Domo vs Tableau Summary
Domo can be characterized as an executive information system. It offers limited capability, comes with ‘executive pricing’ and what it lacks in functionality and sophistication it makes up for with eye candy. Tableau on the other hand is a general purpose visual analytics platform, quite capable of serving up the attractive visuals of Domo, but doing much more besides. Neither solution is low cost, but Tableau does represent better value for money.
However Domo has done its marketing homework, and caters to the frustrations of managers with budget, provided they have $50,000 or more to spend on dashboards and visuals. The promise is essentially that of easy implementation and use – and in most respects it meets this promise. Tableau also majors on ease of use, but in reality its greater sophistication means it is more difficult to learn, although a consumer of dashboards really needs to know almost nothing.
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.
The User Experience
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.
Domo supports user profiles so that the role of colleagues is understood and they can be easily reached. Publication Groups support the sharing of information with individuals and groups, and this can be accomplished by sending Cards or a slide-show of Cards via scheduled emails. DomoBuzz is essentially a simple social network so that discussions around topics can be shared and notifications received when a relevant topic is being discussed. Tasks support the creation of projects and can include access by other interested parties. Action items are defined with corresponding deadlines, and action items can email all concerned.
Domo Apps provide solution templates for senior roles in the typical business. These include:
- CEO: Competitive Benchmark – with measures of financial health and competitor comparison.
- Sales: All Reps Scorecard App – sales rep comparisons, rep performance.
- Marketing: Campaign Engagement App – aggregated, historical data from prior campaigns, engagement statistics, lead conversion.
- Finance: P&L – income statement comparisons, gross profit margin and operating margin reporting, alerts for spending anomalies.
- Solutions also exist for retail and TV Forensics specifically, but also most other industries.
Without doubt Tableau Software set the pace for easy-to-use data visualization and exploration software. In practical terms this means business users can get to their data, typically without assistance from IT, and create graphs, charts and dashboards in a way that is most meaningful to them. Authoring takes place on Tableau Desktop which, as a stand-alone environment, can perform its own analysis, either against the Tableau in-memory database, or against external data sources – databases, cloud data sources, spreadsheets and so on. In a group or enterprise setting Tableau Server acts as a central facility for data access, delivering visualisations, enforcing security and managing user access. Tableau Server distributes visualisations through the web browser to almost any device that supports a web browser – desktops and mobile devices.
As with most platforms of this type Tableau presents a drag and drop data exploration interface. It is Tableau Desktop, which can be installed on Mac and PC, that provides the visualisation authoring environment. It provides most of the chart types and tabular representations a user might need, with intelligent assistance during the visualisation creation phase. Tableau Desktop serves multiple purposes in addition to authoring. It allows users to manipulate metadata, and publish a workbook (a complete visualisation package that can be executed by Tableau Server).
Users of Tableau Desktop can elect to load data into the columnar, in-memory, compressed database. Provided the data fits, it’s very fast – although data can also be cached on disk with an inevitable degradation in performance. This has become almost a standard way of delivering fast desktop analytics, and it’s very effective. If high performance analytics databases such as HP Vectra are used then the user can connect directly to these.
Tableau Server users are presented with ready-made workbooks displaying dashboards and reports. These are not static entities, and provide all the facilities for data manipulation a users might wish to perform – drill down and through for example. Accessing data sources is fairly straight forward, and it is a simple matter to blend data from several data sources.
The support for geographic data is particularly well regarded by Tableau users, and finds wide application where location is an important part of the information set.
Tableau will handle almost any form of data – databases, cloud data sources, OLAP cubes (with some limitations), big data databases, Excel spreadsheets – and so on. It also allows users to combine data from as many data sources as necessary. There are two basic forms of data access in Tableau. The first is the live connection where Tableau issues dynamic SQL or MDX (for OLAP cubes) directly to the data source. The in-memory database is a highly compressed in-memory engine that can hold very large amounts of data – because of the compression factor.
Extracts are a major feature in Tableau, and as the name suggests they are ready built extracts, possibly from much larger databases. These are stored in the columnar database, and most data sources can be treated in this way with the exception of OLAP cubes. The sharing of packaged workbooks depends on these extracts for sharing.
The Tableau Server architecture supports excellent scaling through a multi-tier architecture. It allows Tableau to scale out and up as required. The components of the server architecture are:
- The VizQL Server – authenticated users can open a view and this server then sends requests to the relevant data sources. The results are sent back to the user as a HTML5 rendered image. This server component can be replicated as many times as necessary.
- Application Server – is the gateway to Tableau and handles various permissions, content browsing and server administration. Again this server component can be replicated as needed.
- Data Server – provides IT with a mechanism for IT to monitor, manage meta data and access to data sources.
- Backgrounder – refreshes extracts from databases.
Various APIs are provided for integration of Tableau objects with production applications.
- A data extract API provides direct access to data sources, allowing data to be pre-processed before being used by Tableau. It also facilitates the creation of data extracts, which are used directly by Tableau visualisations.
- The REST API supports the direct manipulation of Tableau Server objects.
Users can extend the functionality of Tableau provided they have some knowledge of XML.
Governance and Management
The management of data access is governed by IT through the Data Server. The Tableau Server provides data isolation and multi-tenancy. Multiple “sites” can be hosted in a singe server, and each site can have multiple projects, which in turn can have multiple workbooks. Each entity can be managed as desired.
Authentication and security support Microsoft Active Directory, SAML, oAuth and a native authentication managed by Tableau Server.