Council Post: Uberisation of Analytics

Uberisation of Analytics

Design by Uberisation of Analytics

Collaboration is a golden rule for organisations to truly leverage the scope of their data and enhance their business. Organisations need to be flexible to be interactive with data. This entails the freedom to access insights, conduct exploratory analysis and collaborate. Collaborative analytics makes it easy to share analytical initiatives and insights that can lead to enhanced business performances. It not only makes it easier to discover new data but also to make the most of it where different stakeholders and teams can use relevant data to draw insights and take actions. 

As a concept, Uberisation is about creating an ecosystem that elevates the principles of collaboration and combines inputs, outputs, and expertise from multiple entities to maximise the available data’s potential. It allows us to create a dynamic and multidimensional environment to uncover fresh insights for better business decisions. 

A successfully Uberised organisation is one that involves the entire value chain in the collaboration process. This includes all the game players in various parts of the processing, from data ingestion to data engineering, analytics models to visualisation, and from data insights to action. It is integral for all constituencies involved to give their input to make the organisation consumer-driven so that they are constantly learning and evolving as per the users’ needs. 

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Industry examples of Uberisation

The self-serve analytics ecosystem created at Uber is one of the best examples of how organisations can integrate collaboration and interaction in their processes for successful insights. The process of ‘uberisation’ takes from this approach created by Uber. 

Below is a view of a few other industries undergoing a process of uberisation.

  1. The traditional buying and selling of automobiles is a good example of uberisation making the entire process more friendly, driven by customer needs & wants, and complete transparency. It eliminates redundancy from the entire value system providing accessibility and information to enable decision making.
  1. Another good example of uberisation is the property rental industry which was once driven only by in-person transactions and is now a mature self-serve industry bringing buyers, sellers and other ancillary partners to a single platform.

Data insights are gathered from all the stakeholders involved in the collaboration process, and this is an ongoing augmented process. Uber itself has the latest addition by teaming up with Mixpanel, an analytics company providing Uber’s teams with the ability to make self-serve for every product manager. Uber is expanding into new and regional markets, and self-serve analytics will help them expand better by tailoring their services to meet the local needs. These include the sign-up flow, the model of service provided, and the app experience. 

Essential Checkpoints to create an Uberised Ecosystem:

  1. Pull, not Push

The working behaviour in uberisation of analytics is all about the ‘pull’. Individuals should have the ability to stimulate and elevate analytics whenever required. In order to prevent redundancy in the company’s environment, it is integral for all players of the system to focus on the pull instead of the push. This means that the individuals/entities will take what they need from the environment vs just what is made available to them.

  1. Interactive and Multidimensional Environment

The self-serving analytics ecosystem involves multiple players working together to keep the system dynamic and prevent passive outputs. This includes collaboration among all the individuals involved that are augmenting the environment by adding back to it. 

Creating a model to make predictions on data is only the first step in a successful service offering. Models degrade over time since the customer’s needs are constantly evolving. Collaboration allows organisations to keep receiving the latest data that they can use to manipulate insights and play with according to the user’s needs.

  1. Input and Output Approach

Consumer-driven organisations thrive on inputs given by the consumer/partners and other entities to ensure the best outputs are provided. Collaboration plays an important role in this step to ensure the participation of the stakeholders in this give and take approach.

For instance, in Netflix, when the watchers are participating in the collaborative process and rating the recommendations given by Netflix, it is adding value to both; the customer and the service. The customer gets better relatable recommendations while Netflix strengthens its recommendation engine. The more ratings the user provides- the input, the more customised recommendations they will receive- the output. 

  1. Continuous Learning with Actionable Insights

Since the system continuously takes in the input to provide better output, the self-serving/uberised analytics ecosystem is never static. It allows for continuous learning and improvement based on real responses and reviews. Actionable insights play a huge role in creating a robust organisation. It always involves taking in the input data, gaining business insights and creating strategies to overcome those issues & provide the best service. Since the organisation is always striving to improve, it gives them a competitive advantage over similar organisations. 

An ecosystem built on uberisation is hugely enabled by machine learning. Machine learning plays the connector between constantly changing data and actionable data insights. Machine learning is used to form the connections between these once the data is uniform. Logical or mathematical wireframes are used for data discovery and scenario planning, making data engineering integral to the self-serve analytics architecture. 

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

This article is written by a member of the AIM Leaders Council. AIM Leaders Council is an invitation-only forum of senior executives in the Data Science and Analytics industry. To check if you are eligible for a membership, please fill the form here.

Hari Saravanabhavan
Hari is an executive leader with a 25-year track record working for successful, high-growth companies in the fields of Analytics and Data Science. With a strong focus on client imperatives, Hari has inspired capable teams to push beyond their comfort zones and deliver meaningful business outcomes powered by state-of-the-art Analytics and Data Science practices.

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