Embedded Analytics enables organisations to integrate analytics capabilities into their own, often SaaS, 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 the integration of analytic capabilities into a host, typically browser-based, business-to-business (B2B), SaaS, application.  These analytic capabilities would typically be relevant and contextual to the use-case of the host application.
 The use-case is, most commonly, B2B, since businesses typically have more sophisticated analytic expectations and needs than consumers.  Here, though, the word "business" in "business-to-business SaaS", 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 (B2B2C) use-cases might also be possible, for example a wealth management SaaS application serving wealth management organisations, where a user might be an advisor to consumers.
 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].
 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.
 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 SaaS Minimum Viable Product (MVP), 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, integrated into operational user experiences and workflows usable without any special knowledge or training required.
 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:
 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
 Advertising & Multi-channel Marketing, Customer Relationship Management (CRM), Enterprise Resource Planning (ERP), Human Resources, Human Capital Management, Payroll & Benefits, Information Technology Service Management (ITSM), Procurement and Purchase-to-Pay
 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: