Uber wants to empower each one of its employees so they can make better decisions and forecast outcomes without any particular technical expertise.
Speaking at a recent event, Uber’s Director of Data Science, Franziska Bell said the world’s largest transportation network company would like all its employees to execute their specific job roles role like a data scientist. According to Bell, this would propel the company to gain deep insights from all data sets available at hand, which would lead to superior experience, both for employees and customers.
Uber aims to achieve this through platformization of its resources where teams from different units of the company interact with one another constantly through rich data points. Uber also has cross-functional tools and platforms for its engineering, product, and design that empowers teams to gain insights and create cutting-edge expertise.
Uber’s data science teams have been working on issues ranging from public policy to dynamic pricing. They have been quick to find pain points within and outside the organisation very rapidly. This strategy, combined with an innovative culture, enabled the company to become a highly-integrated organisation that scaled rapidly.
Uber Bets On Historical Data To Achieve Better Forecasting
Uber wants to provide great user experience and in order to do so, the company aims to accurately forecast supply and demand-related metrics in a spatial or temporal, fine-granular fashion. Even though the emphasis is on forecasting critical aspects of the business, Bell said that no forecasting expertise is needed on the employee side.
The most important requirement according to Bell is the historical data — whether it’s in the form of a .CSV file or a link to a query. It’s this focus on data inputs that employees (beginners and experts) can leverage to create the best-in-class data analytics models.
Uber has been busy creating a dynamic platform that provides powerful transportation around the world so highly dependable individuals can easily ignore the unpredictability of the underlying optimisation challenges. To achieve this, Uber has been consistently hiring data scientists who’re open to juggling the two sides of the partition between an engineer and a scientist.
Whether its supply and demand, real-time detection of outages or hardware capacity planning, Uber’s employees are ready to tackle upcoming challenges and create real-time solutions at a high speed.
What Indian Companies Can Learn From Uber’s Vision
India is in a unique position when it comes to leveraging data science and analytics. Indian enterprises have certainly witnessed their fair share of innovation in data science, particularly in sectors such as banking, manufacturing, retail and healthcare. There are a variety of tools being leveraged by Indian companies for forecasting, conversational AI, anomaly detection, facial recognition and computer vision, among others.
The distinction here is that although advanced data science tools are being used in India, those still exist in somewhat disparate projects and don’t necessarily interact with the other departments in a company.
Apart from this, data usually lies fragmented and siloed across different departments of legacy businesses in India. This is a huge impediment to the growth of the company as it would find it difficult to gain valuable insights, say leading experts. According to Digital Radar Report 2019 by Infosys, inability to work across silos and legacy systems remain speed bumps of digital transformation in the Indian context.
Outlook
There is an important lesson to be learnt from Uber’s data science vision. Focusing on the success of Uber, it can be said that businesses need to make their workflows more interconnected through data-driven platformization which lets software tools to communicate and share data across systems.
Digital enterprises can follow Uber’s footsteps to provide data science expertise at the touch of a button and create solutions for all users whether they are data scientists or not. As Indian enterprises prepare for Industry 4.0, what needs is the innovation culture where rich data is collected at all stages of a product’s supply chain to enhance the data analytics models. The data also needs to be connected at all times so business metrics can be measured and analysed in real-time.
