dataPARC

Is the industrial data historian dead? Spoiler: No. But it's a fair question to ask. With cloud platforms, data lakes, and new analytics tools constantly entering the space, a lot of people are wondering if the traditional historian has run its course. Jim Gavigan of Industrial Insight, Inc. and Kevin Jones of dataPARC tackle this head-on. What the historian still does better than anything else, where the real gaps are, and why the conversation is more nuanced than the headline suggests. Watch the full discussion about industrial data compression: https://lnkd.in/d-m_i_eU #dataPARC #ProcessControl #Manufacturing #DataHistorian

Your plant generates millions of data points every day. But if your operators cannot read the dashboard, none of it matters. Here is what makes an industrial dashboard useful: Show the right data, not all the data A dashboard cluttered with every sensor reading helps no one. Surface the KPIs that matter for each role. Operators, engineers, and managers should all see data customized to their needs. Use visual hierarchy intentionally The most critical alerts should be impossible to miss. Color, size, and placement should guide the eye to what needs attention, not overwhelm it. Real-time and historical context A current reading without historical context is just a number. The best dashboards show where you are and where you have been, so your team can spot trends, not just problems. Design for the person using it A control room operator making split-second decisions needs a very different view than a plant manager reviewing end-of-day performance. One size fits no one. Reduce the need for training If a new operator needs hours of training just to read a dashboard, it is not a good dashboard. Familiar layouts and clear labeling drive adoption from day one. Data is only as powerful as your ability to act on it. A great dashboard bridges the gap between raw data and real decisions. What is the biggest dashboard mistake you have seen on the plant floor? Learn more about dataPARC's dashboards and production monitoring: https://lnkd.in/de45byKt #IndustrialDashboards #DataVisualization #Manufacturing #OperationalExcellence #dataPARC #ProcessOptimization

Your plant generates millions of data points every day. But if your operators cannot read the dashboard, none of it matters. Here is what makes an industrial dashboard useful: Show the right data, not all the data A dashboard cluttered with every sensor reading helps no one. Surface the KPIs that matter for each role. Operators, engineers, and managers should all see data customized to their needs. Use visual hierarchy intentionally The most critical alerts should be impossible to miss. Color, size, and placement should guide the eye to what needs attention, not overwhelm it. Real-time and historical context A current reading without historical context is just a number. The best dashboards show where you are and where you have been, so your team can spot trends, not just problems. Design for the person using it A control room operator making split-second decisions needs a very different view than a plant manager reviewing end-of-day performance. One size fits no one. Reduce the need for training If a new operator needs hours of training just to read a dashboard, it is not a good dashboard. Familiar layouts and clear labeling drive adoption from day one. Data is only as powerful as your ability to act on it. A great dashboard bridges the gap between raw data and real decisions. What is the biggest dashboard mistake you have seen on the plant floor? Learn more about dataPARC's dashboards and production monitoring: https://lnkd.in/de45byKt #IndustrialDashboards #DataVisualization #Manufacturing #OperationalExcellence #dataPARC #ProcessOptimization

What is the most time-consuming part of root cause analysis at your plant? For a lot of teams, it is not the analysis itself. It is everything that comes before it: hunting down the right data, pulling it from multiple systems, reformatting it, and trying to remember the context of what you were looking for by the time the report loads. The best systems collapse those steps. Others quietly add hours to every investigation. So tell us: where does your team lose the most time when a process upset happens? #RootCauseAnalysis #ProcessEngineering #Manufacturing #ContinuousImprovement #dataPARC

We're excited to announce a new strategic partnership with NISHIKAWA KEISOKU Co Ltd. (西川計測株式会社), a Tokyo-based technical solutions provider with over 70 years of experience in measurement, control, and analytical instrumentation for Japan's industrial sector. Nishikawa Keisoku serves approximately 3,000 industrial customers through a nationwide network of offices stretching from Fukushima to Okinawa. Their deep plant-floor expertise and long-standing customer relationships make them an ideal partner to bring dataPARC's historian, PARCview real-time analytics, and cloud and AI-enabled capabilities to Japan's process manufacturers. This partnership is part of our continued expansion in Japan, where we're building a network of specialized channel partners who understand the operational environments our platform is built for. Read more: https://lnkd.in/g2g2sU8Q #SmartManufacturing #IndustrialData #ProcessManufacturing #IndustrialAnalytics #IndustrialIntelligence #ManufacturingJapan

Sugars International LLC is excited to announce that we will be attending the 2026 Annual Sugar Industry Technologists Conference in Cincinnati, Ohio from April 19–22. We’re proud to highlight Scott Kahre's upcoming presentation, “A Roadmap to Digital Transformation in the Sugar Industry,” in collaboration with dataPARC. As the sugar industry continues to evolve, digital technologies are unlocking new opportunities to improve safety, efficiency, and productivity. Scott’s presentation will explore how organizations can navigate the growing landscape of tools and technologies, covering everything from building a strong data foundation to leveraging real-time analytics, KPI monitoring, digital twins, and AI integration. We look forward to connecting with industry peers and discussing the future of digital transformation in sugar production. #SugarIndustry #DigitalTransformation #Manufacturing #DataAnalytics #AI #IndustryInnovation Sally Weiss Wallace

Did you know variable, product-based limits can transform the way you monitor your manufacturing process? Join us for this dataPARC University session where we break down limits, grades, and alarms. We'll show you how to configure them the right way. - Setup & configuration - Best practices - Real-world manufacturing benefits Register now https://lnkd.in/gp8hJYZ8 #dataPARC #ProcessManufacturing #ContinuousImprovement #ManufacturingTech #dataPARCUniversity

This or That? When you are troubleshooting a production issue, which do you reach for first? A. A custom dashboard built for your process B. Trend data pulled directly from the historian Both have their place. But the tools your team gravitates toward say a lot about how well your data infrastructure is actually set up. Share your answer below and tell us why. #DataVisualization #ProcessEngineering #Manufacturing #IndustrialDashboards #dataPARC

How do you choose between two data management systems when they check all the same boxes? Most evaluation scorecards look at the same criteria: data visualization, reporting, ease of use, integration, and total cost of ownership. When systems score similarly across those measures, the decision often comes down to something harder to quantify. How much does each system slow your team down? That question is at the heart of the Workflow Productivity Index, a metric worth adding to your data management scorecard. The WPI looks at four key areas: - Time to Insight: How long does it take to find a root cause using the system? - Task Completion Speed: How many steps does it take to go from raw data to a usable report or visualization? - Data Accessibility: Can users retrieve and compare months of historical data without losing their train of thought? - Problem-Solving Effectiveness: Is troubleshooting, analysis, and documentation contained in one place, or does the workflow require jumping between systems? The answers reveal something a feature checklist never will: whether the system enables your team to operate at the speed of thought, or quietly works against them. Read the full blog post to learn how to apply the WPI to your next software evaluation: https://lnkd.in/gpeetTBt #Manufacturing #DataManagement #ProcessOptimization #WorkflowProductivity #dataPARC #DigitalTransformation

Open-source time series databases aren't replacing the data historian anytime soon. Here's why that matters for manufacturers. The data historian was purpose-built for the complexity of plant operations, data quality, real-time access, industrial connectivity, and security. That kind of domain expertise takes decades to build, not months. Here is a breakdown of the data historian vs. time series database discussion, we also have a video attached in the article that further expands on the topic.