Today’s digital-first companies rely on analysts to make sense of data, generate insights, and help leaders make informed business decisions. But, as analytics evolves, many organizations have realized the power of cross-functional collaboration.

Read on to learn more about collaborative analytics, its benefits, and the key to effective collaboration throughout the data ecosystem.

What Is Collaborative Analytics?

According to business intelligence platform Ajelix, collaborative analytics is “the process of working together with colleagues to analyze data, share insights, and make data-driven decisions." Sometimes referred to as collaborative business intelligence (BI), it involves the combination of BI and collaboration tools to help different stakeholders work together on data analysis and share information with one another.

As Ajelix explains, collaborative analytics has three main goals:

  1. Data exploration and discovery: Organizations that embrace cross-functional collaboration enable teams to uncover hidden patterns or correlations.
  2. Shared analysis: Data teams can share insights and ensure all stakeholders have a unified understanding of the data.
  3. Real-time decision-making: Collaboration tools allow stakeholders to discuss their findings, ask questions, exchange unique perspectives, and make decisions more efficiently.

According to TechTarget, teams can engage in collaborative data analysis in many ways, such as:

  • Working together to discover new datasets, use cases, and requirements
  • Modeling data to visualize insights for various business functions (sales, marketing, product design, etc.)
  • Leaving comments, asking questions, or annotating projects throughout the analytics process

Are you curious about a career in data analytics? Check out this guide for more information.

Collaborative Analytics vs. Collaboration Analytics

Collaborative analytics and collaboration analytics are different concepts despite having similar names. Collaborative analytics is an organizational approach to democratize information, thus producing deeper, more valuable insights across all business operations. Collaboration analytics is about gathering and analyzing data on how teams work together.

According to the Journal of Learning Analytics, “the ultimate goal of collaboration analytics is to generate actionable group insights based on educational or teamwork constructs.” Although both fields involve and embrace collaboration, the terms aren’t interchangeable.

Why Are Organizations Embracing Collaborative Analytics?

The analytics process has historically been individually focused and conducted by one or two people for particular use cases. But now that organizations consider data a key asset, there’s been a notable shift toward collaborative analytics.

In fact, nearly 40% of organizations use collaboration to support analytics processes. More than half expect to use collaborative capabilities in the future.

Robert Kayl, MS, faculty member at the Purdue Global School of Business and Information Technology, says this shift coincides with the rise of the Agile methodology. As a project management framework, Agile emphasizes teamwork and collaboration.

“The whole Agile process in IT tends to involve stakeholders on both the functional and technical side, so you have cross-functional teams to address challenges,” Kayl explains. “In terms of analytics, I think that diverse perspectives, especially with domain expertise, are an important part.”

Kayl points to health care as an example. If data teams are analyzing skin cancer statistics, they might involve dermatologists in the analytics process. Together, doctors and analysts can share insights from their respective expertise, creating what Kayl calls a “cross-pollination of ideas.”

Benefits of Collaborative Analytics

The advantages of collaborative analytics include:

Improved Decision-Making

According to Kayl, collaborative analytics improves decision-making across the board because the organization receives more input from multiple stakeholders.

“I used to work for an organization, specifically for post-traumatic stress and traumatic brain injuries,” Kayl says. “We worked directly with psychologists, patients, and other beneficiaries to develop tools, take their results, and conduct analysis with the intent of implementing more innovative technology to help the military service members, family, and veterans.”

The combination of those different perspectives throughout the analytics process allowed Kayl and his colleagues to gain valuable insights they may not otherwise have had.

Enhanced Data Accessibility and Data Quality

Data sharing and collaboration can also have positive impacts in a business sense, according to TechTarget. For example, an analytics tool with integrated communication features can help employees share insights across an organization, thus breaking down departmental data silos. In turn, everyone can make informed decisions aligned with other business units.

According to Oracle, the problem with data silos is that they wall off information, making them inaccessible to certain people. This makes it harder for departments to work together, for planners to devise data-driven strategies, and for analysts to deliver business intelligence. Plus, because they can fragment a data source, silos may result in duplicative, conflicting, missing, or incomplete information. This ultimately erodes data quality, making analytics less trustworthy.

However, TechTarget points out how analytics tools with collaborative capabilities can help in this regard. First, they democratize data access using self-service features. Employees can generate reports, conduct analyses, ask questions, and search for information without help from the data team.

By allowing users to pull information from a real-time database, organizations can ensure that data remains accurate, well structured, and up to date.

Faster Data Analysis

Sigma Computing explains valuable insights gleaned from data are typically time sensitive. If an organization wants to capitalize on an opportunity or solve a problem before it’s too late, it must act quickly.

With more people contributing to data analysis, teams can leverage domain expertise and helpful perspectives to accelerate the process. Whether in product development or market launch, collaboration can enhance efficiency.

Improved Service Performance

Data sharing is about more than just expanding data accessibility. The increased quantity and quality of available information empowers diverse teams to contribute to broader organizational goals. For example, combining insights from research, operational data, and customer feedback can help employees improve service performance, thus maximizing service value.

Analytics Tools and Components

Collaborative analytics involves collecting data from a variety of sources, storing that data in an accessible location, and allowing multiple people to provide their insights related to that data.

Data Sources

A data source is where information originates, referring to the application or system where it’s collected. Sources can come in many forms. In terms of collaborative analytics, the most common include:

  • Internal databases that store customer information, employee records, and sales data
  • External databases that store industry information, market research, and third-party data
  • Datasets collected through the Internet of Things (IoT), such as IoT sensors
  • Spreadsheets and files imported into a collaborative analytics platform or tool

Data Integration

Data integration combines information from multiple sources to make it accessible for analysis. This multi-step process involves extracting the data from each source, transforming it into a particular format, and loading it into a storage system. Sometimes, organizations transform data after it’s loaded into storage. In either case, integration prepares information for analytics and decision-making purposes. This facilitates the sharing of unified datasets.

Collaboration Tools

Collaboration tools allow people from different areas of an organization, with different areas of expertise, to work together and share information.

As Kayl explains, cloud-based collaboration, video conferencing, and instant messaging platforms enable asynchronous communication, allowing teams to share insights anytime. There are also BI tools, which make information more understandable through data visualization.

Software developers are increasingly adding new capabilities to make BI systems and other applications more collaborative. For example, embedded analytics integrates data visualization and analysis into the platform’s dashboard. This improves the user experience while providing real-time insights through continuous data processing.

Fostering a Collaborative Environment

Building a culture that embraces collaborative analytics is important for an organization to maximize data to its full potential, according to Sigma Computing. Here are a few steps businesses can take to foster an environment that thrives on collaboration:

  1. Take a data-first approach: Organizations should adopt data-driven decision-making and train employees to use analytics for daily operations. Kayl says data literacy is an important building block for collaborative analytics, but a lack of technical skills can cause problems, especially for data governance. “Everyone could be a data steward,” Kayl says. “Anyone who receives data should feel like they’re responsible for [taking care of it].” He advises organizations to have strong governance, access controls, and decision-making processes to mitigate these risks.
  2. Promote cross-functional collaboration: Kayl states most organizations have silos where departments work at a more operational level, but someone at the tactical level brings them together. “Some people are resistant to change. They like to work alone and be siloed,” Kayl says. “But nobody works alone in today's world. Too many students want to do that. I try to get them to understand that when you get out there, you have to [work together].”
  3. Use the right technology: Teams need tools that integrate seamlessly into their workflows and data sources. This minimizes disruption, enhances teamwork, and empowers employees to participate in the analytics process.

What’s the Future of Collaborative Analytics?

Looking ahead, Kayl sees two prominent trends shaping the future of collaborative analytics:

  1. Artificial intelligence (AI) and cloud computing: “I see a lot more AI-driven analytics platforms,” he explains. “A lot of work used to be done in Big Data environments, and now it’s moving to the cloud; almost everything is done in cloud environments.”
  2. Governance and ethics: Kayl also notes a growing emphasis on data governance and ethics in analytics. “You’re getting greater collaboration between information technology and data teams to ensure compliance,” he says, citing health care and the payment card industry as examples. “Legal and functional [teams] working together is becoming more of the norm today than it used to be.”

Check out more trends on the future of cloud computing.

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