Utthunga LLC and Data Gumbo Intelligent Systems today announced the launch of UTT-DataGumbo, a strategic joint venture uniting Utthunga’s 1,200-strong industrial engineering team and AI analytics with Data Gumbo’s automated smart-contract workflows and sustainability frameworks.

Established under a non-binding framework, UTT-DataGumbo will accelerate transformation across energy, manufacturing, chemicals, and metals & mining sectors. The platform’s modular architecture and standardized connectors simplify deployment across industrial verticals—enabling consistent workflows, automatic policy validations, and reduced integration overhead as clients scale operations from pilots to enterprise-wide.

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With the evolution of the cyber world, cybersecurity threats have evolved in lockstep, mutating from simple malware attacks to highly sophisticated ransomware, including state-sponsored threats, each threatening to derail industrial operations with ramifications of a never-before kind. The advancement of these threats has also spawned the emergence of equally advanced security models, which incorporate AI, ML, and real-time monitoring to negate the potential impact of these threats and keep operations on track.

Agentic AI – an autonomous system capable of independent decision-making while working within specific environments – has emerged as yet another modern-day AI model that can transform industrial cybersecurity in revolutionary ways.

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The oil and gas industry lies at the centre of global energy production, but its environmental footprint is impossible to ignore. It’s estimated that greenhouse gas emissions from this sector account for about 15% of the planet’s energy-related emissions. With growing international pressure to decarbonise, the time for the industry to act decisively has never been greater.

What makes this challenge particularly daunting is the breadth of the industry’s emissions profile. Virtually every stage of the value chain contributes to the problem, from the combustion in boilers, heaters, and flares to indirect emissions produced by compressors and pumps. These operational necessities result in a hefty carbon footprint. On top of that, fugitive emissions—unintentional leaks from pipelines, scrubbers, and valves—make managing emissions not only a complex but urgent task.

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1. Data accessibility : Data historians can collect data from multiple sources and store it in a structured and secure format. Object linking, OPC UA, etc., are some protocols used to get the data ready for consumption.
2. Cost reduction : Data compression algorithms used by data historians help store large data volumes efficiently for more extended periods. The maintenance costs are reduced significantly by data compression. Moreover, databases can be accessed by systems like MRP, ERP, SCM, etc., which reduces data loss and data integration costs.
3. Easy access : Compared to relational databases, data historians are faster in storing or retrieving data in real-time. Thus, data is available 24X7 for visualizations or analysis.

The older data systems were time-series databases that were deployed on the industry’s premise. As a result, very little data was clocked, and the main focus was on data visualizations only.

With the advancement in technologies and the onset of the digital world, the focus has shifted to cloud computing, artificial intelligence, and IIoT platform. Due to these changes, the industrial engineering services teams expect data historians to have enhanced data wrangling capabilities.

This includes data identification, metadata addition, data relationship mapping, and dataset mobilization to various servers.

The old and standard data aggregation process has become obsolete. Product engineering services teams are looking for end-to-end data management and digital transformation services.

Once the industrial processes are automated, the OEE benchmark will become more critical. Data historian is beneficial in improving OEE scores:

1. Bidirectional communication is possible with advanced data historians.
2. Data storage, processing, and analysis can be done in real-time. Thus, building and integrating machine learning models with batch analytics becomes easy.
3. 24X7 data access helps in monitoring the industrial equipment and creating real-time alerts.
   Data encryption technology makes the system safe.

As per the Industry 4.0 requirements, features like data integration, asset modeling, visualization, analysis, etc., should be part of industrial automation solutions.

The future of data historians has much more data crunching and analysis in store for it. In addition, operations data historians are challenging to work with and expensive to implement.

Moreover, they have limited visualization and analysis capabilities. These data historians are not scalable across multiple platforms also. Thus, it becomes difficult for the system to process large volumes of data.

The key technologies that future data historians need to incorporate are:
1.Data wrangling: Data is the new gold for industries. If data quality is terrible, extracting insights from it will be a painful task. Thus, data historians should have capabilities like data aggregation, data cleansing, data enrichment, etc.

2.Digital Twin: The digital twin concept is to replicate the industry’s processes and products virtually. The virtual world provides the capability to model a product’s attributes based on the data associated with it.

3.Blockchain: It is a record-keeping technology that facilitates transactions through decentralized networks. No central authority can control the data in the blockchain ecosystem. Thus, the data remains safe and secure.

4.OPC UA: It is the primary communications protocol for Industry 4.0. OPC UA enables hassle-free communication between heterogeneous machines. This technology saves a lot of time and reduces costs for industries in collecting and sharing data for analysis.

This team can support digital transformation consulting and Testing as a service automation product. So, if you are interested in hiring a consultancy for industrial automation services, you can reach out to our team for a discussion.

The industrial and energy sector, currently among the largest contributors to the global carbon crisis, is under immense global pressure to reduce emissions of carbon dioxide and methane. When it comes to emission reduction, we are seeing increasing interest in pinch analysis technology – a transformative new method to optimise energy use in industrial operations. By systematically identifying the ‘pinch point’ – the location in a system where the temperature difference between hot and cold streams is smallest – this approach maximises heat recovery, minimising the need for external energy sources like natural gas, electricity, or steam. The result is a dramatic reduction in energy consumption, directly translating to lower emissions. Beyond just energy savings, pinch analysis also helps design low-carbon systems by integrating renewable energy sources and enabling waste heat recovery

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In modern manufacturing, integrating information technology (IT) and operational technology (OT) has become essential for driving productivity and building a more connected and efficient operation. IT has transformed data management and analysis for years, and OT has kept production stable and reliable. Now, combining these strengths offers manufacturers a unique opportunity: the chance to make smarter, faster decisions with real-time insights across operations.

This convergence also offers manufacturers a strategic edge in sustainability. By optimising resource use and reducing waste, IT-OT integration supports both operational goals and environmental responsibility, helping companies address pressures to adopt sustainable practices. As manufacturers recognise the potential of smarter, interconnected systems, IT-OT integration is proving to be an important step toward resilient and forward-thinking manufacturing.

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