SAP is something of a pioneer in Big Data, database, and now self-service business intelligence (BI) tools.
It shows in the impressive SAP Analytics Cloud (which begins at $21 per user per month).
This product is best suited for companies that have already standardized on other SAP enterprise products, but its a solid entry in its own right because of its affordable price, solid feature set, and strong data visualization functionality.
With that said, we're rating it lower than our Editors' Choices IBM Watson Analytics, Microsoft Power BI, and Tableau Desktop due to its limited data prep features and unintuitive toolbar.
This cloud application is primarily targeting SAP HANA shops and, for the most part, it still speaks in the language of data science rather than talking like a human.
That won't bother data professionals, but in the world of rank-and-file data democratization, it means that the SAP Analytics Cloud isn't quite ready for prime time.
The UI is not intuitive enough for beginners and there's no way to use natural language queries such as IBM Watson and Salesforce Analytics Cloud do or even competitors Microsoft Skype and Sisense, no less.
Still, SAP has made some effort to reach non-data professionals with its "create a story" approach.
While not great, this still makes it arguably easier for business users to understand than either Chartio or Tableau Desktop, both of which have UIs that will make beginning users think they're looking at Egyptian hieroglyphics.
As stated earlier, SAP Analytics Cloud begins at $21 per user per month.
It's therefore very competitive with the rest of the field, especially with high-priced, enterprise-oriented players such as Domo.
Where SAP Analytics Cloud shines brighter than high-end competitors IBM Watson and Tableau Desktop is in its immediate and easy additions of responsive pages for publishing to mobile devices, its real-time analytics with streaming data capabilities, and the aforementioned centralized user experience (UX).
Your IT department can also get a centralized view of the company's analytics assets via the new SAP Analytics Hub.
Considering SAP's BI Suite is a bit fragmented across several products, that's a big plus for IT users.
Amongst its latest updates, SAP has added support for scripting with the R language, which is now practically mandatory for data professionals dealing with predictive analytics and machine learning (ML), though not so much for business analysts and general users.
There's also a new Apple iOS mobile app and live connections to SAP's BW/4HANA, S4/HANA, and Universes databases.
However, SAP Analytics Cloud is capable of ingesting data from non-SAP sources, too, typically from hybrid environments.
There are over 400 extensions and a huge partner and developer ecosystem that's constantly working to add more.
Getting Started
Uploading my test data was a breeze and took only seconds.
This was the same CSV data set that I used to test all self-service BI apps from a business analyst perspective, meaning it was big but not huge.
As I mentioned, the app is setup in a story narrative manner to help users progress through the process.
My first move, wearing my business analyst hat, was to click the "Create a New Story" button to import my data and create a model.
Had I donned a data scientist hat or an advanced user hat (which I can rightfully claim as mine), I could have skipped story mode and gone straight to the modeler where I could add all sorts of things, such as formulas, geo-enrichments, and hierarchies.
I could also make fancier visualizations at the end of the drill, but alas, I'm getting ahead of my own story.
Story Mode is as easy as SAP Analytics Cloud gets but there is a catch.
The data should be clean before you upload it, meaning no missing fields and no variances in the data of any field.
So make sure all of the addresses are typed in the same way, for example.
Enter them in the exact same way because there's no simple way to do any substantial prep work after data is uploaded—something we'd love to see change in future versions.
Yes, there is a "Validate Data" button that will quickly return a short report on your data's hygiene based on a sample.
But things get a bit murky there.
At first glance, the system seemed to be holding its virtual nose and merely hinting that my data needed to be sanitized.
It told me there was one issue but not what that issue was.
Since each of the report bar's fields had information in them, I assumed that was all it had to say on the matter.
It wasn't until I clicked around that report that I discovered more information, and thus, that the problem was ultimately a mapping issue.
The box for "Fill empty ID cells with a default value" was checked by default.
There's no clear way to indicate "apply," or "ok," or "move along now." Not an ideal data prep tool and its prompts are certainly lacking for business users.
The Discovery Process
On the bar at the top are several symbols and some text.
The first one reading from the left is Data View.
It turns out that this is the data manipulation view, which let me do some minor data moves like switching the designation of a column from dimension to measure or vice versa.
This is not something I would call data prep, however it is indeed data manipulation, which isn't the same thing but a good feature nonetheless.
Measure columns contain quantitative numerical information whereas a dimension column is qualitative data and can be in numbers or text.
In other words, changing a column's designation can be useful, but it has its limits.
When I clicked on Data View, the system automatically applied everything I told it to do earlier when I hit "validate data" after importing, which wasn't much really.
There's a save icon on the bar but you could easily overlook it while you're busy following the prompts.
The next prompt points to "Insert" on the top bar and urges you to add charts and other things to the page.
Turns out that's where you choose measures, dimensions, chart structures, and filters.
Yes, this means "Insert" is where you actually begin data discovery.
Not intuitive.
I won't take you through each command, but suffice it to say that except for the universal save icon, the old familiar disk drawing, almost nothing on that bar made sense to me in terms of what functions lay beneath.
For example, the wrench icon made me expect settings or a system tool but instead I found "Story Details" and "Preferences." Just so you know, "Story Details" is where you give your document a title and a brief description, whereas "Preferences" is a hodgepodge of things like page size and properties and tile settings.
It all was a bit confusing and left me exploring the system more often than the data.
Data Visualizations
SAP Analytics Cloud has basic visualizations and automatically suggests a format to fit the data you selected but you can change to another format if you prefer.
You can then copy and paste the visualization to an existing story page or create a new story.
This enables you to write a data story in pictures and then create new stories as needed, all of which are changeable at any time.
The templates are helpful in telling your particular story in a coherent and cohesive way.
Customizations are easy using the design panel and the builder tool.
I really liked that whole story-telling approach because bottom line that's what using data visualizations is all about: telling the business story.
For more options in visualizations, users might also want to check out SAP's new Lumira 2.0 tool, which combines Lumira visualizations with SAP Design Studios.
Pros
Real-time analytics for Internet of Things (IoT) and streaming data features.
Massive ecosystem with plentiful extenders.
Responsive pages make mobile publishing easiest.
Impressive storytelling paradigm.
Centralized view with consolidated analytics.
View More
Cons
Data prep features are lacking.
Confusing toolbar design.
Not friendly for beginners.
The Bottom Line
If your business already uses SAP's HANA database platform or some of its other back-end business platforms, then SAP Analytics Cloud is a powerful, well-priced choice.
But be warned that there's a steep learning curve and a noted dependence on other SAP products for full functionality.
SAP is something of a pioneer in Big Data, database, and now self-service business intelligence (BI) tools.
It shows in the impressive SAP Analytics Cloud (which begins at $21 per user per month).
This product is best suited for companies that have already standardized on other SAP enterprise products, but its a solid entry in its own right because of its affordable price, solid feature set, and strong data visualization functionality.
With that said, we're rating it lower than our Editors' Choices IBM Watson Analytics, Microsoft Power BI, and Tableau Desktop due to its limited data prep features and unintuitive toolbar.
This cloud application is primarily targeting SAP HANA shops and, for the most part, it still speaks in the language of data science rather than talking like a human.
That won't bother data professionals, but in the world of rank-and-file data democratization, it means that the SAP Analytics Cloud isn't quite ready for prime time.
The UI is not intuitive enough for beginners and there's no way to use natural language queries such as IBM Watson and Salesforce Analytics Cloud do or even competitors Microsoft Skype and Sisense, no less.
Still, SAP has made some effort to reach non-data professionals with its "create a story" approach.
While not great, this still makes it arguably easier for business users to understand than either Chartio or Tableau Desktop, both of which have UIs that will make beginning users think they're looking at Egyptian hieroglyphics.
As stated earlier, SAP Analytics Cloud begins at $21 per user per month.
It's therefore very competitive with the rest of the field, especially with high-priced, enterprise-oriented players such as Domo.
Where SAP Analytics Cloud shines brighter than high-end competitors IBM Watson and Tableau Desktop is in its immediate and easy additions of responsive pages for publishing to mobile devices, its real-time analytics with streaming data capabilities, and the aforementioned centralized user experience (UX).
Your IT department can also get a centralized view of the company's analytics assets via the new SAP Analytics Hub.
Considering SAP's BI Suite is a bit fragmented across several products, that's a big plus for IT users.
Amongst its latest updates, SAP has added support for scripting with the R language, which is now practically mandatory for data professionals dealing with predictive analytics and machine learning (ML), though not so much for business analysts and general users.
There's also a new Apple iOS mobile app and live connections to SAP's BW/4HANA, S4/HANA, and Universes databases.
However, SAP Analytics Cloud is capable of ingesting data from non-SAP sources, too, typically from hybrid environments.
There are over 400 extensions and a huge partner and developer ecosystem that's constantly working to add more.
Getting Started
Uploading my test data was a breeze and took only seconds.
This was the same CSV data set that I used to test all self-service BI apps from a business analyst perspective, meaning it was big but not huge.
As I mentioned, the app is setup in a story narrative manner to help users progress through the process.
My first move, wearing my business analyst hat, was to click the "Create a New Story" button to import my data and create a model.
Had I donned a data scientist hat or an advanced user hat (which I can rightfully claim as mine), I could have skipped story mode and gone straight to the modeler where I could add all sorts of things, such as formulas, geo-enrichments, and hierarchies.
I could also make fancier visualizations at the end of the drill, but alas, I'm getting ahead of my own story.
Story Mode is as easy as SAP Analytics Cloud gets but there is a catch.
The data should be clean before you upload it, meaning no missing fields and no variances in the data of any field.
So make sure all of the addresses are typed in the same way, for example.
Enter them in the exact same way because there's no simple way to do any substantial prep work after data is uploaded—something we'd love to see change in future versions.
Yes, there is a "Validate Data" button that will quickly return a short report on your data's hygiene based on a sample.
But things get a bit murky there.
At first glance, the system seemed to be holding its virtual nose and merely hinting that my data needed to be sanitized.
It told me there was one issue but not what that issue was.
Since each of the report bar's fields had information in them, I assumed that was all it had to say on the matter.
It wasn't until I clicked around that report that I discovered more information, and thus, that the problem was ultimately a mapping issue.
The box for "Fill empty ID cells with a default value" was checked by default.
There's no clear way to indicate "apply," or "ok," or "move along now." Not an ideal data prep tool and its prompts are certainly lacking for business users.
The Discovery Process
On the bar at the top are several symbols and some text.
The first one reading from the left is Data View.
It turns out that this is the data manipulation view, which let me do some minor data moves like switching the designation of a column from dimension to measure or vice versa.
This is not something I would call data prep, however it is indeed data manipulation, which isn't the same thing but a good feature nonetheless.
Measure columns contain quantitative numerical information whereas a dimension column is qualitative data and can be in numbers or text.
In other words, changing a column's designation can be useful, but it has its limits.
When I clicked on Data View, the system automatically applied everything I told it to do earlier when I hit "validate data" after importing, which wasn't much really.
There's a save icon on the bar but you could easily overlook it while you're busy following the prompts.
The next prompt points to "Insert" on the top bar and urges you to add charts and other things to the page.
Turns out that's where you choose measures, dimensions, chart structures, and filters.
Yes, this means "Insert" is where you actually begin data discovery.
Not intuitive.
I won't take you through each command, but suffice it to say that except for the universal save icon, the old familiar disk drawing, almost nothing on that bar made sense to me in terms of what functions lay beneath.
For example, the wrench icon made me expect settings or a system tool but instead I found "Story Details" and "Preferences." Just so you know, "Story Details" is where you give your document a title and a brief description, whereas "Preferences" is a hodgepodge of things like page size and properties and tile settings.
It all was a bit confusing and left me exploring the system more often than the data.
Data Visualizations
SAP Analytics Cloud has basic visualizations and automatically suggests a format to fit the data you selected but you can change to another format if you prefer.
You can then copy and paste the visualization to an existing story page or create a new story.
This enables you to write a data story in pictures and then create new stories as needed, all of which are changeable at any time.
The templates are helpful in telling your particular story in a coherent and cohesive way.
Customizations are easy using the design panel and the builder tool.
I really liked that whole story-telling approach because bottom line that's what using data visualizations is all about: telling the business story.
For more options in visualizations, users might also want to check out SAP's new Lumira 2.0 tool, which combines Lumira visualizations with SAP Design Studios.
Pros
Real-time analytics for Internet of Things (IoT) and streaming data features.
Massive ecosystem with plentiful extenders.
Responsive pages make mobile publishing easiest.
Impressive storytelling paradigm.
Centralized view with consolidated analytics.
View More
Cons
Data prep features are lacking.
Confusing toolbar design.
Not friendly for beginners.
The Bottom Line
If your business already uses SAP's HANA database platform or some of its other back-end business platforms, then SAP Analytics Cloud is a powerful, well-priced choice.
But be warned that there's a steep learning curve and a noted dependence on other SAP products for full functionality.
