OT/IoT Data Good Enough for Operations Isn’t Good Enough for AI and Analytics 🌐

Navigating the Data Quality 🚢❄️ Iceberg in Industrial Manufacturing

In the vast ocean of industrial operations, where control systems, SCADA, alarms, and safety mechanisms navigate the ship, data integrity often sails under the radar. Yet, alongside these technological captains, the human in the loop, the domain expert, stands as the co-captain, ensuring a vigilant watch over the seas. As we venture into the deep waters of data historians and warehouses, we encounter an iceberg of challenges that can sink the unsinkable: data defects. 🛠️📈

The Peril of Overlooking Data Quality 🌊

The journey begins with a seemingly harmless assumption: if data is good enough for day-to-day operations (because of the presence of all the layers of protection), surely it’s fit for digital operations. However, this belief is the tip of the iceberg. Beneath the surface lies a complex reality where data defects become permanent fixtures once archived in a historian or warehouse. These defects magnify their impact, affecting everything from business dashboards, data products, and data shared with suppliers and clients to AI models and predictive maintenance strategies. 🚨🔍

The Ripple Effect Across the Enterprise 🌐

Compromised data quality casts a wide net of consequences, affecting every facet of an organization. Here’s a breakdown of its far-reaching impact:

• Data Teams and Scientists: Lose valuable time due to constant data issues, a daily pain point. Data
• Operations: Missed opportunities to reduce costs and boost EBITDA with data defects causing issues like increased energy expenses, unplanned downtime, and higher maintenance costs.
• Business Decision-Makers: Eroded trust in decision-making processes when basing decisions on flawed OT/IoT data.
• Client Relations: The practice of sharing data for compliance or transparency can backfire, risking client loss and revenue impact if the data is of low quality.
• Reputation: The most severe impact is on the company’s reputation, where unreliable data exposed to the public can cause irreparable damage.

Beyond Operations: The Broader Impact 🌐

Data quality extends its tentacles beyond the operational level, influencing reports, data products, AI models, and even how data is shared with clients and suppliers. Ensuring data reliability is becoming a corporate responsibility, underpinning the notion that data is an asset, not a liability. 🤝💼

A Proactive Approach to Data Quality 🔄

However, the industry needs a paradigm shift, moving from a reactive to a proactive stance on data quality. Addressing data issues at their source would not only save time and resources but also prevent the cascading effects of poor data quality downstream. It’s not just a journey from “garbage in, garbage out” to a culture of data integrity and reliability; it’s also a must to succeed in digital operations and digital transformation.

There is one huge problem: Resource scarcity in manufacturing operations is the bottleneck. Doing more with fewer people has been the business case for industrial automation for years. But this has a breaking point. Data stewardship and ownership need to come from the people who understand the data, who are already drowning and in firefighting mode. Executives who spend budgets on “digital” in a broader sense need to incorporate this.

AI: The Beacon of Hope 🤖❤️

Artificial Intelligence (AI) emerges as a powerful ally in this quest. It’s now widely accepted that AI needs data quality, but data quality also needs AI, primarily if you work with high volumes of OT/IoT data. In the case of OT/IoT, both Discriminative AI and Generative AI are necessary to tame the time series data.

Use cases break up into three buckets: auto-completion, co-pilots, and auto-summary, applied to the configuration of data quality checks, setting up data pipelines, enhancing result interpretation and complexity reduction, implementing adaptive data quality checks using foundational models, smart data imputation, metadata learning, translating insights into human-readable text, assisting in data cleansing, and ensuring that data is not just clean but also contextually relevant and actionable and many more.

Embarking on the Data Quality Voyage 👣

For organizations embarking on the journey to enhance data quality, leveraging technology platforms that assess, monitor, and improve data quality is crucial. Here’s how different roles can benefit from and contribute to this initiative:

• Data Scientist: Enhance your workflow and reduce time spent on cleansing by utilizing platforms like timeseer.AI. Improve AI model quality by ensuring your analytics are based on reliable data.
• Data Engineer: Set up and maintain data quality gates and establish data pipelines where reliable data flows seamlessly, supporting the foundation for accurate analytics and decision-making.
• Data Steward (Domain Expert): Partner with business leaders to detect fleet-wide anomalies using sensor and data acquisition systems, ensuring data integrity and relevance across the organization. And you’ll be able to validate data in a manageable way (yes, you can!)
• Business Leader: Build a case for data quality initiatives, leveraging insights from data stewards and the capabilities of platforms like timeseer.AI to drive organizational change and improve decision-making processes.

Watch out for timeseer.AI here. 🌟🛠️

In Conclusion ✨

The dialogue on data quality in industrial analytics is more than a technical discussion; it’s a strategic imperative. As we navigate through the complexities of IoT and big data, adopting a proactive, AI-enhanced approach to data quality is not just beneficial; it’s essential for navigating the industry's future with confidence and clarity. 🚀🌍

To the readers, remember: in the digital age, the value of your data is only as good as the insights it can generate. Prioritizing data quality is the first step toward unlocking its full potential. 🗝️💡

More about this topic was uncovered in the following video together with Aveva Benelux: