Warehouse Native Market Map

Introduction

The data industry has seen significant developments over the last decade. Not only are cloud data warehouses faster and cheaper than they were in the past, but access to data is also more than ever.

We are moving towards a world where data will not be siloed by a select group of vendors or restricted to a few specialized use cases but be available for business teams to solve their use cases as required.

In this blog, we explore some of the paradigm shifts occurring in the industry which facilitate this change and highlight some of the companies driving that change.

The Post-Modern Data Stack

The modern data stack has enabled a fully functioning cloud data warehouse. With the rise of reverse ETL, the value of self-serve SaaS can go full circle. Tools like Census, Hightouch, and Mascot will empower the democratization of the cloud data warehouse for internal use cases of organizations and create the “Post-Modern Data Stack”.

The post-modern data stack will enable companies to build applications on top of the data stack, with the cloud data warehouse at the center. Perhaps the biggest signal here is Snowflake positioning itself as a data cloud now - allowing applications to be built on top of it.

Other strong signals in the industry have been experts like Martin Casado, General Partner at Andreesen Horowitz, commenting that all of SaaS is going to be remade as data apps on top of the cloud data warehouse. Patrik Chase, Partner at Redpoint Ventures, also wrote the canonical post on this paradigm and the companies driving it almost a year back.

Driving Real Business Use Cases

Since the announcement of the connected app model by Snowflake, we have seen several companies come up to solve existing use cases with a new focus: putting customers in control of their data.

Many companies with this focus are coming up in the domains that require cross-functional customer data to provide the best value to users.

Some of the use cases at the forefront of driving this paradigm are:

Customer Engagement

To drive revenue growth, business teams need to know about their customers. Being able to look at different segments of data for customers allows companies to take the right actions on the right audience.

The warehouse native solutions existing in this space allow business teams to create segments of their audiences on top of their data warehouse (which provides access to data across functions like sales, product, and finance) and act on this data by sending it to other SaaS sources or for reporting.

Some companies solving this with a warehouse-native approach are Narrator, Flywheel Software, and even Segment!

Marketing Automation

Marketing teams often rely on many signals across product and buyer intent to drive the best results for their business.

This involves pulling data from several different SaaS sources - and acting on the signals to trigger marketing actions like getting keywords for content, determining the audience for a specific outreach campaign, and even powering messaging workflows.

Some companies solving this use case natively on the cloud warehouse are Vero, Supergrain, and Castled.

Product-Led Sales

PLG companies need to capture multiple signals to drive sales directly impacting revenue. Revenue teams need to look at data across multiple functions to catch the best signals to optimize revenue.

These signals vary from tracking the product usage of different customers - from engagement rates by decision-makers to approaching deadlines, and customer subscription plans.

The warehouse-native approach to solving this involves connecting to the CRM platforms of choice for sales teams and allowing them to engage with their contacts with enriched information from the cloud data warehouse - right from one central application.

Some warehouse-native companies in this space are Pocus and Calixa.

User Journey Mapping

Product teams need a cross-functional view of customer activity on their platform to prioritize roadmaps and guide product development.

Some of the solutions in this problem space include funnels and flow diagrams across different product functions that can be used by product teams to understand user actions better and optimize the product based on them.

Some companies solving this in a warehouse-native fashion are Rakam, Indicative, and Kubit.

Security Information and Event Management (SIEM) Platforms

Security teams need to always be on top of their organization’s infrastructure in order to detect, resolve and investigate security issues.

The companies in this space are leveraging the warehouse native paradigm by centralizing all of their data in their cloud data warehouse and bringing all points of analysis to one place. Also, by solving on top of the warehouse, companies can skip the complicated IT processes around sharing sensitive data - since all the data remains in the customer's data warehouse.

Panther is one of the companies that has been addressing this problem in a warehouse native way.

Monitoring and Observability

Data monitoring is an essential component of any high-functioning product company - it allows for proactive monitoring and deep investigation of issues when they appear.

Usually, solving this problem involves dealing with sensitive information and joining data across different functions, like alerting, monitoring, and reporting - which are usually fragmented across different data silos.

The warehouse native companies in this space are able to bundle the solution into one offering: providing security and a single source of information to data and infra teams in a single place. Observe is one of the companies solving this problem in a warehouse native way.

Machine Learning

Machine learning allows organizations to unlock massive value across many use cases. However, access to data is always one of the most acute problems in implementing and training machine learning models.

The warehouse native paradigm applied to this space allows companies to build high-impact machine learning tools quickly around their specific use cases by providing data science teams access to the central data repository for the entire company, as well as a simple form factor like SQL to work with data.

MindsDB has been trying to solve this problem in this paradigm.

Business Data Apps

Revenue teams are always facing the challenge of having too many tools at hand and having to use even more to make sense of the existing ones.

Optimizing revenue involves identifying the different use cases in a company - across functions like marketing, sales, product, and finance - and solving them to drive efficiency and value to the organization. Data apps empower business users to create these solutions to business problems right on top of their source of truth - the data warehouse.

Houseware, Weld, and Mozart Data are some of the companies innovating in this space by empowering business users to create their own solutions to use cases.

Patch is another company trying to solve this problem by providing tools to developers for building apps in the warehouse native paradigm.

Revenue Recognition

Finance teams need accurate reporting of the revenue numbers flowing into a company. Recognizing revenue when it is realized and earned, rather than when cash is collected enables businesses to have the most accurate insights into their business profitability, financial health, and a clear picture of their performance.

This process gets complicated as businesses scale, and payment volumes and product lines increase. For subscription businesses that need to manage changes, refunds, disputes, and prorations, revenue recognition can be especially complex because these subscription updates can complicate the process of recognizing and deferring revenue accurately and compliantly.

As of yet, we haven’t been able to find companies in this space. However, we believe they should exist!

Pipeline Optimization

Businesses need to accurately predict their pipeline around key bottom-line goals (eg.: marketing-qualified leads, sales leads, converted customers, and revenue) in order to make reliable projections about their performance and drive business decisions.

However, this process is usually complicated and difficult to solve reliably because of the different functions involved in predicting the pipeline of any business. The warehouse native approach allows us to solve this limitation of cross-functional collaboration by centralizing all of the organization’s data in one place in order to draw insights from it.

We haven’t been able to find any companies providing this view into companies yet!

Conclusion

We’ve been keeping an eye on these different use cases in the warehouse native space; we hope this list helps you choose a vendor if you’re looking for one or simply update your watchlist.

However, the warehouse native space is nascent, significant, and constantly expanding! If you know of any company that should be on this list but isn’t - do reach out to us and tell us about it!