- What is data collaboration?
- Data sharing vs. data collaboration
- Why is data collaboration important?
- What are the benefits of data collaboration?
- Data collaboration benefits for retailers
- Data collaboration benefits for brands
- Data collaboration benefits for data suppliers and consumers
- How to get started with data collaboration
- Best practices for data collaboration
- The future of data collaboration
What is data collaboration?
Data collaboration is the act of gathering and connecting data from various sources to unlock combined data insights that can be used to create new products, run analytics, or build targeted campaigns.
You or your company have likely engaged in data collaboration from working in closed environments such as Google Ads Data Hub, Facebook Advanced Analytics, and Amazon Marketing Cloud.
Data sharing vs. data collaboration
While some companies use data sharing and data collaboration interchangeably, we consider data sharing to be a subset of data collaboration, with the key difference being in how relationships between data partners are structured.
- Data sharing: partners can be internal or external and are in the same cloud-based environment. Often, the data being shared is not unique to one partner but rather available to multiple vetted partners.
- Data collaboration: partners can be internal or external and in the same or different clouds. Data collaboration can help surface previously hidden insights that are exclusively discovered and available to the two or more parties actively collaborating in a closed environment.
Why is data collaboration important?
As new privacy regulations pass and third-party cookies disappear, data collaboration offers an opportunity to enrich first- and second-party data and gain new customer insights in a privacy-centric way. Data collaboration is important as it allows companies of all sizes to build customer intelligence in a way that respects consumer privacy.
Key trends accelerating the rise of data collaboration include:
- The loss of identifiers impacting advertisers’ ability to target audiences and measure results
- Continued dominance of walled gardens, companies rich in first-party data and customer insight
- Regulatory changes and a heightened focus on privacy
What are the benefits of data collaboration?
Data collaboration has several key use cases that can prove beneficial for a variety of industries:
- Data monetization – license access to owned data for a fee for analytics and insight
- Drive media investment – allow access to owned data assets to increase media spend on your channels
- Deepen partnerships – form strategic partnerships with key partners by allowing them to safely and securely access to a unique data set
- Customer acquisition and insights – leverage first- and second-party data to identify target audiences and enhance them with additional datasets
Data collaboration benefits for retailers
Once the purview of walled gardens and other large corporations with the resources to build their own infrastructure for managing complex partnerships, data collaboration for retailers has increasingly become the norm. Companies see the value and ability to start small in building a stronger, fuller, more complete understanding of consumers.
Key benefits of data collaboration for retailers include:
- Improved relationships with brands by providing deeper and more meaningful insights on product performance.
- Increased business of partner brands providing an overall performance boost.
- Creation of new revenue streams through new data products.
Data collaboration benefits for brands
Every company, especially those with limited first-party data, can benefit from data collaboration.
Key benefits of data collaboration for brands across industries include:
- Better understanding of how their brand and products are searched, reached, added to cart and purchased on retail websites.
- The ability to establish benchmark performance against competitors and categories.
- More complete audience profiles through filling in customer details that were previously unavailable
Data collaboration benefits for data suppliers and consumers
Campaigns that leverage connected first-party data from both companies involved in a partnership can perform better, driving higher conversion and overall ROAS for advertisers.
Data supplier benefits:
- New revenue stream
- Increased media spend on owned platforms
- Deeper partnerships
- Higher conversion rates
Data consumer benefits:
- Access to new data sources
- More successful advertising campaigns
How to get started with data collaboration
Depending on your business, there are a number of ways to get started with data collaboration and formulate your strategy and goals:
- If you’re a CPG or similar brand sold mostly through online and brick and mortar stores, you can work with a single retail partner to bring in sales data to understand how to optimize campaigns for different audiences. The results can inform future tests with other partners and build your first-party relationships.
- If you’re a retailer, you can kickstart a data collaboration with a trusted supplier to pool intelligence about shared audiences. Your initial goal could be driving greater loyalty with one brand, laying the groundwork for acceleration with all of your suppliers in meeting consumers’ changing needs.
- If you’re a publisher or TV provider with a strong authenticated dataset, you can offer better intelligence to top advertisers, encouraging them to not only spend more with you, but also to do so in a way that enhances your viewership experience.
Here’s a general four-step process that can be applied to most companies looking to get started with data collaboration:
1. Identify the business case
❏ Determine your long-term goals and time-bound experiments you can run to achieve quick wins.
❏ Understand the potential audience and what you want to gain, which will lead you to decide on which partner to work with.
❏ Find an executive sponsor who has a vested interest in the success of your chosen business case who can help remove any blockersee that may arise.
2. Find the right partner
Once you have a list of companies you’d like to approach for collaboration, the next step is connecting with their marketing, analytics, and data teams to discuss how you might collaborate with your data assets. Questions that might come up in that discussion include:
How is the data being shared sourced? It’s key to know where the data that is going to be shared is sourced. Concerns with data privacy and data collection need to be addressed. Brand reputation depends on working with trusted partners that properly sourced.
How is the data validated to ensure accuracy? What quality checks do you have in place? You need to know you are selecting a provider that will provide you with clean, accurate data to ensure your product is providing the best value to clients.
Whether they are trying to make an informed business decision about where to open a newly opening business, conducting market analysis, or targeting, your clients need high quality data to meet their business goals.
What does the data cover? When selecting a data source, developers need to make sure the datasets they are leveraging have comprehensive coverage, meaning that they encompass the entirety of the data needed to fulfill the purpose that a product, application, or service was designed for. Comprehensive coverage is paramount in creating both a cutting-edge product and a smooth user experience.
In what format will the data be delivered? Every business needs data that can be smoothly and quickly integrated with their current solution. The data provided by your partner should be offered in a variety of formats and customizable depending on your needs, whether you want to manage a full file, make real-time API calls, or a combination of the two. You should have the flexibility to take in the data at a frequency that works for you –whether that be monthly, weekly or daily.
3. Set the rules
❏ Create contracts, deploy processes, and install technology to ensure your partnership meets your requirements for data provenance, governance and permission.
To double click into the technology portion of setting the rules, privacy-enhancing technologies (PETs) are increasingly part of strategic discussions on data collaboration. From their origins in academia and early adoption by governmental agencies and highly regulated industries, PETs have entered broader business conversations for their ability to accelerate safe data collaboration, build customer intelligence, and maximize the value of data without relinquishing control or compromising consumer privacy.
At a macro level, the maturing social and political context around privacy has brought them into the spotlight now more than ever. In a post-GDPR, CPRA, and third-party cookie world, even common activities such as sending customers ads and measuring campaign effectiveness are beginning to depend on new tools and techniques that are designed with privacy in mind.
Using PETs in concert with legal and security mechanisms
When considering how PETs can be used to enable innovative forms of collaboration, it’s helpful to imagine how they support existing privacy and security techniques and legal mechanisms. Here’s one scenario:
If you are a retailer and have a contract with a CPG partner stating that any usage of data must prohibit attempted reidentification, you’ve already reduced risk via a legal mechanism. To further define your data collaboration and exactly how you’ll be working together, you can layer on security approaches such as multi-factor authentication and audit logging. This means that each analyst, whether from your side or the CPG’s, must receive clearance to access the secure collaborative environment and will know that their usage can be audited. PETs can build on this foundation by providing analysts with a differentially private query engine to protect the underlying data against deidentification.
All of these techniques work in concert with one another to dramatically reduce the overall risk associated with using consumer data. No single tool can provide a complete solution. By using the right tool for each job, you can design a comprehensive approach to mitigating the risk.
Privacy is not one-size-fits-all
While many scenarios could be similar to the one we described above, it’s not a perfect recipe: privacy is not one-size-fits-all. Every company and its partners will have unique security and technical requirements which warrant holistic exploration of the available security and privacy mechanisms (including PETs) that can be applied.
4. Put it in action
❏ Write queries, develop hypotheses, and test behavioral models to fill missing gaps in business intelligence
Best practices for data collaboration
Beyond the list of how to get started with data collaboration above, it’s important to recognize the interpersonal best practices involved, including:
- Create value for the organization: Expanding on the notion above of identifying the business case, data collaboration starts with time-bound work that creates tangible value across teams. Even better is if the work itself ladders up to the company’s mission. As such, this initial best practice is ideally shared across teams to have a higher chance of finding a senior leader who will understand and champion the initial vision.
- Finding an executive sponsor: Connect with senior leaders who can help formulate and iterate on the initial business case for data collaboration. These sponsors can also keep data collaboration program owners apprised of conversations at the C-suite level that can inform strategy.
- Iterate and communicate: With all of the cross functional work happening across marketing, IT, data, privacy, security, and other teams, it’s critical to ensure team leads understand enough of what’s going on to keep championing the initial business case and support any shifts along the way.
The future of data collaboration
Federation is on the horizon for data collaboration as an accelerant for use cases that are not currently in widespread use due to their nascency. This privacy-preserving technology enables companies to securely connect databases across cloud platforms, infrastructures, and geographies. Think of it as bringing the model to the data, rather than the data to the model, and enabling access while reducing ownership or privacy risk.
The opportunity is enormous for companies that can scale and connect first-party consumer relationships within and across organizations. With modern privacy-preserving platforms for data collaboration available, the time between insight and results from testing initial partnerships is faster than it’s ever been.