From dirty data to AI-ready: Building a unified manufacturing data ecosystem

As middle market manufacturers seek opportunities to integrate artificial intelligence tools across operations, many will need to address a critical barrier that stands in the way: the quality, accessibility and security of their data.

Manufacturers, especially those in the middle market, face unique hurdles when it comes to data management. Unlike their larger counterparts, midsize manufacturers often operate with limited capital expenditure budgets and rely on legacy systems that don’t communicate seamlessly. The result is fragmented data spread across multiple platforms, whether in the form of corporate enterprise resource planning systems, specialized plant floor equipment or supply chain management tools.

Clear data management processes are especially important considering manufacturers generate enormous amounts of data from production lines, inventory systems, supplier networks and customer interactions. While access to vast amounts of data can be a powerful advantage, it requires companies to harmonize disparate data sets to derive meaningful insights. The challenge lies not only in the sheer volume of data, but in the complexity and diversity of systems that generate it. Bringing data together from various sources into a unified data ecosystem is critical to enabling AI tools to harness this web of information.

Why data quality matters: The AI imperative

Manufacturers eager to deploy AI-driven solutions often discover that their data infrastructure is inadequate. Incomplete, inconsistent or siloed data—that is, dirty data—cannot be effectively analyzed, monetized or used to power predictive models and automation. Conversations about implementing AI often circle back to the need for clean, harmonized and accessible data.

AI’s potential—from predictive maintenance and intelligent supply chain optimization to enhanced customer engagement and agentic AI—relies on robust data foundations. Without this, manufacturers may invest in pilots or projects that fail to deliver long-term value, leading to wasted resources and canceled initiatives. The inefficiencies that result from poor data management can hinder technological progress and erode competitiveness in an increasingly digital marketplace.

Companies are aware of the issue; concerns about data quality were the top challenge in using generative AI among respondents to the RSM Middle Market AI Survey 2025: U.S. and Canada.

Building a foundation for AI success: Key areas of focus for manufacturers

To unlock the full potential of AI and advanced analytics, middle market manufacturers must embark on a journey to modernize their data architecture and improve their data governance practices. This starts with addressing the core issues of eliminating data siloes and ensuring data completeness and consistency.

Below, we highlight some specific areas—and accompanying action items—manufacturers should focus on to ready themselves for opportunities that AI tools and capabilities might bring:

• Data centralization: Extracting data from across the organization, including structured and unstructured information, and centralizing for enterprise delivery of information, creates a single source of truth and helps enable better decision making at scale. Transitioning data to the cloud for this centralization means the data architecture is scalable and flexible in a way that can more easily integrate future sources of information with dashboards and other reporting. As this centralization increases connectivity between previously isolated systems, securing these connections will be increasingly important. Data centralization, then, is foundational for AI usage and expanding that usage as more data becomes available.
• Data collection and organization: Within the data warehouse, manufacturers need to implement data modeling, cleansing and transformation efforts to enable more efficient, self-service analytics and AI-driven insights for end users. Having a single source of truth from an organized data repository reduces the dependency on manual reporting and scalable architecture models can make it easier to integrate AI capabilities.
• Modern tools and technologies: Manufacturers should harmonize native processes and toolsets within the new data architecture and introduce automation and AI tools where applicable to reduce manual efforts. By adopting a unified approach to tools and technology, manufacturers can enable greater agility, faster innovation and more informed, data-driven decision making. Further, as manufacturers modernize some of their office and plant systems, selecting the right systems to support this improved connectivity and security becomes increasingly important.
• Data governance and quality: Implementing data governance procedures requires defining and establishing data ownership and stewardship practices, enforcing robust data management standards such as access controls and quality management guidelines, and developing a comprehensive data and reporting catalog.
• Data security: The power of AI brings data and insights to users’ fingertips faster than ever, but this access also brings risks. Many AI tools are access-aware and can inherit data permissions from existing file servers and sites. However, businesses commonly suffer from data sprawl and access creep across these locations, with file and folder permissions more open than originally intended. When these permissions are not appropriately locked down, users can quickly find sensitive data that they didn’t know they had access to, and that they might not be authorized to see. Further, as manufacturers connect previously isolated systems to access their data for AI-driven insights, the security of those connections and systems must be checked.
• Capabilities and training: Standardizing documentation of the organization’s data infrastructure—including platforms, technologies and key operational workflows—can help with upskilling, training and role expansion as needed. Upskilling, in particular, can equip team members with skill sets and an understanding of best practices for advanced analytics competencies.
• Self-serve data analytics: Manufacturers should encourage employees to use reporting and analytics, toward the goal of normalizing data-driven decision making. Embedding analytics into daily workflows can empower teams to embrace this information and ultimately improve visibility into key performance indicators, outcomes and trends.

The path to data transformation

Transforming data processes is not a simple undertaking, and advisors with backgrounds in analytics, data strategy, cybersecurity and technology implementation can play an important role in guiding manufacturers through the process, whether building data foundations from scratch or optimizing existing systems.

As the industry continues to evolve, data transformation will become even more of a nonnegotiable investment for manufacturers seeking to remain competitive. Improved data visibility and unified reporting frameworks ensure stakeholders can access accurate, timely insights across functions, strengthening both operational control and strategic planning. Middle market manufacturers must recognize the importance of complete, consistent and integrated data as the cornerstone of their digital strategy and any AI-driven efforts in the future.

RSM data and analytics supervisor Diana Dale contributed to this article.