There are endless conversations across the web about how to get more data. And which platforms you can get more data with. Which is great, unless… you don’t have a plan for that data once you’ve got it.
Staring at an endless list of numbers and deltas from all these raw data sources is nightmare fuel. Let’s be real. You’ve got to figure out what you can act on using the data you’re bringing in, and fast.
If you’re here, you’re probably aware that visualizing your data in more digestible ways is the best way to gain snap insights on ever-expanding datasets.
In this article, we’ll give you an easy comparative rundown on Tableau vs. Adobe Analytics, two highly discussed solutions in data analysis, visualization, and reporting.
Adobe Analytics Overview
Adobe Analytics, while it’s not the only analytics tool Adobe offers, is the primary web-focused analytics software of the Adobe Experience Cloud. It began as the web analytics platform Omniture, and Adobe acquired it in 2009, eventually changing its name as it became a part of the Adobe suite.
The key function of the software is to collect large amounts of data from a myriad of online sources, whether it’s from your website, your paid ads, email campaigns, and so on. Adobe Analytics (AA) then stores this data in one central hub, connecting your online efforts and creating one point of truth.
What’s more, it also helps you analyze and visualize your data in meaningful ways and gives you the predictive insights you need to better connect with customers.
Features
One reason why so many businesses opt to upgrade to Adobe Analytics for their web data is because of its wealth of powerful features.
No matter how much data you bring to the table, or from how many sources, AA uses the robust and flexible storage capability of the Adobe Experience Platform in combination with its world-class features to create actionable, immediate insights.
Here’s a brief rundown of some of AA’s standout features:
- Varied multichannel data collection: Collect data even from online kiosks, client-server apps, and many more internet-connected sources.
- Server-side processing: Segment data in real-time, without needing to implement complicated rules on your site.
- Simplified tag management: Collect and distribute more data, easier.
- Expanded data capabilities: More data processing power with storage extensions, customer data reporting, and data reprocessing.
- Flow analysis: Examine the full customer journey across multiple data points.
- Cohort analysis: Group like-customers who share similar traits, and analyze trends among their behaviors over time.
- Flexible drag-and-drop custom reports: Organize data how you need to see it, with any combination of visuals, tables, charts, and more.
- Create new metrics with advanced calculations: Use existing metrics to create statistical operations that will form new metrics relevant to understanding your data.
- Anomaly alerts and contribution analysis: Be notified when data anomalies occur, and gain smart insights from AA on what caused them.
- Real-time data: Get a birds-eye view of hit-level data with AA’s Live Stream.
- Actionable shared audiences: Make audience segments that can be utilized to optimize the customer journey in other Adobe Experience Cloud products.
- Simple advertising analytics tie-ins: Easily incorporate data from your digital marketing channels and understand how this data relates to your other data channels.
- Configurable remarketing triggers: Re-engage users automatically with triggers built based on data collected in AA.
- Third-party integration capability: Mesh seamlessly with limitless 3rd party apps using AA’s pre-built API connectors.
Use Cases
For all businesses, having an analytics solution is a must. If you’re without one currently, adopting an analytics platform should be a priority.
Some example of instances when Adobe Analytics specifically might fit your needs are:
- You run a high volume of journey tests and need real-time analysis capabilities to help you respond to changes in customer journeys when they happen.
- You need a centralized data platform that combines cross-channel measurement with powerful and diverse visualizations to better understand the full experience.
- You need to analyze pattern insights from the whole customer journey, to find out where to invest the most testing effort.
- You could benefit from more flexible, and more powerful predictive modeling to help you spot trends before they happen.
- You need a robust data storage solution capable of scaling with your business.
- You already use other products in the Adobe Experience Suite (like AEM, or Adobe Target), and are looking for a web analytics solution that links seamlessly with those.
Tableau Overview
Founded in 2003 at Stanford, Tableau was the brainchild of co-founders Chris Stolte, Pat Hanrahan, and Christian Chabot. It was created using VizQL, which employed visual data encoding in combination with query to foster a stronger visual understanding of data.
One of Tableau’s main goals originally, and still to this day, was to remain an accessible platform even to those without query expertise, via its drag-and-drop visualization capabilities.
Tableau, while similar in a few ways to Adobe Analytics, serves a different purpose overall. Both can be considered data analytics platforms, but Tableau is focused almost entirely on data visualization and not data storage or advanced predictive data.
One key difference to keep in mind is that Tableau doesn’t collect its data directly from the source (e.g. Google Ads, your website, your app, etc.) Instead, it integrates with other platforms that do get their data from the source (e.g. Adobe Analytics, Salesforce, Google Analytics) and brings that data in for visualization.
Features
Over its various iterations, Tableau has worked to continually develop ever more ways to visualize data that you can easily act on. Its most notable features work to improve the ease of comprehensive, diverse visual analyses. A few such features are:
- Geospatial analysis: Analyze data geographically, plotted out on a map view.
- Hyper SQL engine: This enables near real-time dashboard loading and data processing.
- Visualization embedded images: Map image assets to your data sets for easier visual analysis.
- Join step: Create comprehensive visualization by easily joining data from 2+ tables that share a common field.
- Accelerator: Employ pre-built dashboards to get started faster, and customize fully as needed.
- Bins: Go from large continuous datasets to digestible distribution displays without complex IF statements.
- Data stories: Automatically generate key insights about your data in easy-to-understand language.
- Explain data: Gain quick insights into why data anomalies may have occurred.
- Accessible drag-and-drop visualization: Easily drag and drop data into dashboards and visualizations.
Use Cases
Tableau is a powerful tool for understanding data, but how do you know if Tableau suits your entire situation?
As we mentioned earlier, Tableau pulls data from your existing storage for use in its wealth of visualizations. Simply put, if you don’t already have a data storage platform, or one that doesn’t work how you need it to, Tableau won’t add value to your tech stack.
That said, some example instances when Tableau would be a prudent choice are:
- If you already use a data storage and collection platform that unifies all your data in one place, but that falls short in the breadth and ease of data visualizations.
- If you can’t change your existing data collection stack, but it’s fragmented across many different platforms, and you need a better way to interpret all this data together.
- Your end goal is to improve data literacy companywide, using features like Tableau’s Center of Enablement, which helps teams of all technical levels learn to capitalize on your data.
Adobe Analytics vs Tableau: Key Differences
Now that you’ve got a bit more background on each platform, you’ve probably already got a few ideas about the most important areas where they differ. The following capabilities don’t overlap:
Although most of the capabilities overlap, they excel in different areas.
But, to help you get a closer look at these differences, let’s wrap them up in one place here.
Analysis and Reporting Interface and Usability
To start, it’s worth highlighting that Tableau and Adobe Analytics both have convenient workspaces for data visualization. Both of these workspaces feature data points (metrics, dimensions, categories, etc) which can be dragged and dropped to craft reports.
However, Tableau’s Workspace can certainly feel more daunting to those in our organizations who aren’t natural data analysts, despite its drag-and-drop functionality. AA’s Analysis Workspace, on the other hand, is a bit cleaner and more intuitive for users of all data literacies.
Additionally, unlike Tableau, Adobe Analytics is comprised of far more tools than the workspace alone, like the components tab, which enables you to segment data, create alerts, schedule reports, and create calculated metrics.
Data Integration and Management
To recap, the largest difference in data integration between Tableau and Adobe Analytics is where each platform can draw its data from.
Tableau must draw data from other data storage solutions and can’t collect or store data directly from the source. AA, however, can integrate directly with online data sources and can store vast amounts of this collected data. AA can also import and store customer data from your offline sources to add context to your web data.
Some of Tableau’s key integrations are:
- Microsoft SQL Server
- Excel
- Salesforce
- Snowflake
- Azure
- Alteryx
On the flip side, AA features integrations with over 200 data sources (too many to list here), with some of the key sources being:
- Your website (via tagging solutions managed within AA)
- Your app(s)
- Your advertising campaigns
- Your CRM
Collaboration and Sharing
At first glance, Tableau and AA’s collaboration and sharing capabilities are fairly similar.
Both platforms enable you to assign specific permissions around collaboration on dashboards. Both platforms also enable you to comment on dashboards and visualizations.
Notable differences here tie back to interface accessibility, as well as how permissions are assigned.
For users (especially on the business side) who need to access and collaborate on projects with lower data literacy, AA provides a more user-friendly, accessible interface, whereas Tableau’s interface is more accessible to users who already have some data literacy.
Second, permissions are role-based within Tableau, whereas AA provides more flexible and customizable permissions on the user and group level.
Types of Visualizations
Last but not least, while AA and Tableau both mirror each other in the many of the visualizations they provide, there are certainly some differences between the two.
For example, Adobe Analytics incorporates quite a few visualizations out-of-the-box that Tableau doesn’t offer in the same capacity, especially related to customer journeys, like:
Can Adobe Analytics and Tableau Work Together?
Knowing what we know now about how Tableau works, and what AA does, the answer to this question is yes—technically, Tableau and AA can work together.
Tableau draws its data from storage solutions like Adobe Analytics, and the functionality to link Adobe Analytics to Tableau does exist.
However, having both platforms tends to be more redundant than helpful in many cases. This is because Adobe Analytics already incorporates powerful visualization features on top of its data collection and storage capabilities.
AA was designed to be an end-to-end platform that follows your data from conception through to predictions, visualization, dashboards, and activation of insights. So, while you can use it with Tableau, you really don’t need to invest in both.
Conclusion
Now you know—Tableau and Adobe Analytics both serve the digital analytics space in different and exciting ways. Keeping the needs of your business and your existing tech stack in mind, we hope you’ve got a clearer idea now of which platform may be best for you.
As a whole, Adobe Analytics brings a bit more capability to the table from square one of your data journey to square infinity. Transparently, it certainly does require more of an investment to implement than Tableau—though it’s not an investment without serious rewards in the long run.
That said, if Adobe Analytics is truly the best path forward for you, don’t let implementation strain keep you from choosing it! Our experts are standing by to help get you on board with AA quickly and set you up for continued success with the platform. Ask us how your implementation process would look!
