How to Build a Collaborative Analytics Culture
Content Marketing Manager, Sigma
Many of the benefits that come with being data-driven are only seen when your approach is collaborative. You’ll be able to uncover overlooked data sources, arrive at insights more quickly, take advantage of your domain experts’ wealth of knowledge around the meaning of the data, and, ultimately, get more accurate answers to the “why” questions.
To reap these benefits, you’ll need more than a set of collaborative analytics tools, though these are an important piece of the puzzle. You must build a collaborative analytics culture where team members value and seek out one another’s perspectives, knowledge, and skills.
Here are six ways to develop a team that prizes a community-driven approach that emphasizes collaboration.
Emphasize the benefits of collaborative analytics
One of the best ways to get buy-in is to “sell” the benefits of a collaborative culture. Help your people see how working together will improve the health of the company and allow their individual teams to be more effective. Use concrete examples to show how technical team members can provide domain experts with data that holds answers to their pressing questions and how domain experts can help the BI team avoid mistakes in interpreting the data.
Break down silos between departments
It seems to be human nature to operate in small groups, independent of a wider whole. It takes effort to facilitate working together as a community. This is just as true when it comes to organizational dynamics. To break down the silos that hold you back, you’ll need to communicate your vision for collaboration, build cross-departmental communication channels, hold cross-functional trainings, and assign cross-departmental projects to get people from disparate teams working together.
From a technology standpoint, data centralization and the modern analytics stack make it easier than ever to create an infrastructure that supports collaboration. Be sure you’re taking advantage of tools like cloud data warehouses, which provide a centralized access point to a variety of data types from disparate sources, and reusable datasets and dashboards. Using these tools will ensure that everyone is working with the same version of truth and that everyone can benefit from and build upon the work of others.
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Democratize access to data
A collaborative analytics culture isn’t possible without democratized access to data. You must take the position that data is for everyone in the organization. Yes, permissions and security must be in place. But it’s possible to build a data governance program that both makes data accessible and minimizes risk. Your analytics tool should serve business users as well as technical users. Domain experts should be able to explore data on their own and leverage analyses and datasets that already exist through technology and data sharing practices. The wider the access, the more minds involved in analysis, the more insights you’ll come to as an organization.
Promote diversity of perspectives
For any team to be successful, the members must value one another’s perspectives, knowledge, and the other assets they bring to the table. Promote diversity by helping your team see why other perspectives are valuable. Point out examples of companies you see that are experiencing success as a result of collaboration.
For this promotion to work, it must be lived out from the highest levels of the company, through action. Your technology has a role to play here. Collaborative data modeling allows people with different backgrounds to participate in the process. Those who are closest to the meaning of the data can help speed up the modeling process and help the team arrive at more accurate insights. Additionally, templates allow your team to share pre-built dashboards, datasets, and worksheets, so everyone can benefit from one another’s work.
Curiosity is the fuel that sustains data-driven organizations. When people from every team are asking the “why” questions, insights come in greater numbers and more quickly. Encourage curiosity as a priority of the organization, and emphasize how it can help teams boost their results. There’s a practical element to this step as well. Your domain experts need to be comfortable working with your analytics tools. They should be able to use a familiar interface (such as one similar to the Microsoft Office tools) that doesn’t require they have coding skills.
A modern approach to data governance makes collaborative analytics possible. See why.
Build a balanced data governance program
Finally, a collaborative data analytics culture depends on a balanced data governance program. While many data teams fear that data democratization will end up looking like the Wild West, this doesn’t have to be the case. Using modern tools, it’s entirely possible that you can make data accessible to those who should have access while ensuring security and sufficient quality. Follow best practices like developing standard definitions, establishing your KPIs around quality, keeping a data quality issue log, and looking to the data onboarding point to solve quality issues.
When it comes to the level of insights that an organization is able to produce, nothing is as foundational as a collaborative analytics culture. When everyone in a company prizes the input of others and works together to achieve its goals around data analytics, the organization becomes a formidable competitor in the marketplace.
Learn more about implementing collaborative data analytics — check out The Definitive Guide to Collaborative Analytics.