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Trying to communicate with someone who speaks a different language can be difficult. When we find ourselves in these kinds of situations, we often rely on commonalities — things that are fundamentally true across cultures. 

Sales and marketing often speak different languages: Convert vs Close, Prospect vs Leads, Sales cycle vs Marketing funnel. This can make alignment between the two difficult. 

Data, on the other hand, is a universal language. And using it collaboratively can help alignment and improve go-to-market results from a campaign planning standpoint. 

Alignment is all about connection, and connection is all about finding something that both parties have in common. In the world of B2B, that commonality will always be data. 

What is data collaboration?

Data collaboration is the practice of using data to enhance go-to-market efforts and strategic initiatives across departments. 

Basically, it’s identifying the ways in which data can help different teams work together to achieve common goals. 

Too many times, data sets get siloed and therefore lose relevance between departments. With a comprehensive data collaboration strategy, you can avoid data fragmentation, promote standardization, and improve overall data quality. 

Why is data collaboration important?

You might read this and think “data collaboration isn’t that important, data is just data.” I’m here to tell you you’re wrong. 

Data collaboration has a variety of benefits, the main one being sales and marketing alignment. And since employees and executives believe lack of alignment within a team impacts the outcome of a task or project, it’s definitely something that should be prioritized.  

Any successful GTM strategy requires sales and marketing teams to be on the same page when it comes to data insights. Below are some key benefits of data collaboration.  

  • Allows for shared access to a wider range of data: In order to be on the same page, salespeople and marketers need access to the right data at the right time. Employing a data collaboration strategy can consolidate important datasets into one, holistic view. 
  • Reduces data fragmentation: It’s hard to look at the big picture when you only have part of a story. The same goes for a GTM strategy. Data collaboration gives the full picture to everyone who needs it, exactly when they need it. 
  • Standardizes metrics: Making data more uniform creates reliable metrics and ensures the various applications housing this data are speaking the same language. 
  • Simplifies the deployment architecture: All of the things mentioned above will make sharing data easier (while also staying compliant with privacy guidelines)! Making data easily distributable allows stakeholders to work with it together more effectively. Think of the way Google Docs makes sharing documents easier — data collaboration is the same idea. 
  • Enables globally distributed teams: In today’s day and age, it’s not unusual for a company to have a variety of offices, across a variety of states and countries. Teams need to be able to see insights and work off the same data set. Yet data changes hour to hour. Data collaboration allows key stakeholders to view consistent data at any point throughout the day, thus streamlining workflows. 

How to Implement a Data Collaboration Strategy

By this point you’re sold on why data collaboration is important. Now for the hard part: how do you actually make collaboration come to life? 

While every company is going to prioritize different kinds of data, below are some general best practices when starting the data collaboration process. 

1. Establish Expected Outcomes

On the surface, data collaboration probably sounds really simple. Isn’t itust a group of people from different departments looking at datasets, together? 

Not exactly. 

It can actually be a very time consuming and expensive process. Therefore, you need to have a clear plan about how data is going to be consolidated, who is going to analyze the combined results, and what outcomes you are hoping to achieve. 

Think about what it is you want to get out of data collaboration. Want to learn more about what motivates target customers to buy other products? Or decide which initiatives and campaigns you should focus on in the upcoming quarter? 

Each one of these questions can be addressed through data collaboration, but not without strategy. 

2. Determine types of data to collaborate on 

Depending on what insights you’re looking for, you’ll need to identify the data that can provide you the answers you are looking for. 

Agreeing on what constitutes “good data” can be a challenge, but you can generally turn to three main categories to provide insights: 

  • Customer data: Contact details that provide insights into your best customers.
  • Activity data: How sales and customer marketing are reaching out to best customer within your TAM.
  • Channel data: What marketing channels are the most effective in attracting and engaging customers.

3. Build a list of questions to answer with data

The whole point of data collaboration is to promote sales and marketing alignment. Once these two departments have settled on the datasets they want to look at, they should compile a list of questions that can be translated into data queries. These queries can help both sides get the answers they need when it comes to understanding bigger business issues. 

Final Thoughts on Data Collaboration

Data collaboration seems like something fairly niche. And for some companies, maybe it is.

For data-driven companies data collaboration should be a no-brainer. You need it in order to ask better business questions that will not only improve sales and marketing alignment but increase overall ROI. 

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About the author

Reyna LaRiccia

Reyna LaRiccia is a Content Marketing Specialist at ZoomInfo, the leading B2B contact database and sales intelligence solution.

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