Board Position: Uberization of Analytics


Collaboration is a golden rule for organizations to truly leverage the breadth of their data and improve their business. Organizations need to be flexible to be interactive with data. This implies the freedom to access information, conduct exploratory analyzes and collaborate. Collaborative analysis facilitates the sharing of initiatives and analytical information that can lead to improved business performance. This not only makes it easier to discover new data, but also makes the most of it where different stakeholders and teams can use relevant data to derive ideas and take action.

As a concept, Uberization is about creating an ecosystem that elevates the principles of collaboration and combines the inputs, outputs, and expertise of multiple entities to maximize the potential of available data. It allows us to create a dynamic and multidimensional environment to discover new ideas for better business decisions.

A successful uberized organization is an organization that involves the entire value chain in the process of collaboration. This includes everyone in the game in various parts of processing, from data ingestion to data engineering, from analysis models to visualization, and data insight to action. It is essential for all stakeholders involved to provide input for the organization to be consumer-centric so that they are constantly learning and evolving based on user needs.

Examples of Uberization in industry

The self-service analytics ecosystem created at Uber is one of the best examples of how organizations can integrate collaboration and interaction into their processes to gain successful insights. The “uberization” process is inspired by this approach created by Uber.

Below is a view of a few other industries undergoing uberization.

  1. The traditional buying and selling of automobiles is a good example of uberization making the whole process more user-friendly, driven by customer needs and wants, and full transparency. It eliminates the redundancy of the entire value system by providing accessibility and information to enable decision making.
  1. Another good example of uberization is the real estate rental industry which was once only focused on face-to-face transactions and is now a mature self-service industry bringing together buyers, sellers and other ancillary partners on one platform.

Data information is collected from all stakeholders involved in the collaboration process, and it is an ongoing, increased process. Uber itself has the latest addition by partnering with Mixpanel, an analytics company that gives Uber teams the ability to create self-service for each product manager. Uber is expanding into new and regional markets, and self-service analytics will help them grow better by tailoring their services to meet local needs. These include the registration flow, the service model provided, and the app experience.

Essential checkpoints to create an uberized ecosystem:

  1. Pull, not push

The working behavior in the uberization of analytics is all about the “pull”. Individuals should have the ability to stimulate and elevate analysis whenever needed. In order to avoid redundancy in the business environment, it is essential for everyone in the system to focus on the pull rather than the push. This means that individuals / entities will take what they need from the environment rather than what is made available to them.

  1. Interactive and multidimensional environment

The selfish analytical ecosystem involves multiple actors working together to keep the system dynamic and prevent passive exits. This includes collaboration between everyone involved who improves the environment by adding to it in return.

Creating a model to make predictions about the data is only the first step in a successful service offering. Models degrade over time as customer needs are constantly changing. Collaboration allows organizations to continue to receive the latest data that they can use to manipulate information and play with it based on user needs.

  1. Entry and exit approach

Consumer-oriented organizations thrive on inputs provided by consumers / partners and other entities to ensure that the best outputs are delivered. Collaboration plays an important role in this step to ensure stakeholder participation in this give-and-take approach.

See also

For example, in Netflix, when observers participate in the collaboration process and rate the recommendations given by Netflix, it adds value to both; customer and service. The customer gets better, relevant recommendations while Netflix strengthens its recommendation engine. The more ratings the user provides – the input, the more personalized recommendations they will receive – the output.

  1. Continuous learning with actionable insights

Since the system is constantly absorbing input data to deliver a better result, the selfish / uberized analytical ecosystem is never static. It enables continuous learning and improvement based on actual responses and assessments. Actionable information plays an important role in building a strong organization. It always involves collecting the input data, obtaining business insight, and creating strategies to overcome these issues and provide the best service. Since the organization is always striving for improvement, this gives it a competitive advantage over similar organizations.

An ecosystem based on uberization is greatly facilitated by machine learning. Machine learning bridges the gap between ever-changing data and actionable insights into data. Machine learning is used to make the connections between these once the data is consistent. Logical or mathematical wireframes are used for data discovery and scenario planning, making data engineering an integral part of the self-service analysis architecture.

In conclusion, the digital world thrives on collaboration; The days of the lone inventor are long gone!

This article is written by a member of the AIM Leadership Council. AIM Leaders Council is an invitation-only forum for senior executives in the data science and analytics industry. To check if you are eligible for membership, please complete the form here.


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Hari Saravanabhavan

Hari Saravanabhavan

Hari is a senior executive with 25 years of experience in successful and high growth companies in the fields of analytics and data science. With a strong focus on customer imperatives, Hari has inspired capable teams to go beyond their comfort zones and deliver meaningful business results fueled by leading analytics and data science practices.

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