The devices, sensors, and machines found in industrial organizations all generate a massive amount of real-time, performance-tracking, and other unstructured data. 

For example, an offshore oil platform usually generates around 2 terabytes of data daily. 

The average manufacturing company generates more than 1800 petabytes of data annually, twice as much as the next closest industry. 

All this data is a valuable goldmine of information that can be leveraged powerfully when made accessible to the right stakeholders. This data, in the right hands, can be used to improve operations and grow the organizational bottom line. 

Using a manufacturing data lake service with the right architecture can make all the difference to ensuring data democratization within a manufacturing-based business. 

But first, a look at the data challenges within the manufacturing sector. 

Data Challenges Within Manufacturing

The manufacturing sector faces multiple challenges pertaining to how data is handled. Here are some: 

  1. Much data is stuck within silos and is not accessible to relevant stakeholders. 
  2. Missing data points hinder accurate analysis. 
  3. There is a lack of standardized data definitions among teams.
  4. Data from multiple sources does not align seamlessly.
  5. Legacy systems are common and do not integrate well with modern technologies.

The major reason for these data challenges is the use of legacy processes that are not capable of surfacing the right information at the right time. Using a modern manufacturing data lake architecture that makes information accessible can help overcome these challenges and democratize data access within a manufacturing business.

What Data Lake Architecture is Suitable for Manufacturing?

Here is an example of a data lake architecture suitable for a manufacturing-based business. 

Manufacturing Industry Data Lake Architecture

The above is an example of a data lake architecture by Amazon Web Services (AWS). There is allowance made within the architecture to handle information from diverse sources including IoT devices and automation equipment and the ability to create useful visualizations and dashboards usable by analysts and data scientists. It is possible to set permission levels for data access and to archive data when required. 

The data lake can access data from the ERP software and ingest data points from factory sensors. 

Such a data lake architecture ensures the creation of the right kind of data and its access by relevant stakeholders. A trusted Amazon partner like Aspire Systems can help set up a similar data lake service architecture.

Can All Manufacturing Units Benefit from a Data Lake Architecture? 

Manufacturing units, regardless of size or scale can benefit from the data access enabled by a data lake architecture. 

Smaller units would do well to incorporate a data lake platform at the outset. It would serve them well as they increase in scale and the volume of data handled from various sources increases over time. 

Using a data lake architecture is essential for larger units due to the large volumes of data generated daily. Using a data lake service will empower such businesses to understand the level of efficiency of operations and make crucial data-backed decisions powered by machine learning. 

What Insights Can a Manufacturing Data Lake Provide? 

A manufacturing data lake platform can provide valuable insights useful for improving decision-making, efficiency, and operations. 

  • A manufacturing data lake can analyze equipment data and predict which machines are likely to fail, thus reducing maintenance costs and downtime. 
  • A manufacturing data lake can improve manufacturing quality through analysis of production data to pinpoint areas for improvement or defects. 
  • A manufacturing data lake can improve supply chain management and ensure timely delivery of materials with real-time monitoring. 
  • A manufacturing data lake can monitor energy consumption data and help in reducing energy usage and costs. 
  • A manufacturing data lake can analyze data from multiple sources simultaneously and pinpoint ways to streamline operations and enhance overall factory performance. 
  • A manufacturing data lake can ensure worker safety by monitoring equipment status and environmental conditions in real time. 
  • A manufacturing data lake can keep track of the performance of assets and their health to optimize their use and extend their useful life. 

As evident, using a manufacturing data lake architecture can provide valuable insights to businesses to enhance their performance and the quality of their products. 

Final Word 

Using a data lake service with the right architecture can help break information silos within a manufacturing-based business and provide the right kind of access (with permission levels) to the right stakeholders. It helps that data lakes can handle structured, semi-structured, and unstructured data from IoT devices, production logs, and enterprise applications. Manufacturing businesses of all sizes can leverage a data lake architecture to ensure operational excellence, better products, and customer delight, all while democratizing data access.

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