Also Read: Blend Launches Mexico Hub, Expands AWS AI Partnership

New features include Automated Voyage AI Embeddings for real-time semantic search, the LangGraph.js Long-Term Memory Store for persistent cross-conversation memory, and MongoDB 8.3, which delivers significant performance improvements without requiring application changes. “The hardest part of running agents in production isn’t the model. It’s the data layer underneath it,” said CJ Desai, President and Chief Executive Officer of MongoDB. “To trust an agent at scale, it has to retrieve the right context, hold memory across sessions, and operate at machine speed, wherever the enterprise needs it.” MongoDB also reinforced its hybrid and multi-cloud deployment strategy to support regulated industries with stringent compliance and data residency requirements.

Read More: MongoDB Makes Enterprise AI Production Ready

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To solve this challenge, the data company for trusted enterprise AI – Cloudera, released the Workflow Data Fabric Zero-Copy Connector for ServiceNow. In doing so, they enabled the extraction and transformation of the ServiceNow data in the Cloudera data fabric without extracting it first. In essence, Cloudera helped create a bridge between action data and insight data.

Real-Time Intelligence via Zero-Copy Architecture

Zero-Copy Connector is the key technology behind this press release that considers ServiceNow as a live component of the Cloudera Data Lakehouse. There is no creation of an additional copy of the data, but only a query of the data where it exists.

Main technical capabilities of the connector are:

Virtual Data Integration: Data scientists have an option to combine ServiceNow tables (for example, tickets and asset management information) with the historical data in Cloudera via SQL without data migration.

Metadata Management Across Multiple Platforms: Thanks to Cloudera SDX (Shared Data Experience) the consistent application of security and governance policy in an integrated manner for all types of data and according to GDPR, HIPPA, and other regulatory frameworks.

Also Read: PwC and OpenAI Partner to Build AI-Native Finance Function, Reshaping the Future of Fintech

Saving Cloud Costs: The elimination of large volumes of data transfer and duplication results in lower costs.

Near-Zero Latency: Analysts work with the most current operational data, allowing for “now-casting” rather than “forecasting” based on yesterday’s reports.

Impact on the Data Science Industry

This collaboration marks a significant transition in the Data Science sector, moving the needle from “Experimental AI” to “Operational AI.”

1. Accelerating the Lifecycle of Machine Learning (ML) For data scientists, 80% of the job is often described as “data janitoring”-the tedious process of cleaning and moving data. Zero-copy architecture removes a massive portion of this burden. By gaining instant access to ServiceNow’s rich operational datasets, data science teams can build, train, and deploy ML models much faster. Whether it’s predicting IT outages or identifying patterns in customer service friction, the time-to-insight is reduced from weeks to minutes.

2. The Rise of “Agentic” Data Science As the industry moves toward Agentic AI-where AI agents take autonomous actions-the need for real-time data is paramount. A data science model that predicts a supply chain disruption is only useful if it can trigger a workflow. By integrating Cloudera’s analytical power with ServiceNow’s workflow engine, the industry is creating a “closed-loop” system where data science doesn’t just inform a report; it powers a self-healing enterprise.

3. Enhanced Governance for AI Training Training AI models on “shadow data” or ungoverned extracts is a major risk for modern businesses. The Zero-Copy Connector ensures that the data used for training remains within the secure boundaries of the enterprise fabric. This allows data scientists to build more trustworthy models that are grounded in verified, governed, and high-fidelity operational data.

Effects on Businesses Operating in the Industry

The ramifications of the Cloudera announcement have much wider implications, serving as a framework for the “Intelligent Enterprise” in 2026:

Improved Operations through “Proactive Service”: Companies can shift from a reactionary support process to proactive service. Through the analysis of ServiceNow log files in real-time using Cloudera, organizations can detect when a particular server is about to fail or when customers complain about the same issue.

Data Democratization: It no longer requires you to be a data engineer to analyze complex workflows. By providing companies with a way to access ServiceNow data through SQL queries and Cloudera BI tools, firms can empower more individuals—from product managers to HR managers-to make informed data-driven decisions.

Resource Optimization: With ETL pipelines being phased out, IT and data engineering staff members can be reassigned to more value-added initiatives, such as developing AI agents and enhancing data quality.

Resilience in a Volatile Market: In a fluctuating economy, the ability to see the “health” of an organization’s workflows in real-time is a strategic moat. Companies that leverage this zero-copy integration will be more agile, responding to internal and external shocks with precision.

Conclusion

Cloudera’s introduction of the Zero-Copy Connector for ServiceNow marks a groundbreaking occasion in the “Data-to-Action” transformation. By removing the inefficiencies of data transfer, Cloudera does more than offer yet another tool-it changes the very nature of the contemporary data ecosystem itself. For the community of data science practitioners, this marks a freeing from the limitations of siloing data. For the business entity, it is nothing less than the building blocks of an autonomous company-a company driven by insights and action.

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The company’s latest release reflects a growing pressure on data teams, who are increasingly tasked with supporting AI initiatives while maintaining speed, reliability, and cost efficiency. Much of the challenge stems from repetitive engineering work—building pipelines, maintaining transformations, and troubleshooting data flows—that slows down delivery and limits scalability.

Qlik’s updated approach introduces agentic capabilities directly into engineering workflows, allowing teams to translate intent into functioning data assets while preserving control and governance required in production systems.

“Most companies do not struggle to imagine AI use cases. They struggle to deliver the trusted, current data those use cases depend on,” said Mike Capone, CEO, Qlik. “As demand rises, data engineering becomes the critical path. Qlik is helping teams reduce friction, protect trust, and keep pace with the business.”

Also Read: BigPanda and ServiceNow Team Up to Cut Alert Noise and Accelerate Incident Resolution

The release includes several key enhancements. Declarative pipelines bring a new dimension of intuitiveness and guidance to constructing data flows, reducing the complexity associated with developing and evolving pipelines. An AI Assistant that can help create jobs, write SQL, and generate documentation for Talend Studio is expected to become available soon.

Another example of advances made by Qlik in the area includes extending its real-time routing functionality to accommodate agentic workflows, which facilitate connecting large language models and retrieval-augmented generation pipelines. In addition, Qlik has recently integrated its Open Lakehouse with streaming data, merging batch, CDC, and real-time event handling into one place.

Overall, such innovations attempt to transition data engineering from a process-oriented, labor-intensive effort to an intention-based, AI-powered activity.

“There is a big difference between an assistant that helps write code and a system that actually helps a data team move faster end to end,” said Robin Astle, Principal Developer, Valpak. “The interesting part of this announcement is the focus on pipeline creation, data quality, metadata, and stewardship together, because that is much closer to how real engineering work happens.”

The post Qlik Expands Agentic Data Engineering to Accelerate AI-Ready Data Delivery appeared first on ITDigest.

Across the industry, organizations are under increasing pressure to accelerate throughput while maintaining strict quality and compliance standards. According to McKinsey & Company, advanced analytics and automation can improve productivity in pharmaceutical operations by up to 30%, yet adoption remains uneven due to fragmented data and workflows. Similarly, Deloitte reports that over 60% of life sciences companies still struggle with siloed systems that limit effective data utilization, while Gartner notes that fewer than half of AI initiatives in regulated industries successfully scale beyond pilot phases.

L7|SYNAPSE addresses this gap by embedding a conversational, context-aware interface directly into the workflow execution layer of L7|ESP. Users can build agents, ask questions, retrieve data, generate workflows, and produce summaries using natural language (including voice), without needing to navigate underlying systems or data structures. More importantly, every response is grounded in a private, organization-specific knowledge base that includes SOPs, protocols, and batch records, ensuring outputs are accurate, traceable, and aligned with governed data.

Also Read: SiMa.ai Secures Strategic Investment from Micron to Scale High-Performance, Power-Efficient Physical AI

The development of L7|SYNAPSE has been shaped in close collaboration with early customers, incorporating real-world feedback from laboratory, quality, and manufacturing environments. These insights have directly informed key capabilities, from knowledge grounding and permission-aware responses to workflow generation and cross-system data access, ensuring the solution addresses practical challenges encountered in day-to-day operations.

By retrieving relevant information before invoking a large language model, L7|SYNAPSE delivers citation-backed answers that reflect real-time operational context and user permissions. This approach enables organizations to move beyond experimental and point AI use cases and toward consistent, compliant execution at scale. The platform also supports flexible integration with all the major cloud-based (Claude, ChatGPT, AWS Bedrock) or locally hosted LLM models, allowing organizations to meet security and regulatory requirements. L7|SYNAPSE is also compliant with industry standards such as MCP and A2A.

“L7|SYNAPSE is designed to close the gap between AI capability and the operational reality of running small and large regulated scientific enterprises,” said Vasu Rangadass, Ph.D., President & CEO of L7 Informatics. “It enables teams to interact with complex workflows in a simpler way while ensuring every action and insight remains grounded in trusted, governed data.”

By reducing the need to search across systems, interpret fragmented documentation, or rely on specialized expertise, L7|SYNAPSE streamlines workflows across laboratory, quality, manufacturing operations, and tech-transfer between Pharma and CRDMOs. The result is faster decision-making, improved consistency, and a more scalable approach to execution across the scientific value chain.

Source: PRNewswire

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The innovative platform features a network of nine intelligent agents which operate around the clock during all stages of the data lifecycle. They are responsible for monitoring data pipelines, analyzing anomalies, generating insights, fixing issues, and creating documentation without the necessity of any human assistance.

With Anomalo’s autonomous platform, companies will be able to advance beyond data observability tools by integrating agentic AI into their data operations. In contrast to conventional solutions which require people to constantly monitor and solve any data-related issues, the platform will be able to analyze anomalies, find out their cause, and resolve the problem.

The innovation will enable businesses to have reliable and high-quality data for AI and other analytics purposes. With a growing dependence on artificial intelligence for decision-making, there has been an increasing demand for trustworthy data.

Also Read: HENNGE Launches Endpoint & Managed Security to Strengthen Cloud Security Portfolio

Implications for the IT Industry

The deployment of self-driving data solutions is an indication that there is a broader change taking place in the world of information technology towards autonomous data infrastructure. In traditional IT infrastructures, data operations and management processes involved a lot of human effort in order to keep systems up and running.

Through the adoption of agentive artificial intelligence in data systems, IT teams are embracing self-recovery capabilities that enable data systems to monitor and self-correct in case of failures. Through this development, IT professionals are expected to experience reduced workload and more reliable systems.

From a management perspective, this development is going to raise the need for focus on governance, orchestration of AI solutions, and frameworks for trust. Data governance and audit processes should also be able to provide clear transparency and traceability for decisions made by intelligent agents to ensure accountability within regulated organizations.

In essence, with such developments being witnessed in the world of IT, data is turning into a self-executing system as opposed to its current status of merely a passive resource.

Business Impact and Strategic Value

In terms of business operations, the benefits that come along with switching to self-driving data cannot be overstated. The automation of the process of data quality management helps in increasing uptime by avoiding problems related to poor data quality; enhances decision accuracy, and also leads to faster time-to-insight.

Having reliable data is an important requirement for any AI-based project like predictive analytics, personalized services for customers, etc. This way, the company will be able to rely on its data management systems and increase the number of AI implementations.

Decreasing the need for manual labor results in cost savings and increased productivity. Thus, employees will be able to work not only on monitoring and maintaining the data quality but on some other projects as well.

Finally, using self-driving data gives companies an opportunity to react faster to changes in the market environment.

Driving the Future of Autonomous Data Systems

Anomalo’s announcement underscores a defining trend in enterprise technology: the transition from manual data management to intelligent, autonomous data ecosystems. As data volumes continue to grow and AI adoption accelerates, traditional approaches to data operations are becoming unsustainable.

By introducing a system where data can effectively “manage itself,” Anomalo is helping redefine how organizations approach data reliability and governance. For the IT industry and businesses alike, this marks a significant step toward a future where data is not just a resource—but an intelligent, self-operating foundation for innovation and growth.

The post Anomalo Introduces Autonomous ‘Self-Driving Data’ System to Redefine Enterprise Data Operations appeared first on ITDigest.

IT operations teams in large-scale enterprises frequently face difficulties in managing high volumes of event noise, and the new BigPanda application aims to provide a single incident experience within the ServiceNow IT Service Management (ITSM) system. This application will automatically provide enriched information in the form of topology, probable root cause, and configuration management database (CMDB) to the ITSM system. This will enable the elimination of duplicate or redundant tickets.

Customers using BigPanda with ServiceNow report up to 99% reduction in alert noise, over 50% fewer incident tickets, and 30–50% faster mean time to resolution (MTTR), generating tangible operational savings and improved service reliability.

Also Read: Unstructured and Teradata Partner to Scale AI-Ready Data

“As we use ServiceNow on a daily basis for incident, problem, and change management, integrating BigPanda’s incident and change capabilities into ServiceNow has reduced manual ticket creation and improved correlation between Incidents and Change. Overall, the integration has been highly effective,” said Ben Narramore, Director of Global Operations and Service Management at Sony Interactive Entertainment.

BigPanda works within existing ITSM and monitoring infrastructures, allowing enterprises to gain immediate value without disrupting workflows, regardless of IT Operations Management (ITOM) maturity.

“Enterprises have made ServiceNow the system of record for IT operations, but many still struggle to operationalize the massive volume of signals flowing into it,” said Tom Melzl, Chief Revenue Officer at BigPanda. “Whether an organization is early in its ITOM journey or operating a mature NOC, they can start seeing improvements in MTTR within weeks without needing to re-architect their environment.”

“BigPanda’s certified application for ServiceNow gives customers powerful new ways to cut through alert noise, accelerate incident resolution, and get more value from their ServiceNow investments,” added Alix Douglas, Group Vice President, Partner Solutions at ServiceNow. This partnership signals a new era of faster, more reliable IT operations for enterprises navigating increasingly complex digital environments.

The post BigPanda and ServiceNow Team Up to Cut Alert Noise and Accelerate Incident Resolution appeared first on ITDigest.

While enterprises are looking to take AI from a proof-of-concept phase into production, the pain points associated with fragmented data systems and complex integration scenarios have become more pronounced. Most traditional methods seek to rebuild relationships between data sets at the inference layer, resulting in non-consistent results and a lack of transparency. Arango’s latest platform addresses this by embedding contextual modeling directly into the data layer, allowing enterprises to maintain a continuously updated and governed data foundation.

The Agentic AI Suite is a major part of the release. It comprises over 20 built-in AI services as well as exclusive tools such as AutoGraph, AutoRAG, and Arango Ada. These tools automate essential tasks such as data ingestion, contextual modeling, retrieval optimization, and workflow orchestration, therefore greatly decreasing the engineering work needed to go from development to production.

Also Read: Unstructured and Teradata Partner to Scale AI-Ready Data

To give an example AutoGraph is responsible for automatically arranging structured and unstructured data into interconnected knowledge graphs, which allows AI systems to comprehend the relations between business entities and events. Meanwhile, AutoRAG enhances retrieval strategies by combining graph-based, vector, and hybrid search techniques, ensuring more accurate and context-aware outputs. Arango Ada further simplifies development by allowing users to interact with complex data systems through natural language queries.

The platform also offers a flexible deployment option of Bring Your Own Code/Container (BYOC) model. Thus, organizations can integrate their preferred AI models while still having control over security, governance, and compliance requirements.

Being highly scalable, the platform can be deployed on cloud, on-premises, hybrid, and air-gapped environments which make it suitable for regulated industries and very large-scale enterprise operations. By offering a unified contextual data foundation, Arango intends to assist organizations in developing AI systems that are not only scalable but also explainable, traceable, and in line with business conditions.

The post Arango Unveils Contextual Data Platform 4.0 to Accelerate Enterprise AI Deployment appeared first on ITDigest.

Teradata has embedded Unstructured’s data processing platform natively inside Teradata Enterprise Vector Store, giving customers a secure path to transform documents, images, video, and audio into AI-ready data without external tools or pipelines

The post Unstructured and Teradata Partner to Scale AI-Ready Data appeared first on ITDigest.

Also Read: Microsoft has introduced SQL Pool Insights Feature Enhances Monitoring in Microsoft Fabric Data Warehouse 

Commenting on the initiative, Oz Dogan, President, Americas of Blend, said, “This expansion strengthens our platform across the Americas. By establishing a deeper presence in Mexico, we are expanding our capacity to serve complex enterprise programs both for local clients and for organizations across the region. Our focus is simple: build where demand is growing, invest in long-term capability, and deliver on our clients’ needs at a consistently high level.” The company has also emphasized that the rise of the technology scene in Mexico, in combination with the presence of a robust talent pool and the increasing demand for advanced analytics and AI services among enterprises, makes it an excellent place to invest in the future. In this regard, Andrés Barrantes, the company’s SVP and LATAM Region Head, said, “Mexico is a critical and high-growth market. There’s real energy here. Organizations are investing in modernization and moving with urgency, and they need partners who are capable of driving real outcomes. Our investment is a reflection of our confidence in the market and our commitment to the long-term opportunity in this growing market.” As part of this investment, Blend360 is also looking to hire around 100 new employees in Mexico in the first year, with a multidisciplinary team of experts in AI and ML, data engineering, solution architecture, prompt engineering, and marketing technology.

Read More: Blend Enters Mexico as a Strategic Client Market and Operational Center, Deepening Collaboration with AWS to Accelerate Enterprise AI Across the Americas 

The post Blend Launches Mexico Hub, Expands AWS AI Partnership appeared first on ITDigest.

KDG, a leading provider of business advisory, technology, accounting, and artificial intelligence services, announced the acquisition of Square Foot Consultants, a leading Pennsylvania-based provider of business advisory, technology, and ERP consulting services to mid-market manufacturers.

After many years of working together, the acquisition integrates Square Foot Consultants’ deep expertise in business process improvement, business intelligence, artificial intelligence, and organizational training into KDG’s portfolio of specialized expertise. Together, the combined team will offer expanded capabilities designed to help organizations streamline manufacturing operations, data, ERP systems, and execute strategic initiatives with greater clarity and confidence.

“After five years of collaboration, it became clear that our combined potential far exceeded what we could achieve as independent entities. Square Foot’s deep proficiency in manufacturing technology, ERP, and artificial intelligence complements KDG’s existing scale and service diversity,” said Kyle David, CEO of KDG. “This acquisition isn’t just about expansion; it’s a strategic alignment of cultures that reinforces our commitment to growth driven by excellence, rather than growth for its own sake.”

Square Foot Consultants has built its reputation by developing systems, processes, and protocols and building durable competitive advantages for manufacturers  working alongside leadership and employees to analyze workflows, eliminate data silos, and create sustainable systems that teams understand and can maintain long term. This hands-on, collaborative philosophy closely aligns with KDG’s own approach to client partnerships and long-term value creation.

Also Read: Trifork Launches Danish Sovereign AI and Data Option 

Kalyn DeHaven, AVP of Design and Marketing at KDG, commented: “From the start, our work together with Square Foot has felt truly collaborative. They’ve operated alongside us in a way that already felt like part of the KDG team. Square Foot was aligned in values, thoughtful in their approach, and deeply committed to delivering meaningful outcomes for clients. Because of that, this transition feels like a very natural next step.”

“From my perspective, this is about people first.” said Matt Harwick, VP of Professional Services at KDG, “We’re incredibly excited to welcome Square Foot’s talent into KDG. They bring a depth of expertise and a client-first mindset that aligns perfectly with how we serve. Adding strong, experienced professionals to our team doesn’t just increase capacity – it elevates the quality, insight, and impact we’re able to deliver to every client.”

Nate Shaffer, President of Square Foot Consultants, spoke of the acquisition: “Over the years, we’ve been intentional about partnerships, knowing our clients place enormous trust in us to help guide their strategy, operations, and technology decisions. In KDG, Square Foot saw a partner that shares a genuine commitment to client success, collaboration, and practical outcomes. What excites me most about this transition is what it unlocks for our clients. Square Foot clients are gaining access to a broader network of talented professionals across technology, business consulting, accounting, and artificial intelligence, as well as experience spanning a wide range of industries. That expanded pool of expertise will enable us to move faster, solve more nuanced challenges, and bring more durable solutions to the table. We’re confident this transition will create meaningful gains for the companies we serve and position us to support them at an even higher level moving forward.”

Source: PRNewswire

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