AWS QuickSight and Tableau are both popular business intelligence and data analytics platforms. But which data solution is best for your company?
Tableau and AWS QuickSight are two well respected business intelligence (BI) and data analytics platforms.
These applications are crucially important tools as organizations seek to harness growing levels of data. It’s no longer the case that data analysis is performed only by data scientists slicing and dicing data. Now staff from management, marketing, and IT are utilizing analytics in their routine activities.
As two popular data analytics platforms, users often are forced to choose between QuickSight and Tableau. There are arguments for and against each BI tool. But which is best for your business?
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Tableau is very focused on delivering analytics with AI, data management, and collaboration. At the heart of Tableau is a proprietary technology called VizQL that makes interactive data visualization an integral part of understanding data. It differs from traditional tools that require users to choose a subset of the data to present, organize that data into a table, and then create a chart. VizQL creates a visual representation to begin with, providing fast visual feedback as you analyze.
QuickSight is not a stand alone analytics platform; it’s a service that resides on Amazon Web Services (AWS) that is used for the analysis of data. It can create dashboards and it ties into a database management system. It includes features to leverage AI to boost data analysis, visualization, and general data management capabilities. Those companies already using AWS will find it a handy analysis tool that is integrated fully into the AWS ecosystem.
Tableau has been adding features like Ask Data in Slack (ask questions using natural language and automatically get data visualizations), Einstein Discovery in Slack (predictions in the work flow), and Model Builder (collaboratively build and consume predictive models using Einstein).
QuickSight offers the ability to ask conversational questions of data and use an ML-powered engine to receive relevant visualizations without the need for time-consuming data preparation from authors and admins. It can also perform forecasting and what-if analysis.
QuickSight Q provides suggestions for phrases and business terms, and performs spell checking so users are freed from worrying about typos or remembering the exact terms in data.
Who wins on features? Tableau is stronger in terms of broad analytics features. QuickSight’s strength is that it’s embedded within AWS. But it can’t compete with Tableau’s deep end-to-end data and analytics capabilities.
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In terms of learning curve, users of Tableau should be well-versed in working with the R programming language, which is often employed in statistical programming and data analysis.
But Tableau has been working to become easier to use. Its VizQL technology enables people of all skill levels to analyze complex data to gain deeper insights. The depth of analytics goes beyond just building charts. Tableau’s exploratory nature, the linear learning curve, and its customization capabilities help to unearth insights from data. Tableau also works well when statistical data is data mined as the raw material. For other formats, ease of use can suffer.
QuickSight, too, has a learning curve, and users overall feel it is more difficult to master than Tableau. But once mastered, its search function allows anyone in the organization to understand data and perform queries using natural language, view interactive dashboards, and detect patterns and outliers via machine learning.
The conclusion: Both data analytics platforms require some technical knowledge. Tableau, though, caters to a broader user base and is probably somewhat easier to use.
Tableau tries to differentiate itself with what it describes as an intuitive analytics experience with richer capabilities based on its patented VizQL engine. It can connect to real time data feeds (performing queries in-database and returning results in real-time) or in-memory (ingesting data from source systems). This allows users to control performance, cost, and data freshness. Tableau also scores very well on live query capabilities and extracts, helping analysts to query faster. Its use of the R language makes it the winner on statistical capabilities.
QuickSight has excellent analytics qualities, too. It is particularly adept at ad hoc reporting and rapid visualization of Amazon-based data. Developers find it handy, as it can deploy and scale embedded analytics to a huge audience of users in apps using AWS APIs. This makes it easy to share data visualization and insights with users. Performance is also boosted by AWS’ ability to automatically scale based on workload. QuickSight provides updates every two weeks, ensuring all users have the latest features without any downtime, version conflicts, or compatibility issues.
Tableau wins on broad analytics capabilities and its ability to work well regardless of the technology platform used by the customer, whereas QuickSight is focused on the AWS ecosystem. As such, AWS users are unlikely to see much need for Tableau.
Quicksight is wholly a cloud offering. It has no on-premise version. It has cloud features far in advance of Tableau’s, which has its DNA rooted in on-prem.
While Tableau offers cloud-hosted solutions such as Tableau Online and Tableau CRM, its strength lies in on-premise deployments and this is where much of its massive installed base resides. Thus, it can be challenging to scale out Tableau workloads in the cloud. QuickSight wins by a large margin in the cloud, Tableau wins for on-premise.
CRM and BI often work in tandem. With Tableau owned by Salesforce, it offers excellent marketing and enterprise product capabilities. Tableau is in the process of being integrated with Salesforce Einstein Analytics (known as Tableau CRM). An Einstein Discovery dashboard extension, for example, brings predictive modeling capabilities from Einstein to Tableau.
QuickSight doesn’t deal with CRM. But most CRM platforms operate on AWS. Tableau is the clear winner when a business wants a unified BI/CRM package. But for those companies not part of the Salesforce universe, QuickSight offers a good alternative as it can be paired up with other CRM solutions if they are also running on AWS.
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Tableau has its hands full in integrating with Salesforce. This creates a somewhat fragmented experience between Einstein Analytics and Tableau, yet steady progress is being made in merging the solutions. It won’t be long until those issues are resolved. As a result, Salesforce customers will be upsold to Tableau and vice versa.
QuickSight is fully integrated into AWS and offers no real integration challenges. If a data source can integrate with AWS, Quicksight can be deployed. QuickSight wins here.
Tableau has a reputation for being expensive. By some estimates, it works out to about $75 per month per user for decent analytics functionality. But those who only want to interact with some basic dashboards can get it much cheaper. That said, the addition of Tableau CRM for a list price of up to $150 per user per month means newer functionality and Salesforce integration can quickly get pricey.
QuickSight has somewhat complex pricing but is cheaper than Tableau. Authors can create and share dashboards with other users in the account. The cost is $24 per month per author.
QuickSight accounts enabled with a feature known as Q enable authors to explore their data by asking questions, setting verified answers, and fine-tuning Q to better align with business domains. That raises the price to $34 a month. For users who are just reading or viewing data, the pricing is variable based on the number of sessions, capacity, questions, and more. Still, it is unlikely to work out to be more expensive than Tableau. QuickSight wins on price.
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Like all solutions from leading data analytics and business intelligence vendors, both Tableau and QuickSight enable customers to create interactive dashboards, leverage AI to make data analysis easier, and offer data management capabilities. Both data platforms offer highly sophisticated analytics features.
Tableau boasts a fanatical user base and a very loyal user community. Its user conferences attract large audiences. Its popularity is growing, too, partially through the distribution of a free platform known as Tableau Public. This is where people can share and explore data visualizations online. It contains over 3 million interactive visualizations. Yet it is in the data scientist, analytics specialist, and power user markets where Tableau’s feature set wins the most plaudits.
QuikSight is excellent within the AWS ecosystem, is easy to deploy and easy to access. It favors a templated visualization approach where visuals may not always be fully configurable. For basic analysis, it is straightforward. But a higher level of technical aptitude is required to perform more complex analysis.
Overall, Tableau is the clear winner. Gartner, in its latest “Magic Quadrant for Analytics and Business Intelligence Platforms,” graded Tableau as a leader. QuickSight did not rate a mention – probably because it is essentially an AWS analytics add-on rather than a stand alone BI and analytics platform. That said, QuickSight already powers millions of dashboard views weekly for AWS customers, so it is clearly performing well for many companies.
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