Tableau is a market-leading tool in modern, self-service business intelligence that covers all bases. Offering great analytical breadth, depth, and flexibility, it makes understanding data easier and simpler.
While the tool excels in creating beautiful visualizations, most of its advertising focuses on corporate environments that revolve around data engineers and bigger budgets. The free public version of the tool comes with limited capabilities.
However, you can opt for the tailored version for more than a dozen industries—including healthcare, banking, and manufacturing—offering support for HR, sales, finance, IT, and marketing departments. Tableau also has a non-profit option and versions for academic settings.
Typically, the more you pay, the more efficient Tableau will be for you. This particularly rings true in the case of benchmarked data from third parties, mapping and analysis of surveys, and time-series data. Lately, Tableau has been drawing on the artificial intelligence techniques of natural language processing to help users steer clear of clicking and dragging to create formulas, and instead describe what they want to see.
To give a straightforward answer to this question, people choose Tableau because it has a higher ceiling than any other business analytics tool.
With multiple tools integrated into the application, Tableau is the perfect option for variety in visualizations and granularity. Organizations that need higher granularity in their tasks for data permissions and embedded analytics will easily reach their goals using Tableau.
The ease of using Tableau is simply incredible. Once you have your data set cleaned up and understand it, you will find it really easy to create new visualizations and other things on the fly. If your ETL process is created smoothly, you can build complex dashboards with Tableau. Additionally, weekly and/or monthly updates are also very easy. All you need to do is refresh your extracts and check to make sure everything is updated accordingly.
Some people believe it is easy to create Tableau dashboards as a beginner. Yet sometimes, the opposite can be true. You won’t be able to master Tableau dashboards by learning a few Excel formulas. On top of that, Tableau dashboard creation works efficiently when the underlying data is well understood and organized.
Additionally, Tableau compresses large data sets into its fast .hyper format from almost any data source—whether it’s Excel, JSON, Database, or others. By connecting data to Tableau, you will be able to get answers and gain insights on the spot by dragging and dropping into the pane with its built-in data visualization features.
Furthermore, Tableau has an administration interface alongside the desktop client, allowing companies to access and collaborate on the published data workbooks.
Here are the main benefits of leveraging Tableau:
And the drawbacks:
Power BI is an existing Microsoft system, like SQL, Excel, and Azure, that helps analyze and visualize data from local or cloud sources and publish the reports to the Power BI platform. Furthermore, it offers data preparation, interactive dashboards, visual-based discovery, and augmented analytics.
The free desktop version of Power BI is ideal for isolated users. However, the pro version that leverages SharePoint, Microsoft Office 365, and Teams to control raw data and published reports makes collaborative analysis easier for a monthly fee.
For enterprises that wish to go all-in with business intelligence and analytics, this premium tier can make all the difference. It allows self-service data prep through pre-built connectors and corporate data held in Microsoft Dynamics 365, Azure SQL Data Warehouse, and third-party sources like Salesforce.
To put it shortly, Power BI is an excellent choice for people already familiar with Microsoft products like Office 365, Azure, and Excel. After all, it comes at a reasonably low price for SMBs and startups that require data visualization but don’t have a lot of additional capital.
People often come across Power BI and then stick to it once they realize its capabilities. Power BI was developed for joint stakeholders and not necessarily data analysts. Therefore, the interface relies more on drag-and-drop features to help the team enhance their visualizations.
However, Power BI is more than just a data reporting or visualization tool. If you have a good data model in place, it will allow you to quickly create sustainable reports, thereby saving you time in the long run by automating regular summaries.
When you look at it from a business standpoint, it can speed up the decision-making process if you have a structure in place. It enables people to see trends on a daily basis without extra work, instead of waiting for summaries of monthly reports. It also has a powerful backend that lets you manipulate and structure the data any way you want.
We highly recommend using Power BI. It is a potent BI tool that lets you do the following:
But there are a few shortcomings:
Domo is a cloud-based platform that primarily focuses on business user-deployed dashboards and ease of use. The tool offers business intelligence solutions ideal for various industries like healthcare, financial services, education, manufacturing, and roles such as sales, IT employees, CEOs, and BI professionals.
CIOs should first check out how Domo handles data from Jira, AWS, GitHub, and New Relic before moving forward to look at the 500+ integrations that can help your entire organization.
Domo allows you to pull built-in ETL data, create reports, and share them within a matter of minutes. It is also relatively user-friendly, even for a less technical audience.
The tool focuses a lot on hype and branding instead of core functionality. It promotes itself as an all-in-one cloud-based data and analytics solution perfect for organizations. However, a lot of Domo’s features tend to be unutilized. Plus, it only makes an excellent choice for firms with lots of cash to spend on a tool that doesn’t necessarily work so well.
Even in the case of ETL, Domo doesn’t allow you to easily create virtual tables and delete them as required. Additionally, the data in the temp tables also happens to hold little to no business value—yet they do make you pay for it. The model is very inefficient, as you have to merge all the facts and dims in a huge table, meaning adding a new dimension will be challenging. Also, the granularity is top-level.
There are several pros of using Domo:
And here are the cons:
Sisense’s BI software stack focuses on everything from the database through ETL and analytics to visualizations. In fact, the organization claims that its in-chip database engine is faster than in-memory databases. Additionally, it is famous for its embedded BI use cases.
Sisense’s latest version comes with new machine learning capabilities and is available in the cloud and on-premise. It also offers solutions for marketing, HR, finance, sales, and IT, alongside customer service, operations, and logistics departments. Additionally, Sisense makes it easier to offer analytics tools to users who work outside the enterprise by embedding them in web applications.
Sisense acquired Periscope Data in September 2019. They are also in the process of integrating advanced analytics capabilities through the acquisition.
Sisense is a good choice if building an analytical application is your priority. However, it is complex and requires training to master. You should also possess a high level of technical skill to set up the in-cache technology. Furthermore, the amount of ongoing technical support you require will greatly increase the price of keeping the tool going.
While the ElastiCube means you won’t require another data warehouse or supporting infrastructure on the data side, non-technical users will have a hard time setting up the ElastiCubes data objects without IT support. This will hinder your organization’s ability to scale Sisense.
These are the advantages of using Sisense:
And the disadvantages:
When it comes to choosing the most appropriate BI tool for your organization, you should look at the market research and product features to see if the application aligns with your business requirements.
Additionally, you should also consider your enterprise’s size and growth. For instance, cheaper software and lightweight tools might be best for smaller companies with less data variety and volume. Likewise, a simple tool with fewer features will be easier to learn and cost-efficient when training novice users. A more sophisticated BI tool is only suitable if the end users are well-versed in the software or analytics.
At STX Next, we promote the use of data visualization best practices. Because of that, Tableau is hands-down the clear winner for us. Whether it’s ingesting data from any source or creating beautiful dashboards, Tableau puts a lot of effort into stepping up the data visualization game.
However, there are several reasons why you may want to go with any of the other tools.
Power BI comes with click-through actions that eliminate the need for a data engineer who knows SQL to clean data from different systems. With Domo, on the other hand, everyone in your organization can access the data sets and gain reliable insights to make faster decisions. Similarly, Sisense makes it easy to discover business insights from complex data so you can get up and running almost instantly.
Ultimately, as is so often the case, the choice should come down to your individual needs.
If you’re interested in learning more about business analytics tools, here are a few resources that can help:
Do you have any more questions about business analytics tools? Or maybe you need some help developing an intelligence platform of your own? If so, our expert teams of software engineering professionals are here to support you. Reach out to us and get all the answers you seek!