Embedded business intelligence (BI) continues to transform the business landscape, enabling organizations to quickly interpret data and convert it into actionable insights. It allows organizations to extract information in real time and answer wide-ranging business questions. Embedding analytics helps tackle the issue of extracting information from data which is a time-consuming process. Our research shows organizations spend more time cleaning and optimizing data for analysis rather than creating insights. On top of that, they are adding more data sources and information systems which in turn introduces more complexity. Our Analytics and Data Benchmark Research shows that organizations face various challenges with analytics and BI. More than one-third of participants (35%) responded that they find it hard to integrate analytics and BI with business processes and connect to multiple data sources. By embedding analytics and BI into business processes and workflows, organizations can enable users to make critical decisions fast, enhancing overall business agility.
Sisense is a BI software company that focuses on enabling organizations to embed analytics in business applications and workflows. It offers a scalable platform with cloud agnostic deployment and integration capabilities with Amazon Web Services (AWS), Snowflake, Google and Microsoft, among others. Sisense offers three solutions in its Sisense Fusion Platform — Sisense Fusion Embed, Sisense Infusion Apps, and Sisense Fusion Analytics. The Sisense Fusion Platform is an artificial intelligence (AI)-enabled, analytics cloud platform that offers the ability to embed analytics into customer-facing applications. Users of different skill levels can analyze data and then infuse customized experiences into different workflows and processes.
Sisense Fusion Embed is its embedding solution that infuses AI-driven analytics into products and business applications. Users can use its code-free tools for analysis and can augment analytics with AI and machine learning (ML) without requiring extensive IT support. Sisense Infusion Apps enables users to use natural language query (NLQ) to fetch, analyze and deliver insights from within productivity tools like Microsoft Office 365, Microsoft Teams, Google Workspace or Slack. It can be deployed in the cloud or on-premises, with a single tenant or multi-tenant architecture, and features end-to-end governance and security that can be automated via APIs. By bringing self-service and embedded analytics to individuals, organizations can increase the value of data and enhance overall productivity.
Sisense Fusion Analytics enables business users to integrate BI into workflows and business tools such as Slack, Salesforce, Google Docs and Google Sheets. Its platform also enables users to collaborate around data and BI. Organizations can use Sisense to import data from several data sources and visualize it using dashboards. Additionally, Sisense uses AI for automated data preparation and delivery of insights. It can also provide forecasts based on historical data, enabling nontechnical users to predict business outcomes, track changes to critical metrics and make decisions based on new insights. As I’ve written previously, AI-driven augmented analytics can give users faster answers and actionable insights into their business metrics. By automating data preparation, insight discovery and sharing, organizations can speed up the decision-making process, improve the state of analytics and gain a competitive edge in the market.
Sisense should continue to invest in its integration capabilities and add in more business partners to expand the portfolio of software and tools it can connect and integrate with. I recommend that organizations looking to embed analytics into business applications, processes and workflows evaluate Sisense. It offers multiple embed options and libraries, including iFrames, Embed SDK and Sisense JS. It enables users to combine data and uncover insights in a single interface without scripting, coding or extensive IT assistance. By augmenting intelligence in business processes, organizations can automate AI/ML analyses and provide guided or assisted features within data and analytics products.