Implementing Data Governance in the Manufacturing Industry

By Matt Turner

Published on May 12, 2022

Three construction workers wearing hard-hats checking out a piece of manufacturing equipment.

Every company is a technology company. Or, rather, every successful company these days is run with a bias toward technology and data, especially in the manufacturing industry. With so much economic uncertainty, coupled with the unrelenting advance of “Industry 4.0” technologies, manufacturers must deploy the right technologies and, most importantly, leverage the resulting data to make better, faster decisions. But without the right data practices in place you run the risk of misusing data and missing opportunities.

A technical engineer typing in her laptop computer in a technical facility

Data governance is the foundation on which manufacturers ensure the effective use of valuable data by giving you the ability to handle, manage, and secure your data. But to deploy sound governance practices requires a thorough understanding of data governance in manufacturing, how its value can be realized, and how it can be used to enhance security, increase operational efficiency, reduce costs, identify risks, and so much more.

What are the benefits of data governance in manufacturing?

At the most basic level, data governance promotes behavioral compliance with respect to data. Think of it as a framework for setting clear expectations on how your teams should work with data. Data governance ensures that:

  • Your people find and analyze the right data, so that

  • All personas access and use data appropriately, and

  • Manage data effectively and efficiently

Data governance in manufacturing, when done well, can then accelerate time-to-market, increase efficiencies, optimize operational performance, improve the customer experience, and expose opportunities up and down the supply chain.

Here’s how.

Centralize, optimize, and unify data

Your manufacturing operation generates a huge amount of data every day. Add to that the data from sales, support, engineering, field service, finance, and even partners and suppliers, and it’s a complex jumble of data sources, locations, and access rules. It’s also too much for any single team of humans to possibly manage.

Puzzle displaying how support, sales, engineering, field, finance, partners, and suppliers are all connected puzzle pieces surrounding the “All data” missing piece.

Today’s manufacturers need the help of artificial intelligence (AI) and machine learning (ML) to analyze large and complex datasets rather than spending valuable time searching across silos for the right data. By implementing intelligent data governance tools, your teams can make more effective decisions because they have access to data from across your organization.

Improve the 1:1 customer experience

The customer experience is fast becoming a key differentiator for manufacturers. More and more, your commercial customers expect the same level of service they receive from consumer brands. This requires instant access to comprehensive data across inventory, logistics, order processing, product compatibility, partner channels, and so much more. Data governance in manufacturing helps you develop the data management and data usage practices that ensure the right information is readily available in service to your customers.

Accelerate onboarding of new data workers

Putting data to work is a modern business imperative. In fact, a recent survey on data practices found that 59% of companies have adopted data literacy programs across most or all of their organization. These types of programs are intended to build a data culture where workers default to using data to drive better decisions.

Such programs also allow new hires to hit the ground running with data. A data culture can bring new workers quickly into the fold by making it easier to find, understand, and access data for even the uninitiated. Tools like a data catalog give new workers an intuitive interface to understand data in plain language, see how data is being used, and get connected to those who know the data best.

Manage and mitigate risk

Manufacturing organizations have regulatory laws to follow, including data privacy regulations such as GDPR and CCPA. It’s therefore important to have an automated data governance system, such as a data catalog, that classifies sensitive data, enforces data rules, and alerts data leaders when access or other rules are potentially infringed. These types of controls not only reduce risk for your company, they help protect you from the potential monetary penalties and reputational damage that can result from a regulatory violation.

Enrich the quality of data

High-quality data supports manufacturing leaders in making more informed decisions, doing it faster, and being more confident in the outcomes. More tactically for manufacturers, higher quality data gives you the insights to lower operational costs, improve product cross-selling, and give customers more accurate and faster service and support. Good data governance practices and enforcement then directly improves customer satisfaction simply by reducing errors and improving overall customer experience.

Centralize data storage systems

Centralization of your manufacturing data prevents data sprawl and helps ease regulatory compliance. You’re simply responsible for fewer data sources, which makes data management and data governance that much easier. Data governance best practices for manufacturing call for data sources to be centralized, which is why so many companies are migrating data to the cloud. Beyond just data centralization, however, manufacturers can reduce data management costs, ease data backup, and give IT teams more time to focus on improving and facilitating data governance efforts.

What are the data challenges facing manufacturers?

Manufacturers are generating mountains of data not only from your business, but also from an increasing array of internet of things (IoT) devices; innovative manufacturing technologies; connected suppliers, vendors, and partners; and increasingly connected customers. Without curating and standardizing the data residing in disparate systems, you can’t easily connect your operational data with data incoming from partners and use it to reach your overall corporate goals. But it’s not as easy as flipping a switch because manufacturers face unique challenges with respect to data and data governance.

Scene from “I Love Lucy” where Lucy and Ethel are working in a kitchen line gathering all the chocolates and stuffing them inside their blouse.

Ineffective monitoring systems

Your operational activities are undoubtedly collecting data and generating reports. But equipment that’s just a few years old, let alone decades old, may already be unable to support today’s data requirements. It is therefore difficult for manufacturers to obtain critical information about production or product issues because data is missing or incompatible. Effective data governance in manufacturing can identify these types of gaps and interruptions so your teams can get to work mitigating them.

Inefficient workflows

Efficiency is an unending goal for manufacturers and manufacturing processes. There’s always room for improvement. But the days of relying on gut feel and judgment when making decisions have been replaced by a data-driven framework. An automated and systematic approach to data governance in manufacturing can prevent quality issues, identify production bottlenecks, and improve time management issues so you have the data to continuously and confidently improve efficiency.

Difficulties optimizing the global supply chain

Especially today, supply chain management challenges are a key concern for every manufacturer. Ongoing supply chain disruptions continue to impact operations around the globe. That makes it especially difficult for manufacturers with international suppliers and vendors. An automated and intelligent data governance strategy ensures you have access to the data needed to optimize your global supply chain.

TJX provides a great example of data governance best practices in a global supply chain. The department store company imports many of its manufactured products from China. With pandemic-induced shortages and disruptions, the company was obviously keen to accurately estimate shipment lead times. But TJX had a flood of data points related to every stage of shipping, making it difficult to decipher and know which data point was most relevant. Searching for an “arrival” date, for instance, returned as many as five data points. So the company pushed its data governance program to standardize business definitions and tag subject matter experts for any questions. The result: TJX not only eliminated hundreds of hours wasted on data searches, it gained confidence in its analyses and generated more accurate shipment lead time estimates.

6 Best Practices for Implementing Data Governance in the Manufacturing Industry

So now what? It’s time to start creating your own data governance program. Here are seven data governance best practices in manufacturing to jump-start your efforts.

1. Train and educate your manufacturing workforce.

Building data literacy across your operations is critical to empowering workers to find, correctly interpret, and leverage data across your organization.

2. Standardize data fields across databases.

Your teams must be confident in the data they are using, even at the most fundamental level of what a field is termed and what that term means across your organization.

3. Integrate a defensive and offensive data strategy.

Data defense minimizes risk while data offense ensures data is used to support business objectives. Be sure to consider both when developing your data governance strategy.

4. Utilize a data catalog.

It combines metadata with data management and search tools so your workers can quickly find the data that they need, evaluate its fitness for intended use, and access those who can answer questions. Along the way, it keeps track of data usage and provides additional controls to help data governance efforts.

5. Establish security controls.

Effective data governance supports effective data security even if by simply locating and understanding what data is available. A good data governance program will easily integrate with and inform your security programs.

6. Implement an automated data governance strategy.

Modern data governance tools use AI and ML to help automate and manage data policies, track and report on data access and data workflows, and enhance data stewardship activities.

How Alation supports data governance in the manufacturing industry

Manufacturers have an abundance of data from a variety of internal and external sources, manufacturing and production technologies, and an ecosystem of suppliers, vendors, partners, and customers. To become truly data driven requires you to find that data, understand it, and put it to use appropriately. It’s a tall order, but a data governance program is your first step and Alation Data Catalog and Alation Data Governance solutions can help.

Alation enables manufacturers to develop a common business language the entire company can use to improve, optimize, and harmonize operations. By facilitating and easing data governance, you can curate and standardize the data residing in disparate systems, silos, and locations, and then link operational activities with overall competitive strategy and market imperatives. Specifically for manufacturers, Alation:

  • Delivers “Industry 4.0” to your company by linking IoT data with operational and business data, easing adoption of new technology, and speeding development of new products.

  • Accelerates product delivery and improves inventory management, helps you adjust to dynamic markets, and streamlines the mechanics of logistics, fulfillment, and distribution.

  • Standardizes data across the supply chain, which improves collaboration with suppliers and vendors, streamlines sourcing, and optimizes production and delivery processes.

Learn more about Alation for Data Governance in Manufacturing and how our solutions are helping manufacturers like Polaris, Motorola, Parker Hannifin, GE Aviation, and others.

  • What are the benefits of data governance in manufacturing?
  • What are the data challenges facing manufacturers?
  • 6 Best Practices for Implementing Data Governance in the Manufacturing Industry
  • How Alation supports data governance in the manufacturing industry


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