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The Modern SaaS Stack and the Unexploited Amount of Data (medium.com)
75 points by anacleto on May 8, 2017 | hide | past | favorite | 11 comments

The author makes an excellent point about the integration barriers raised by running a business on an aggregation of cloud services. Sure, they all provide REST APIs. But as your suite of services evolve you must constantly re-implement your client code. And you'll never get the performance you can get from on premises where you may be able to connect directly to a vendors SQL DB because you're hosting it yourself, or eavesdrop on a pub sub message bus.

There's a company called Outlier.ai that basically exists to deal with this problem. I quoted the CEO, Sean Byrnes, in this article about business intelligence: https://www.inc.com/sonya-mann/business-intelligence-cloud.h...

Covers a lot of the same territory from a newsier perspective.

At the higher end of the price spectrum, Adobe has done an amazing job gluing it all together. I'm familiar with them from the Omniture days, but recently attended their Summit event and it really opened my eyes on how integrated their stack has become: - Analytics

- Personalization

- eCommerce and Cart recovery

- Programmatic Ad Buying (including automatic credit for any bot traffic detected by WhiteOps above 3% threshold)

- DMP, that syncs in real-time with your Analytics/segmentation and cart recovery software.

There is also a slew of video related stuff that was of special interest to me, but isn't really relevant to this thread.

Best of all, you can setup alerts by email and SMS, and then reply to them asking for more info that the system auto analyzes for you.

Source: Current Adobe customer and attended Summit 2017.

A popular (in 2017) solution to the tech version of this problem (proliferation of services and data systems) is put it all in Kafka. I wonder if that would work for integrating Saas platforms.

You kind of need a bunch of Saas<->Kafka interactions, but if the tech side of your company uses Kafka for reporting and BI, why not have your Saas platforms put data there?

This is where the bigger opportunity lies for AWS or Google Cloud to move up the stack. They already power the core of most of the smaller SaaS products. Hence it's just a matter of identifying the common tooling patterns and add value on top. These products can integrates well with their existing data pipeline tools, offering the incentive to business to standardize on a single cloud platform rather than shopping around for SaaS products.

This post focuses on the problem, but it's also a huge opportunity to build a product that can integrate from a bunch of different SaaS and provide a BI layer. I'm sure lots of folks are trying to do this, but I haven't seen it done well yet. It probably needs to be an enterprise product w/PS, because of the infinite number of SaaS combinations.

The thing is, without a truly standard stack, the best integration of the stack comes from gluing it together ourselves.

We use a mix of APIs from the SaaS services we use and Zavier to capture the data points we need and make the services work well together. The availability of access filters into our descision, but it seems like at some point it may make sense to roll our own replacement for some.

Intercom has been great and our customers love it. But it is getting increasingly difficult to keep deeply integrated with the rest of our stack.

hey Jaxn - I'm the PM for the Intercom Messenger. Would love to learn more and see how we can make it easier for you. You can email me tom@ intercom.io if you'd like!

This works for more technical or earlier stage startups but it's much harder as you scale.

Author of the post here.

>This post focuses on the problem, but it's also a huge opportunity to build a product that can integrate from a bunch of different SaaS and provide a BI layer.

True. This is the exact problem that we are trying to solve at plainflow.com.

There are plenty of products that do this.

I do some work with Power BI, and a pretty common set up is a bunch of SaaS connections (Google Analytics, Mailchimp, Salesforce, Zendesk, Stripe being the most common in my engagements), plus a database or two, plus some static files (excel mostly). Depending on the department, throw in some social media accounts (some directly supported.

Connect it all together in a tabular model, and you've got yourself a nice little BI layer.

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