(Asia) Sankar Sreeramula, Chief Data Scientist at Asia Pulp and Paper, speaks with Maria Espona, Professor, ArgIQ, and CDO Magazine Editorial Board member, about Indonesia’s data and analytics growth, data-driven decision-making processes, predictive modeling, and characteristics of a data analytics leader.

Sreeramula states that data analytics is a growing trend in Indonesia. Referring to his previous job, Sreeramula recalls being the first data science team member, which eventually became a 52-member team in four years.

Regarding South-East Asia, he believes the Indonesian economy to be a digital economy that is growing fast. Currently, small and medium size industries in Indonesia’s rural and urban areas treat data differently. He cites rural projects such as remote plantations where data is significantly leveraged for business transformation.

Sreeramula notes that data helps organizations make better decisions. In data analytics, people talk about modern analytic insights and tools like dashboards, but they lack contribution to business decision-making, he says. Sreeramula believes it is fundamental to adhere to data regulations and derive decisions for business growth.

In this context, it is critical to re-analyze data if needed to extract meaningful information. He states that one must understand data first and derive business value with the insights. He affirms that data optimization is more important than model optimization.

Sreeramula states that the challenge lies in communicating data with business users. To him, it is a business language rather than a technical language. Considering that, he stresses the necessity to collect relevant data and then explain it well to the business users. 

Next, he explains the technique of predictive maintenance wherein machinery is run continuously and generates predictive insights. He deems it necessary for an organization’s top management leaders to generate quick insights after knowing the predictions and guide their teams better. That is why data decision-making is crucial in analytics, states Sreeramula.

He highlights the necessity of a business leader to have a solid business understanding as a leader, along with industry and data representations. He maintains that most data scientists do not merge the science aspect of data with business. Sreeramula affirms that a good leader should match cognitive science with social science.

In conclusion, Sreeramula says deriving business value from data initiatives is crucial. He urges leaders to create a culture suitable for teams to discover significant threats and drive evidence-based outcomes. With rapid data transformations, leaders should make long-term forecasts while improving data management strategies.