Director of Data and Analytics at Bridgestone Americas

How important is data and data analysis in this new age of technology?

In today’s business landscape, data plays a critical role in revealing valuable insights that can directly impact a company’s operations, especially in the face of uncertainties such as those posed by COVID-19 or supply chain disruptions like the recent Ukraine prices. Leveraging data and analytics can help businesses manage uncertainties while minimizing their impact on operations.

Apart from this, data and analytics can also help companies increase their operating profit and revenue while reducing costs such as operating expenses and capital expenditures. Additionally, businesses can use data to enhance customer intimacy by going beyond traditional surveys like NPS and conducting end-to-end analysis to understand how customer satisfaction affects revenue.

It is crucial to note that having happy customers does not always guarantee revenue, and it’s essential to conduct an end-to-end analysis to understand the link between customer satisfaction and revenue. Thus, data analytics can help businesses manage uncertainties, increase operating profit and revenue, reduce costs, and enhance customer intimacy, ultimately leading to better decision-making.

The importance of data-focused strategies has led to a surge in the number of companies utilizing analytics in various areas, such as IoT, to collect real-time data and improve product development. Capturing data in real-life helps create digital twins, enabling businesses to understand product conditions and improve product development continually.

Data is vital at every stage of the game, from R&D to better decision-making, managing uncertainties in the supply chain, and enhancing customer intimacy, all leading to better revenue, increased operating profit, and reduced costs for businesses.

As the head of data and advanced analytics, what are the current challenges that you see in the industry, and how can you solve them?

The challenges of managing uncertainties, creating new products, improving customer intimacy, and enhancing decision-making require businesses to invest heavily in data and analytics. However, the fundamental challenge that most industries face is data quality. Silos between manufacturing data, supply chain data, and R&D data have been longstanding issues.

To overcome this, companies need to break down these silos and prioritize data quality by establishing data stewards and owners.

To improve data quality, businesses need to develop data platforms, such as data lakes, that can capture, process, and check data quality from source IP systems. Moreover, businesses need to implement processes that align with compliance and ownership requirements. These elements are still in the development stage across the industry, and businesses must focus on transparency, ownership, and data quality.

While data is considered an asset, it lacks owners compared to other assets like a manufacturing plant, real estate, or stocks. Businesses need to develop data literacy programs across the organization to improve data ownership and usage. Democratizing data usage across functions and training data-savvy individuals to become data scientists or analysts will enable businesses to create insights and dashboards ad hoc, rather than relying on a central function.

Data and analytics should also bring in machine learning experts, data architects, and data scientists to create high-level ML and AI use cases that add value and standardize certain dashboards. Businesses must prioritize data quality, transparency, and ownership to overcome silos and develop a more comprehensive end-to-end understanding of their operations.

What advice would you like to give out to your fellow data scientists in the industry?

The perception of data science varies among business heads and organization heads. I would say it is essential to recognize that data scientists are not models themselves but are responsible for creating models that add value to the business. The ultimate goal is to create value, not just models.

To achieve this goal, a simple two-by-two matrix can be created to map the value cases or use cases being worked on. The x-axis consists of indirect and direct impact, while the y-axis includes tangible and intangible measures. Direct and tangible measures have a direct impact on customers and the profit and loss (P&L) statement, which should be measured by the CFO organization. The other three squares may not have a direct impact, but data science teams should work on measurable KPIs to mature those use cases over time and bring value to the organization.

For newcomers and data scientists, it is crucial to shift their thinking from merely creating models to creating an impact. They should focus on understanding the business and how their models can add value. It is essential to learn about validation and how to make an impact in the organization. On the other hand, business heads must understand how to incorporate the two-by-two matrix into the cost structure and how data analysts and data scientists can create value for the organization. What needs to be remembered is that the key is to shift the focus from creating models to making an impact that adds value to the business.