The Importance of Data Reliability

In part two of their conversation about data reliability and digital transformation, Girish Bhat, Acceldata Senior Vice President of Marketing, and Ravi Prasad, Novartis Global Head of Data, AI Strategy and Operations discuss the leadership and strategy necessary to drive the transformation effort. Critical to that effort is ensuring the reliability of data, and managing data pipelines, and these two thought leaders explain the necessary steps required of data teams. Prasad offers an insightful perspective on this by explaining how digital operations and data have been critical to Novartis’ transformation journey. 

One topic that Bhat and Prasad drill into is organizational management, which they note is especially crucial at times of dramatic change. Attracting and retaining talent is clearly important, but Prasad discusses the need to empower data team members with the freedom to be creative in their problem-solving and to foster an environment that prizes innovation.

Prasad suggests that, from a process standpoint, alignment with the business is key and that it is critical that both business and technical teams are working towards a two-pronged approach that seeks to do the following:

1. Develop operational and cost efficiency.
2. Actively engage with customers and enhance the speed of service.

When it comes to product development and technology innovation, Prasad explains that Novartis has maintained a focus on these things, but also invested heavily on the infrastructure side as well. However, embedded in his decision-making, he says, is a focus on ensuring the reliability of the data and building trust across all stakeholders. He goes into detail on his recommended steps:

1. Create a robust internal data lifecycle process and data pipeline
2. Adhere to strict privacy, security, and ethical standards
3. Ensure application readiness. The accessibility of data is critical.
4. Develop transparency with external data platform providers

Prasaid says that Novartis’ data team has many data pipelines and with the digitization of services, he anticipates them to grow exponentially. Additionally, he explains that his organization is looking to leverage ML and other data science methods to automate and scale existing pipelines.

Learn more about how to manage data pipelines effectively with a demo of Acceldata’s data observability platform.

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Photo by Matt Duncan on Unsplash

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