The word collaboration refers to the action of working with someone to produce or create something. Collaboration is fundamental to teams working together successfully and producing the best possible result our output. Collaboration also leads to a much better work environment and culture within an organization.
The above definition accurately captures what we mean when we talk of collaborative analytics– namely, the act of working with people in an organization to produce or create valuable analytics with the organization‘s data and analytics assets as the foundation.
Collaborative analytics is a still nascent process where best practices and methods are still emerging. It is a topic that was introduced 10 years ago on the BeyeNetwork but mostly focused around process. Ventana Research, and leading research firm on BI and analytics, has devoted research on the use of collaboration in analytics this year. As organizations are evolving their analytics for deeper insights that answer increasingly complex questions, they are recognizing that collaboration can help them get there faster and with greater accuracy.
The core need for collaborative analytics is to increase the analytics team’s knowledge around data. A greater understanding of the analytics assets at their disposal facilitates greater trust, brings new ideas on how to use the analytics, and generally produces faster insights with greater detail and accuracy.
A recent survey by the Neebo team showed that knowledge around analytics assets is dispersed in many locations across an organization. Collaborative analytics can foster shared knowledge on an organization’s analytic assets.
Analytics teams, comprising of data analysts, business analysts, data scientists, knowledge workers and management, will collaborate in multiple ways, all in an effort to share analytics assets and knowledge about those assets. This includes:
In general, these forms of collaborative analytics can be grouped into two major categories: implicit collaborative analytics and explicit collaborative analytics.
Implicit collaborative analytics methods are those that emerge automatically and organically through an organization. These methods are typically implemented in a software platform by leveraging information about team members’ actions and combining it with machine learning around activities, workflows and underlying data sets.
These methods are typically surfaced through the paradigm of social media, utilize machine learning and recommendation engines, and are centered around sharing and taking advantage of the power of collective team activity throughout the platform. Implicit collaborative analytics helps to build trust in assets, determine fit and usefulness, create seamless workflows between teams, and generate additional exploration and discussions.
Implicit collaborative analytics methods could focus around the following areas:
A greater user experience would be offered through a generalized social media-style interface combining aspects of the above into a holistic “newsfeed” interface for analytics assets. It would also support seamless discoverability and frictionless sharing throughout the platform.
Explicit analytics collaboration methods are those that require an explicit team member action for initiation or completion. These methods typically revolve around deliberate, team-driven requests, delivered through a variety of shared communication channels, which should leverage collaboration features of an analytics hub or platform to enable cooperative asset discovery, annotation and description.
These methods are typically implemented through a combination of social media like interfaces, including chat-style experiences, like/dislike (or “thumbs-up”/“thumbs-down”) feedback and a variety of documentation mechanisms. The overall experience of explicit collaboration is centered around explicit interactions between team members to better understand, document, curate and collaborate around the assets. The software platform should codify, centralize and expose these methods for wide use by analytic team members.
A shared analytics hub is a perfect place to foster all these forms of collaboration through different interfaces to facilitate greater knowledge around analytics assets and sharing and reuse of assets.
At the highest level, collaborative analytics facilitates faster answers to analytic questions. At the next level, it delivers six key benefits:
Collaborative analytics will increase the value the analytics community offers to the organization and bring greater ROI to analytics programs.
The Neebo Virtual Analytics Hub is a SaaS solution allowing analytics teams to find, create, collaborate and publish trusted analytics assets in complex hybrid landscapes. Neebo provides unified access across analytics silos, increases use of analytics assets and furthers data knowledge to build trust and rapidly answer new business questions. Neebo includes an array of both implicit and explicit collaborative analytics facilities to share and build knowledge on analytics assets within the analytics community as well as information workers and management.
To learn more about Neebo and collaborative analytics, download our white paper on Analytics Collaboration.