What is Data Collaboration?
Data collaboration is the practice of using data to enhance partnerships, alliances, go-to-market efforts, and strategic initiatives. Anytime two companies combine their data-driven insights to create new value, you’re seeing data collaboration in action.
In the modern world where privacy and security are of monumental importance, the methods companies use to collaborate around data are more important than ever. Before embarking on any of the strategies in this guide, be sure to confirm that your methods represent best practices in security, provide transparency for your users, and don’t run afoul of any modern compliance requirements.
Examples of Data Collaboration
Partners and Alliances
Companies form all types of partnerships, but the most widely recognized are strategic alliances that are forged because of shared interests such as similar customers, product integrations, or complimentary views of a future market state.
When these kinds of partners explore data collaboration, the results can be extremely valuable. Without even disclosing any raw data about who their customers are, companies in this situation can often help each other gain macroeconomic insights like the size of their addressable market, the characteristics of potential new customers, and the potential impact of a deeper collaboration.
It’s important to note that measuring things like the overlap in customer base or sales pipeline often requires use of a data escrow service like Crossbeam to ensure that only the overlapping data is revealed. Without such a service, conversations like these often stall as companies debate which side will share their entire list so that a comparison can be done.
Channel sales relationships are a special kind of partnership. These are relationships with resellers, system integrators, or other companies that actually assist in the selling, implementation, support, or enhancement of a company’s products.
While these relationships can be extremely valuable, they can also be difficult to navigate. This is because channel sales partners very often sell competing products right alongside one another. If a system integrator is in the business of building out ecommerce websites, for example, they could potentially be implementing a Magento shopping cart for one customer at the same time they’re implementing a DemandWare shopping cart for another. The fact that Magento and DemandWare are competitors makes it difficult for either to fully entrust their data to the system integrator, and therefore makes data collaboration more difficult.
However, when data collaboration can be done with these kinds of partners, the benefits are immense. By sharing insights about sales prospects very early on in the sales process, for example, companies can avoid “channel conflict” situations in which they are accidentally selling against their own partners (or pitting their own partners against each other) without realizing it.
Again here, the key to unlocking these opportunities without requiring the oversharing of data with a third party is a tool like Crossbeam that serves as a secure escrow service and identifies overlap when it exists but keeps proprietary data secret to each participating party.
Investors and Portfolio Companies
When one company has a financial interest in another, collaborating around data can become a powerful thing. This is especially true in the case of venture capital firms and investors who have many, many companies in their portfolios.
It is a common practices for VCs to collect data from their portfolio companies about things like market salaries, financial metrics, best practices, and performance benchmarks. By serving as a central hub that collects and organizes that data, the VC can unlock a high-volume network of data collaboration as a central trusted party. As a result, many companies who accept venture capital investment are able to access extremely valuable and proprietary benchmarks about how they should be running and measuring their business.
Consultants can also play the role of a trusted sage that can accumulate, anonymize, compile, and report on trends and benchmarks. Again here, powerful data collaboration can result in which the participants all benefit from having participated.
Unlike with VC firms or strategic partners, however, consulting firms will often package up these data sets and resell them to anyone who is willing to pay the steep price. The quality is good, but this can feel less like collaboration and more like a pay-to-play arrangement. Sometimes, however, the quick access to high quality data can be very worth it.
Data Collaboration Best Practices
Have Clear Goals
Collaborating around data may sound like a simple concept, but the actual execution can be time consuming and costly. Figuring out how and when the data will be combined, who is going to analyze the combined results, and what outputs are expected can be a full-time job. Entire tools have been built just to manage this process.
The key to making this all worthwhile is developing a clear sense of what specific outcomes you want to achieve from a data collaboration. Are you looking to measure your market size? Learn about what motivates your target customers to buy other products? Enrich your existing data with insights from your partners? Decide which initiatives deserve your focus next quarter?
All of these could be valid, high-ROI objectives, but each would benefit most from different types of data from different sources. Pick your objectives and work backwards rather than jumping on any opportunity that comes your way.
Use A Data Escrow Platform
In our modern era of data security, privacy regulation, and just plain paranoia around data, it’s more important than ever to use secure, reliable systems to facilitate your workflows around data collaboration.
Tools like Crossbeam were created with this modern world in mind, allowing you to exercise powerful and specific controls over every single data point that is shared or analyzed in partnership with any given third party. You should never have to let your customer list or sales pipeline get in the hands of a partner to learn how you can best collaborate -- instead, use a trusted third party to identify the overlap and disclose only the relevant results.
Measure the Results
Sharing data and running initial reports can be an intriguing and illuminating process, but it means nothing if it doesn’t actually impact your business. Make sure that you have specific Key Performance Indicators (KPIs) in place before you embark upon a data collaboration project. This will ensure that you can actually observe the changes over time as your collaboration takes place, and hopefully will provide you with a clear case for collaborating more in the future.
Data collaboration can be a powerful force multiplier for your business. By augmenting your data and insights with that of partners, investors, or consultants, you can make smarter decisions, learn from the mistakes and wins of others, and create a continuous pipeline of new opportunities for growth. Just be careful not to share too much -- it can strategically unwise and even illegal depending on the nature of the data.