With the advent of the big data era, most companies quickly adopted data-driven processes, platforms, and initiatives to get deeper insights so they could make better business decisions. However, that adoption was most commonly driven by data experts, who decided which tools to integrate, what processes to revamp, and what areas to concentrate on.
While that focus can surely boost any area and company, it can also lead to a couple of issues all too common in big data initiatives. The first problem is the potential emergence of knowledge silos—data-driven teams inside a company often tend to keep the insights for themselves. The second issue is that a lot of people who’re actually close to the data might not enjoy the benefits of big data analytics, mainly because big data engineers decide how their data-driven processes should look.
Those problems are more frequent than you might think, even if companies choose to work with a custom software development team to tailor a big data solution to their specific needs. That’s because those problems aren’t the result of the development process, but rather the approach businesses take to the process itself. The goal of companies looking to become data-driven should be, then, to overcome those shortcomings to leverage the data on a company-wide level.
How can companies do that? By implementing a collaborative analytics approach.
What Is Collaborative Analytics?
Collaborative analytics is an approach to business intelligence that puts the community at its center. It leverages business intelligence and collaboration tools to ensure that everyone in a particular organization accesses, uses, and contributes to the data analytics strategy.
Thus, collaborative analytics is all about bringing people in an organization together to boost the big data initiatives that the organization might have adopted. The ultimate goal is to make sure that everyone provides their input into the strategy, adding value through their own perspective and their specific relationship with data. By collectively collaborating in the big data strategy, a company can build bridges between different areas and experts, thus better positioning itself to leverage the know-how across the entire organization.
On a functional level, collaborative analytics encompasses diverse tasks, including the identification of relevant data, the creation of datasets, the implementation of data-driven tools, the development of a data governance strategy, and the use of data assets, among others. People are the central element of any collaborative analytics effort, as they are the ones who better understand the available data and how it can be used to boost the company.
Naturally, there are other important parts in any collaborative analytics effort, including AI algorithms to analyze the data, team workspaces to foster collaboration, centralized data servers, communication tools, and even visual data modeling tools for easier data monitoring. All of that comes together to provide numerous benefits, especially when compared with the siloed analytics efforts many businesses use today.
Benefits of Collaborative Analytics
You can find the following among the benefits of putting the community at the center of your data analytics efforts:
- Identification of more relevant and valuable data. Since everyone is involved in big data efforts, virtually anyone can point to untapped data sources, which can bring forth data opportunities that data experts might have overlooked before.
- Better data use. As you bring the entire company’s expertise together to work on the data, all team members can describe how to better use the available data while also pointing out relevant characteristics and insights that derive from the available datasets.
- Deeper and more on-point insights. While big data often relies on artificial intelligence to analyze vast amounts of data in search of trends and insights, algorithms still need human input to make more sense of the information. Given that in this approach everyone collaborates in the big data strategy, it’s easier to make sense of visualizations and reports. What’s more, different experts can formulate relevant questions to get even deeper insights.
- Faster analyses. Yes, AI is often the one that marks the speed at which any analytics process moves forward. But the collaboration that stems from this approach can help companies explore new directions more quickly, thanks to the input from experts across all the activities in the organization.
- Increased data literacy. Any company that aspires to be data-driven needs its staff to be data literate. In other words, everyone needs to be able to use and analyze data to make quick and impactful decisions. By extending the reach of the big data initiative to the entire company, you can push your staff to better understand the value of data and help them get accustomed to data tools and reports.
The Challenges for Collaborative Analytics
With all those benefits and being that today’s company has the imperative to become data-driven to face the post-pandemic world, it’s hard to imagine any business resisting embracing the collaborative analytics approach. Yet, even if you have the will to adopt the said approach, you’ll still have to overcome certain specific challenges.
Some of the most important ones include the following:
- Issues with collaborative features in BI tools. Business intelligence tools are essential for collaborative analytics, yet they can have certain issues with collaboration. Many BI platforms have limited collaboration features that can’t be customized or that are too advanced for everyone in an organization to use. Older solutions might not have collaborative features at all, making it difficult to extend collaboration without having to completely revamp the entire digital environment.
- Lack of a data governance strategy. Collaborative analytics implies centralizing all your data in a nuclear spot that everyone can access at any given time. However, doing that might pose a security risk, as you need to make sure that only authorized users can see data based on their clearance level. That means you need to develop a data governance strategy that complies with security standards while not hindering access.
- Need for proper infrastructure. A sound big data initiative needs the right infrastructure to secure the capabilities you’ll need to collect, store, and analyze vast amounts of data. Investing in such infrastructure in-house might break your budget, so it’s important to take a look at third-party vendors to hire the proper support. This is a challenge in itself because not all vendors will fit your specific needs.
Time to Embrace Collaborative Analytics
As you can see, the challenges aren’t insurmountable, so you might as well start your journey toward collaborative analytics today. The only thing you’ll need is an informed road map that contemplates different factors, such as the need to prevent knowledge silos from appearing or the central role diversity of perspectives plays in the success of the approach.
Being community-driven in your analytics efforts can certainly pay off, so taking that route feels like a natural step forward for companies looking to thrive in the post-pandemic context. And what better time to do so than right now?