Understanding the complexities of enterprise data supply chains

Managing data supply chains

When you purchase through links on our site, we may earn an affiliate commission.Here’s how it works.

In an era when organizations are collecting ever larger pools ofdataand deriving information from an increasingly complex array of sources, managing the data supply chain has become a vital function of daily operations in industries as diverse as manufacturing, healthcare, andfinance. Data has never been more essential to businesses, but without a strong data supply chain program in place, companies lack the ability to leverage outside information in a useful way.

Defining supply chains of data

To understand the concept of data supply chains, first imagine traditional supply chain management, which includes the handling of the production and flow of goods and services – beginning with raw materials to the delivery of the finished product. Similarly, data supply chains consist of three stages:

Nirmal Ranganathan is the Chief Architect for Data & AI at Rackspace Technology.

Managing supply chains of data

In the past, the data mostly came from operational data systems, either generated from within customer relationship management (CRM) and enterprise resource planning (ERP) systems or other products that serve businesses as transactional systems. Under this model, the data supply chain was less complex and more easily manageable using a single model.

But in the last five to seven years, especially in the wake of the pandemic, companies understand that they cannot survive on just the data they generate internally. This evolution – the explosion in external data sources – is causing organizations to think about their data using two discrete data supply models: direct and indirect. The direct model consists of:

Adapting to a new data world

So how should organizations adapt to this evolution? With the needs of the enterprise becoming much more interconnected — not only within the company, but also among suppliers, producers and consumers – enabling the direct model and indirect model to interact with each other requires a modern infrastructure, invariably trending toward multicloud.

Today, company data is distributed across multiple clouds and SaaSapplications. Organizations cannot rely on internal data alone to enable better decisions. Rather they must rely more on external data sets from global partners, suppliers and consumers and causal data that interprets the environment surrounding what the organization sells and who they sell to.

To adapt to the data supply chain management evolution, organizations should expand their supply chain of data to leverage all three forms of direct data sets: first-party, second-party, and third-party data. They should also focus on increase the adoption of indirect data sets, both causal and synthetic data. Using both data supply models, organizations can build and leverage modern data architectures and data integration patterns. To get started on the data supply chain management journey, a good first step is developing a data “playbook” that institutes data management best practices while also engaging analytics teams and transformational processes to drive growth. From there, organizations can start to build and leverage more modern data architectures and data integration patterns while maintaining a focus on data that is most critical to the enterprise.

We’ve featured the best productivity tools.

Are you a pro? Subscribe to our newsletter

Sign up to the TechRadar Pro newsletter to get all the top news, opinion, features and guidance your business needs to succeed!

Nirmal Ranganathan is the Chief Architect for Data & AI at Rackspace Technology and responsible for technology strategy and roadmap for Rackspace’s Public Cloud Data & AI solutions portfolio.

7 myths about email security everyone should stop believing

Best Usenet client of 2024

Phishing attacks surge in 2024 as cybercriminals adopt AI tools and multi-channel tactics