Top Technical Requirements for Embedded Analytics By Teresa Wingfield April 14, 2023 What is Embedded Analytics? More employees making decisions based on data insights leads to better business outcomes. Increasingly, data analytics needs to be surfaced to users via the right medium to inform better and faster decisions. This is why embedded analytics has emerged as an important way to help organizations unlock the potential of their data. Gartner defines embedded analytics as a digital workplace capability where data analytics occurs within a user’s natural workflow, without the need to toggle to another application. How do you embed data analytics so that users can better understand and use data? It all starts with building the right data foundation with a modern cloud data platform. While technical requirements to support embedded analytics depend on specific use case and user needs, there are general requirements that a cloud data platform should always meet. Below is a summary of each one. Technical Requirements API Integration: The cloud data platform must provide flexible API choices to allow effortless application access to data. Extract, Transform and Load (ETL) integration: The solution should also include ETL capabilities to integrate data from diverse sources, including databases, internal and third-party applications, and cloud storage. Data variety: Support for different data types, including structured, semi-structured, and unstructured data, is essential as data comes in many forms, including text, video, audio, and many others. Data modeling: The solution should be able to model the data in a way that supports analytics use cases, such as aggregating, filtering, and visualizing data. Data quality: Data profiling and data quality should be built into the platform so that users have data they can trust. Performance: REAL real-time performance is a critical need to ensure that users can access and analyze data in the moment. Scalability: The solution should be able to handle large volumes of data, support a growing number of users and use cases, and reuse data pipelines. Security: The solution should provide robust security measures to protect data from unauthorized access, including role-based access control, encryption, and secure connections. Governance: Embedded analytics demands new approaches to data privacy. The cloud data platform should help organizations comply with relevant data and privacy regulations in their geography and industry while also making sure that data is useful to analysts and decision-makers. Support for embedded analytics vendors: In addition to sending data directly to applications, the cloud data platform should allow developers to leverage their embedded application of choice. How the Avalanche Cloud Data Platform Helps The Avalanche Cloud Data Platform, with built-in integration, including APIs, and data quality, is an ideal foundation for embedded analytics. These features combined with dynamic scaling, patented REAL real-time performance, compliance and data masking help meet the needs of even the most challenging embedded analytics use cases. In addition, you can fuel your applications with data directly from the Avalanche platform or use your preferred application for embedded analytics. Don’t take our word for it, start your free trial today and see why the Avalanche platform is a great fit for your embedded analytics needs! About Teresa Wingfield Teresa Wingfield is Director of Product Marketing at Actian where she is responsible for communicating the unique value that the Avalanche Cloud Data Platform delivers, including proven data integration, data management and data analytics. She enjoys applying her extensive knowledge in these areas to help customers find solutions that will help them achieve long-lasting success. Teresa brings a 20-year track record of increasing revenue and awareness for analytics, security, and cloud solutions. Prior to Actian, Teresa managed product marketing at industry-leading companies such as Cisco, McAfee, and VMware. She was also Datameer’s first VP of Marketing for big data analytics built on Hadoop, and has served as VP of Research at Giga Information Group, acquired by Forrester, providing strategic advisory services for data warehousing and analytics. Teresa holds graduate degrees in management from the MIT Sloan School of Management and software engineering from Harvard University.