Business intelligence (BI) has the strategies as well as technologies for data analytics and business information management. Artificial intelligence (AI) can significantly increase accuracy and reduce errors. As BI tools, both Sisense and DataFocus Cloud are driven by AI, and help business elites make decisions.
With a comprehensive system, DataFocus Cloud provides search-based analytics and abundant visualizations to help business elites realize the values of data.
The page design, visualizations, ease of use, etc of the two platforms are quite different. Read more to understand the differences between the two tools and choose a more suitable one for yourself or your company.
Sisense aims at infusing analysis into everyday apps and workflows to help business make better and faster decisions. It is built with flexible deployment options for easy approach.
DataFocus Cloud (DFC) targets making business elites into data professionals. With search-based analytics, data exploration is like Google search. It is built on-cloud, so that everyone can use it at any time at any device.
The comparison is based on free-trial versions.
2.1.1 Novice Guide
On the “Data” and “Analytics” module, as shown in gif figure 2-1, Sisense provides quick tour video as well as some other options like videos, documentations, etc. Also, Sisense provides highlighted steps when first enter modules, as shown in figure 2-2.
DataFocus Cloud provides different kinds of videos on the home page, such as making charts, searching keywords, etc., which assists beginners to get to know the system.
2.1.2 Navigation Bar
The navigation bar of Sisense lies on the top of the page, and mainly lists 3 modules: “Data”, “Analytics”, and “Pulse”. “Data” module exhibits ElastiCube and Live models, “Analytics” module is the visualization module, and “Pulse” module shows important metrics and events.
DFC has 2 navigation bars on top as well as left of the page. The left navigation bar displays 10 modules, including “Home”, “Search”, “Answer”, etc. The detailed explanation can be viewed here: Function Module. The top navigation bar shows pinned dashboards, and users can quickly check these dashboards whenever they want, as shown below.
2.1.3 Analytics Page
Analytics page refers to the specific visualization making page of Sisense and search analysis page of DFC here, and both can be divided into three parts roughly.
The visualization-making page of Sisense is mainly formed by dashboard lists, canvas, and filter pane. As shown in figure 2-6, the dashboards lists display dashboards the users own and sample dashboards, the canvas is the visualization part, and the filter pane lists all filters added.
DFC’s search analysis page is mainly comprised of column names, search bar, and the canvas. The columns are shown as attributes and measures, the search bar is the analytics center, and the canvas shows charts, as shown in figure 2-7.
2.2 Data Source
Sisense offers two types of data connection. One is ElastiCube, where data is imported to Sisense, and data from multiple sources is allowed to merge. Another is Live, where the data source is directly connected and the update is near real-time. The entire list of data source supported by Sisense is shown below.
There are mainly three ways to obtain data sources in DataFocus Cloud.
Local files and data warehouses can be imported into the system, which can fit big data analytics such as billions of data. Users can overlay or append new data afterwards at the detailed data page.
Databases can be connected to update resources in real-time. Similar to Sisense, multiple databases can be connected at the same time. What’s more, users can directly conduct join analysis concurrently.
DFC provides API to get access to reliable external data source.
The entire list of data source supported by DFC is shown below.
2.3 Ease of Use
|Attribute & Measure
|Text, date, number
|String, time, number, boolean
|One by one
|Chart types & samples
|Search bar & chart types
As shown in figure 2-10, fields are sorted alphabetically, and users can type to search for fields. In DFC, columns are listed as attributes and measures and similarly, users can type to search for a column, as shown in figure 2-11.
As shown above, Sisense offers three main field types, text, date, and number. Also, number includes int, big int, float, real, and decimal. DFC offers string, timestamp, boolean, and number, where number includes int, double, big int, and small int.
To add a field in Sisense, users need to select fields one by one, as shown in gif figure 2-12. In DFC, users can either select multiple columns together, or use the “Select All” option to add all columns.
Sisense: Hover over chart icons, then the chart type would be prompted. Hover over sample dashboards, then relevant information including name, owner, created date, and last modified time would appear, as shown in gif figure 2-14.
DFC: Hover over chart types, then the making condition of that chart would appear. Then when type in the search bar, the system would prompt what to search, as shown below.
Sisense uses drag-and-drop as the interaction mode, while DFC applies search-based analysis. DFC’s search-based analysis focuses more on data analytics than visualization creation. With regards to complicated business questions, such as how is the growth rate of the quarterly sales, simply type in the search bar, and the system would choose an appropriate chart.
This way of analysis returns what you think and straightly analyze business questions. Even with complicated business questions, the system would return the analysis results quickly to help make data-driven decisions.
Filters in Sisense are mainly widget filters and dashboard filters. Widget filters are not visible or editable in the dashboard, and dashboard filters are mainly fulfilled by the filter pane on the right side of the page.
Similarly, filters in DFC can be divided as answer filters and dashboard filters. Answer filters are not editable in the dashboard, and there are different ways to add filters in dashboards.
Sisense: both widget filters and dashboard filters have the same filter configurations. Filters are introduced in terms of field types.
Users can choose to select values, use text conditions such as contains or starts with, and rank values.
Similarly, users can choose to select values, use values conditions like smaller than, and rank values.
Users can select time according to different time intervals or time frames, and rank values.
DFC: answer filters and dashboard filters are quite different, as shown below.
Keywords are the center of search-based analytics. Data is filtered automatically after typing keywords in the search bar. The full list of keywords is here: Keywords.
(2) Filter on tables
For tables such as grid table and pivot table, filters can be added under the setting button. Also, batch addition can be used for non-time attributes.
(3) Filter on charts
Click the axes to add filters, as shown in gif figure 2-23.
Select a data point or an area, charts on the dashboard would be filtered together. If you want to remove the effect, just click the “Revert” button.
(2) Filter components
Under “Text” component, various filter components are listed, which would be part of the dashboard and other users can interact with.
This section mainly compares formula editors.
Both Sisense and DFC displays description and examples of formulas. The difference is that users need to keep hovering over a formula to view the descriptions in Sisense, while in DFC, formula assistant is provided and stays still on the right side of the formula editor.
To add a field, users need to click the field name under the “Data Browser” in Sisense.While column names can be added though typing or clicking prompts in DFC. As shown in gif figure 2-26, the prompts not only show columns names, but also display syntax.
In terms of formula types, Sisense provides statistical, math, time, and some others. DFC supports string, math, time, transfer, group analysis, json, and many other formulas, check out here: Formula List.
2.4 Data Processing
Data processing is mainly accomplished under the “Data” module in Sisense, and in DFC, it is conducted inside the detailed data page and search analysis page. Here is the comparison:
Sisense and DFC can join multiple data tables, as shown in the gif figures below. The difference is that join in Sisense is per field (one line stands for one join from one field), and DFC offers various join types, such as left join, inner join, full join, etc. Also, there are two ways to join tables in DFC, as shown here.
Sisense provides custom code to enable users to run Python code or SQL sentences to clean or transform data.
DFC mainly utilizes intermediate tables to clean data, more on intermediate tables here. In terms of transforming data, DFC provides conversion between rows and columns, column splits, etc. Check more here.
There are 3 kinds of tables in Sisense: table, aggregated table, and pivot table. DFC also supports 3 types of tables: grid table, pivot table, and cross table.
Table & Aggregated Table & Grid Table
Sisense’s table lists raw and non-aggregated data, and aggregated table displays aggregated data. The design configuration includes change border style, column width, whether to display colors or word wrap, and etc.
DFC’s grid table presents data in two-dimension, and has many different configuration options, such as adding total rows, header rows, change colors as well as fonts, set the heat map mode, and etc. In default, data is aggregated in DFC, and users can choose not to aggregate data, as shown in gif figure 2-31. Check out the detailed configurations of grid table here.
Both platforms provide pivot tables to summarize large amounts of data. The difference is mainly the specific table display and the configurations, as shown below. Check out more on DFC’s pivot table here.
DFC provides cross tables to present multi-dimensional data and fulfill complicated business needs. Users can add, edit, and pin headers.
Sisense supports around 20 chart types and DFC supports more than 50 chart types. Also, DFC has a high level of customization, users can design their charts freely, such as add labels and suspend texts, change colors and fonts, etc. The differences between the two tools are listed in four parts:
Take heatmap as an example, Sisense provides calendar heatmap and color heatmap, and DFC provides calendar heatmap, correlation heatmap, longitude&latitude heatmap, and matrix heatmap. Also, users can set the heatmap mode of grid table and pivot table.
Sisense offers scatter map, area map, ArcGIS map, route map, etc.
DFC supports GIS location map, ll (longitude&latitude) location map, trajectory map, 3D scatter plot, etc., and users can import geographical data by themselves.
As shown below, there are sunburst diagram, word cloud, polar chart, trellies chart, chord diagram, network diagram, etc.
DFC also supported some dynamic charts. For example, time series bubble chart, 3D globe fly line, etc.
|PDF report sizes
|Free & Grid
|Text, labels, tabber, images, ..
|Text, image, video, media…
The width of the Sisense’s dashboards is fixed, the page would grow longer with more widgets. When saving a dashboard as a pdf file, there are many different options for sizes, as shown below.
Dashboards in DFC have various size options, users can either select preset sizes or directly enter the height and width.
By dragging widgets around the canvas or dragging the borders, the dashboard layout is changed in Sisense.
There are two layouts in DFC. One is grid layout, which users can utilize grids to place components. Another is free layout, which users can place components freely.
Sisense utilizes widgets inside dashboards. Charts are also widgets, and are not saved separately. DFC utilizes components as interaction objects, and charts are saved as answers separately.
As listed above, both tools support various interaction objects. Check more about components here: Custom Components.
Sisense has different types of sample dashboards, as shown below. Users can view as well as interact with these samples, and download widgets as image or csv file.
DFC has various templates of dashboards, as shown in figure 2-47. Users can directly use them and substitute components designed by themselves.
2.6 Resource Management
Both Sisense and DataFocus Cloud provide 2 display mode for resources, which are list and tile/thumbnail. Also, users can search to quickly find resources in both platforms.
Sisense has mini navigation bar to manage resources. Users can switch between “Recent”, “All”, “Created by Me”, and “Shared with Me”.
DFC has several modules for users to check resources, such as answer module, table module, dashboard module, resource module, etc. Take resource module as an example, users can move between different resource types through the mini navigation bar. Also, DFC provides tags to quickly classify from different categories.
User roles are for executing permissions. There are four kinds of roles in Sisense, and 7 in DataFocus Cloud.
Sisense: The four categories of roles are admin, designer, viewer, and basic user. Also, roles can be customized using Sisense REST API.
DFC: Roles can be classified as system roles and custom roles. Users can directly add as many custom roles as they want and decide what these roles can do to specific resources. For example, “Sales_01” can edit resources related to sales, and “Sales_02” can only view those resources. Check out more here: Roles.
Groups can be assigned with their own roles in Sisense, and then users can be assigned to groups. Therefore, resources can be shared directly with groups.
Group structure in DFC is similar to Departments in companies. Users are assigned with positions in different departments. Combined with roles, each user can have different permissions towards different resources. For example, “Back End” “Developer Manager Shelly” from “Technology” department can manage the department and resources related to project W. Check out more here: Department Structure.
To share resources in Sisense, users need to first download them as pdf, image, csv, or other formats first, then share within other tools.
Similarly, resources can be downloaded and exported in DFC. Also, there are two other ways to share resources in DFC. For members within the team, users can directly share resources within the system or send emails. For external members, users can utilize the external viewing link. However, external users can only interact with the resources. Check out more about sharing here.
Sisense’s pricing is customized, and users need to get in touch with Sisense team first.
Charging by capacity, DFC’s pricing plan is fair, able to spread across the organization, and not very sensitive to member numbers. Check the plan here:
As AI-driven BI products, both Sisense and DataFocus Cloud are excellent visualization platforms built on cloud. This article demonstrates the similarities and differences between them.
Sisense provides massive sample use cases of dashboards that include different businesses as well as aspects. Also, it has beautiful visualizations to show data in different ways.
DFC provides search-based analytics which is effective and convenient. Visualizations are abundant and customizations are high-level. Teams can use the extensive system to easily realize cooperation. DFC would keep improving the system and the user experience. Welcome check out here: DFC.