Kyle Pudenz, SVP, Enterprise Analytics at Cencora, speaks with David Zhao, Managing Director of Coda Strategy, in a video interview about the role of data speed in business decision-making, addressing supply chain challenges, and balancing real-time insights with long-term strategic planning.

Defining speed in supply chain operations

Speaking of turning raw data into market speed, Pudenz states that understanding the true need for speed and how it varies across different contexts is crucial. For instance, in supply chain distribution, where pharmacies rely on up-to-the-minute information to serve patients, real-time or near real-time data is essential, he adds.

Conversely, Pudenz says that manufacturers tracking product movement through the supply chain may not require real-time data. Instead, they need reliable data no more than a day old to assess challenges and collaborative opportunities while analyzing product movement. In this scenario, speed is defined differently, he notes.

The role of data in long-term strategic analysis

For more strategic, long-term analysis, such as customer segmentation or market trends, data needs to be aggregated and contextualized over extended periods. In these cases, real-time updates are not necessary; instead, models may be refined over weeks as additional downstream events provide further context.

Sharing a key takeaway, Pudenz states, “Speed is all relative given the specific challenge that you are trying to solve. He cautions against over-indexing on making all data real-time but rather focuses on indexing for the right level of latency.

Additionally, Pudenz states that making everything real-time might also lead to increased noise. It is critical to synthesize the information and context to ensure making the right strategic adjustment.

When asked to share a data-driven business use case, Pudenz highlights the persistent business problem of customer challenges related to product availability.

In the pharmaceutical industry, supply chain challenges existed even before COVID-19, but the pandemic significantly worsened them. In the post-COVID landscape, supply chains have yet to fully recover, and product availability struggles.

However, patients need medication, and the product must be available when needed, says Pudenz. To address this, Cencora is building solutions that help identify the immediate impacts on that supply chain upstream.

Delving further, Pudenz recalls an incident where a tornado hit a manufacturing plant, immediately taking it offline. This required the organization to comprehend the impact of product availability.

Addressing product availability challenges with data

Apart from that, long-term challenges include the inability of a manufacturer to deliver products on time, which is an accumulating problem, says Pudenz. “Speed in that scenario is really understanding the longer-term implications,” he adds.

Adjusting to anticipate where there might be a looming challenge is a different level of speed, says Pudenz. He notes that Cencora has built a solution that considers two different extremes — one tactical and the other on the strategic side, enabling proactive action.

Speaking of balancing product availability with speed, Pudenz says, “In terms of data, speed is really dependent on the specific situation you may be encountering.” For instance, he adds that weight loss drugs have gained significant attention in the market, for which, in the short term, there is an urgent need for order fulfillment to meet demands.

However, a broader perspective is needed to analyze long-term trends such as evolving order patterns and customer demographics, says Pudenz. In such cases, recognizing shifting trends is more essential than honing in on real-time data.

He maintains, “It is super important that you are able to act on those with some sense of speed.” Pudenz believes that is where it is crucial to have a complimentary understanding of what is the right data set. Instead of a trade-off, it is about using the right type of data for the right purpose.

Contrasting data needs: Supply chain vs. finance teams

Next, Pudenz shares two distinct use cases for data speed and analysis within the organization. From the supply chain perspective, he states that the team does a phenomenal job in such scenarios.

The supply chain team relies heavily on real-time or next-day data, monitors daily order patterns, and analyzes what has happened and how it aligns with recent trends. This allows them to manage inventory, coordinate with manufacturers, and plan future orders to maintain optimal stock levels.

On the other hand, the finance team takes a broader view, focusing on the impact from a financial perspective, and their visibility and timelines are different, says Pudenz. They may not require next-day data but instead conduct weekly or monthly reviews to understand market shifts, update projections, and evaluate the impact on the broader organization.

Concluding, Pudenz stresses that these two examples illustrate real-time to longer-term planning, demonstrating how the need for data and speed differ for each team.

CDO Magazine appreciates Kyle Pudenz for sharing his insights with our global community.

Don’t Start with Analytics and Look for a Business Problem — Cencora SVP Enterprise Analytics