Data Hub vs. Data Lake: 7 Areas Where Data Lakes are Insufficient for Modern Manufacturers

manufacturing data hub vs. data lake

The Manufacturing Data Hub (MDH) presents a sea-change in how manufacturers think about, interact with and use their data. This article compares the MDH to a traditional data lake many manufacturers use for data management and analysis.

Before we cover how a data hub is superior to a data lake, let’s define what we’re talking about.

What is a Manufacturing Data Hub?

The term “manufacturing data hub” is thrown around a lot. That’s why we outlined criteria that must be met in order for a platform to be considered an MDH. For the purposes of this article, we’ll keep the definition simple.

An MDH is a platform that collects all manufacturing data into a centralized system of record based on a standard schema. This gives the end user simplified access to all the data and events that comprise their processes.

What is a Data Lake?

At its core, a data lake is a centralized repository that allows organizations to store and analyze vast amounts of raw and structured data at scale. 

In the last decade, manufacturers have started using data lakes to consolidate data from diverse sources like sensors, supply chain information, and quality control data. Through this consolidation, manufacturers using data lakes hope to enable fully comprehensive analytics and insights to improve operational efficiency, optimize processes, and support data-driven decision-making.

Unfortunately, many of these efforts and aspirations fall woefully short because a data lake simply can’t match the function and capabilities of an MDH in these areas.

Data Lake vs. Data Hub: 7 Key Areas of Differentiation

Data lakes and data hubs offer two distinct ways of managing and accessing manufacturing data. Here are the biggest areas where the differences are most prominently felt.

1. Data Standardization

Standards exist in manufacturing. They are crucial and ensure consistency and interoperability between the software that manages devices, production lines, sites and supply chain systems.

By definition, an MDH operates using a schema based on a well-established standard like ISA-95. This schema acts as a common language for data across systems. As manufacturing operations become even more complex, the need for these standards only grows.

A data lake operates in direct opposition to standardization. It stores raw, unstructured and structured data in its native format. That lack of standardization is at the heart of all the shortcomings left on this list.

2. Data Governance

In manufacturing, data governance is crucial for ensuring data quality, compliance, and security throughout the entire data lifecycle across all areas of the operation. Data lakes again fall short because governance across a multi-site manufacturing operation is completely out of their capabilities.

A manufacturing data hub is specifically designed to understand and enforce the specific data governance requirements of the manufacturing sector, providing standardized, contexualized data models, metadata management, and access controls that align with industry standards and regulations while being flexible enough to align with organizational needs.

3. Scalability

Multi-site scalability is critical. Manufacturing is global, and delivering sustainable improvements across those sites requires data capacity to scale up to millions of events per second.

Data volumes are expected to grow exponentially. With a data lake, it could scale with that data volume vertically; however, you would lack the ability to scale horizontally to new sites and devices.

A manufacturing data hub can offer horizontal scalability with the capability for real-time processing. This means your data infrastructure will be able to deal with the scalability challenges presented by new data sources like IoT devices and sensors in machinery as the MDH expands include to new sites.

4. Security

With new and ever-growing data volumes, the need for security becomes all the more apparent. While data lakes are secured by proper encryptions and access controls at all stages, if you manufacture in a regulated industry, you may find that you’ll need to add additional layers of security.

With an MDH, you get role, attribute, and graph-based access controls and high-level encryption for data both in flight and at rest. Only an MDH can offer the needed security compliance for manufacturers in life sciences out of the box. A data lake requires customization.

5. Support for Event-Driven Architecture

One of the key differentiators of an MDH compared to traditional data storage and management solutions in manufacturing is the ability to support an event-driven architecture, meaning it can respond to events in real time.

A data lake is insufficient for any sort of event-driven processing. By definition, it only stores raw data. Any sort of event-driven processing or manipulation of that data happens outside of the data lake.

6. Speed

As we discussed in the point above, a manufacturing data hub supports an event-driven architecture. This is key for manufacturing where the inability to react in real time to a negative event could mean millions of dollars in scrapped products. Seconds count, and you eat up valuable time when you need to record data in a system for each thing you produce or when you need to wait for schema-on-read processing when fetching data.

An MDH allows for low latency, real-time data processing so your floor operators can keep production moving.

7. Flexible Business Logic

There are many different forms of manufacturing, and there are many different intricacies in data storage and processing required to meet the specific needs of your manufacturing operation.

A data lake doesn’t provide that flexibility. Data lakes are ideal for storing raw data. Beyond that, they fall short of the more advanced needs modern manufacturers have when it comes to building out a faster, event-driven operation.

An MDH provides the means to develop flexible business logic that can be configured to suit the specific operational needs of the business as a whole down to a specific manufacturing process.

Conclusion: Time to Get Out of the Data Lake

When new solutions arise, it’s helpful to compare them to current solutions. An MDH represents an entirely new way to think about manufacturing data. With it, manufacturers can close the gap between insight and action, allowing for faster innovation cycles. For all the reasons we listed in this article, simply throwing raw manufacturing data into a data lake will no longer be sufficient if your organization wants to remain competitive in the years to come.