When I'm working with clients to develop their data product strategy, the question always comes up—which platform do I recommend?  Of course, it depends on their situation, goals, constraints, etc. but it often comes down to just a few options.  Until now, I've just told people and have never written down any recommendations

So, I thought I'd put together a simple list of a few of my "go to" analytical tools for data product teams. It's a list of 3-5 analytical platforms or tools that I'd use myself if I was going to build a data product.

This list will change as I find new options or if platforms no longer become a good choice for data product builders.  I'll update this list as appropriate.


GoodData has been around for a while now—it was the platform I selected for my first data product.  And the second.  And the third.  Clearly, it's got something I like.  That something is a full-ecosystem for data product teams to build, deploy, and maintain data products.  

I like that it's easy to prototype new analytics (charts, tables, KPI infographics) and dashboards without much support. It allows me to work side-by-side with clients or potential users to come with analytics that are just what they need.  Combine this with a team that's always ready to help out and solves problems for you and a deep understanding of the embedded space, and GoodData earns a well-deserved space on my shortlist. 

I've used GoodData many times before, and I'll use it again.  

(Learn more about GoodData in our white paper)


Izenda is a relative newcomer to my list of go-to data product platforms.  When I first saw a demo of the Izenda platform, I think I actually made the person performing the demo stop and repeat a few things.  I couldn't believe they had that much functionality—functionality targeted directly at the needs of a data product builder—packed into the platform.  They scale, they perform, and they get data products (a less common thing than one might think...).  

Things I particularly like about Izenda: they make it easy to see what people are using (and not using) within your data product, they let you create dashboard templates thereby saving time on deployments, and they make it easy to embed using a variety of techniques.  And on top of this, they are heavily focused on data products.  It's not a sideline gig, it's not an "oh yeah, I guess we can do that" thing—it's their focus.

This is a platform that you might not have heard a whole lot about. You need to change that right away if you're in the market for a good platform for embedded analytics.  It's a great option for a data product and worth a good, hard look.


Looker is a platform that you probably have heard a lot about lately.  And for good reason — they seem to be the hot property in the analytics space these days. I love the fact that Looker makes it easy to get into the "guts" of the analytics and see what's really going on.  You can jump right into the SQL and see why a metric isn't calculating quite right.  Don't let this scare you off — they let you build analytics and dashboards visually as well.  But even for a non-coder like me, it's nice to be able to look under the hood once in a while.  

I also like the fact that they leave the data in place—you don't have to upload all of your data to a proprietary storage system before using it for analytics. I've seen problems occur when the volume of data is so large that moving it isn't feasible and Looker jumps right past this problem (and its associated costs).  I also love that they make it easy to deploy (and roll-back if necessary) across all of your instances.  Data product owners sometimes forget what a chore it can be to update thousands of instances of their analytics to "version 2.0" for their customers.  Many platforms make this a manual effort, but Lookers has the tools built right in.  

And finally, they're great to work with and some of the smartest folks I've met (never overlook the importance of this if you're a data product owner!).  They get data products, they get analytics, and they get business needed.  Highly recommended for data product teams.

(Learn more about Looker in our white paper)


Wait, what?  Who?  What's a "Keboola"? It's an awesome platform for gathering any data, any place, in any format, blending it, preparing it, and pushing it to whichever visualization platform is right for you.  It's the 8" chef's knife of the data product world—it's that useful.  

I love the fact that Keboola makes it easy to accommodate multiple use cases at once and with ease.  Some of your customers want data feeds instead of dashboards?  No problem.  You want to use Looker for some customers and Izenda for others but not have to create (and maintain) multiple data models?  Keboola is your tool.  Need to enrich your data with natural language processing or other advanced analytics?  Keboola lets you add "recipes" to your data with ease.  

When you combine this uber-functionality with a deep understanding of data products and what's needed by data product teams, I wouldn't build an analytical application without Keboola under the hood.  Three thumbs up.

That's it. These are what I'd use and what I recommend. This is the short list of the "must know" platforms for data product teams building embedded analytical products. Did I miss any platforms that you'd recommend for a data product owner?  If so, let me know.  I'll take a look and possibly include more platforms in the next version of my short list.


I work with some of the companies I've recommended above but the recommendations are mine and mine alone.  These companies haven't reviewed, edited, or even seen the information before I published it.  There's no "pay to play" going on here.  If I work with a company, it has no bearing on what I write here.  This list is what I'd use if I was faced with the task of building a data product.  Period.