Analytics Collaboration

A Primer on Data Collaboration

Data collaboration is a complex, still-evolving category that has not yet reached maturity or gained critical mindshare in the industry while serving a real need in an organization.

Ebook Background

About The Primer on Data Collaboration Book

This paper examines the state of collaborating around data and analytics in organizations today. It also identifies and describes current practices and describes how they should evolve to achieve an optimal data-driven culture within an organization.

As you read this paper, the most important thing to keep in mind is that analytics collaboration is a still nascent process where best practices and methods are still emerging. However, as organizations are evolving their analytics for deeper insights that answer increasingly complex questions, they recognize that collaboration can help them get there faster and more accurately.

To discuss analytics collaboration, we must first define what it is…but also what it is not. Afterward, we will discuss the different ways it manifests itself, the benefits that can accrue to organizations practicing it, and finally, the different methods of collaboration.

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Data Collaboration Needs

In a recent survey, data and business analysts were asked to describe how their team collaborates on data for analytics…

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What data collaboration is “not”

Given the rather amorphous state of the collaboration capabilities at the moment, some vendors might categorize features as “collaboration.”

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Types of Analytics Collaboration

Implicit, Explicit, Team Driven, Data-Driven, Cooperative Data Management...

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Data Collaboration Benefits

Knowledge Sharing, Deeper Data Understanding, Increased Trust in Data and Analytics, Enhanced Curation...

WHAT EXACTLY IS ANALYTICS COLLABORATION?

The word “collaboration” itself has multiple meanings. In the most used one, it refers to working with someone to produce or create something. This accurately captures what we mean when we talk of analytics collaboration, namely, the act of working with people in an organization to produce or create something of value with the organization‘s data and analytics assets as the foundation.

We also note that we are focusing this paper on internal analytics collaboration among team members within the same organization. There is also the collaboration with external business partners, clients, and suppliers – which is also a worthwhile goal – but carries a different set of requirements and challenges which we do not tackle in this paper.

The very term “analytics collaboration” is itself in its infancy, with the industry not having fully coalesced around a canonical set of capabilities that define it. To that end, this paper examines the different methods by which analytic teams collaborate around data and proposes a critical set of capabilities required to fulfill analytics collaboration needs.

IMPLICIT & EXPLICIT COLLABORATION TYPES AND ASSOCIATED BENEFITS

We consider implicit analytics collaboration methods to be those that emerge automatically and organically through an organization. These methods are typically implemented in a software platform by leveraging all team members’ actions and combining them with machine learning around activities, workflows, and underlying data sets. They are also surfaced through the social media paradigm, utilize machine learning and recommendation engines, and are centered around sharing and taking advantage of the power of collective team activity throughout the platform.

We consider explicit analytics collaboration methods to require an explicit team member action for initiation or completion. These methods typically revolve around deliberate, team-driven requests delivered through various shared communication channels, which should leverage features of a collaboration platform to enable cooperative asset discovery, annotation, and description. These methods are typically implemented through a combination of interfaces, including chat-style experiences, like/dislike (or “thumbs-up”/“thumbs-down”) feedback, and a variety of documentation mechanisms.

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