Data management in manufacturing: the difference between being data-driven and data-burdened

In today's digital world, the ability to create business value from data makes it one of the most prized assets of any organization. With the right data, businesses can make informed decisions, improve operations and gain a competitive advantage, to name just some of the benefits.

But simply having data is not enough. Like your people, your data must be managed as a strategic asset. Often, this can be a challenge—especially for manufacturing organizations that, today, are dealing with exponential growth in the volume of data and whose operations are becoming increasingly reliant on making sense of all that data.

Here's where data management becomes a key pillar to becoming a truly data-driven manufacturer.

What is data management?

Data management is the process of collecting, storing, protecting and maintaining the integrity of your organization's data. It involves creating policies, procedures and systems to ensure that data is accurate, complete and accessible when you need it. As a data-driven manufacturer, effective data management underpins your ability to anticipate disruptive events and react strategically and quickly, becoming an agile and resilient organization.

Data management is about having an action plan for your data through its entire life cycle. Let me explain the various phases:

Phase 1: Governance

Data governance is the overall management of data, including who has access to it and how it is used. This includes putting in place policies and procedures for data collection, storage and access. Strong data governance strategies ensure that your data is accurate and complete, you can access it in real-time, and it meets all regulatory and compliance requirements.

Phase 2: Quality

Data quality refers to the accuracy, completeness and reliability of your data. To make effective decisions, you must have high-quality data that is free of errors and inconsistencies and properly validated and cleaned.

Phase 3: Security

Data must be managed securely across its entire life cycle, from the very beginning, when data is generated, until its obsolescence. This is particularly crucial in today's increasingly complex and digital manufacturing environments. Protecting your data and minimizing the risk of data breaches and cyber attacks requires implementing security measures such as encryption, firewalls and intrusion detection systems to protect data from unauthorized access. Meeting data privacy regulations is another equally important consideration.

Phase 4: Archival and end of life

Data cannot be kept indefinitely. How you deal with data when it is no longer required or reaching its end of life is an essential aspect of data management. It calls for policies and procedures for storing and retaining data for a certain period and then destroying or purging it when it is not needed or is at the end of its regulatory retention period. This is important from both a compliance and cost management perspective.

Connecting the dots with semantic data

As data management evolves, organizations may consider data management strategies that allow for semantic understanding. Semantic data refers to data that is rich in meaning and context and which can be easily interpreted by both humans and machines. It differs from traditional data, which is often siloed and difficult to understand.

In manufacturing, semantic data underpins digital twins or virtual representations of physical objects or systems used for simulation, analysis and prediction. For an accurate and detailed representation, semantic data is typically structured and annotated using ontologies, which provide a common vocabulary and structure for data. This enables better integration of digital twins with other systems like AI, IoT and edge computing to provide real-time insights and predictions and support effective decision-making.  I invite you to read our case study on how a 4D digital twin of the plant is helping a mining client improve worker safety and maintenance efficiency.

Say no to silos!

Manufacturing leaders understand that breaking down data silos across the enterprise directly impacts efficiency and the ability to collaborate and communicate. It also is crucial to achieving true integration from the top floor to the shop floor.

Approaching data management as an enterprise-wide initiative ensures that data is integrated and consistent across the entire organization rather than being siloed in different departments or business units. In doing so, data can be shared, used and reused effectively, while avoiding duplication and enabling better decision-making across the organization.

Setting the standards for data success

As digital leaders pave new paths, markets evolve, and the need to work in ecosystems becomes more pressing, data standards will be fundamental to ensuring data is consistent, comparable and interoperable across different systems and applications. Adhering to these data standards (or the rules and guidelines for collecting, storing and sharing data) will facilitate data sharing across departments and with external partners and ensure that data is accurate, reliable and valid for decision-making.

To summarize, no data initiative or strategy can succeed without comprehensive data management practices and policies. Data management is crucial for organizations to ensure that their data is accurate, complete and secure—giving them a decisive advantage in uncertain times.

Where are you in your data journey? Get in touch with me to discuss the transformative power of data in manufacturing. I also invite you to read the blogs in this series on data-driven manufacturing:

• 4 steps to become a data-driven manufacturer 
• Are you asking the right questions to build your manufacturing data strategy? 
• Enterprise intelligence: Going from “data rich” to “insights rich” in manufacturing