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Governed Data Integration for Manufacturers

ABSTRACT: This blog defines governed data integration and describes how it enabled two manufacturers to synchronize data flows from the factory floor to the customer.

Sponsored by Semarchy

It’s not easy to build things these days. Picky consumers demand fast delivery of perfect products, with little sympathy for the component shortages, factory shutdowns, or trade wars that stand in the way. Manufacturers struggle with siloed, inconsistent datasets as they try to forecast demand and streamline production. Their complex data environments also can slow collaboration with suppliers and shippers. And expanding regulatory requirements can complicate how they handle trade, safety, and security issues. Challenges like these prevent manufacturers from synchronizing operations and hurt their ability to compete in the 2020s.

To overcome such challenges, manufacturers must consolidate and standardize their data environments. They need to build unified views of their customers, plants, and logistical networks that help plan and fine-tune production schedules with greater precision. They need to share accurate, governed data as they collaborate internally and externally while still complying with rigorous regulatory requirements. Manufacturers that meet these requirements can build a more flexible and efficient foundation for growth.

Governed Data Integration Can Help

Manufacturing firms adopt the discipline of governed data integration to build this foundation. Governed data integration is the convergence of three processes: discovering data across the enterprise, integrating it for analytics or operations, and governing it with master data management (MDM) and cataloging.

Governed data integration platforms help design, execute, and monitor all these tasks as part of automated workflows, using a graphical interface, monitoring, and alerting. As individual tools merge into these multimodal platforms, they enable cross-functional teams to collaborate, simplify processes, and democratize data access. They also improve data quality to support both operations and analytics. This blog defines governed data integration and describes how it enabled two manufacturers, a global carmaker and the lingerie company Chantelle Group, to synchronize data flows from the factory floor to the customer.

Breaking It Down

First, let’s break down how governed data integration handles data discovery, data integration, and governance to help manufacturers gain 360-degree views of the business.

Governed Data Integration

  • Discover. Data engineers, and possibly data analysts or data scientists, search, expose, and profile various tables, files, and IoT sensor feeds in their sprawling environments. They evaluate these assets—describing products, suppliers, shippers, and so on—based on metadata such as file names, tags, lineage, usage statistics, and schema.

  • Integrate. Next, the data engineer ingests and transforms data from sources such as legacy databases, ERP systems, software as a service (SaaS) applications, or Internet of Things (IoT) systems. They reformat, merge, filter, or (re) structure the datasets to prepare them for analytics and operations.

  • Govern. Data engineers and data stewards cleanse data by identifying and fixing errors. Then they implement MDM by matching and merging duplicate records. Finally, data engineers, data stewards, and other stakeholders catalog the associated metadata to support governance policies.

Business managers and analysts alike can use this governed, integrated data to drive their decisions and actions more confidently.

Case Studies

Now we explore two actual case studies, our European carmaker and Chantelle Group, to understand what this looks like in action. We will call the carmaker Eurocars.

Eurocars

Eurocars accumulated technical debt over nearly a century of operations. Its data resided in research centers, factories, and offices on three continents, resulting in different versions of the truth. These fragmented views impaired the ability of business stakeholders to analyze performance, adjust operations, and ensure regulatory compliance. Even as its popular cars and trucks rolled off assembly lines, the company lacked the flexibility to prepare for the future. Its business and IT teams needed to modernize their data environment to adjust supply chains and enact sustainability programs in an agile, efficient manner.

Eurocars adopted a governed data integration program to meet these requirements. Its data team integrated, matched, and merged data across 13 business domains—products, materials, retailers, and so on—to create golden records. IT and business stakeholders now collaboratively maintain this single version of the truth on a flexible cloud platform. Data engineers deliver more accurate data to analysts, who in turn generate holistic business views for key decision-makers. Eurocars reduced costs by eliminating redundant data and reduced compliance risk by improving data governance. Logistics teams can change components, and factory managers can change production schedules, all with less effort and disruption.

Chantelle Group

Shoppers have become much more demanding since Chantelle Group started selling lingerie in 1876. They want the right model and the right fit when they walk into the store, and know they can find an alternative fast if they don’t get it. This modern reality forced Chantelle to adopt a governed data integration program. Chantelle integrated and mastered its data across back-office, front-office, and partner systems. This created a unified and governed view of the business, enabling various internal and external stakeholders to operate more efficiently. Retailers, for example, can check inventories in real-time, predict demand, and order missing units to keep shelves fully stocked. And Chantelle can correlate more data points to build reliable demand forecasts. (Also read Semarchy’s case study on Chantelle.)

Summary

Manufacturers will always be vulnerable to material shortages, mechanical risks, and political tension. But firms that integrate and govern the data that describes the many aspects of their business stand a better chance of navigating such challenges. They can streamline their operations, then anticipate and adapt to inevitable changes that arrive. A governed data integration program increases their odds of success.

Kevin Petrie

Kevin is the VP of Research at Eckerson Group, where he manages the research agenda and writes about topics such as data integration, data observability, machine learning, and cloud data...

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