Business Intelligence (BI) has undergone a process of continuous democratization – from customary, standalone BI platforms that required significant effort to deliver inert reports to a few, to up-to-date analytics that moved access from the few to the many, and from content creation to data consumption.
But have up-to-date analytics platforms with more data, more users, and more operate cases delivered commensurate business value? In most cases, business users often get stuck trying to find the right data due to the proliferation of data sources and dashboards. This is where carefully curated data experiences—built-in analytics and built-in artificial intelligence (AI)—can assist, not only for customers and external operate cases, but also in internal, user-facing applications.
First, before organizations can deliver embedded analytics and embedded AI, they need a universal semantic layer—an independent but interoperable translation layer—between the data repository and the endpoints that consume the data. The semantic layer provides a consistent and trusted view of unified data by organizing, simplifying, and accelerating its consumption. Once the universal semantic layer is in place, curated data experiences are relatively simple to deliver, internally and externally. While many organizations start with customer-facing applications, it’s worth mentioning that significant value can be quickly realized by focusing on internal processes first.
Decisions in context
The first thing many organizations do when they implement a universal semantic layer is build embedded analytics that improve internal decision-making. With embedded analytics, users can access data when and where they need it most: in the context of their workflows. Instead of browsing a dashboard in an analytics platform and then switching to a business app to act on it, companies can build an analytics experience within an app or custom solution, resulting in less app hopping and more immediate relevance. Taking this a step further, organizations can power this experience with embedded AI, so users can view relevant data in a business app with a uncomplicated voice command or chatbot.
Better employee experience
By incorporating metrics into the process, embedded analytics escalate productivity and engagement. Well-designed embedded experiences drive efficiency across the business by providing relevant information for specific tasks. For example, marketing teams can receive near-instant updates on lead generation, buying patterns, and customer acquisition costs using embedded analytics. Instead of manually compiling data from multiple systems, marketers can make faster, more informed decisions to generate more high-quality leads by having immediate access to this data, even via natural language commands with embedded AI.
Better workflows and processes
Embedded analytics incorporate data-driven insights directly into workflows to optimize business processes. Think supply chain management and all the intricate details of logistics and operations. Companies can monitor inventory levels, supplier performance, and demand forecasts in real time, embedding them in supply chain management systems. A holistic view of the supply chain enables managers to optimize productivity and reduce expenses more skillfully. For example, they can adjust inventory levels or optimize route planning with voice instructions using embedded AI to keep everything running smoothly.
Artificial Intelligence to Further Democratize Data
When combined, the semantic layer and AI unlock profound capabilities to inform business users in real time and context. For example, a universal semantic layer enables AI-powered analytics to be embedded in a tool like Salesforce, enabling analysis of deals, leads, and other key metrics without context switching—and through a near-real-time process that can be as uncomplicated as asking an AI chatbot.
Of course, an AI-ready universal semantic layer can also power customer-facing applications that enable organizations to make the most of customer data and interactions. Imagine a bank embedding an AI chatbot that lets a customer create a monthly budget based on income, regular expenses, and savings goals, or a shopping recommendation engine that curates outfits based on a customer’s inventory and preferences.
One last thought
AI and embedded analytics powered by a semantic layer are transforming the way data is used across organizations, replacing the conventional, often fragmented approach with one that is more integrated, insightful, and useful. Businesses can improve employee experiences and transform siloed data interactions into targeted insights that every employee in the business can easily access to drive growth, innovation, and continuous improvement. By integrating analytics and AI directly into the operational tools that employees operate every day or customer interactions to transform their experiences, businesses can finally capture the full value of data.
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