Collaborative Analytics: Things You Need To Know

Collaborative Analytics: Things You Need To Know

Many of us are acquainted with the phrase, “Many hands make light work”. The same principle works at the heart of collaborative analytics. Essentially, it is the act of working together with the constituent people to produce on-point analytics. The analytic assets and data of the organization will act as the base of the analysis. Implementing collaboration as a mode of operation has been proven to be advantageous, with a 10% improvement in productivity. Investment in collaboration software is already registering an upward trend, as evidenced by the projected increase of 1.1 billion over five years.

How does it help the business?

Let us imagine a scenario. In any business, if all the departments work in sync, the output will be quickest, complete, and of top quality. In other words, a collaboration between the component entities of that business drives it to perform at its most optimal level. According to the recent statistics, businesses functioning within the pandemic are settling for collaboration software in their platforms, giving rise to a 176% increase in installing these programs on enterprise systems. Although indirect, this information aptly highlights the practical nature of collaborative work in a business.

Likewise, the main function of collaborative analytics is to increase the knowledge of the analytic team about their data. A greater understanding of analytic assets fosters greater trust, culminating in the generation of new ideas on utilizing the analytics mentioned above with faster insights, culminating in the greatest detail and precision. With the shared understanding of the analytic assets available, the company experiences a remarkable improvement in its operation, culminating in smoother task completion and scalable growth.

What comprises a collaborative analytics team and its functions?

In a collaborative analytics team, the following persons should be included:

  • Data analysts
  • Business analysts
  • Data scientists
  • Knowledge workers
  • Management

All required personnel, including the mentioned ones, will have to collaborate in numerous ways to utilize the analytics assets and knowledge about them. The use of online collaboration tools is common in this matter.

However, the functions of the responsible employees will be diverse and challenging. The main tasks they have to undertake include but are not limited to:

  • Distributing any individual awareness of analytics assets and recommending the best use.
  • Creation of automated recommendation modes on the best use of assets.
  • Inclusion and implementation of collaboration tools and AI-driven asset knowledge.
  • Working together to effectively determine, construct, share, and utilize analytic assets.
  • Discussion and collaborative referencing of assets.
  • Optimizing social feeds, allotment, and discoverability of asset features.

Types of collaborative analytics

Depending upon the functionalities, methods of collaborative analytics can be divided into two fundamental divisions:

  • Intrinsic methods

    These methods take place spontaneously and naturally in an organization. They are usually executed in a collaboration platform by adding the actions of the team members and combining them with machine learning to assimilate activities, workflows, and core data sets. Intrinsic collaborative analytics methods help build trust in the assets, create a hitch-free workflow between the teams, define its suitability and usefulness, and generate improvement through discussions and explorations. An example of its utility would be the 1500 corporations worldwide collaborating human and AI and complementing each other’s strengths.

    The following aspects of collaborative analytics are touched upon by the intrinsic methods:

    1. Endorsements of assets to use in analysis and exploration.
    2. Recommendations for parallel resources of the existing ones.
    3. Facts on appropriate assets produced and used by other team members.
    4. Identification of popular assets for the consideration of the team.
    5. Emphasizing resources added to or created in the platform.
  • Extrinsic methods of collaboration

    This mode requires the action of a specific member of the team for the initiation or the end of the collaborative effort. The techniques make use of measured, team-driven requests delivered through various shared communication channels.

    These requests employ the platform or hub’s cooperation aspect to drive cooperative discovery of assets, explanation, and depiction. These are usually implemented through various interfaces, like social media, chat-style feedback, like/dislike feedback, and other record-keeping features.

    The principle of extrinsic methods revolves around specific interactions between team members to employ better curating, documentation, and collaboration around the assets.

    The collaboration platform should encode, centralize, and make them available to the team members. It has been recorded that effective communication in the team can retain the best talents by 4.5 times.

    Some of the essential facets of an extrinsic collaboration could include:

    • Distinct accounts of each asset, its utility, and uses.
    • Comments on certain understandings from the asset.
    • Organized discussion around an asset, preferably within the platform.
    • Supportive innovation and investigation of assets.
    • Asset modelling and shared workspaces.

    It is a good practice to create a hub to implement team collaboration tools and use diverse interfaces to use the different forms of collaboration. This facilitates the generation and dissemination of analytics asset-related knowledge culminating in the sharing and reuse of assets.

Efficacy of online collaboration tools

The different team collaboration software available in the market is already being used for collaboration among business ventures. They have significantly improved the integration of work, technology, and the human element of a business, resulting in an effective business operation. The most exciting collaboration tool in today’s scene is Artificial intelligence (AI). Apart from setting the optimum number of meeting attendees and the best meeting duration, it is also reported to improve business processes and enhance job quality. The use of these collaboration tools is being instrumental in enhancing workplace communication.

Implementing the advantages

The efficacy of implementing collaboration tools for business has been proven long before, and its benefits are also apparent. It increases the sharing of amplified and better knowledge about analytics, provides:

  • Better understanding and increased trust.
  • Better creation of analytic assets.
  • Utilization of diverse analytic skills.

Using the available tools properly will increase the number of analytic assets and provide better understanding, thereby increase the value of the analytics community for the organization and generate more ROI for the analytics program. A collaborative analytics platform that helps to create a better work environment and culture within an organization. Wersel GlassX will improve the way you collaborate and bring more significant ROI.



Wersel Marketing Team