In the rapidly evolving world of industrial manufacturing, the importance of a robust Data Management strategy cannot be overstated. With the increasing interest in AI and its transformative potential, companies are eager to harness the power of AI to improve operations, reduce downtime, and improve efficiency. However, before diving into AI initiatives, it is crucial to establish a solid Data Management strategy. This foundational step ensures that the data used to train AI models is of high quality, is dependable, and comprehensive.
The adage "garbage in, garbage out" holds true in AI. The effectiveness of AI models is directly linked to the quality of the data they are trained on. Issues such as data gaps, duplicated data, and inconsistent data formats can significantly degrade AI performance. Industrial manufacturing operations generate massive amounts of data annually, but due to data management challenges, storage costs, and preconceived notions about data importance, there are often critical data gaps.
Managing data quality across disparate platforms is a major challenge. Inconsistent data standards, formats or time intervals can lead to errors and inefficiencies, undermining the potential benefits of artificial intelligence and machine learning. Ensuring high data quality at all touchpoints is essential for accurate AI/ML predictions and insights.
Sophisticated AI models require a large and diverse mix of data from various sources. The more data sources connected, the more powerful and versatile the AI models can become. A comprehensive approach to data management enables the integration of diverse data types, providing a robust foundation for AI training.
Organizations must identify and manage all sources of data, including existing enterprise systems, IoT data, and external third-party data. There are three broad types of data:
• Structured Data: Structured data can sometimes be called linear data. It mostly comes from ERP systems, PLCs, and is often stored in relational SQL databases.
• Semi-Structured Data: Semi-structured data is often referred to as out-of-order data, which has a hierarchical structure that doesn't conform to relational databases, it is suitable for NoSQL databases.
• Unstructured Data: Unstructured data is non-linear data, it lacks a predefined format, such as text, images, and videos.
Industrial AI initiatives often require a comprehensive mix of structured, semi-structured, and unstructured data. The data management strategy should identify all sources, and carefully consider how it is stored so it can be best accessed and utilised.
Data ingestion, the process of moving data from its original source to storage location, needs to be carefully planned and managed. Organisations need to ensure that the data after it is ingested is compatible.
As data is continuously generated, the ingestion process should also be continuous, and where possible, real-time data ingestion of time-series and IoT data should be enabled. This continuous data stream can then be analysed by AI/ML models to provide continuous insights to the organisation.
Data managers need to carefully plan and manage this process to ensure that it is reliable, if the data connection drops out, the data ingestion can cease, and data gaps can quickly occur and go easily unnoticed.
Organisations should look for flexible scalable storage solutions. There are data storage solutions now that support a large variety of data types, with unlimited storage capacity. The data storage solution should include or integrate seamlessly with AI platforms.
For a detailed comparison of data storage options, see our data storage comparison here.
The good news for businesses is that the cost of data storage is reducing, and there are many options available including private cloud storage, on-premise solutions and public cloud storage depending on the organisations data security requirements.
The transformation and wrangling of data can take up a significant amount of time for data professionals, however it is essential for data science projects and advanced analytics. The data transformation process converts raw data into clean, structured formats which can be used to train AI models with. Often a manual process, organisations should consider how their data will be used and the format it is required to be in, and seek ways to streamline the data transformation process.
VROC’s DataHUB+ pre-processes both structured and unstructured data, streamlining the data transformation process. Learn how VROC’s platform supports both data scientists and data engineers.
As part of a data management strategy, organizations should consider the tools they use for data analytics and visualization (such as Power BI), and the tools they will for advance analytics (such as Python or TensorFlow). The insights acquired in this step are the ultimate purpose of a data management strategy. Analytics tools should be directly connected to the historical and real-time stored data, to provide the organization with real-time insights.
Data democratization is something businesses should try to achieve from the data management strategy. Data democratization is achieved as businesses make data accessible, providing personnel with the tools and skills to analyse data and gain insights from it. Businesses need to consider upskilling staff, building a data-literate workforce.
As organizations invest and overtime come to rely on advanced analytics, companies need to consider the automation of machine learning, and the operationalization of machine learning, to make the process as streamlined and efficient as possible.
A data management strategy needs to encompass data governance and security. An organisation whose data is secure, consistent and useable can rely on their data to obtain real intelligence, distinguishing themselves in the marketplace.
Data Governance is the process of establishing clear policies and ensuring that all data-users are trained and comply with the policies. Data governance and security is an on-going critical component that must be carefully managed by all organisations today.
Data managers need to ensure that all data sources, storage solutions, systems, tools and users comply with the data security protocols.
Once a solid Data Management strategy is in place, enterprises can leverage their comprehensive data sets for AI-driven forecasting and predictions. By ensuring high data quality, reliable ingestion, flexible storage, efficient transformation, actionable analytics, and stringent governance, industrial manufacturing companies can maximize the potential of their AI initiatives. Investing in a robust Data Management strategy today paves the way for successful AI adoption and long-term operational excellence.
DataHUB+ is unique in that it is an end-to-end scalable data solution, that ingests a wide variety of structured, semi-structured and unstructured data in real-time. It sorts and stores the data so that it can easily be visualized using the inbuilt analytics tools. The platform is SOC2 and ISO27001 compliant, providing robust data security. It seamlessly connects in the same interface to OPUS, VROC’s no-code AI solution. If you are seeking an end-to-end data and AI management solution we invite you to get in touch with our team for a demo.
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