What to Look for in Collaborative Analytics and BI Software

Devon Tackels

Content Marketing Manager, Sigma

Being a truly data-driven organization requires leveraging the knowledge, experience, and work of everyone in the company — not just those with technical expertise. Domain experts have valuable perspectives that can result in more accurate insights, and those who are working closely with software and systems know what data is available and relevant to a given problem.

Collaboration is essential to solving business problems quickly and effectively, and organizations that take a community-driven approach stand to see even greater benefits. When you partner with industry leaders, clients, and others, you’re able to explore additional data and gain wider perspectives, which offer competitive advantages you’d get no other way.

If your organization is ready to be more data-driven and take a community-based approach, you’ll need a collaborative analytics software tool to unlock the benefits. In this post, we examine how analytics and business intelligence (A&BI) tools can facilitate collaboration, what to consider as you’re evaluating tools, and key capabilities to look for as you evaluate solutions.

How tools can facilitate collaboration

First, let’s see how software tools can make collaboration easier and, as a result, deliver insights faster.

Develop a culture of curiosity

The right tools will help develop a culture of curiosity within your organization. When people understand that they’re encouraged to ask questions and dig deeper to uncover the “whys” behind trends or patterns, they’ll be more inclined to explore on their own. And this curiosity will drive deeper, more meaningful insights.

Foster a workplace that values diverse inputs

Tools that emphasize collaboration will make it clear that you value input from a diverse array of people in a variety of roles. Team members that feel their perspective matters will be more likely to contribute, leading to a more accurate picture of the problem you’re trying to solve, along with better solutions. By bringing experts in sales, marketing, product, and operations into the A&BI conversation, you can help break down the data language barrier and empower new perspectives to join in.

Encourage non-technical users to participate

It will be difficult (if not impossible) for non-technical users to get engaged in most analytics software without extensive training or technical knowledge. Domain experts and others who don’t know SQL must have access to an intuitive analytics tool, be able to quickly see what data is available, and analyze data without writing code. 

The tools your organization chooses will communicate much more effectively than a goal or mandate to be data-driven will. When your people know that collaboration is a priority and their viewpoints and knowledge are valued, you’ll be on a straighter path toward becoming truly data-driven.

What to consider when evaluating collaborative analytics software

What specifically should you keep in mind when evaluating various software tools? Here are seven questions to help you choose software that will make collaboration easier, not harder.

 Does the tool allow people with varying skill sets to contribute?

If the tool has a steep learning curve or if it requires manual SQL writing, it won’t meet the needs of people without a background in data science or analytics. They won’t be able to fully participate, and you won’t benefit from what they can bring to the table. Look for an intuitive interface that doesn’t require manual SQL to surface actionable insights.

 Does the tool hold users back in any way from getting the answers to follow-up questions?

Pre-built dashboards are a great way to visualize high-level data. But they fall quite short when it comes to getting deeper insights by asking follow-up questions. All users should be able to drill down to find the answers to the more-meaningful “why” questions — without involving the data team, which often creates bottlenecks. Look for a tool that allows users to create their own dynamic dashboards and dig into the underlying data on their own.

 Does the tool allow team members to build upon one another’s work?

Reinventing the wheel isn’t efficient. Team members should be able to join multiple data sources and get a full, real-time picture of performance across applications — as well as share, reuse, and build on each other’s analyses. Look for a tool that serves as a single source of truth for a company’s entire data ecosystem. This includes the ability to model data once and share relevant datasets that have already been curated for analysis.

  Is the tool built for cloud data warehouses?

With the mind-bendingly-high volume, velocity, and variety of data being generated today, cloud data warehouses (CDW) are a necessary foundation for any modern analytics program. On-prem databases just can’t keep up, and the performance, scalability, and flexibility of modern cloud data warehouses dramatically speed up processes. Your analytics software should be designed with the modern CDW in mind, allowing you to take full advantage of its capabilities.

 Does the tool facilitate semi-structured data analysis?

Unstructured and semi-structured data make up 80% of the data collected by enterprises. JSON data is particularly widespread, as it’s the standard format for data coming from connected devices, apps, and more. Your tool must enable you to easily connect to and work with this data. Look for BI software that makes it possible to easily identify and parse relevant JSON, without having to manually code SQL.

 Does the tool support semantic modeling?

If you’re working with a variety of data types in a cloud data warehouse, semantic data modeling and a final clean up of the data is required before you can conduct analyses. If business users are going to ask ad hoc questions, they must be able to contribute to the data modeling process. Modern tools like Sigma help data and business teams collaborate together in a familiar spreadsheet UI using SQL and/or Sigma’s visual data modeling functionality to build highly contextual, centralized models.


Read our free guide to choosing the right BI software for your company. 

  Does the tool offer robust security?

Privacy and security are central in any collaborative analytics program. A&BI tools that migrate data or rely on CSV extracts leave your organization vulnerable. Your collaborative analytics tool should include features like object and row-level security, single sign-on, and detailed permissions. Also, be sure to look for security certifications like SOC, HIPAA, CSA.

Your collaborative analytics tool can make or break your success with community-driven analytics. Understanding what to look for and why these capabilities are important will help you make a choice that will meet your needs and make your quest to become data-driven simpler.

Learn more about the benefits of collaborative analytics and what holds most companies back by reading The Definitive Guide to Collaborative Analytics.

Ready to pick a new analytics and BI tool? Make sure to read our guide to evaluating BI software before you buy. 

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