Manufacturing Data Lake
What is a Manufacturing Data Lake in Process Plants?
A manufacturing data lake, like data lakes in other industries, is a way of storing vast amounts of data so that it can be processed and analyzed to produce valuable insights into manufacturing plant operations. It operates as a central repository that serves manufacturing analytics tools.
Data lakes are a relatively new form of data storage that is ideal for today’s world of enormous datasets and artificial intelligence (AI) and machine learning (ML) analytics. Unlike some data storage options, data lakes can hold datasets in many different formats. You don’t need to choose a specific schema or decide how to structure the data held in a data lake.
Why is a Data Lake Important for Process Manufacturing Plants?
Process plants generate massive amounts of data, as much as 1800 petabytes per year. Advanced artificial intelligence (AI) and machine learning (ML) analytics can crunch these datasets and unlock the value they hold. AI and ML in manufacturing enable predictive analytics solutions that increase visibility into plant operations to improve efficiency, speed up root cause analysis, drive product R&D for greater innovation, and power improved forecasting for better business decision-making, among other use cases.
However, process plant data comes from many different sources, including industrial Internet of Things (IIoT) devices, sensors, maintenance logs, and more, with still more coming from external sources like customer orders, GPS tracking for supply chain shipments, market fluctuations, etc.
Each data source may format data differently, making it difficult for analytics tools to access all the data. Often, valuable data goes unused because it’s in a different format to the rest of the datasets, creating silos that can lead to skewed conclusions.
A data lake helps prevent these blind spots by bringing all the data together in a single repository, no matter how it is formatted or where it originates. Data lakes are thus part of the foundation for advanced technologies like predictive monitoring, predictive maintenance, digital twins, inventory optimization, and more.
How Can Process Plants Implement a Manufacturing Data Lake Most Effectively?
Set clear goals
Like every tool, data lakes will only make a difference to your organization if you use them in the right way. It’s vital to first understand what you intend to achieve with the data that you store, so you can make sure that data is used in a way that delivers valuable insights.
Build a connected data system
To truly derive value from a data lake, you need protocols to determine which data will be stored and when it is removed, pipelines to process raw data, analytics tools to crunch the data and produce insights, and a procedure for disseminating those insights to relevant stakeholders. Otherwise the data in your data lake will go to waste.
Ensure documentation is maintained
Because data lakes can hold so much data, there’s a risk they will turn into data swamps, where you can’t find the data you need. You need a domain expert who can document relationships between various data sources, preserving context and metadata to permit deeper exploration.
Prepare the right human resources
A data lake might sound simple, but making the most of it requires having employees with the right skills to decide when data should be removed from the data lake, manage connected data analytics tools, and convert insights into meaningful predictions and advice. This means hiring enough data scientists and analysts, but also ensuring that domain experts are available to work together with your DS team and help them understand data context and relationships.
How Do Process Plants Benefit from a Manufacturing Data Lake?
Manufacturing data lakes bring together data from disparate sources across the plant, overcoming silos to ensure that advanced analytics tools can access all the data they need. Together with AI and ML, process manufacturing organizations can use a data lake to increase productivity and efficiency in the plant, improve forecasting accuracy, guide business decision-making, boost innovation, and sharpen their competitive edge.