<ul class="anchorlinks"><li><a href="#what">What is embedded analytics</a></li><li><a href="#embedded">Embedded analytics featured use case</a></li><li><a href="#machine">Embedded machine learning</a></li><li><a href="#how">How to recognize the best plugins</a></li><li><a href="#unbounded">Unbounded use cases</a></li><li>&nbsp;<a href="#ever">Ever-evolving assemblies</a></li></ul>



<h2 id="what">What is embedded analytics?</h2>



Embedded analytics refers to the process of plugging a third party’s software into your own app to take advantage of a set of advanced data analytics tools and use it as your own. 



Of course, a description of AI-based analytics is more involved and will be explored in more detail shortly — and with plenty of use cases to make it real. But broadly speaking, after choosing the analytics solution that meets your needs, the steps to embedding are very simple:



<ul><li>Purchase the plan license matching your needs</li><li>Embed the API in your app</li><li>Connect data sources</li><li>Set up dashboards</li><li>Extract insights from your data</li><li><a href="https://www.sisense.com/case-studies/luzern-blox/">Impress your customers</a>!</li></ul>



Developing an advanced data analytics solution is a complex endeavor. That is probably the best reason to embed another company’s solution instead of building one yourself. In fact, that simple statement captures the essence of embedding — not just analytics, but any API delivering an expert service that raises the quality of your own web app. Let’s look at the immediate benefits of embedding:



<ul><li>Deploy expert domain knowledge instantly in your app</li><li>Avoid the time/cost of building a developer team</li><li>Bring your product to market faster</li><li>Rely on experts for support, upgrades, and compliance</li><li>Reduce risk and simplify business logic</li></ul>



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<h2 id="embedded">Embedded analytics featured use case</h2>



AI-based embedded analytics goes beyond simple dashboard data visualizations to a new realm of business intelligence (BI). Nowadays, white-labeling your <a href="https://www.sisense.com/product/embedded-analytics/">embedded analytics solution</a> means that the plugin acts exactly like the other elements of your web app. 



White-labeling is a natural aspect of every important use case because it <a href="https://www.sisense.com/glossary/integrated-analytics/">seamlessly integrates</a> AI-powered intelligence into your products. Going still further, AI-based embedded analytics evolves workflows from previously isolated business processes to ones that blend outcomes and insights across departments, which can now collaborate with ease.&nbsp;



A practical example would be an e-business sales process with the otherwise dull-sounding activity “Create sales order.” In this case, the sales order is for an auto insurance policy. However, suppose a customer service representative, armed with an AI-based analytics engine like Sisense embedded in their CRM, realizes not only the opportunity for cross-selling by bundling related products, but a blended solution that will inject new possibilities into the workflow.



While the sales recommender engine naturally suggests products based on previously established customer preferences, it is simultaneously using the e-claims process to evaluate potential relationships in the big data warehouse. The account rep initiates a sales process which, in the embedded analytic app, triggers a deep reinforcement learning algorithm in parallel with creating the new policy. 



As this is going on, existing claims are being evaluated by a fraud detection algorithm, resulting in an opportunity to dynamically flag suspicious activity and route the workflow to the appropriate department. 



This is one situation where a traditional customer account rep can become a sophisticated BI user, verging on membership in a data team and crossing traditional department boundaries to creatively fuse intelligence with actions and outcomes.



<h2 id="machine">Embedded machine learning</h2>



Embedded analytics is the easiest way to build the best-in-class AI-based analytics components into a web app or service without coding. 



Of course, you can dive deep into code if necessary; the best vendors of embedded apps provide full API and SDK resources, and by definition, every library, function, call, webhook, and microservice can be interfaced to your app as needed to provide absolute customized outcomes via all the most popular languages, and especially Python, Go, Rust, and C++.



Essential to the definition of embedded analytics is the paradigm of assembling the best pre-built components available, rather than writing original code. Embedding makes it possible to efficiently build a complete app by interfacing embedded components and microservices. In other words, you can create an industry-leading app by writing only the code necessary to interface components.



To this end, you can expertly express a complete business procedure by assembling existing components. Machine-learning-based analytics probes your connected data stores to build and train models to surface meaningful insights from your data. 



Furthermore, to implement embedding in the scope of white-labeling means seamlessly infusing AI-powered intelligence into your products while enjoying the fastest time to market. The benefits of relying on experts who created the plugin are ceaseless:



<ul><li>Highest data compatibility, lowest risk in QA, fastest deployment and testing</li><li>Pre-tested code, standard models, deploys easily in continuous integration/continuous delivery pipelines</li></ul>



<ul><li>Third-party component vendor accepts responsibility and risk&nbsp;</li><li>Vendor provides tech support, bug fixes, upgrades, compliance, governance</li><li>Optimizes cost-value</li></ul>



<h2 id="how">How to recognize the best plugins</h2>



Faced with a bewildering array of products claiming to offer embedded analytics solutions, how do we sort best from worst? Making that decision defines the market. In other words, the choice of features and functionality defines embedded analytics, so how do build that definition?



<ul><li>Fully customizable option to code or use interface&nbsp;</li><li>Option to white-label — make the component your own!</li><li>Data volume and app scaling not a concern for user</li><li>Secure access</li><li>Developer stack agnostic — interface many client libraries and languages</li></ul>



<h2 id="unbounded">Unbounded use cases&nbsp;</h2>



<a href="https://www.sisense.com/whitepapers/sisense-ai-throughout/">AI-based analytics</a> today infuses every field of business endeavor with actionable insights, raising standards to new levels of performance and expectation. Marketing’s holy grail is reaching the customer with the right offer at the right moment. A bank’s marketing manager uses an embedded text mining tool to score and then rank the success of a new account promotion campaign. 



Based on customer profiles and feedback from previous promotions, the manager uses dashboard visualizations embedded in the operational campaign management system or enterprise CRM to then harvest an optimal set of customers to launch the next promo campaign.



As illustrated previously, such a CRM with embedded AI components is not limited to marketing, but feeds sales, customer service, and even enterprise HR modules at the same time! As you embed analytics and connect data sources, the plugin builds still more data sources, this time based on intelligent patterns already identified. This augmented approach has exponential benefits.



For example, as marketing BI users develop promotions like those described above, the embedded algorithms score leads to prioritizing sales representative actions. Naturally, the outcome is to surface actionable insights from customer contacts, which include phone calls and emails. The machine learning then extracts and identifies the actions that drive sales: sales call attribution, compensation, etc.



Customer service nodes in the vast neural net of the embedded system define intelligent routing, call classification, voice authorization, and intent discovery, which trains the model for enterprise chatbots. (And chatbots are now scoring better CRM evaluations than humans!)



<h2 id="ever">Ever-evolving assemblies</h2>



As we have seen in the high-level overview, embedded analytics is the easiest way to build top-tier AI-based analytics into a web app or service. Optimize cost-value overall benefit by including best-in-class components. Simply subscribe, embed code, and impress customers. 



Of course, embedding components can be as simple or complicated as the goals of the project. But generally, you can <a href="https://www.sisense.com/whitepapers/the-embedded-analytics-platform-built-for-rapid-development-at-scale/">embed components to reduce complexity and enjoy immediate benefits</a>.&nbsp;



Some APIs are compulsory because the vendor has unique access to data sources (satellite feeds, for example). If the supplier is the only source, and the service is necessary to the outcome, then embedding is not optional.



Building your own components will be more expensive, will require staffing expert human capital, and may not result in the world’s best product as an outcome. The only potential disadvantage arises when you can’t find a third-party component that has exactly the functionality required — now choose a third party that is customizable and “stand on the shoulders of giants.”



See the pros and cons of building or buying an analytic solution &gt;&gt;Read this free guide for all the pros and cons



<a class="action-btn " href="https://www.sisense.com/whitepapers/pros-cons-of-building-vs-buying-embedded-analytics/" target="_blank" rel="noopener noreferrer">Get the Pros and Cons Here</a>



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