Introduction

Data sharing is a growth vector in a tough economy for SaaS

The climate for enterprise software companies is more challenging than its been in a decade. Competition is fierce and budgets are tighter than ever. Every product leader is looking for features and product enhancements that not only differentiate their core platform but drive incremental revenue and retention.

Data sharing is a proven path toward both account expansion and churn reduction. Companies like Stripe, Heap and Braze have built 8-figure business lines by helping their customers access the data their apps generate in the platforms they use for analytics. Heap was able to increase total contract value (TCV) by 20% and reduce churn by nearly 50% for customers who used sharing.

This report will provide everything you need to scope and launch a successful data sharing program. It will walk through:

• Why is data sharing?
• Why is it important for enterprise software?
• How does data sharing work?
• What are common use cases for data sharing?
• How do software companies offer data sharing?
• How do you price data sharing?
• What is the business case for data sharing?
• What are best practices for bringing data sharing to market?
  

The fast track: What you need to know about data sharing

• The rise of data platforms and "out-of-app" analytics Your customers have spent the past decade building out their own data infrastructure. More and more, they are working with internal data teams to build sophisticated analytics that draw on data from across their organization. That means customers increasingly want software companies to provide access to the data its app generates natively in the platforms where these analytics are taking place.
• Turning "data sharing" into a part of your product offering Progressive software companies now monetize not only software, but access to the data it generates through "data sharing" offerings. Instead of pointing customers to an API or standing up ad hoc integrations, companies have started to build product lines that offer access to structured data products directly to customers in their platform of choice.
• ‍The single fastest growing incremental revenue stream for enterprise software companies Companies like Stripe, Heap and Braze have turned data sharing into an important part of both their revenue and retention stories. These offerings not only generate incremental revenue (typically, 20% of TCV) but they reduce churn for their core product. Heap found that customers who purchased its data sharing product renewed at a 50% higher rate than those that did not.
• Expanding your reach across the data and analytics ecosystem One of the most powerful aspects of data sharing is that it dramatically expands software companies footprint in the data and analytics ecosystem. Selling data sharing starts with your champion but often allows companies to extend their reach into data teams and unlock entirely new co-selling relationships with the major cloud data platforms. These relationships will be critical as the GenAI wave continues across the software industry.
  

What is data sharing?

Data sharing is the process by which companies provide external teams (customers, partners, other business units) access to data to perform analytics.

Data sharing is distinct from traditional data integration techniques such as ELT or ETL because in two main ways:
‍

1. Data sharing is led by the provider of the data while data integration is typically led by the consumer of data.
2. Data sharing assumes that the provider of data does not govern the  infrastructure used by the team consuming the data.

Why is data sharing important for B2B SaaS?

A new analytics ecosystem for software companies

As an enterprise software company, data has always been an important part of your product strategy. You have built and launched new dashboards, reporting and analytical tools that help customers track metrics and derive insights from the data your app generates.

But your app is increasingly one point in a broader cloud analytics ecosystem. Your customers have spent the past decade building their own data infrastructure in the cloud. They have hired data teams and invested in analytical platforms like Snowflake, Databricks, and BigQuery. And the rush toward ML/GenAI is only accelerating the central role these platforms play in companies' analytics strategies.

The question for software companies is whether they will ignore the trend or start or embrace the opportunity. When customers want to build Al models or cross-functional dashboards in these platforms using data from your app, how will your team respond? Will you simply send them to a hard­ to-use API or will you have an offer ready to dramatically improve-and monetize-that experience?
‍

The rise of 
“out-of-app” analytics

Traditionally, software companies helped users explore the data they collected by building dashboards, visualization and other analytical tools. These "in-app" analytical tools helped (and still help) users to interpret and understand the process your app helps to improve.

But as companies built out more sophisticated data capabilities in-house, teams started to ask questions that extended across their business. The questions were not just "how many emails were opened;" but "how do email opens predict return rates for a given product line?" These questions require data from across an organization and powerful new analytical capabilities best suited for purpose-built platforms.

That's why companies like Stripe, Heap and Braze have started offering the ability to consume their data in other platforms as a service. These "data sharing" offerings support advanced, "out-of-app" analytics by data teams alongside an in-app analytics experience geared toward non-technical users.

Your platform will still remain the system of record for the parts of the business you touch. But data sharing offers a powerful way to monetize more advance use cases such as ML / AI and cross-functional reporting. These use cases typically require either the blending of multiple data sources or advanced or custom analytics like ML / AI models only available in data platforms.

How does data sharing work?

Most B2B software companies already share data today. The most common way software companies have traditionally shared data with customers is via REST APIs that provide a set of endpoints from which data teams can extract data and load it into their system - a process referred to as ETL or ELT. The problem is that these APIs were built for transactional use cases (e.g. integrating data between apps) not analytical ones - which makes getting data into analytical-ready format time consuming and error prone for data teams. 

Modern forms of data sharing eliminate the ETL work for customers by sharing analytics-ready files and tables directly to their analytical platform. Data is either shared as a file into cloud storage (or a secure FTP server) or as a table within their data warehouse or database. Historically, in order to support these forms of "native" sharing, customers have had to grant access to their infrastructure to software companies in order to "write" data into their database or file storage. But today, most data platforms offer protocols that allow software companies to securely share both files and tables "natively" to cutomers without having to get access to their infrastructure.

‍

What are common use cases for data sharing?

• Executive reporting. A big driver for "out-of-app" analytics is bringing together multiple sources into one comprehensive reports
• ‍Cross-functional analytics. As teams collect more data across their business, they increasingly want to analyze it across functions.
• ‍Advanced Analytics. There's a growing consensus in ML/ Al communities that data is the differentiator.

The use cases-and demand for them will vary by vertical. Software companies that touch mission critical or revenue adjacent workflows like product, sales, marketing, finance, or supply chain typically will likely see more demand and have more urgency to move faster.

How do software companies offer data sharing?

You already share data. You’re just not benefiting from it (yet).

As we mentioned, there's a good chance you already share data. Most enterprise software companies offer APls and many share data on a custom basis with larger customers. Some even offer connectors or sharing to platforms like Databricks, Snowflake, AWS and more.

The problem for many software companies is that they do not orient to data sharing as a product line. They bury "data sharing" within a broader integration story (use our API or integration with Snowflake) that typically focuses on the extensibility of their software-not the data. The consequence is that they miss a massive revenue opportunity and a chance to deliver value to a strategically important part of their customers' organization.

‍

Moving from “slow mover” 
to “leader” in data sharing

If these trends seem familiar but you cannot name a specific initiative, there's a good chance that your stuck in the "slow mover" category. Data sharing has been on your radar (a competitor launched an offering recently), but you still fulfill demand through a mix of third-party API connectors and hoc integrations or PS/ SE projects.

A leap frog moment for "slow movers"

The good news is that in the past 18 months the technical burden to launch an offering has decreased dramatically. A new generation of Data Sharing Platforms now provide the toolset engineers need to stand up sharing to the overwhelming share of your customers in a matter of weeks, not years.

It presents a unique opportunity for "slow movers" to leap frog competitors. Most leaders and visionaries in data sharing still only support 10-15% of common destinations and are tied to internally built infrastructure that does not scale effectively. With most Data Sharing Platforms, you can support 80- 90% of destinations with relatively light engineering effort.

There's likely more demand than you think

Remember, customers do not pay for data sharing-they pay for out-of-app analytics. That's important to keep in mind because there's a good chance that your customer will not even know to ask for data sharing. Many teams have become so accustomed to paying other vendors to access their data that they often do not know to ask the software companies in the first place.

That's why it's critical to look for both explicit and latent signs of customer demand. Of course, it's always good to start by identifying more explicit signals customers asking for data sharing in RFPs, sales conversations and SE requests-but signals of demand often can be found elsewhere.

Here are a few common ways to identify demand for data sharing:

• AskAEs/SEs Solution engineering is often the glue that holds together enterprise software products. Ask your solutions engineers if they are getting requests to access their data directly or are asking for help supporting analyses inside their data platform.
• Search RFPs /Call Analytics If you have RFPs compile in one place or have a call recording software like Gong, search for keywords like "Snowflake", "Databricks", "BigQuery", "AWS", "connectors", "data sharing" etc.
• ETL providers Search to see whether the major ETL companies-FiveTran, Stitch, Portable, etc.­ have built a connector for your application. If they've built one, it means customers are actively pulling data from your API.
• Competitive analysis Take a look at your competitors to see if they offer a connector or sharing into one of the major data lakes or warehouses. Look for two types of things: a named data sharing detailed in their documentation or one of the major data platforms listed in their integrations pages.
• ‍Soft sell Start selling data sharing to customers as a service. A solution engineering team can service a handful of requests with limited product investment. Follow the sales path in the later section and see if you can validate a few early wins to buttress the business case.

How do you price data sharing?

Data sharing is typically priced as an incremental add-on available to customers in a premium pricing tier. Some companies begin by offering it as standard part of a premium tier, but most sell data sharing as an add-on priced as a combination of a share of the existing contract value and specific sharing specific-features like number of tables or frequencies of updates.

When pricing data sharing, you should consider an important trade-off: operating margin vs. ease of selling. More advanced models allow companies to increase margin capture by attaching pricing to the specific value-drivers of data sharing. However, they also are harder for sales teams to sell since they are built on data-centric concepts like frequency of updates, which can reduce the close rate and adoption by your salesforce.

What is the business case for data sharing?

Increase ACV, reduce churn and unlock a new revenue channel

Most software companies today are nearing the end of the "ad hoc" sharing stage in their maturity. They've built APls and support some third-party connectors, but are seeing the value of potentially monetizing uni-directional sharing as demand increases.

Increase ACV for mid-market and enterprise customers by 15-20%

Most data sharing offerings are sold as an add-on priced at 15-20% of the total contract value (TCV). It depends on the type of data you collect, but Heap found within a few years the 50% of its paying customers purchased its data sharing offering.

Reduce churn for data sharing customers by 50%

At Heap, customers that purchase its data sharing offering churn at 40-50% the rate of those that do not. Zac Fuld, VP of partnerships at Heap, attributes the reduction in churn to two factors: Heap Connect extends its reach beyond product and marketing orgs making the product stickier inside the organization and it expands the capabilities of the product to support advanced use cases that competitors might address.

Create co-sell motion with major data platforms

Each of the major data platforms offer powerful partnership programs that can drive significant revenue for software companies. Data sharing offerings drive compute consumption for these platforms, which makes software companies that offering them strategically important partners. Within a year of launching Heap Connect, Heap became Snowflake's Partner of the Year generating significant revenue with the platform.

Case Study: Heap

In 2017, digital experience platform Heap launched a Hackathon project called HeapSQL that let customers query their data in a cloud data warehouse. Six years later, that product, now named Heap Connect, generates 8-figures in revenue and has emerged as a strategically important part of the company's revenue story.

How do you sell data sharing?

The  3 pillars of selling data sharing

Selling data sharing is a three-legged stool. You get champions excited about the advanced use cases they get to build, win over data teams by eliminating one of the most hated parts of their jobs, and activate data platforms as powerful partner in your go-to-market motion.

1. Introduce data sharing to your existing champion

Your champion does not care about data sharing. They care about that attribution model or supply chain analysis that requires data from multiple sources. They care about the idea they had for a model that lets them predict the likelihood a user might buy a product based on the connection between website activity and purchase behavior.

Your app might support some of these workloads in-app. For the ones that it does not, data sharing provides a way for your champions to support it by partnering with their data team.

Common discovery questions for data sharing

Identify the use case: Do you collaborate often with your data team on analytics projects? What are other questions you wish you could answer?

Pull out the pain: What's the lifecycle of one of these projects? How long did your most recent project take? What worked? What did not?

Imagine the whole solution: Who do you typically work with on the data team? How is it organized? What data platform does your team use?

Common data sharing use cases for your champions

2. Make the data team into your next big champion

Most larger companies now have a data team. Data teams are typically frequently organized in a "hub-and-spoke" model where a centralized group of engineers and analysts manage data infrastructure, science and Al initiatives across the business. These teams are frequently tasked with supporting these use cases and will know the pain associated with integrating data from applications well.

Meet Alex. Data Engineer at ACME.

‍Alex is a data engineer at a mid-sized consumer products firm he’s part of a 5 person data team responsible for managing their data infrastructure.

Typical day:

• Managing infrastructure across Snowflake and Google Cloud
• Creating dashboards and models with business teams
• Troubleshoot issues in data pipelines.

‍

Why Alex loves data sharing

• Increase ETL Efficiency: Alex is measured in part on processing times and error rates in the ETL process. Sharing dramatically improves processing time since he no longer has to build and manage the ETL process.

• Improve Data Quality: Data sharing significantly improves the quality of the data Alex access from your app since it’s delivered as an analytics-ready table managed by your team.

• Accelerate collaboration: With data sharing, Alex is now able to work with the rest of his team to deliver the insights and models his line of business counterparts need to succeed.

Turn the data platforms into a co-selling engine

It's important to remember that data sharing is a joint sale with the data platforms. Most of these platforms are priced based on consumption, so AEs are constantly in search of new workloads that will increase compute usage in customer accounts.

This makes these platforms an extremely powerful partner in not only supporting existing data sharing sales, but eventually to support purchase of the core platform as well.

Consumption is table stakes

Before you can engage these platforms, you need to show how your partnership drives consumption (typically of compute) on the platform. Typically, if you can show that your customers are running new analyses because of access to your data, you can demonstrate a ton of value for these platforms.

‍Extend into new use cases

Consumption, as we said, is table stakes. To accelerate your partnership, you need to show how your partnership expands their reach into new use cases. For instance, if you're a payments platform, you could demonstrate how you're increasing access to financial analysis workloads that the platform previously did not see.

Introduce new personas

Data platforms are typically bought by data teams. But these platforms want to expand beyond these buyers into the line of business as well.

Show that you not only help them expand their footprint into your persona, but that you are an expert and partner in building a business around that persona.

Getting Started with Bobsled

Companies like ZoomInfo, Carto and CoreLogic rely onBobsled to power their data sharing programs. Bobsled provides a single white-labeled platform for software companies to provision, permission and manage analytics-ready data to customers natively in their analytics and cloud platforms without building a pipeline or managing infrastructure.

To learn more, check out our docs or schedule time for a demo here.