Birst vs Domo Summary
Birst and Domo serve two quite different needs. Birst is a general purpose BI and data visualization platform that will address the needs of the enterprise. Domo is a management and executive information system, and its strong social and collaboration features mean information can be shared easily and as necessary.
Birst comes with a sophisticated data architecture so that the needs of diverse groups can be satisfied using a single version of the truth. Its analytic capability is also well above average, with embedded predictive analytics and good integration with R. In summary it is a complete enterprise BI solution.
Domo is specifically tailored for ease-of-use, but its analytics capability in no way compares with Birst. The collaboration and sharing facility is very strong, and it also offers executive solutions for senior management. But it falls short of being an enterprise business intelligence solution.
Domo is a cloud BI solution that is targeted at larger businesses, and majors on ease-of-use, social sharing of information and transparent access to diverse data sources. The actual analytic capability is on a par with most other offerings of this genre, but since the platform is geared toward business users the emphasis is primarily simple analytics made available in the simplest way possible. For more complex analytics users will need other tools, but Domo will address the general need for reporting and data visualization for many business users who simply want to see their data in an easily digested format, and with least possible effort. Support for mobile devices and a native iPad app enhance the availability of information considerably.
Connecting to data sources is a very straightforward affair in Domo. This process is enhanced by 1-Click Apps which provide reporting templates based on industry, role, domain and data source. In effect they suggest what should be measured and how it should be represented. Obviously the resulting reports and charts can be modified as needed, but they do provide a good launch pad for further refinement. Card Builder works in a similar manner, allowing users to select data and then recommends a visualization for the data. The Analyzer provides filtering and sorting functionality so that cards (graphs, reports, charts) present data in the required format. The Page is the equivalent of the dashboard, containing multiple Cards. Pages can contain subpages. Cards can also be grouped into Collections. It is easy to see here that Domo is providing plenty of opportunity to manage objects in a way that suits the individual user, and to provide a leg-up in the creation of various reporting objects.It is not particularly sophisticated, but it is well organized and easy to use – the usual trade-off.
Domo Connectors do all the hard work of providing access to a diverse set of data resources. This includes on-premise applications, databases and cloud based data. There is a large number of these connectors, satisfying the needs of most businesses out-of-the-box. To some extent the Connectors are intelligent and will notify users when the password to a data source has expired. The uploading of data into the Domo database can be scheduled or executed immediately, as required. The merging of data from several different data sources can be accomplished by using Magic – a largely automated, self-service tool with a drag-and-drop graphical interface, that supports functions such as joining, cleaning and transforming data – the usual ETL functions that have traditionally needed technical assistance. The DataFusion utility is a simpler way to join data, and if needed the DataFlows capability allows the use of SQL for ETL processing. Data is uploaded to Domo using the WorkBench, and if security is a major concern data can be de-identified and/or encrypted.
Birst is a cloud (public or private) based BI platform that addresses the needs of both production oriented BI and end-user data visualization and querying. The company calls this a 2-Tier approach, and it is possible because of the excellent data management architecture of the platform.
Many contemporary data visualization platforms are, in effect, point solutions. They confuse the data picture rather than clarify it, by creating their own versions of the truth within a database of their own. They also do not cater for production BI needs which, although currently somewhat unfashionable, are nonetheless here to stay. Production reporting (weekly sales reports for example), executive dashboards and other ‘fixed’ information needs will always be with us, despite the current fascination with all things visual.
So Birst competes very well with the ease of use of products such as Tableau and Qlik Sense, while also providing a true enterprise BI facility, with support for regular, production oriented reporting.
The impressive feature list of Birst includes native and offline mobile support, embedded predictive tools, intelligent data functions, automated data refinement, drag and drop data visualizations, and quite unique levels of data management. This integrated environment means that everyone gets the same version of the truth, that predictive models can be embedded into dashboards and charts, and that there should never be data ambiguity.
The architecture of Birst is sophisticated and involves several layers, most of which concern themselves with the data. While we all want to create attractive visualizations, it is the underlying data architecture that empowers accurate and meaningful data visualization.
The total cost of ownership of Birst is reported by users and analyst firms to be low, and of course the time to value is very low. Birst is a contemporary alternative to most conventional enterprise BI platforms, while delivering modern user interfaces and well governed self-service capability.
Birst provides an easy-to-use interface for data exploration, discovery and visualization. It supports the creation of charts, dashboards and high definition reports. The self-service capability of Birst equals many of the products which specifically address this need. An Open Client interface is also provided if users want to use other tools, such as Excel or Tableau.
The visual discovery interface is mainly navigated using drag-and-drop, and a recommendation engine will suggest the most appropriate visualizations for the data. It supports filtering, user created metrics, instant metrics, and an intelligent search capability. Available visualization formats include: column, bar, line, spline, area, area spline, points, scatter, bubble, pie, funnel, pyramid, list tables, crosstabs, and geo maps. The Expression Editor provides the ability to create BQL (Birst Query Language)-based report expressions that can be used to create more insightful visualizations, and users can also choose to limit data to “Top N” data points.
Dashboards are created in a WYSIWYG, drag-and-drop environment, and Birst dashboards and widgets are rendered in HTML5, so they automatically resize for a responsive, optimized experience wherever they are used, on a laptop, desktop, or tablet.
Birst also includes a report designer for advanced pixel-perfect report creation, enabling highly formatted report creation typically used in production-delivered reports. Examples of rich formatting include: conditional formatting, conditional display, duplicate suppression, and null value replacement. Embedded images and sub-reports in various bands are supported. Reports are compiled into Java byte code for fast and direct execution. No interpretation at runtime is required, and server-side report caching enhances performance.
The Birst platform includes a predictive analytics engine. In contrast to conventional data-mining environments, data does not have to be moved; instead datasets for model training and scoring are generated directly from the Birst Unified Business Model.
Birst’s advanced analytics capabilities leverage the ROLAP engine for data preparation. The modeling engine makes full use of aggregates and derived measures. Sophisticated new measures are defined and calculated on the fly as inputs into the modeling process. Share, time-series and dimensional breakout metrics are used to enrich the information. The use of OLAP-style measures for modeling enables the addition of complex and highly predictive behavioral calculations. For each modeling task, Birst automatically evaluates a comprehensive set of algorithms. Supported algorithms include linear and logistic regression, decision trees, feed-forward neural networks, support vector machines and rules/regression trees. Modeling scores are written directly back to the User-ready Data Store, ready to be used in ad hoc queries and dashboards or to be fed into additional processing (for example, list generation). Both rules-based and model-based recommendations can be combined into complex decisions. Birst also delivers tight integration with the R statistics package, making it easy to deploy R-based measures to any number of users. Birst measures can make calls to the R server, submit data for processing, and retrieve the results to present to users. By leveraging the integration with R, users can greatly augment the already robust advanced analytics capabilities available in Birst out of the box.