Embedded analytics delivers the benefits of data-informed decision making to users of an existing platform by allowing a BI tool to be embedded into another program. For any developer looking to add analytics to their software or service without building an in-house tool, BI tools with embedded capabilities provide a nifty solution.
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QlikView does not support embedded analytics, but QlikSense and Qlik Analytics do, with flexible and powerful capabilities through RESTful APIs that allow developers to build and extend embedded analytics tools for almost any application or browser-based UI.
The Winner: Though Tableau and Power BI are neck-and-neck for embedded analytics, Power BI is the winner; comparatively, it is easier to embed into other applications and supports secure write-back through a custom visual, whereas Tableau only supports this through custom development.
When technology experts talk about the Internet of Things, or IoT, they refer to interconnected devices that can transmit data over a network without needing human-to-human or human-to-device interaction. A few examples of IoT devices include smartphones, smart thermostats, sensors, vehicles, machinery and more. The Internet of Things brings all these devices together, and BI tools with IoT analytics capabilities can subsequently explore and analyze the huge volumes of data generated by this giant network of devices, creating insights with even more breadth and depth.
Power BI integrates with Azure Stream Analytics, a real-time analytics and complex event-processing engine that is designed to analyze and process high volumes of fast streaming data from multiple sources simultaneously. It also integrates with Azure Event Hub and Azure IoT Hub to ingest data from sources like connected devices, sensors, social media feeds, clickstreams, applications and log files. Power BI can identify patterns, which can trigger actions and initiate workflows such as creating alerts, feeding information to a reporting tool or storing transformed data for later use. Users can create real-time dashboards and alerts for these IoT devices based on temporal or spatial patterns and anomalies.
Tableau can gather data from sensors or devices and then analyze and visualize it to develop insights based on data collected. Tableau can build dashboards from IoT data and detect trends or anomalies.
QlikView supports IoT Analytics with direct access to IoT data in close to real-time. It can analyze large amounts of IoT data and present it in an intelligible way, and it also supports monitoring and forecasting in real-time with automated alerts and more.
The Winner: With Azure Stream Analytics, real-time streaming data analytics is a cinch with Power BI, whose alert, dashboarding and anomaly detection capabilities propel it to the front of the pack.
Geospatial analytics helps turn spatial, location-based data into maps, visualizations and actionable insights. It gives geographic context to data points, adding another layer of information.
Power BI integrates with ArcGIS maps to provide map visualizations, which users can pin to dashboards. Users can also create map visuals based on TopoJSON maps. If their geospatial data is in any other formats, Power BI users can convert shapefiles or GeoJSON files to TopoJSON files which they can then convert into visualizations for their dashboards.
Tableau users can import geospatial data from various file formats and then analyze and visualize it directly in the platform. Tableau supports various types of interactive map visualizations, such as proportional symbol maps, choropleth maps, point distribution maps, heatmaps, flow maps and more. In Tableau, users can perform a map search to find locations and explore and inspect data related to those places. When users begin to type in the search box, map search suggests possible locations that are in their map view, based on location names and text from their data sources. Users can search for location types such as continent, country, state or province, county, city and postal code. Tableau also offers forward geocoding and reverse geocoding, as well as various spatial functions which allow users to perform advanced spatial analysis and combine spatial files with data in other formats like text files or spreadsheets. Tableau’s spatial connector allows users to connect to and join ESRI Shapefiles, KML, GeoJSON files, MapInfo tables and other forms of geospatial data.
Tableau uses geospatial analytics to help users add the “where” to their “why.”
Qlik GeoAnalytics lets users make better location-related decisions by offering mapping and location-based analytics for QlikView. QlikView’s multi-layer mapping provides geographic visualization and calculations, including spatial analysis to collect, display and manipulate location-based data such as street addresses, zip codes, satellite images and GPS coordinates. It also allows users to perform geo data look-up through which they can automatically populate and update dashboards and maps with data about specific places.
The Winner: With its integrations for geospatial data in a large variety of formats, interactive map visualizations and its powerful map search function, Tableau takes the lead.