Building Data Supply Chains

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Our world is becoming more interconnected through various services (including Artificial Intelligence), making us increasingly dependent on one another. Businesses rely on a series of steps, or a value chain, to deliver their products and services, and managing this chain is crucial for success. This is why supply chain management emerged in 1982—to help businesses gain better visibility into their operations and manage them effectively to achieve stronger results.

I came across a post in which Chad Sanderson argues that data is more like Supply Chain Management than Software Engineering. He listed the following similarities between Data Pipelines and Supply Chain Management:

1. Workflow Management: Both data and supply chains rely on coordinating a series of steps to transform raw inputs into valuable products.
2. Quality Control: Ensuring quality at every stage is crucial to prevent errors and maintain the integrity of the final output.
3. Inventory/Storage Management: Effective resource management is needed to balance capacity and demand, whether for physical goods or data storage.

Let's extend this concept from an internal technical data pipeline to an end-to-end business process.

Data Supply Chains across the Entire Organization Ecosystem

Last week at the Innovate Americas event organized by TM Forum , I led a master class on Building Data Supply Chains; Enabling Sustainability and Beyond. We discussed building end-to-end data supply chains across an organization's value through secure collaboration for different use cases.

If we were to visualize a telco value chain as a connected entity, then it would look like this.

There are lots of data collaboration opportunities that could be brought into the telco's value chain. This can help improve business process visibility. Such collaboration must occur without creating additional copies of data to ensure the metadata can be shared across the chain.

This is a telecommunications example, but similar data networks can be built across all industries. It is important to visualize the businesses that make up your data networks.

Benefits of Building a Data Supply Chain Across the Value Chain

1. Holistic Visibility: Creating a data supply chain allows for complete visibility into data flow across the company's value chain, enabling better decision-making and strategic alignment. Business requirements, such as Scope-3 Emissions reporting, can only be fulfilled with an end-to-end Data Supply Chain.
2. Enhanced Collaboration: A unified approach fosters collaboration between teams and organizations, breaking down silos and ensuring that data is accessible to everyone who needs it, which enhances efficiency and innovation. This becomes critical in the public sector to foster collaboration in public policy-making.
3. Improved Data Quality: Quality checks can be implemented at multiple stages, leading to more reliable and accurate data.
4. Agility and Responsiveness: A well-structured data supply chain enables organizations to respond quickly to market changes and customer needs by leveraging real-time data insights.
5. Data-Driven Culture: Establishing a data supply chain promotes a culture of data-driven decision-making across the organization, empowering employees at all levels to leverage data in their roles.

Summary

Building a data supply chain across the entire organization value chain ensures that data flows efficiently and is accessible, accurate, and valuable at every step. This approach enhances collaboration, decision-making, and agility, ultimately driving better business results while fostering a culture of data-driven success. Seamless collaboration across multiple organizations is no longer a nice to have but a critical business requirement to deliver key insights and fulfill regulatory requirements in a timely fashion.

I write weekly on different topics related to Data and AI. Feel free to subscribe to FAQ on Data newsletter and/or follow Fawad Qureshi on LinkedIn or FawadQureshi on X.