Datadobi Launches StorageMAP 7.3, Enabling Smarter Data Automation, Governance, And Compliant S3 Migration
Workflow automation
StorageMAP 7.3 introduces policy-driven workflows that allow administrators to define tasks executed by its workflow engine in response to specific triggers, such as a time schedule. A 'dry run' feature facilitates reviewing the scope of a policy before full execution.
These new workflows support a wide range of use cases, including periodic automated archival, creating data pipelines to feed GenAI applications, identifying and relocating non-business-related data to a quarantine area, and more. Once policies are published, StorageMAP runs the workflows on schedule without requiring manual supervision.
Granular deletion and targeted data control
In addition, StorageMAP 7.3 adds support for granular file-level deletes. Administrators can identify files that match specific criteria and save them as input to a targeted delete job, which StorageMAP will execute. Each delete job generates a report that documents the job's details and outcome.
This functionality addresses situations where a coarse-grained directory-level deletion is not possible due to the presence of both relevant and disposable data. By enabling precise file selection, StorageMAP ensures that administrators can apply accurate and effective deletion policies.
Object migration enhancements
StorageMAP 7.3 also enhances its core object migration functionality by supporting the migration of locked objects between S3-compatible storage systems. This allows compliant data stored in a Write Once Read Many (WORM) format to be relocated across different vendor platforms while retaining its retention date and legal holds.
To support cost and performance objectives, the solution includes the ability to select the S3 storage class during object migration or replication. By specifying the desired storage class at the time of the job, organizations can avoid unnecessary post-migration lifecycle policies and ensure data is written directly to the appropriate tier.
'StorageMAP 7.3 marks a major step forward in enabling true automation for unstructured data management,' said Carl D'Halluin, CTO, Datadobi. 'Organizations can now define and execute policy-based actions at scale, removing the bottlenecks inherent to existing manual processes, making their file and object storage environments far more responsive to operational needs.'
'Unstructured data continues to proliferate across hybrid environments and organizations need solutions that not only provide visibility but also enable decisive action,' said Simon Robinson, Principal Analyst at Enterprise Strategy Group, now part of Omdia. 'Datadobi is addressing several operational pain points by introducing policy-driven automation and greater control over how data is managed, deleted or migrated, especially in compliance-sensitive scenarios.'
Customers dealing with increasingly complex data landscapes require solutions that enable them to stay in control without incurring additional operational overhead. StorageMAP 7.3 offers a practical way to address these important challenges by reducing the time teams spend on routine tasks and helping them move critical data without disrupting compliance or performance.
For further information about StorageMAP 7.3, click here.
Datadobi, a global leader in Unstructured Data Management, is revolutionizing unstructured data management with its StorageMAP platform. Enterprises can make data-driven decisions that transform unstructured data from an expense and risk into a competitive advantage and revenue. Founded in 2010, Datadobi is a privately held company headquartered in Leuven, Belgium, with subsidiaries in New York, Melbourne, Dusseldorf, and London. For more information, please visit https://datadobi.com and follow us on LinkedIn.
Hashtags
Try Our AI Features
Explore what Daily8 AI can do for you:
Comments
No comments yet...
Related Articles
Techday NZ
2 days ago
- Techday NZ
GlobalLogic unveils VelocityAI Testing to boost software quality
GlobalLogic has announced the introduction of VelocityAI Testing, a GenAI-powered solution intended to address quality assurance challenges and improve software delivery effectiveness for organisations facing complex digital demands. VelocityAI Testing focuses on embedding AI-driven intelligence across all phases of the Software Development Life Cycle (SDLC). This approach is designed to help clients accelerate cycle times, improve efficiency, and maintain high standards of quality in software delivery. Industry challenges Enterprises today face increasing pressure to deliver software solutions more rapidly while also ensuring data security and high quality. A shortage of skilled resources, rising costs, and evolving technology landscapes have compounded these challenges, particularly in the area of quality assurance and software testing. According to GlobalLogic, VelocityAI Testing has been developed as a direct response to these industry pain points. The platform claims to provide cost savings, scalability, and an outcome-driven approach, with flexible commercial options that accommodate varied organisational needs. "The future of software development hinges on intelligently integrating enterprise-grade GenAI tools across the entire lifecycle, and Quality Assurance is no exception," said Raj Sethi, Senior Vice President of Technology, SDLC, GlobalLogic. "VelocityAI Testing integrates seamlessly into your existing processes, requiring little to no change management, and delivers immediate value to organizations of all sizes, regardless of their current engineering partnerships. VelocityAI Testing operates transparently behind the scenes, providing accelerated time-to-market, enhanced quality, and adapting to the ever-changing technology landscape with flexibility of both operating and commercial models." Main features VelocityAI Testing includes several key capabilities: Test Strategy Development: The system leverages automated agents during onboarding to streamline time and enhance the success rate of test automation. Autonomous Test Case Generation: Test cases for both functional and non-functional testing are produced from user stories, subtasks, and software development artefacts within JIRA/ADO and CAKE, which functions as an intelligent integrator. This capability supports multiple testing stages, such as sanity, smoke, regression, and acceptance testing. Script and Output Integration: Outputs are saved to Jira or other third-party tools like QMetry. Test case execution is performed in separate environments, without requiring system deployment in advance. Flexible and Secure Deployment: VelocityAI Testing offers deployment within a client's Virtual Private Cloud (VPC) to match privacy and security policies. The solution supports client VPCs, GlobalLogic VPCs, on-premise, and air-gapped setups, accommodating heightened security requirements. Context-Aware Test Design: The system interprets documentation such as business requirement documents, product requirement documents, wireframes, Figma designs, and user guides, incorporating relevant business context and persona information via CAKE to enrich test scenarios. Defect Management and Reporting: Defects are logged automatically to JIRA/ADO, and the system employs GenAI capabilities to pinpoint root causes, identify untested sections, and provide smart dashboards that track key performance indicators, coverage, and quality metrics. Client results GlobalLogic reports notable results achieved with VelocityAI Testing in varied client environments. In one instance, an international provider of building products and services recorded a 30% improvement in greenfield and brownfield development projects following the adoption of GenAI-supported testing combined with GlobalLogic's accelerator. A media and entertainment organisation observed a 30-40% reduction in overall Software Testing Life Cycle (STLC) cycles. This was attributed to end-to-end quality assurance, automation, and test case generation provided by GlobalLogic. Additionally, for a Contact Centre service provider, the solution supported QA infrastructure modernisation and automated validation tasks, yielding a 50% reduction in time spent. The provider also migrated 94% of its customers to a multi-tenancy platform and reduced production backlog by 12-13% quarter-on-quarter. Experience and positioning GlobalLogic has reported that it has been developing machine learning and GenAI technologies since 2021. The company describes the launch of VelocityAI Testing as an important milestone in its approach to promoting a shift from reactive, stability-focused engineering practices to proactive, growth-oriented operations with a GenAI-first strategy. Follow us on: Share on:
Techday NZ
3 days ago
- Techday NZ
Only 30% of firms ready for AI scale despite rising investments
A new global study has revealed a significant gap between artificial intelligence (AI) pilot programmes and full-scale enterprise adoption, with only 30% of organisations ready to maximise the technology's potential despite widespread experimentation. The report, titled Practical Insights from AI Leaders and released by surveyed 1,000 senior business and technology leaders from 10 countries. Its findings show 71% of companies are either actively using or piloting AI, but expanding those initiatives remains a challenge for most. The research indicates that the appetite for further investment in AI is strong. A total of 89% of respondents plan to increase their organisation's AI investments in 2025, with three-quarters intending to allocate up to half of their IT budgets to AI initiatives next year. Scaling barriers Despite clear intentions to increase funding, the report identifies three central obstacles to wider AI deployment. A lack of AI talent was flagged by 44% of respondents, unpredictable costs associated with large language models (42%), and persistent concerns regarding data privacy and regulatory compliance (41%). The survey highlights four critical investment areas AI leaders are prioritising to mitigate these barriers: hiring internal and external AI talent (66%), improving data quality (51%), strengthening solution security (40%), and upgrading IT infrastructure (37%). The report suggests that initiatives rooted in these focus areas are more likely to deliver sustainable, organisation-wide impact. Adoption strategies There is a growing trend for organisations to buy and customise AI solutions rather than build them entirely in-house. According to the survey, 72% of respondents prefer purchasing existing solutions due to their relative ease of deployment, regulatory compliance, quality, and integration capabilities. The study also finds that many organisations now have technologies such as generative AI (GenAI), large language models (LLMs), and conversational AI in production or initial scaling phases. Emerging areas, including multi-modal AI, retrieval augmented generation (RAG), agent orchestration, and agentic AI, are the focus of early-stage experiments and pilot projects. "AI is no longer experimental, it's foundational," said Raj Koneru, Founder and CEO of "We're seeing businesses reimagine how they operate, innovate, and grow - and AI is at the heart of this transformation. The future enterprise will have AI embedded across every function, with humans and intelligent agents working side by side. To prepare for this future, organizations must prioritize data readiness, build scalable infrastructure, implement responsible governance, and invest in empowering their workforce to thrive alongside AI." Focus on return Leaders surveyed by emphasised the importance of measurable return on investment when devising their AI strategies. Operational efficiency, output quality, employee productivity, customer satisfaction, and time-to-completion were identified as the top indicators of AI project success. Participants listed the most significant current AI application areas within their organisations as business process orchestration (44%), encompassing automation, operations, compliance and risk management; workforce productivity (31%), including task automation, information discovery and employee support; and customer experience (24%), with a focus on support and self-service. The data shows that while 71% of companies are experimenting with or utilising AI across multiple departments and use cases, the likelihood of moving from experimentation to full-scale deployment is enhanced if companies set out with an enterprise-wide strategy and clarity on which processes stand to benefit most from AI adoption.
Newsroom
6 days ago
- Newsroom
A copyright game of chicken
Opinion: If you have the right tools, generative AI has made it easy to create high-quality images, simply through providing it with textual prompts – commonly called Text To Image. This raises numerous ethical and legal issues, including whether images generated this way are protected by copyright and who owns the copyright in those images. The GenAI models – such as Midjourney, Gemini, and DALL-E 3 – are not creating works out of thin air. They rely on training data, which are often works found online and copied without the permission of the copyright owners. Artists and other creatives are understandably concerned about the copying of their works, and some are starting to sue the owners of these models for copyright infringement in the US, the EU, the UK, and elsewhere. It may come as a surprise for readers that under copyright law if a GenAI image has drawn inspiration from hundreds or thousands of works but has not copied a substantial part of an identifiable work protected by copyright, the GenAI image will not infringe copyright. Can the GenAI images that do not infringe copyright be protected by copyright? I investigated this question for an article in the New Zealand Business Law Quarterly. In the US and some other countries, the answer is currently no: GenAI images created with prompts are not protected by copyright. However, New Zealand's copyright law is different. It may be possible for such works to be protected by copyright. However, there is no automatic protection. The image must be 'original'. Before looking at what 'original' means in copyright law, another of copyright law's oddities is that artists do not have copyright of their artistic style. For instance, if a user prompts a GenAI model to create a red chicken in the style of Roy Lichtenstein, if Lichtenstein did not create a similar red chicken in his distinctive style, there will be no copyright infringement. You could demonstrate originality though, if you used skill and judgment in your prompt. Simple prompts such as 'red chicken' or even 'red chicken in a garden with the sun shining, with trees and flowers' would not show sufficient skill or judgment. But you might be able to get copyright of an image produced from detailed prompt such as the following: 'create a close-up photograph of a red shaver chicken in profile view in a lush garden with the sun's crepuscular rays showing, the chicken is centred in the image, to its right and slightly behind the chicken are apple trees, to its left and slightly behind the chicken are large sunflowers, the background is out of focus creating a bokeh effect, the overall appearance is interesting with detailed texture of the birds' feathers, there is no text or additional objects in the image'. (I wrote this prompt. The 'bokeh effect' is a term in photography, which describes the aesthetic quality of the blur in the out-of-focus parts of an image.) Alternatively, a work may be considered original if enough time and effort was spent in creating it. If a user created an image using GenAI and then spent a long time modifying that image through a series of prompts, that could make the image original and protected by copyright. If an image is sufficiently original and therefore protected by copyright, who owns the copyright? The AI user or the owner of the GenAI model, or someone else? The answer depends on which country the user is attempting to claim copyright in. Under New Zealand's Copyright Act, because the user provided the prompt (or prompts), the user is likely to be treated as the first owner of the copyright. However, because GenAI images are treated as computer-generated works, these images won't be protected to the same extent as more traditionally created works, such as a painting painted by an artist. The copyright in a traditional painting will last for 50 years after the artist's death. In contrast, copyright in computer-generated works lasts for 50 years from the end of the year in which it was created. Also, while painters will have moral rights in relation to their own painting, users do not gain moral rights of images created with AI. One type of moral right is the right of integrity, when artists can complain if a change to their work harms their reputation or integrity. Another difference in the copyright of an original artwork, compared with an AI-created image, is that with the latter, the level of protection depends on the level of originality. The level of skill and judgment when using prompts is lower than that of an artist painting a painting; therefore, a person 'copying' the GenAI image would have to copy the GenAI image almost exactly to infringe copyright. For the painting, infringement could occur if a substantial part had been copied, even if there were large differences. Copyright is complicated, and AI makes it even more so. Because the level of protection for AI images will be lower than for traditional created works, I argue that GenAI images need to be marked as such and the Copyright Act 1994 amended so that the copyright owner of a GenAI image is unable to sue for copyright infringement if they have not sufficiently watermarked the image.
