Analytics platforms, sometimes known as business intelligence (BI) platforms, provide a tool set for businesses to absorb, organize, discover, and analyze data to reveal actionable insights that can help improve decision-making and inform business strategy. Some of these products require IT implementation to build the analytical environment, connect necessary data sources, and help prepare the data for usage; others are designed to be primarily configured and used by non-expert users, without the help of IT for deployment (known as self-service). Business and data analysts, data scientists, or other business stakeholders can utilize this software to prepare, model, and transform data to better understand the day-to-day performance of the company and inform decision-making. Fundamentally, for a product to be categorized as an analytics platform it must be an end-to-end analytics solution, which incorporates five elements: data preparation, data modeling, data blending, data visualization, and insights delivery.
Although standalone data preparation tools exist that assist in the process of discovering, blending, combining, cleansing, and enriching data—so large datasets can be easily integrated, consumed, and analyzed—analytics platforms must incorporate these functionalities into their core offering.
Analytics and business intelligence platform must support data blending and data modeling, giving the end user the ability to combine data across different databases and other data sources and allowing the end user to develop robust data models of this data.
The reports, dashboards, and visualizations created using analytics platforms can break down data to a granular level, depict connections and trends between multiple datasets, and create data visualizations that make the data easier to understand for non-expert stakeholders. Products which only provide the visualization component are categorized as data visualization software, which includes products primarily designed to create charts, graphs, and benchmark visualizations.
Some business and data analytics platforms offer embedding functionality to place dashboards or other analytics capabilities inside applications; these products are considered embedded analytics software. Products specifically designed for ingesting and integrating big data clusters are categorized as big data analytics software. Other features of analytics platforms can include natural language search functionality and augmented analytics. Natural language search refers to the ability to query data using intuitive language, frequently in the form of a question. Augmented analytics refers to the process of using machine learning for deriving insights from the data and supporting non-expert users in working with and visualizing data, such as automated data preparation and discovering hidden patterns in the data.
To qualify for inclusion in the Analytics Platforms category, a product must:
Provide robust data ingestion, integration, and preparation features as part of the platform
Consume data from any source through file uploads, database querying, and application connectors
Allow for the modeling, blending, and discovery of data
Create reports and visualizations with business utility
Create and deploy internal analytics applications
