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Vendors

The Embedded Analytic Vendors List

The Embedded Analytic Vendors List

I've had several conversations lately where, at some point, I get the question "which vendors provide data product capabilities?"  It only took me hearing this question 5 or 6 times before I thought that perhaps I should create a list of embedded analytic providers.

My hope is that this will provide a good starting point for product leaders trying to select a vendor for their analytic application. 

Note: this isn't a list of recommended vendors or platforms, it's just... a list. If I've left out a vendor that you feel should be included, please let me know.

Good Gets Even Better: The Radical New Features of GoodData Spectrum

Good Gets Even Better: The Radical New Features of GoodData Spectrum

When I get an email from an analytic platform vendor asking if I’d like to check out a new feature release, it feels a bit like Christmas. It’s like getting a present that’s oddly-shaped where you have no idea what lies inside. Will it be that awesome new gadget I wanted or will it be an ill-fitting sweater?

When I heard from GoodData that they had a new release that they wanted to share, all sorts of images popped into my head. Would this be a new iPad or a pair of pink bunny-shaped earmuffs? Hmmm…

Periscope Data Releases a Game Changer

Periscope Data Releases a Game Changer

I was prepared to be unimpressed when I received a product announcement email from Periscope Data a few weeks ago. I assumed that they had added new theme options, could now connect to some obscure database, or had received some fresh new certification. Boy, was I wrong. This is a game changer.

Vendors You Should Know: Looker

Vendors You Should Know: Looker

When you look at the analytic landscape, it becomes apparent that most platforms are either directly focused on enterprise analytics or are derived from platforms intended for use inside a businesses walls. 

It’s rare to find a platform that works both for internal analytics and for data product teams. Looker is that rare platform that, while initially focused on internal use cases is an excellent choice for those seeking to build data products with embedded analytics.  

Looker’s cloud-based approach offering fast, flexible, and powerful analytics allow data product teams to focus on creating value for their customers rather than on the care and feeding of analytical systems.

Vendors You Should Know: GoodData

Vendors You Should Know: GoodData

Data products are different from enterprise analytics, and as a result, product owners need to seek out slightly different functionality when selecting a platform on which to base their data-driven functionality.

Beyond the data loading and visual analytic requirements, product teams need:

  1. Robust embedding capabilities
  2. Tools to manage customers through the product lifecycle
  3. Flexibility both to get up and running (and producing revenue) quickly and to adjust to the changing needs of customers.

Vendors You Should Know: Izenda

Vendors You Should Know: Izenda

Building a data product can be a real nightmare for a product leader. 

An actual nightmare as in, it can keep you up at night working about all of the things that can go wrong. 

Will it function in an embedded environment? Will it scale to thousands of customer tenants? Will it make life difficult when I need to rollout a new version? Will the vendor “pivot” and stop focusing on data products?

If you are building an application with customer-facing embedded analytics and you value your sleep, you need to make the right choice when you pick a platform.

I’d like to introduce you to Izenda—a platform purpose-built for embedded analytics backed by a team that understands what it means to make analytic-powered applications successful.

Vendors You Should Know: Periscope Data

Vendors You Should Know: Periscope Data

Often analytics fall into one of two camps: either you’ve got powerful functionality that can serve the needs of the data scientists inside your business, or you can have flexible, user-friendly analytics that are suitable for use by customers as part of a data product. 

The “analyst” oriented tools are extremely powerful, but they require expert knowledge of databases, data modeling, and languages like SQL, R, and Python. The “customer” oriented platforms look great and are easy to use, but often lack the capabilities to perform the sophisticated analysis required by analysts (or many power users).

The use cases just don’t overlap—you’d never use one product for both of those scenarios. Or would you?