{"id":30897,"date":"2026-01-19T09:30:00","date_gmt":"2026-01-19T09:30:00","guid":{"rendered":"https:\/\/www.engineernewsnetwork.com\/blog\/?p=30897"},"modified":"2026-01-15T17:28:13","modified_gmt":"2026-01-15T17:28:13","slug":"top-trends-changing-industrial-data-management","status":"publish","type":"post","link":"https:\/\/www.engineernewsnetwork.com\/blog\/top-trends-changing-industrial-data-management\/","title":{"rendered":"Top trends changing industrial data management\u00a0"},"content":{"rendered":"\n

Krzysztof\u00a0Kubosz<\/strong><\/strong> explores the\u00a0top three\u00a0trends\u00a0that are\u00a0shaping industrial data management<\/strong><\/p>\n\n\n\n

The latest developments in digital transformation put data management at the forefront of design to effectively feed a comprehensive artificial intelligence strategy.  It is now paramount that companies unlock the data streams across systems, providing accurate data, with visual context.  This is the key to turning raw data into actionable insights as 2026 begins.  
<\/p>\n\n\n\n

1. Addressing data overload<\/strong>. In the wake of digitalisation, the sheer amount of data produced by industrial assets is staggering and will only continue to grow. Today’s all-important human operator must sift through huge amounts of siloed data from sensors and other devices in new and legacy systems to find information that is relevant to their job role.  <\/p>\n\n\n\n

This creates a challenge for businesses that want to improve operations through data but instead, create new time-consuming tasks for already-busy operators. This brings a risk of inefficiencies and errors, and it also distracts the workforce and therefore hinders businesses from reaching their goals.  <\/p>\n\n\n\n

To prevent data overload, information must be processed, contextualised, and presented in a meaningful way that turns data into actionable insights.  <\/p>\n\n\n\n

So, how should industrial businesses tackle data overload? By aggregating data from disparate real-time data sources. As a result, they no longer have to juggle between multiple systems, they can now get a clear view of data with fewer distractions so that they can make proactive decisions.  
 
 
2. Workforce integration<\/strong>. Conversations around industrial data management almost exclusively focus on asset data, but this misses one of the most valuable data sets: skills and workforce information. This is particularly important as experienced plant engineers retire, and a new generation enters the workforce.  <\/p>\n\n\n\n

As well as delivering monitoring and control, digital solutions can also record how experienced operators respond to alarms and incidents, and what datasets they find most valuable to get the most out of plant and equipment. Over time, this builds up a detailed history of assets and working practices that can help to bring new employees up to speed.  <\/p>\n\n\n\n

Having a digital record of operator decisions gives workers the ability to capture on-the-job knowledge before they leave the workforce and mitigates the impact of the skills gap.  <\/p>\n\n\n\n

Another benefit is that teams can monitor and manage operations from anywhere. On one hand, this extends the reach of expert operators to oversee assets spread across multiple locations, and sites with access limitations. Tools such as digital twins provide detailed insight and asset history so that remote experts can diagnose issues remotely and order the right spares and tooling to complete a maintenance job with only one visit to site.  <\/p>\n\n\n\n

On the other hand, digital tools give local operators access to world-leading knowledge and support. For example, augmented reality provides guided support and reduces the need to call in specialist technicians to remote or hard-to-access locations.  <\/p>\n\n\n\n

3. Unified name space and data enrichment<\/strong>. The third trend in industrial data is unified name space (UNS). Through this, multiple data sets are combined into a structured model so that any client tool including AI can use the data for purpose. For example, under predictive maintenance, live asset data can be combined with historic performance logs to predict potential failures of components very accurately.  <\/p>\n\n\n\n

This can evolve into data enrichment by integrating operational data with measurements of environmental conditions, advanced analytics, process information, and energy management. Analysis of patterns in the broad dataset will create predictions and insights covering an entire facility. For applications like manufacturing, food and beverage, or life sciences where any deviation to the process or maintenance issue can impact the final product, data enrichment fulfils the crucial requirement for operators on the plant floor to have access to clear and actionable alerts to enhance safety, efficiency, compliance, and sustainability.  <\/p>\n\n\n\n

Software for building a scalable data foundation<\/strong><\/p>\n\n\n\n

Data overload, building data strategies that feed AI and the rising skills gap remain significant challenges for operations in 2026, but can be mitigated with an innovative approach to industrial data management. Digital Software solutions from AVEVA are designed to address all three of these trends from single sites through to enterprise level.   <\/p>\n\n\n\n

While all businesses have unique goals, move at their own pace and follow a distinct path to digital transformation, these types of data management solutions can store and collect data from any location and source across multiple assets and facilities with no need for coding.  <\/p>\n\n\n\n

Krzysztof Kubosz is Product Manager – SolutionsPT<\/a><\/strong>. <\/p>\n","protected":false},"excerpt":{"rendered":"

Krzysztof\u00a0Kubosz explores the\u00a0top three\u00a0trends\u00a0that are\u00a0shaping industrial data management The latest developments in digital transformation put data management at the forefront of design to effectively feed a comprehensive artificial intelligence strategy.  It is now paramount that companies unlock the data streams across systems, providing accurate data, with visual context.  This is the key to turning raw data into actionable insights as 2026 begins.   1. Addressing data overload. In the wake of digitalisation, the sheer amount of data produced …<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[199],"tags":[14258,14257],"class_list":["post-30897","post","type-post","status-publish","format-standard","","category-news-views-and-opinion","tag-industrial-data-management","tag-solutionspt"],"yoast_head":"\nTop trends changing industrial data management\u00a0 - Engineer News Network<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.engineernewsnetwork.com\/blog\/top-trends-changing-industrial-data-management\/\" \/>\n<meta property=\"og:locale\" content=\"en_GB\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Top trends changing industrial data management\u00a0 - Engineer News Network\" \/>\n<meta property=\"og:description\" content=\"Krzysztof\u00a0Kubosz explores the\u00a0top three\u00a0trends\u00a0that are\u00a0shaping industrial data management The latest developments in digital transformation put data management at the forefront of design to effectively feed a comprehensive artificial intelligence strategy.  It is now paramount that companies unlock the data streams across systems, providing accurate data, with visual context.  This is the key to turning raw data into actionable insights as 2026 begins.   1. Addressing data overload. 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