What is structured data?
When talking about big data, it’s usually broken down into two data sub-types: structured data and unstructured data. Some data scientists also talk about a third type: semi-structured data. It’s crucial to understand structured data in relation to unstructured data.
Structured data is data that has clearly defined attributes, tags, and schema, kept in data warehouses unlike unstructured data, which is usually stored in a data lake. Structured quantitative data which can be held in a relational database after it’s been converted numerically, and its defined attributes make it easy to query and analyze. Structured databases are usually managed with a relational database management system (RDBMS) and analyzed using structured query language (SQL).
Process control data and time-series data are just two examples of structured data. Thanks to the enormous number of IIoT sensors and smart devices, process plants have an abundance of structured data. Structured data is ideal for training and testing machine learning (ML) tools which produce valuable predictions for process manufacturing companies.
Why is Structured Data Important for Process Manufacturing Plants?
Structured data is relatively easy for ML tools to understand, while unstructured data requires more preprocessing, is more difficult to analyze, and is usually processed by deep learning (DL) tools which are a subset of ML.
With structured data, process plants can apply ML solutions like predictive analytics, demand forecasting, and supply chain monitoring to generate reliable predictions about the condition of the plant, fluctuations in market conditions, and change to the supply chain.
With this information, process engineers can act before a serious part failure occurs, executives can respond to emerging opportunities and mitigate potential risks, maintenance teams can improve their scheduling, and more.
How Can Process Plants Make the Most of their Structured Data?
Ensure Data Quality
Whether you’re using structured, unstructured, or semi-structured data, data quality is always crucial. Defined regulations governing data gathering and storage to ensure that data is collected as complete datasets and stored correctly, with the right schema and tags.
Assemble the Talent You Need
Although structured data is easier to handle than unstructured data, and there are a number of self-service BI and predictive analytics tools, you still need someone to take responsibility for your data strategy and employees who understand how to interpret ML predictions that are based on structured data.
Decide on Where to Begin
Structured data can serve a whole range of use cases in process plants, from predictive monitoring to process optimization. However, it’s better to begin with just a few use cases which will quickly demonstrate the value of your new ML solution.
How Do Process Plants Benefit from Structured Data?
With the help of structured data combined with ML algorithmic models, process plants can improve product quality, increase safety, raise their competitive advantage, and stay ahead of changing demand trends to boost profitability and productivity.