Collaborative analytics is the latest concept that will help enterprises solve problems faster.

As the word suggests, collaborative analytics means looking at data analytics with a community approach. The primary merit of this method is that collaborative analytics can bring diverse expertise to the business and develop a shared understanding of the result. But making this work seamlessly is not as effortless as it sounds. There needs to be a right balance between new tools, people, and business functions to come to a common conclusion.

What's The Need To Collaborate?

Collaborative analytics means putting data transformation to use. This saves teams a lot of time by avoiding misunderstandings that come from bad decision-making to align collaborative efforts in a better way. This can accurately assess the overall performance of the organization.

Many of the business problems extend beyond the domain and department of the issue which makes it hard to solve with only one kind of expertise. While businesses have collaborative actions, one area where they miss out is having a collaborative understanding of what's happening. This gap can be filled with collaborative analytics.

Boosting Data Teams

With more brains working on the problem, the understanding will come faster, that's the essence of collaborative data. It also has the ability to throw light on a broader inventory of data that might come with all the different experiences people have with data. A broad perspective helps companies come up with new ideas that might otherwise go overlooked.

There are no know-it-all data scientists. Some problems might seem unsolvable to one data scientist, but those problems can be broken down for more people to pitch in. "Enterprise benefits from collaborative analytics by knowing that data users in the organization all contributed to creating, building, and documenting the analytics," says Peggy Tsai, Vice President of data solutions at BigID, a data intelligent platform.

The Right Tools For The Right Efforts

For everyone to collaborate on a problem, there needs to be a common interface and workspace where people can access data and KPIs. This workspace must be able to easily showcase data by providing a contextual business view of information in a simple format for the business. This model should be similar to a marketplace that allows the creation and sharing of data products.

It's crucial for a collaborative workspace to enable transparent data manipulation. It also enables data visualization so users can examine different sides of the information. Once the data is analyzed, data teams should be able to create more versions of it to share it with colleagues for collaboration, review, and implementation.

The Human and AI Elements

For collaborative analytics to work in an organization, humans must have the willingness to learn. Data literacy helps everyone involved differentiate the meaning of data in a unique way. Being open to the process also helps understand the varied experiences each member brings to collaborative analytics in terms of domain knowledge, technical approaches, and perspectives. It also helps people share their opinions without hesitation.

Once the basic tools, people, and functions are in place, artificial intelligence is the next piece in the puzzle that can drive the insights. AI can help collaborative analytics by suggesting what information is relevant, focusing on data sources and visualizations. Sometimes the data teams don't have all the information they wished they had. AI can help fill that gap. It can also create hypothetical scenarios to play out the different strategies that were discussed and their outcomes for better decision-making.