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.”

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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.

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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.

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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.

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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 

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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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Also Read: AWS Glue Enhances Data Lakes with Apache Iceberg Materialized Views for Simplified Pipelines and Faster Queries 

For instance, analytics queries that are very read, heavy are usually processed in dedicated pools that are optimized for the reporting workloads, whereas the write operations like inserts, updates, and deletes are carried out in separate pools that are optimized for ETL and data ingestion tasks. Through SQL Pool Insights, operational telemetry that is pooled around these pools is being aggregated and surfaced through system views and monitoring tools so that teams can be ahead of the game in managing the system health and performance. By providing data on maximum resource allocation, workload configuration, and time, based operational states, among other metrics, the capability enables the organizations to have a clear picture of how the data warehouse capacity is used and where the potential for optimization exists. The rollout of SQL Pool Insights is a part of Microsoft’s secret mission to upgrade Fabric’s ecosystem observability and operational intelligence, thereby allowing the enterprises to execute heavy analytics workloads with higher reliability and transparency. Along with AI and business intelligence, analytics will most likely be the key use case for unified data platforms. There is a need for enhanced monitoring capabilities like SQL Pool Insights, which will be very useful in delivering consistent performance, making troubleshooting easier and helping data teams managing large, scale data warehouse environments to become more efficient. These capabilities are all geared towards helping the enterprises harness the power of a unified data platform to the fullest.

Read More: Microsoft introducing SQL Pool Insights in Microsoft Fabric Data Warehouse   

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The new feature is aimed at making it easier for organizations to access and share data without having to create multiple pipelines or duplicate data sets. The new feature will allow Databricks workloads to access OneLake data without having to go through multiple processes.

What the New Capability Enables

In the traditional data infrastructure, to provide access to different tools or platforms, teams tend to create multiple copies of the data. This increases storage costs, synchronization, and governance. With the new feature in preview, Azure Databricks is able to query or analyze data that already exists in OneLake without having to move or duplicate the data.

This enables multiple analytics teams and tools to work with the same set of curated data products in OneLake without having to set up parallel pipelines or storage layers.

OneLake is a single logical data lake for the entire organization. It brings analytics data into a single centralized environment that can be accessed by different engines and services.

With this integration of Azure Databricks and OneLake, Microsoft is positioning its ecosystem as a more interoperable analytics platform, enabling organizations to harness the power of Databricks’ advanced data engineering and AI capabilities together with Fabric’s unified analytics environment.

Why This Matters for the Analytics Industry

The move marks an increasing trend in the industry towards “zero-copy” and “zero-ETL” data models. In current enterprise environments, analytics platforms are often dependent on ETL (Extract, Transform, Load) processes to transfer data between systems. While these processes add complexity and latency to operations, they also cause inconsistencies in data between tools.

The zero-copy data model disrupts this approach by allowing analytics tools to directly access and share data storage layers. Rather than transferring data between systems, tools can access the same data while preserving governance and security processes.

This approach is already underway in the cloud data ecosystem. Solutions like Delta Lake, open table formats, and data virtualization platforms are making it possible for analytics tools to collaborate without having to constantly transfer data.

Microsoft’s new preview feature enhances this approach by enabling Databricks, a popular platform for large-scale data engineering and machine learning, to seamlessly integrate with the OneLake system.

Also Read: AWS Unveils Glue 5.1 – What It Means for Data Integration and Analytics

Impact on Enterprise Data Teams

For enterprises operating in the analytics and data engineering space, the integration offers several strategic advantages:

1. Reduced Data Duplication

Data teams often replicate datasets across multiple warehouses or analytics environments. Zero-copy access allows organizations to maintain a single source of truth, reducing storage overhead and eliminating version inconsistencies.

2. Faster Analytics Workflows

Without the need to build additional pipelines or data synchronization processes, analysts and data scientists can access datasets faster. This can accelerate experimentation, model training, and reporting.

3. Simplified Data Architecture

Complex data stacks often consisting of multiple lakes, warehouses, and ETL tools can be simplified. By centralizing data in OneLake and enabling cross-platform access, organizations can reduce infrastructure complexity.

4. Improved Data Governance

When data is duplicated across multiple environments, enforcing governance policies becomes challenging. With a single shared dataset, organizations can apply consistent security policies, access controls, and compliance frameworks.

Business Implications Across the Analytics Ecosystem

The general implications of this emerging trend go beyond the convenience of technology. For companies that are highly dependent on analytics, the ability to have unified data access can greatly impact the efficiency of business operations and decision-making.

For companies in sectors such as finance, retail, healthcare, and manufacturing, real-time data insights have become essential in informing business decisions. By making it easier to access data and analytics platforms, companies can make decisions faster.

In addition, companies that implement AI and machine learning processes can also benefit from the ability to have unified data access. Data scientists who work with Databricks to build machine learning models can easily access enterprise data stored in OneLake without having to duplicate data into other environments.

In terms of cost savings, companies can also benefit from reduced infrastructure costs due to reduced data duplication and pipeline maintenance.

The Competitive Landscape

The announcement also reflects Microsoft’s approach to more aggressively compete in the new data platform space. Cloud analytics vendors are also focusing on open data models and interoperability.

Microsoft’s goal of more closely integrating Fabric, OneLake, and Databricks is to build an ecosystem where analytics, data engineering, and AI can run on multiple platforms seamlessly. This will make Microsoft’s stack a single ecosystem for enterprise analytics, which could compete with other lakehouse and data platform offerings.

Looking Ahead

The preview release of zero, copy access between OneLake and Azure Databricks is giving another spin to open, integrated analytics ecosystems. As companies keep growing data operations and embedding AI at the center of decision, making, they will need to have a very good data interoperability

In case that feature is massively used by the market, it could be the fastest way to go to simplified, shared, data architectures, where analytics tools rely less on data movement and more on getting insights. For the analytics industry, the change implies a time when platform borders are no more and the data is really accessible throughout the enterprise.

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