Transform your business model to pay-per-use

Many industrial companies have a data strategy, yet very few can honestly say their business is better because it. The reason often isn’t technology or talent, but a missing piece in what we call the Triangle of Digitalization — a simple framework that reveals why most data strategies quietly fail.

Barbara and Tech Mahindra, a global provider of technology consulting and digital solutions, have announced a strategic partnership. The alliance aims to accelerate Edge AI adoption worldwide, enabling enterprises to orchestrate intelligence at scale and drive industrial transformation. Barbara brings its secure and scalable Edge AI platform, while Tech Mahindra contributes global integration expertise and deep industry know-how.

The strategic alliance combines Hâpy Services' expertise in Edge connectivity orchestration with Barbara´s Edge AI orchestration solutions to offer a unique value proposition to the European market.

As industries seek faster insights and real-time responsiveness, Edge MLOps (Machine Learning Operations at the Edge) is emerging as a game-changer. Unlike traditional MLOps that rely on centralized cloud infrastructure, Edge MLOps enables machine learning models to be deployed, monitored, and retrained close to the data source.

Edge Computing and DataOps are different, and they’re most effective when used together. But by starting with a secure, flexible, and robust edge computing platform like Barbara, industrial companies can create a future-proof foundation that supports any DataOps toolset, avoids vendor lock-in, and is ready to scale into the next generation of intelligent industrial systems.‍

In industrial environments, disconnected systems make real-time decision-making difficult. Unified Namespace (UNS) offers a solution by creating a Single Source of Truth, but implementation can be complex. Barbara’s Edge Platform simplifies real-time data acquisition, processing, and security, making UNS deployment seamless. This article explains how.

Unlike conventional AI models that rely on cloud computing, Edge AI processes data locally, enabling real-time decision-making, minimizing latency, enhancing security, and reducing operational costs. This technology is already driving greater efficiency and autonomy in industrial settings. In this webinar, we discuss the biggest challenges and solutions to fully harnessing the power of AI at the Edge.

Developing a data-centric strategy takes companies on a journey from data collection to the creation of advanced AI models. This article explores the stages of an industrial organization's data journey, the challenges they face, and how Barbara’s solutions meet their needs at every step of their digital journey.

The “Do It Yourself" approach entails that the Total Cost of Ownership of taking an Edge Platform to scale and maintain it, can erode company´s return on investment. Instead, the trend is to collaborate with domain experts and specialized vendors to co-develop solutions for your unique operational challenges. In this article we delve into the pros, cons, and complexities of DIY infrastructure versus pre-built platforms, to help you make an informed decision.

By 2025, analysts predict that 50% of enterprises will have adopted edge computing, up from just 20% in 2024. At Barbara, we’ve seen this momentum firsthand, with inquiries about Edge Computing and AI increasing fourfold in 2024 alone. While the potential of Edge AI is undeniable, its widespread adoption brings both opportunities and challenges. In this post, we dive into key predictions for Edge AI in 2025 and examine the challenges the industry must overcome to unlock its full potential.

The 181 zettabytes of data generated annually by over 13 billion devices exceeds global internet bandwidth, posing challenges for cloud computing due to high costs, latency, and security concerns. Edge computing processes data locally, offering efficient, secure analysis without constant connectivity. Barbara’s Industrial Edge Management and Orchestration Platform simplifies managing this infrastructure by providing cloud-like capabilities on-premises and bridging the IT-OT gap.

Edge computing is crucial for most enterprises' digital transformation goals, with projects already underway across various industries and organizations. However, most deployments currently focus on individual use cases, leading to fragmented islands of edge computing technologies, processes, and skills. What should you do when these independent projects start to multiply? We will disclose the answer in this article.

Edge Computing is becoming a critical component, enabling organizations to leverage edge data in unprecedented ways. However, embarking on an Edge Computing project can be daunting, as building an edge solution requires significant time, investment, and a highly skilled team.This article explores the essentials for getting started with Edge Computing.

View our on-demand webinar "How to Maximize your Edge Data". Whether you're just starting to collect data and want to maximize its potential or you're deep into your digital transformation journey and aiming to harness AI at the edge, this webinar provides actionable insights applicable to every stage of your enterprise’s edge journey.

For industrial companies embracing AI, understanding the MLOps workflow is key to turning use cases into real-world results. In this post, we show how Barbara integrates seamlessly into each stage of the workflow especially where the edge plays a critical role.

Many companies find themselves underprepared for the complexities involved in expanding their projects within the Edge. Proof of concepts (POCs) typically focus on one or a few locations, but if successful, they must scale to hundreds or even thousands of locations. This article highlights key considerations for technology leaders navigating the Edge AI landscape.

AI empowers machines to learn from data, make smarter decisions, and adapt in real time, driving unprecedented efficiency in manufacturing. The convergence of AI and automation is reshaping the industry and accelerating innovation. In this article, we explore how organizations can successfully embark on their Edge AI journey.

Barbara partners with channel experts to offer a secure, simple and scalable orchestration solution for edge deployments. Add a highly profitable revenue stream to your business, receive generous discounts off list prices and accelerate the pace of projects to production.

Industrial servitization is a hot topic right now as new technologies emerge that harness real-time data. In an increasingly competitive market servitization is the way to open new business lines, improve customer loyalty and, in addition, comply with sustainability goals. Manufacturers can now make a profit from services by making their products last longer and avoid waste as much as possible. They can also recycle parts of broken products for reuse or recycle an integral part of these services.

The digital revolution is accelerating industrial transformation. Ubiquitous innovation, global markets, and increasingly demanding customers who no longer seek just a product, but a comprehensive service tailored to their needs are forcing manufacturers to transform their business models and move from one-off sales to one of higher value-added services.

Edge computing plays a key role in sustainability and energy efficiency because it is specifically designed to bring applications and data closer to devices and their users. Increasing the deployment of edge solutions could be one of the answers to achieve the energy optimization goal that the industry is looking for.