We use cookies and other similar technologies (Cookies) to enhance your experience and to provide you with relevant content and ads. By using our website, you are agreeing to the use of Cookies. You can change your settings at any time. Cookie Policy.

Collaborative Analytics – An Emerging Practice

Originally published January 20, 2009

Collaboration is the act of working jointly – two or more people combining their efforts toward achieving shared or intersecting goals. Collaboration is fundamental to teamwork and is generally viewed as a good and culturally desirable thing. Yet analysis is practiced almost exclusively as a solitary activity – the determined and dogged analyst persistently digging through masses of data in search of answers and insights.

The old model of business analysis is about to change. Collaborative technology is ready. But more importantly, today’s business and economic climate demands collaborative understanding and problem-solving. You can expect an economic recovery to begin in 2009, but don’t expect a return to business as usual. There will be turbulent times; to navigate you’ll need to make the right discoveries, ask the right questions, find the right answers and devise the right solutions. That’s a tall order for the solitary business analyst. Collaborative analytics is an idea whose time has come.

What is Collaborative Analytics?

To understand collaborative analytics, let’s begin with a definition of analytics. Business analytics encompasses the science, disciplines and processes of business analysis. It begins with gathering of data and continues through analysis of data to develop conclusions and recommend or decide about actions to be taken. Figure 1 illustrates a typical analysis process.


Collaborative analysis builds upon the analysis process by adding activities to overcome the solitary and linear nature of traditional analysis. Figure 2 illustrates the extensions to move from conventional to collaborative analytics. The shift from solitary and linear to participative and iterative is accomplished with three feedback loops:

The data loop encourages data sharing. Communicating about the data helps to be sure that the best data sources are chosen and that the quality of the data is known. Data sharing avoids redundant work of multiple analysts unnecessarily building similar collections of data. Communication and sharing improves consistency of analysis across the organization. 

The analysis loop brings more thought and multiple perspectives to the process of deciding what the data means – what stories it has to tell. A recursive process of developing hypotheses and then validating them as conclusions is sure to achieve more robust and complete conclusions. Collaborative analysis also develops deeper understanding and stronger analytic skills among all of the business analysts.

The action loop focuses on coordinated action across the enterprise. Collaborative and cross-functional analysis and decision making leads to a more complete and connected set of actions. Dependencies among people and organizations are less likely to be overlooked or to fall through the cracks.


Collaborative analytics, then, is a set of analytic processes where the analysts work jointly and cooperatively to achieve shared or intersecting goals. Collaborative analytics includes data sharing, collective analysis and coordinated decisions and actions. The goals of conventional analytics are to find answers and make decisions. Collaborative analytics encompasses these goals but seeks to achieve more – to increase visibility of important business facts and to improve alignment of decisions and actions across the entire business.

What is the Current State?

Collaborative analytics is an emerging practice; it is not yet in the mainstream. It is written about (occasionally) and it is talked about (a bit). As with all emerging practices, the meaning is often muddled. It’s a catchy phrase – “collaborative analytics” – so marketers are likely to adopt it to describe whatever analytic product they promote.

Some of the early proponents seek to formalize it. Mohan Sawhney, in a keynote address at the recent Teradata Partners Conference, spoke of “a process where inter-organizational teams organize, analyze and interpret federated customer and operations data to make better joint business decisions.” That’s a lofty goal, but I’m inclined to start with small steps. Let’s get the business analysts that we have to share data and analysis results, and decision makers to communicate and coordinate.

Others seem to believe that it’s already mature and mainstream. In a recent white paper sponsored by Tableau Software, Neil Raden itemizes nine best practices for collaborative business intelligence (BI). While Raden’s advice is logical and well-reasoned, it is largely theoretical and yet to be proven in practice. Collaborative analytics is too new to have actually developed best practices.

What about the Technology?

Both analytic technology and collaboration technology are maturing, but they haven’t converged to a large degree. Although Tableau sponsored the previously mentioned white paper and the products do a wonderful job with visual analysis, they don’t really enable collaboration. The same may be said for many of the other technologies and tools that fit in the BI-for-the-masses space. Simply operating on the desktop and reaching a large number of users does not lead to collaboration. Perhaps the opposite will occur – more independent, individual and linear analysis; more redundancy as the same analysis is performed by different people; more inconsistency and multiple versions of the truth.

Collaborative analytics takes more than accessibility, ease of use and desktop operability. It demands a shared workspace – the core component of collaborative technologies. From groupware applications to wikis and Google Docs, sharing is fundamental.

A small number of analytic software vendors are taking the lead in the area of shared analytics. Expect the field to grow, but today look to these companies for leadership in collaborative analytics technology:

  • GoodData – Web 2.0 based software as a service (SaaS) approach to collaborative analytics.
  • Aha! Software – Defines collaborative key performance indicators (KPIs) for the business and makes those KPIs visible through a product called Axel.
  • Invoke Solutions – Will release Engage Analytics in January 2009. Engage is a collaborative analytics solution for market and customer analysis.
  • Panorama Software – In addition to their suite of SaaS analytic solutions, Panorama offers a free Gadget for Google Docs that makes pivot table analysis easy and sharable in Google’s collaboration environment.

I wish that I could offer a longer list of collaborative analytics technologies, but this is today’s reality. I do believe that it will grow quickly over the coming months. But beware of products labeled “collaborative” that don’t have a shared workspace. No sharing … no collaboration.

What Else is Needed?

Technology aside, it is likely that the biggest barrier to collaborative analytics is the BI community itself. The technology is coming, but technology isn’t enough. Collaboration is something that people do. Remember the definition – “two or more people combining their efforts …” Any activity that involves two or more people comes with those old, familiar challenges – politics and culture. Collaborative analytics seeks sharing of data, sharing of results and sharing of decisions. Any or all of these may be contrary to your current business intelligence culture.

The challenge, then, is to create an analytic community where collaboration and sharing are core values. Stepping up to this challenge is a big subject – too big to address adequately in this article – that encompasses cultural issues and change management. I offer some thoughts in my article Analytic Culture – Does It Matter?, and you’ll find abundant words of wisdom in the articles at Maureen Clarry’s BeyeNETWORK Leadership and Management Channel.

A Final Thought

Don’t pursue collaborative analytics as a replacement for conventional analytics, but as an extension. Collaborative processes aren’t well-suited to some workflows. Some data may be too privacy sensitive for sharing. Some analysts may be better suited to solitary work. Some projects may demand fast-track answers. The reasons for more traditional and linear analytic processes are many. Just be sure that they’re reasons, not excuses.

  • Dave WellsDave Wells

    Dave is actively involved in information management, business management, and the intersection of the two. He provides strategic consulting, mentoring, and guidance for business intelligence, performance management, and business analytics programs.

Recent articles by Dave Wells



Want to post a comment? Login or become a member today!

Be the first to comment!