded analytics

Last updated May 19, 2024

Embedded analytics enables organisations to integrate analytics capabilities into their own, often software as a service, applications, portals, or websites. This differs from embedded software and web analytics (also commonly known as product analytics).

This integration typically provides contextual insights, quickly, easily and conveniently accessible since these insights should be present on the web page right next to the other, operational, parts of the host application. Insights are provided through interactive data visualisations, such as charts, diagrams, filters, gauges, maps and tables often in combination as dashboards embedded within the system. This setup enables easier, in-depth data analysis without the need to switch and log in between multiple applications. Embedded analytics is also known as customer facing analytics.

Embedded analytics is the integration of analytic capabilities into a host, typically browser-based, business-to-business, software as a service, application. These analytic capabilities would typically be relevant and contextual to the use-case of the host application.

The use-case is, most commonly business-to business, since businesses typically have more sophisticated analytic expectations and needs than consumers. Here, though, the word "business" in "business-to-business software as a service", could also refer to organisational, operational use cases that ultimately benefit consumers (such as healthcare, for instance), e.g.: clinics & hospitals, care & correctional facilities, educational establishments (on/offline), government departments, municipalities, museums, not-for-profit organisations, overseers & regulators amongst others.

Business-to-business-to-consumer use-cases might also be possible, for example a wealth management software as a service application serving wealth management organisations, where a user might be an advisor to consumers.

History

The term "embedded analytics" was first used by Howard Dresner: consultant, author, former Gartner analyst and inventor of the term "business intelligence" said Howard Dresner while he was working for Hyperion Solutions, a company that Oracle bought in 2007. Oracle started then to use the term "embedded analytics" at their press release for Oracle Rapid Planning on 2009 ^{[ citation needed ]}.

Considerations with embedded analytics

When evaluating embedding analytics, consideration would normally be given to integration at various levels, these would likely include: security integration, data integration, application logic integration, business rules integration, and user experience integration.

The spectrum of options for Embedded Analytics Embedded Analytics.jpg — The spectrum of options for Embedded Analytics

This is in contrast to traditional BI, which expects users to leave their workflow applications to look at data insights in a separate set of tools. This immediacy makes embedded analytics much more intuitive and likely to be valued by users. A December 2016 report from Nucleus Research found that using BI tools, which require toggling between applications, can take up as much as 1–2 hours of an employee's time each week, whereas embedded analytics eliminate the need to toggle between apps.

There's a spectrum of options for embedding analytics, on the one hand, at the outset, for example in developing a software as a service minimum viable product, developers will often select a visualisation library, since this is assumed to be the most flexible way to create unique and differentiated analytic experiences. At the other end of the spectrum are Business Intelligence tools, these might make some sacrifices in flexibility for developers, but make up for this with the maturity and sophistication of products optimised for data scientists and analysts.

With embedded analytics, developers and product managers are looking for some kind of compromise between those two extremes of flexibility and user sophistication: flexibility sufficient for product teams to innovate and differentiate, sophistication sufficient to provide advanced analytic capabilities yet without the user being a data scientist or necessarily having any analytic background experience or training. The objective would be intuitive, contextual analytics, consumed as regular web content, immersed into operational user experiences and workflows usable without any special knowledge or training required.

Key elements of embedded analytics

Integration: Embedded analytics seamlessly integrates with existing software applications, allowing users to access analytics functionalities within the context of their daily workflows.
vData Visualization: Embedded analytics offers robust data visualization capabilities, including charts, graphs, and dashboards, to present insights in a visually appealing and easy-to-understand format.
Self-Service Analytics: Embedded analytics empowers users to perform self-service analytics tasks without relying on IT or data analysts, enabling them to explore data, generate reports, and gain insights independently.
Customization: Embedded analytics solutions are highly customizable, allowing organizations to tailor analytics functionalities to their specific needs, preferences, and branding requirements.
Scalability: Embedded analytics solutions are scalable, capable of supporting growing data volumes and user bases without compromising performance or functionality.
Security: Embedded analytics prioritizes data security, ensuring that sensitive information is protected through encryption, access controls, and compliance with regulatory requirements.
Real-Time Insights: Embedded analytics provides real-time or near-real-time insights, enabling users to make timely and informed decisions based on the latest data.
Seamless User Experience: Embedded analytics offers a seamless user experience by integrating analytics functionalities directly into the application's interface, eliminating the need for users to switch between multiple tools or platforms. ^[1]

Use-cases for embedded analytics

The use-cases for embedded analytics are as diverse as the vertical (industry-specific) or horizontal (function, process or role-specific - across industries) host applications in which they are embedded, some examples include:

Vertical use-case examples

Automotive, reservation/rental & dealerships, education, energy management, Fintech (banking, asset management, wealth management), hospital management & healthcare (clinics, care-homes and in the field), learning management, property & facilities management, retail, staffing, supply chain management, transportation & fleet management, unified communications

Horizontal use-case examples

Advertising & multichannel marketing, [[customer relationship management, enterprise resource planning, human resources, human capital management, payroll & benefits, information technology service management, procurement and purchase-topPay

Analytics vs analysis

A common perception is that analytics is mostly, or solely, about analysis. A key value from analytics is the ability to analyse, however the potential for analytics can go substantially beyond this once embedded in the processes of a host software as a service]] application.

When considering user profiles, the difference becomes clearer:

With analysis, the user would be expected to be trained, experienced or at least familiar with the principals of analysis and might have job titles such as analyst, data analyst or data scientist. This persona, for example, would understand chart selection, in other words, given a specific data set, which chart type(s) would best illustrate what can be learnt from the data. This persona probably has a good understanding of data structures, might have the ability to write queries, might be familiar with data modelling and would likely have a strong statistical awareness.
With embedded analytics, the user would be expected to be trained, experienced or at least familiar with business processes and might have no formal or other data science skills. This person is more interested in outcomes that can be driven from analytics rather than exploration into inconsistencies or anomalies that can be found in the data.

With embedded analytics, a software as a service user is probably less interested in spending much time analysing, their purpose, with analytic content immersed as part of a business process, is to drive outcomes, potentially at scale. For this persona, an analytic journey might start with a dashboard helpful in highlighting an anomaly which needs prioritised attention. Clicking on that anomaly, guides the user in understanding the root-cause that's causing the need for action. Once a root-cause has been investigated, the embedded analytics can place the user in the part of the host application to act, potentially at scale. So rather than reschedule, reorder, reassign one thing, the embedded analytics can apply business rules and pass parameters to the host application to act 100 or 1,000 times, instead of once, where each action may be individually customised.

Types of embedded analytics products

When considering the integration of analytics into your solution, you can choose from various categories of software products. These options can be broadly classified into three main groups:

Embedded analytics for SaaS software: Specifically designed for software as a service applications, this category offers specialized embedded analytics solutions. They are ideal for enhancing the analytics capabilities of software as a service platforms, enabling data-driven insights and features tailored to software as a service environments. Like GoodData, icCube, Logi Analytics, Looker (company), Sisense.
Business intelligence software: If your goal is to incorporate pre-existing, comprehensive Business Intelligence software into your solution, you can opt for this category. It allows for seamless integration of generic BI tools for data analysis and reporting.
JavaScript graphics library: If you prefer to build analytics solutions from the ground up, utilizing JavaScript graphics libraries provides the flexibility to create custom analytics components tailored to your specific needs.

Related Research Articles

Customer relationship management (CRM) is a process in which a business or other organization administers its interactions with customers, typically using data analysis to study large amounts of information.

Enterprise content management (ECM) extends the concept of content management by adding a timeline for each content item and, possibly, enforcing processes for its creation, approval, and distribution. Systems using ECM generally provide a secure repository for managed items, analog or digital. They also include one methods for importing content to manage new items, and several presentation methods to make items available for use. Although ECM content may be protected by digital rights management (DRM), it is not required. ECM is distinguished from general content management by its cognizance of the processes and procedures of the enterprise for which it is created.

DocuShare is a content management system developed by Xerox Corporation. DocuShare makes use of open standards and allows for managing content, integrating it with other business systems, and developing customized and packaged software applications.

Appointment scheduling software or meeting scheduling tools allows businesses and professionals to manage appointments and bookings. This type of software is also known as appointment booking software and online booking software.

Enterprise feedback management (EFM) is a system of processes and software that enables organizations to centrally manage deployment of surveys while dispersing authoring and analysis throughout an organization. EFM systems typically provide different roles and permission levels for different types of users, such as novice survey authors, professional survey authors, survey reporters and translators. EFM can help an organization establish a dialogue with employees, partners, and customers regarding key issues and concerns and potentially make customer-specific real time interventions. EFM consists of data collection, analysis and reporting.

Business intelligence software is a type of application software designed to retrieve, analyze, transform and report data for business intelligence. The applications generally read data that has been previously stored, often - though not necessarily - in a data warehouse or data mart.

Adobe LiveCycle Enterprise Suite (ES4) is a service-oriented architecture Java EE server software product from Adobe Systems used to build applications that automate a broad range of business processes for enterprises and government agencies. LiveCycle ES4 is an enterprise document and form platform that helps you capture and process information, deliver personalized communications, and protect and track sensitive information. It is utilized for purposes such as account opening, services, and benefits enrollment, correspondence management, requests for proposal processes, and other manual-based workflows. LiveCycle ES4 incorporates new features with a particular focus on mobile devices. LiveCycle applications function in both online and offline environments. These capabilities are enabled through the use of Adobe Reader, HTML/PhoneGap, and Flash Player clients to reach desktop computers and mobile devices.

Unified communications (UC) is a business and marketing concept describing the integration of enterprise communication services such as instant messaging (chat), presence information, voice, mobility features, audio, web & video conferencing, fixed-mobile convergence (FMC), desktop sharing, data sharing, call control and speech recognition with non-real-time communication services such as unified messaging. UC is not necessarily a single product, but a set of products that provides a consistent unified user interface and user experience across multiple devices and media types.

SharePoint is a web-based collaborative platform that integrates natively with Microsoft 365. Launched in 2001, SharePoint is primarily sold as a document management and storage system, although it is also used for sharing information through an intranet, implementing internal applications, and for implementing business processes.

Qlik [pronounced "klik"] provides a business analytics platform. The software company was founded in 1993 in Lund, Sweden and is now based in King of Prussia, Pennsylvania, United States. The company's main products are Qlik Replicate and Qlik Sense, both software for business intelligence and data integration.

Middleware analysts are computer software engineers with a specialization in products that connect two different computer systems together. These products can be open-source or proprietary. As the term implies, the software, tools, and technologies used by Middleware analysts sit "in-the-middle", between two or more systems; the purpose being to enable two systems to communicate and share information.

KNIME (/naɪm/), the Konstanz Information Miner, is a global computer software company, originally founded in Konstanz (Germany), now headquartered in Zurich (Switzerland) with offices in Germany, the U.S. and Switzerland.

<span class="mw-page-title-main">TACTIC (web framework)</span> Web-based, open source workflow platform and digital asset management system

TACTIC is a web-based, open source workflow platform and digital asset management system supported by Southpaw Technology in Toronto, ON. Designed to optimize busy production environments with high volumes of content traffic, TACTIC applies business or workflow logic to combined database and file system management. Using elements of digital asset management, production asset management and workflow management, TACTIC tracks the creation and development of digital assets through production pipelines. TACTIC is available under both commercial and open-source licenses, and also as a hosted cloud service through Amazon Web Services Marketplace.

Collaborative decision-making (CDM) software is a software application or module that helps to coordinate and disseminate data and reach consensus among work groups.

HP CloudSystem is a cloud infrastructure from Hewlett Packard Enterprise (HPE) that combines storage, servers, networking and software.

Business communications operations management (BCOM) is a category of management products that automate the configuration and operations of modern enterprise communications solutions.

Unified communications (UC) management is essentially the management of unified communications; it refers to the systems used by enterprise organizations to automate their enterprise communications services and the voice network infrastructure that those services run over.

Logi Analytics, Inc. is a computer software company headquartered in McLean, Virginia, United States with offices in the UK and Ireland. It offers interactive data visualization products for business intelligence and business analytics. On April 7, 2021, Logi Analytics, Inc. was acquired by insightsoftware.

Lark Suite is a cloud-based business-to-business (B2B) super-app with a collection of collaboration and productivity tools, sold and marketed by Lark.

References

↑ "embedded analytics - key elements". www.enqdb.com. enqdb. Retrieved 17 May 2024.

External links

This page is based on this Wikipedia article
Text is available under the CC BY-SA 4.0 license; additional terms may apply.
Images, videos and audio are available under their respective licenses.

[1] "embedded analytics - key elements". www.enqdb.com. enqdb. Retrieved 17 May 2024.

[1]