We’re pleased to be featured in the July 10, 2024 issue of insideAI NEWS, formerly insideBIGDATA. The revamped publication offers comprehensive coverage of AI technology and implementation strategies across multiple industries, including healthcare, security, financial services, automation, and agriculture. Below is a summary of our article, “The Rise of Embedded Analytics and Embedded AI: Proportional Business Value at Last?” Click here for the full text.
Although business intelligence (BI) solutions have gone a long way to make more data available to non-technical users, challenges remain. Business users often give up when trying to find the right data because of the proliferation of data sources and dashboards. This is where curated data experiences — embedded analytics and embedded artificial intelligence (AI) can help — not just for customers and external use cases but in internal, user-facing applications, too.
Access data where you need it with Embedded Analytics
With embedded analytics, users can access data when and where they need it most: in the context of their workflows. Instead of viewing a dashboard in the analytics platform and switching to the business application to act on it, businesses can curate the analytics experience within an application or a custom solution so there’s less jumping among applications and more immediate relevance. Taking it one step further, organizations can drive the experience using embedded AI so that users can bring up the right data within the business application using a simple voice command or chatbot.
But before organizations can deliver trusted embedded analytics and embedded AI, they need a universal semantic layer—an independent yet interoperable translation layer—between the data repository and the data-consuming endpoints. The semantic layer provides a consistent and trusted view of unified data by organizing, simplifying, and accelerating its consumption. Once a universal semantic layer is in place, curated data experiences are relatively easy to deliver, internally and externally. Although many organizations start with customer-facing applications, it is worth mentioning that substantial value can be derived quickly from focusing on internal processes first.
AI and embedded analytics transform how data is used in an organization
There are many examples of internal processes that can benefit from embedded analytics and AI, including better employee experiences, decision-making in context, and better workflows and processes. When paired, a universal semantic layer and AI unlock profound capabilities in informing business users in real-time and context. For example, a universal semantic layer makes it possible to embed AI-assisted analytics into a tool like Salesforce, allowing for analysis on deals, prospects, and other key metrics to be done without context-switching — and via a near real-time process that can be as easy as querying an AI chatbot.
AI and embedded analytics powered by a semantic layer transform the way data is used in an organization, replacing the conventional, frequently fragmented method with one that is more integrated, perceptive, and useful.