Data Stack Deep Dive Season 1: Data Connectors

With Chloe, Elise, and Olivier from Idinvest we’re beginning a series of deep dives about the B2B Software data stack with a focus on a key component of this ecosystem: Data Connector startups.

Table of content:

  1. Data Connectors: The piping system of B2B software
  2. Horizontal Data Connectors
  3. Vertical Data Connectors
  4. The Data Warehouse is the new Platform

The Videos

1. Data Connectors: The piping system of B2B software

Defining Data Connector startups

By data connectors, we mean the startups that enable businesses to connect together the various software, SaaS, databases that they use as well as external sources, and let data flow easily.

Our definition is broad on purpose because there are many flavors of this concept, but a major common aspect that distinguishes data connector startups from the majority of software which simply connects with each other through native integrations is that Data Connectors are neither the source of data, it’s not where data is collected, nor the final destination of data. They sit in the middle as data pipes.

The data stack theory of evolution

What’s interesting to notice with data connector startups is that they tend to emerge once the software stack of a category or an industry reaches a certain critical mass and managing data flow between all of them becomes a pain for businesses.

Zapier, one of the OGs of the space, emerged once the explosion of horizontal SaaS was underway.

It’s the same with Segment which made a ton of sense once the Martech landscape started to become too complex.

And more recently we saw several data connectors emerging in the Fintech space where a Cambrian explosion of apps is happening.

It’s almost like the theory of evolution, every time a software stack becomes too crowded, a data connector startup will organically emerge.

Are data connectors defensible businesses?

A central point of our investment thesis around data connectors is that being a pure connector is not enough to build a defensible business. It’s a great starting point, but in the long term, only enabling the flow of data is not defensible because piping becomes a commodity and it has less value than a System of Record.

Coming back to our theory of evolution, to become defensible, once data connectors emerge in a space, they need to evolve quickly and add more features to lock users in.

2. Horizontal Data Connectors

By horizontal Data Connectors, we mean the products which are not specific to a particular industry or type of data. They connect plenty of different applications and data sources regardless of where they come from. It includes companies such as Zapier which is mainly for small businesses.

But also more “enterprise” types of products such as ETL software with Talend, Stitch, Fivetran, Panoply, and others.

Value propositions of Horizontal Data Connectors

The core value of these connectors is to collect and aggregate data that you have stored in various places. On top of that, they also provide additional sets of features that are key for their defensibility.

Automation and workflow. The first important category of additional features is the “Automation & workflow” one, which enables users not only to connect apps between each other but also to create their own personalized workflow with triggers. For example When someone subscribed to my Mailchimp newsletter on my blog, automatically add the lead contact to Salesforce and send him a thank you tweet. It’s what Zapier focuses on: they connect hundreds of apps and users can easily create their own data workflow.

Another common set of value-added features is around data orchestration. For instance, Stitch lets businesses make sure that their data pipeline works properly from scheduling to error monitoring and handling. 

The third category of features is linked to data hosting and backup. In that case, the value of an ETL is not only to collect the data that you have stored in all your external and internal applications but also to normalize them and to store them in a single place where you know that they are safe.

The last set of features we’ll cover is around data quality and integrity. In that case, the aim is to provide a kind of “Quality Assurance” for your data and make sure that the data that flows is clean and reliable.

3. Vertical Data Connectors

In parallel with this well-established category of Horizontal Data Connectors, there’s a growing trend of Vertical Data Connectors that are specialized in specific industries or specific types of data.

Specialized/vertical data connectors

The rise of vertical data connectors is driven by the fact that software is eating more and more industries, leading to an increasingly fragmented data stack in every business category

It’s why we see more and more specialized data connectors that specialize in a specific type of data like Segment or mParticle for customer data.

Or on specific industries with for example TrueLayer in the Fintech space or Impala and Duffel in the travel tech space.

Value propositions of Vertical Data Connectors

Most of the time the value propositions of vertical data connectors are slightly different than the horizontal ones, and they tend to be used for different reasons. Most of the time they are not competing head to head.

The first major differentiation is that they provide integrations to specialized apps. Horizontal Data Connectors generally offer integrations with the most common software out there. On the other hand, vertical Data Connectors can provide integration to more specific apps. For example, if you’re in the Fintech space and need to build integrations to financial services it’s a better move to use data connectors such as Plaid or Tink.

The second differentiation is around integration depth. Building and maintaining a catalog of integrations is already a challenging tech problem, but another dimension to this complexity is integration depth. Horizontal Data Connectors often provide basic integrations that enable data to flow in and out of apps, but as soon as you need to do more complex operations you often need to turn to vertical data connectors. An example is Duffel which is a data connector in the travel tech space that provides more integration to airline booking software.

The last major differentiation is that, obviously, vertical Data Connectors can provide additional features that are more specific to the data type or industry they focus on. For example, Segment not only enables you to collect and centralize your customer data but also provides a range of features specific to customer data such as GDPR compliance or a consolidated overview of your customers’ interactions. Features that you won’t find on most horizontal data connectors.

4. The DataWarehouse is the new platform

Now that we have described in broad strokes the data connector landscape, let’s cover a couple of interesting trends happening.

The data warehouse is the new platform

In the enterprise space, a key function of Data Connectors is to collect and store data in what is called a data warehouse.

It’s the main role of ETL software that lets the data team connect to a myriad of applications to extract, normalize and load data into this central database/repository.

What’s interesting to notice is that in the past couple of years more and more startups are building products on top of DWs. For example, while most ETLs are good at collecting and centralizing data, Census focuses on the “data outbound” aspect to make sure that the third-party apps you use have the latest data synchronized with what is inside your data warehouse.

Iteratively is another example as they focus on a step before in the data funnel to make sure that the data which is collected from your third party apps is reliable before it’s sent to your DW. 

These are two examples but an increasing number of startups are building products on top of the data warehouse. 

Operationalizing the data warehouse

This growing trend of products build on top of the data warehouse seems to be driven by first the increasing importance of data in every department. Whether it’s at the product, sales, and marketing, or customer support level, more and more decisions are fueled by data and you can find more and more specialized analytics products available.

But this trend is also driven by the increasing number of data engineers in these departments. If before the data team was mainly centralized and dealing with analytics at large, you can now find data engineers in marketing or product teams, and these employees are craving for these new data connectors’ products. 

So the market is getting deeper not only in terms of use cases but also in terms of users.