Acceldata’s CEO Rohit Choudhary offers commentary on scaling, securing, and optimizing IT infrastructure for the digital age. This article originally appeared in Insight Jam, an enterprise IT community that enables human conversation on AI.

IT infrastructure is the foundation of modern business operations, enabling everything from real-time data processing to cybersecurity and automation. As organizations accelerate digital transformation, the complexity of managing infrastructure has grown significantly. Enterprises must address key challenges in scalability, security, cost management, and integration to ensure their IT environments remain efficient, resilient, and future-ready.

The Evolving IT Infrastructure Landscape

IT infrastructure has moved beyond traditional on-premise data centers. Hybrid and multi-cloud environments now dominate, enabling organizations to distribute workloads strategically. The adoption of AI-driven automation, edge computing, and sustainability initiatives adds additional layers of complexity. Keeping pace with these changes requires a well-planned approach to infrastructure strategy.

Scalability remains a primary concern as businesses grow and workloads expand. Infrastructure must be designed to support increased demand without introducing inefficiencies. While cloud environments offer flexibility, they require careful management to avoid unexpected cost spikes. Security and compliance are equally pressing issues. Evolving cyber threats make it essential for organizations to safeguard sensitive data and adhere to industry regulations. A misconfigured cloud instance or outdated security framework can expose critical vulnerabilities.

Managing IT infrastructure costs is another challenge. Spending on cloud services, maintenance, and energy consumption continues to rise, and without visibility into resource allocation, businesses risk inefficiencies and budget strain. Hybrid environments add another layer of complexity, requiring seamless integration between on-premise and cloud resources. Many organizations struggle to align legacy systems with modern cloud-based applications, slowing down digital transformation efforts. Additionally, the rapid evolution of infrastructure technologies demands specialized skills in cloud architecture, AI, and cybersecurity. The shortage of experienced professionals can delay critical initiatives and increase reliance on external providers.

Strategic Solutions for IT Infrastructure Success

A well-balanced hybrid infrastructure offers the best of both worlds, providing control over critical workloads while taking advantage of the flexibility and scalability of cloud resources. A cloud-first approach can be beneficial in many cases, but businesses should assess their specific needs before migrating entirely. A strong security strategy must be embedded into every layer of infrastructure. Implementing zero-trust frameworks, multi-factor authentication, and continuous monitoring reduces risk while improving compliance. Automated security tools also help organizations stay ahead of evolving threats with real-time detection and response capabilities.

Cost visibility and efficiency should be a top priority. FinOps principles help align cloud spending with business goals by providing real-time insights into resource utilization. Automated monitoring tools can prevent wasteful spending and optimize infrastructure costs. AI-powered management solutions further enhance efficiency by predicting failures, automating maintenance, and optimizing workloads. Automation reduces manual intervention and minimizes operational disruptions.

Investing in IT talent is also essential. Organizations should provide ongoing training in cloud management, AI-driven infrastructure, and security best practices. For specialized needs, collaborating with managed service providers can help bridge expertise gaps while ensuring operational continuity.

The Future of IT Infrastructure

Emerging trends will continue to shape infrastructure strategy in the coming years. Edge computing is driving real-time decision-making closer to data sources, AI is enabling predictive analytics and self-healing infrastructure, and sustainability initiatives are influencing how data centers are built and operated. Organizations that take a proactive approach to infrastructure – balancing security, scalability, and cost efficiency – will be best positioned for long-term success. A resilient IT infrastructure is more than just a technical foundation; it is a strategic asset that enables innovation, agility, and sustainable growth.

The post Scaling, Securing & Optimizing IT Infrastructure for the Digital Age appeared first on Best Data Management Software, Vendors and Data Science Platforms.

Progress Software’s Developer Advocate, Principal Eve Turzillo offers commentary on how MFT solutions can help businesses stave off catastrophe. This article originally appeared in Insight Jam, an enterprise IT community that enables human conversation on AI.

When it comes to data protection, most organizations today face an extraordinary amount of pressure. On the one hand, the sheer quantity of raw data has exploded: organizations are responsible for safeguarding more sensitive information than ever before. At the same time—and not without relation—federal and state rules around data protection have grown ever more stringent.

A quick rundown of just the most significant regulations includes the GDPR, the Payment Card Industry Data Security Standard (PCI DSS), HIPAA, the California Consumer Privacy Act (CCPA)—the list goes on. And new regulations are being introduced on a regular basis, with consumers increasingly concerned about the risks their data is being exposed to.

Failure to comply with these regulations can lead to significant penalties. In fact, a single HIPAA violation can cost an organization $50,000. The GDPR, meanwhile, can fine businesses up to 20 million euros or 4% of annual global turnover (whichever is highest) for compliance failures. Most of the costliest security breaches have taken place in the last three years, which just goes to show that the stakes have never been higher.

Organizations are working to align their operations with regulatory best practices, and over the years, one key contributor for compliance has emerged: managed file transfer (MFT). Transferring data is essential to enterprise operations and that data needs to be transferred securely for organizations to maintain compliance.

The Perils of Traditional File Transfer Protocols

While many businesses have migrated to SaaS solutions over the last 15 years, files still form the backbone of many major institutions—and this isn’t likely to change anytime soon. The Automated Clearing House (ACH) network, which facilitates financial transactions in the U.S., was responsible for 34 billion transactions totaling $86 trillion last year—and to a very large extent, those transactions were file-dependent.

Traditional file transfer protocols (FTP) have their uses, but they are unequipped for the needs of the modern enterprise, which must constantly work to prevent potential disaster. With FTP, user credentials are sent as plain text and files are unencrypted during the transfer process. This fails to meet most regulatory requirements, even before you factor in persistent connectivity and functionality issues. If the goal of the modern enterprise is to minimize vulnerability as much as possible, FTP falls short.

And that goal is, again, non-negotiable because ultimately, regulatory consequences are just the tip of the iceberg. A data breach of sufficient severity can slash market valuation, permanently damage consumer trust and halt business operations as IT personnel work to get systems up and running again.

Why Managed File Transfer (MFT) Is the Answer

The risk factors in FTP have led to the emergence of several solutions. But the openness of certain solutions comes with steep costs for businesses concerned with file protection. What businesses want is the simplicity combined with the reliability —without having to build out an impossible-to-navigate range of solutions. In this search, managed file transfer (MFT) has emerged as the clear winner.

MFT has three key benefits that distinguish it from other solutions and promote regulatory compliance:

1. Authentication and Encryption

At the heart of any compliance program is the concept that only authorized users can access and manage files. Through a series of authentication protocols, ex: multi-factor authentication (MFA) —MFT helps keep data out of the wrong hands.

Of course, even the most robust authentication protocols can’t deliver a 100% success rate, which is why encryption is so important. Encryption, another central aspect of MFT solutions, protects information from potential bad actors. This security measure means that the data is only accessible by those it is intended for. 

2. Secure Automation

Self-sufficient workflows are a primary ambition for many organizations in 2025. MFT solutions have been at the forefront of automation efforts for years, enabling complex file transfer orchestration without active human intervention. Through advanced, logic-based workflows, MFT solutions can automate transfer activity without putting sensitive information at risk.

3. Auditability

Even when an organization is operating without incidents, the compliance paperwork that regulations require can still drain resources. MFT solutions can provide precise data on compliance, allowing organizations to present regulators with detailed records of data movement—who accessed what, when they accessed it and more. In a world of constantly expanding regulations, organizations need to be able to track the movement of their files with extreme precision.

New data privacy laws are proliferating at a rapid rate. At the same time, enforcement of those laws is intensifying dramatically as slow-moving bureaucracies catch up with new technological developments. Organizations that minimize the importance of data protection are exposing themselves to significant risks, both in the short and long term. In this respect, MFT might be one of the best preventative measures available.

The post How MFT Solutions Can Help Businesses Stave Off Catastrophe appeared first on Best Data Management Software, Vendors and Data Science Platforms.

Ocient’s SVP Cloud Operations George Kondiles offers commentary on cloud vs. on-premise and the data debate goes back to the future. This article originally appeared in Insight Jam, an enterprise IT community that enables human conversation on AI.

Enterprise data infrastructure is undergoing a notable shift. While the past decade saw a rapid transition from on-prem deployments toward cloud-first strategies, a new reality is setting in. Organizations are reexamining the benefits of on-premise infrastructures — not as an antiquated or regressive move, but as a highly strategic choice, particularly for AI, analytics, and mission-critical workloads.

Real Talk – Why Are Enterprises Headed Back On Prem?

So, why are so many companies going back on prem? The answer isn’t a wholesale rejection of the cloud, but rather a nuanced reevaluation of workload placement. Optimizing for where workloads are deployed has a direct impact on data processing speed, latency, and overall system efficiency – all of which are essential for enterprises dealing with growing data and AI workloads to get right.

Plus, there are some compelling drivers shaping this new reality.

• The ‘surprise cloud costs’ conundrum – Public cloud pricing models, though attractive at first, have lost the predictability promise for running compute-intensive, always-on workloads. With unexpectedly high operational costs, enterprises are reconsidering the total cost of ownership (TCO) of their cloud deployments.
• AI and operational performance – Put simply, some workloads that require low latency and high throughput (e.g., edge computing or intensive AI) achieve better performance and results when run on dedicated, local infrastructure.

Cloud vs. On Prem Trade-Offs: Performance, Cost, and Sustainability 

It’s important to weigh the business-critical trade-offs when evaluating (or reevaluating) cloud vs. on prem infrastructures.

Performance 

Cloud environments offer unmatched flexibility and scalability, making them a natural choice for sporadic or unpredictable workloads. However, always-on workloads that demand consistent, high-performance computing may suffer from latency and network bottlenecks in the cloud, even with cloud services optimized for speed.

On-prem infrastructures, on the other hand, offer direct control over hardware, enabling IT teams to tailor configurations for maximized throughput and minimal response times.

Cost 

Cloud services entered the game as a cost-effective option for organizations with fluctuating data needs, with pay-as-you-go pricing models that initially promised scalability and cost savings. However, costs scale dramatically as workloads increase in complexity, leaving enterprises paying premiums for compute cycles, large data transfers, and increased storage needs.

Conversely, on-prem solutions may require a higher upfront investment but also deliver long-term savings due to fixed costs and reduced reliance on third-party services, particularly for high-volume workloads.

Sustainability

The trade-offs surrounding sustainability can be even more nuanced.

For instance, cloud providers invest heavily in renewable energy and energy-efficient data centers. However, running compute-intensive tasks over distributed infrastructures can consume significant resources, which we know is an industry-wide problem that needs urgent addressing.

On-prem solutions, on the other hand, can offer better control over energy consumption based on efficiencies delivered at the hardware and software layer, with some able to deliver critical data performance in a significantly smaller data center footprint.

The Takeaway 

Considering performance requirements, budget constraints, and sustainability commitments within the greater evaluation of workload optimization is essential when constructing a resilient and adaptable infrastructure.

Example: Cloud vs On Prem in AdTech

Consider an AdTech company managing real-time bidding (RTB) operations.

RTB demands ultra-low latency because ad placements must be processed in milliseconds to ensure optimal campaign performance. TL;DR: Operational performance is a critical driver of success for the company.

With a cloud-first infrastructure approach, network variability that can stem from multi-tenant architectures could mean unpredictable latency spikes and result in the company missing critical bidding opportunities. In addition, the high-volume, real-time operations typical for AdTech and other organizations may push the company’s cloud costs outside of acceptable thresholds.

Running the same workloads on-prem, the company can minimize network latency and achieve faster data processing speeds, allowing for seamless bidding and reduced ad server response times. This control also means consistent and predictable performance, which is critical for real-time bidding, as well as less varied OpEx costs.

A Peek At the Future 

Change and innovation with technology happens rapidly. Though we don’t have a crystal ball, there are trends taking hold of enterprise IT that may shape the future of data infrastructure in five to 10 years. Consider the below as food for thought:

• Increasing Needs At The Edge: Real-time data and IoT operations at the edge are influencing rapid computing infrastructure innovation. The need to process data closer to its source will continue pushing the industry at large for decentralized processing capabilities.
• Hyper-Personalized AI Systems: Future AI-driven systems will not only determine infrastructure needs in real time, they will deploy those resources autonomously (or at least semi-autonomously) — which creates the potential for enterprises to optimize their infrastructure for performance, cost, etc.
• Built-In Efficiency Layers: Hardware-software codesign is creating operational and energy efficiencies that are unlocking new levels of performance and minimizing resource consumption. As these innovations shape a new era of intelligent hardware and software integration, enterprise leaders will have an increased ability to reconfigure and balance business needs and sustainability goals.

The debate surrounding cloud vs. on-premise infrastructures is dynamic and evolving. Just like the need for agility carved out a path for innovative cloud applications, the future of enterprise IT will be shaped by intelligent, workload-specific optimizations made by enterprises creating the infrastructure for data-driven innovation.

The post Cloud vs. On-Premise: The Data Debate Goes “Back To The Future” appeared first on Best Data Management Software, Vendors and Data Science Platforms.

Solutions Review’s Executive Editor Tim King highlights the overarching AI impact on data management jobs, to help keep you on-trend during this AI moment.

One of the least surprising things someone can say in 2025 is that artificial intelligence (AI) has impacted data management jobs. What’s less clear is the specific impact AI has had on those jobs, and whether data management professionals should feel threatened or empowered by the rise of generative AI, LLMs, and automation. With AI being woven into every aspect of how organizations collect, govern, protect, and steward data, the structure and purpose of data management roles are evolving at breakneck speed.

To keep pace with these changes, the Solutions Review editors have mapped out the key ways AI has transformed data management, what data stewards and managers can do to maintain relevance, and what the future may hold for their careers and core responsibilities.

Note: These insights were informed through web research using advanced scraping techniques and generative AI tools. Solutions Review editors use a unique multi-prompt approach to extract targeted knowledge and optimize content for relevance and utility.

AI Impact on Data Management Jobs: How Has AI Changed the Data Management Workforce?

The impact of AI on data management has been both sweeping and paradoxical. AI has taken over huge swathes of tedious manual work—think data cataloging, tagging, lineage tracking, policy enforcement, and metadata management. This has created the freedom for data management pros to focus on strategy and governance rather than grunt work. Yet, it’s also made certain foundational skills and roles—like manual data quality checks, rule-based stewardship, and legacy master data management—less valuable, if not redundant.

Automated Metadata Management and Data Cataloging

For years, data management teams painstakingly cataloged data assets, tagged metadata, and tracked data lineage to enable discoverability, compliance, and trust. Today, AI-driven data catalogs like Collibra, Alation, and Informatica CLAIRE can crawl, classify, and suggest business definitions automatically, keeping pace with data at cloud scale. These platforms not only surface hidden relationships but also recommend privacy tags and policy assignments. The upside: data becomes more discoverable and governed at scale. The downside: the classic “metadata librarian” role—a fixture in traditional IT—has become far less relevant.

Data Quality and Governance

AI-powered data observability platforms now monitor for data drift, outliers, and data integrity issues in real time, auto-generating reports and suggesting remediations. AI agents can trigger corrective actions and even notify stakeholders before flawed data can impact business decisions. This doesn’t mean the human element is obsolete; if anything, oversight becomes more critical, as professionals are needed to interpret, validate, and challenge AI’s judgments—especially as AI can introduce new types of errors, biases, or even hallucinations. Still, entry-level stewardship jobs that revolve around manual review are in decline, and the “craft” of traditional data cleansing is shifting rapidly toward a role of guiding, auditing, and curating AI-driven processes.

Privacy, Security, and Compliance

One of the most profound AI impacts is in privacy and compliance. AI tools now scan for sensitive data, automatically classify PII, and apply (or recommend) policies for GDPR, CCPA, HIPAA, and other regulatory mandates. While this makes compliance faster and more comprehensive, it also means the compliance professional’s job is less about hunting for issues and more about defining ethical frameworks, auditing AI systems, and handling edge cases. AI is good at enforcing rules but still poor at understanding nuance. As regulatory and ethical scrutiny increases, human oversight is more crucial than ever. In the next wave, expect to see new roles like “AI compliance analyst” or “ethical data steward” emerge as organizations grapple with risks unique to machine-driven automation.

Master Data Management and Data Integration

AI-driven MDM tools can reconcile entities, resolve duplicates, and stitch together fragmented records from disparate sources at machine speed. They’re great at recognizing patterns and performing fuzzy matching that would have taken humans weeks or months. But this also means classic MDM administrators—once prized for their meticulousness and tribal system knowledge—are being retooled or redeployed. Instead, the value now is in designing matching strategies, validating results, and integrating MDM processes into AI-augmented data pipelines. The “glue work” that kept MDM viable is dissolving, and those who rely on old-school MDM scripting are facing a shrinking field.

A 2024 IDC report found that 68% of organizations with AI-powered data management platforms reduced time spent on data cataloging and quality tasks by over 50%, while 72% reported improved compliance and audit readiness. But 57% said they were struggling to retrain legacy staff for the new AI-centric tools, and 48% expressed concern about over-reliance on automated recommendations.

The Emergence of AI-Centric Data Management Roles

The decline of some legacy roles is matched by the rise of new, AI-focused positions. Data management teams increasingly need “data product owners,” “AI policy architects,” and “AI trust leads”—people who understand both the technical and ethical dimensions of managing data in an AI-rich environment. LinkedIn’s 2025 “Skills on the Rise” report lists AI governance, explainability, and data privacy as the fastest-growing requirements for data management professionals.

Still, don’t mistake this influx of new roles as permanent. There’s every reason to believe that, as AI gets smarter, even some of these hybrid jobs could become ephemeral. The safest strategy: cultivate deep business acumen, critical judgment, and the ability to question automated outcomes—skills AI can’t easily replicate. The professionals who will thrive are those who can translate between the business, the technologists, and the AI systems, ensuring that automation aligns with organizational values and regulatory boundaries.

Upskilling for the Future

Upskilling is not just advisable—it’s existential. The mantra “learn AI or be replaced by someone who has” is no longer speculative. For data management pros, this means focusing on:

• AI literacy and tool fluency: Get hands-on with AI-driven governance, cataloging, and data quality tools.

• Policy, ethics, and regulatory intelligence: Stay ahead of privacy law, bias detection, and automated policy enforcement.

• Communication and leadership: Guide multidisciplinary teams and bridge the gap between business and technical stakeholders.

• Critical thinking and oversight: Develop the instincts to question, audit, and intervene in automated processes.

For organizations, data management can’t be seen as a “back-office” or merely IT discipline any longer. The best teams are evolving into cross-functional “data enablement” units that empower self-service analytics, support compliance, and drive value from data assets—powered by AI, but grounded in strong human judgment.

AI Will Augment Data Management Jobs, Not Replace Them—But the Role Is Evolving

Ultimately, AI is automating away the tedious and the routine in data management, but it can’t automate accountability, ethical decision-making, or business alignment. The professionals who are proactive about learning new tools and frameworks, who can interpret and guide AI outputs, and who see the bigger picture will always be in demand. The future for data management is not about clinging to legacy processes, but about becoming the trusted steward and strategic enabler for the AI-powered enterprise.

Bottom line: AI is transforming data management from the ground up, eliminating the old “janitorial” tasks and creating a new class of high-impact, high-responsibility work. To thrive, data management professionals must embrace change, develop AI fluency, and double down on the human skills that machines can’t (yet) replace. The future is wide open—but only for those willing to reinvent themselves.

The post What the AI Impact on Data Management Jobs Looks Like Right Now appeared first on Best Data Management Software, Vendors and Data Science Platforms.

Successfully implementing and maintaining a master data management (MDM) project can be challenging, with Gartner reporting that 75% fail to meet business expectations. With failure rates this high, businesses can’t afford to get it wrong. A failed implementation doesn’t just waste time and resources — it can also lead to missed opportunities, poor decision-making, and competitive disadvantages. So, what does it take to ensure success from the start?

Here are some key lessons and best practices to help get your MDM implementation right the first time.

1.    Elevate data management to board-level

For an MDM project to succeed, it must be positioned as a strategic business enabler, not just a technical initiative. Gaining board-level support ensures that MDM is recognized as a critical foundation for:

●    Operational improvements: A single, accurate view of data reduces inefficiencies, streamlines processes, and enhances collaboration across departments.

●    Intelligent decision-making: Reliable data empowers leadership to make faster, data-driven decisions, improving forecasting, customer insights, and strategic planning.

●    Risk management and compliance: Strong data governance minimizes regulatory and security risks by ensuring data accuracy, consistency, and traceability.Without high-quality, well-managed data, even the best strategies can fall apart. Embedding MDM into board-level discussions ensures that organizations treat data as a core business asset, significantly increasing the likelihood of first-time success.

2.    Decide on your implementation styleSince each organization has unique requirements and desired outcomes, understanding the different styles of MDM implementation will assist with selecting the best path.

●    Consolidation style: This method gathers and integrates data from multiple sources into a centralized MDM system, ensuring a single source of truth. It’s best suited for organizations that prioritize data quality and consistency to enhance reporting and analytics while simplifying complex data structures.

●    Registry style: Here, the MDM system functions as a centralized hub rather than just storing the data, offering a unified view without altering source systems. This low-risk solution makes it an excellent choice for business environments with strict data governance controls, maintaining autonomy across existing systems while minimizing disruption.

●    Coexistence style: This model enables seamless source-system synchronization, allowing centralized and distributed MDM. It provides flexibility for organizations with evolving business needs, ensuring consistency while enabling a phased implementation with little operational interference.

●    Transaction style: This style, designed for real-time data management, positions the MDM system as the primary data source, ensuring continuous updates. It’s ideal for organizations operating in fast-paced environments that require the most current data for decision-making and operational efficiency.

3.    Challenge conventional success metricsTraditional MDM success metrics often focus too narrowly on technical milestones — such as system deployment or data integration — without considering the broader business impact. Instead of asking, “Is the MDM system fully implemented?”, organizations should be asking questions like:

●    “How has MDM improved our ability to make strategic decisions?”

●    “Are we seeing measurable improvements in efficiency, risk management, or customer experience?”

●    “How is better data management contributing to revenue growth or cost savings?”

Broadening the success criteria ensures that MDM initiatives stay aligned with business priorities rather than becoming isolated IT projects.

Cross-functional workshops can further reinforce this alignment by bringing together diverse teams to creatively solve challenges and refine strategies. Additionally, organizations should continuously reassess objectives to adapt to evolving market conditions, using real-time feedback loops to stay on course.

Ultimately, the most successful MDM programs are those that evolve — leveraging lessons learned to drive ongoing improvement, empower employees with a deeper understanding of data’s role in decision-making, and ensure that MDM remains a sustained driver of business success.

4.    Design for both now and later

By designing their MDM strategy with a long-term vision, organizations can ensure seamless integration with emerging technologies that may not yet be mainstream. Building flexible data architectures that can adapt to evolving industry trends — such as mandatory sustainability reporting and the growing adoption of AI — can minimize the need for costly overhauls in the future. Prioritizing scalability further ensures that systems can handle tech innovation, increasing data volumes, and organizational growth without disruption.

To support this adaptability, businesses should build flexibility into budgeting and allocate resources for future technology investments. This ensures they can make timely upgrades that keep their systems at the forefront of innovation.

5.    Embrace change as everyday practice

MDM success requires adaptability, making change a continuous process rather than a one-time event. Embedding agility in project management through iterative development and continuous feedback ensures systems remain flexible and responsive.

Encouraging experimentation and testing is also important — giving teams the space to trial new data models, automation tools, and governance practices allows organizations to refine their approach without fear of failure. This culture of continuous learning helps ensure MDM evolves alongside business needs.Regular stakeholder feedback helps refine strategies while celebrating small wins keeps teams motivated. Finally, staying informed about industry innovations ensures businesses remain competitive, integrating emerging best practices to drive long-term success.

A strong foundation for data-driven growth

Getting MDM right is a key strategic move for any data-driven business. With a well-planned implementation strategy and the right tools, organizations can transform their data into a valuable asset that fuels reduced risk, facilitates better decision-making, supports AI initiatives and can provide a competitive business advantage.

The post Mastering MDM: How to Get it Right the First Time appeared first on Best Data Management Software, Vendors and Data Science Platforms.

In the era of GenAI, data management goes beyond storage, security and resilience; it’s a strategic necessity for turning data into enterprise value. A holistic approach to data management is essential not only for operational resilience but also for maximizing the value of data in today’s interconnected and cloud-reliant world. AI is the ultimate workload for hybrid cloud, requiring enterprises to securely unify data across hybrid environments.

This is why today’s AI-centric organizations must think hybrid when it comes to their data management strategy. We can look at an incident last year as an example—when a significant outage at major cloud provider disrupted operations for several multinational companies, including delays in financial transactions and interruptions to enterprise applications. This event, which caused a seven-day outage, underscores the critical need for organizations to diversify their data and storage strategies to ensure resilience against potential single-provider failures.

Data is the Cornerstone of AI

Data serves as the essential resource that enterprises depend on for their operations and is at the heart of market competitiveness. Effective data management is a prerequisite for advancing AI innovation and enterprise business transformation. When dealing with complex and disorganized data, enterprises must harness the “transformative power of data organization” and utilize data management platforms to centralize “data storage.” This is the crucial first step when advancing AI development and driving digital transformation.

According to IDC, only one-third of enterprise data is stored in the public cloud, while the remaining seventy percent is dispersed across data centers, internal systems, and edge locations. With the increased adoption of GenAI, much of the data driving these use cases is expected to be created at the edge rather than in the cloud, further contributing to this fragmentation. Our regular discussions with management have shown that enterprises are now not only focused on improving the efficiency, visibility, elasticity, and scalability of their data but are also planning to implement multiple backup systems with equal functionality to prevent single points of failure.

Additionally, it’s important to note that many enterprises often end up scattering data across multiple locations without a clear plan. This unplanned approach, often referred to as “hybrid-by-accident”, complicates data management and increases the risk of security breaches during data transfer. Additionally, it leads to the inefficient use of IT and financial resources. As a result, companies struggle to effectively utilize their valuable high-quality operational data, which in turn hampers AI training and application processes.

To address this, enterprises need to transition to a “hybrid-by-design” strategy, where infrastructure, applications and data deployment across hybrid environments are intentional and well-managed. This shift enables companies to better leverage their high-quality operational data, allowing for enhanced AI training, inferencing, fine-tuning and retrieval augmented generation applications.

Creating Data Management Architecture to Drive Transformation

While data distribution is essential to mitigate risks, it requires a unified approach to be effective. Many enterprises are recognizing the value of implementing unified data architectures that simplify storage and data management and centralize the management of diverse data platforms. These architectures, combined with intelligent data platforms, enable seamless access and analysis of data, making it easier to support analytics and ingestion by generative AI.

IT managers can further enhance a system’s data analysis, network security, and introduce a hybrid cloud experience to simplify data management. Today, the tech industry is focused on streamlining how enterprises manage and optimize storage, data, and workloads and a platform-based approach to hybrid cloud management is critical to manage IT across on-premises, colocation and public cloud environments.  Innovations like unified control planes and, software-defined storage solutions are being utilized to enable seamless data and application mobility. These solutions allow enterprises to move data and applications across hybrid and multi-cloud environments to optimize performance, cost, and resiliency. By simplifying cloud data management, enterprises can efficiently manage and protect globally dispersed storage environments without over-emphasizing resilience at the expense of overall system optimization.

Data Management Should Not be Overlooked by Management

Effective data management is essential not only for AI development but also serves as a critical risk management strategy. Neglecting AI can result in significant reputational and financial damage. The most talked about outage of 2024 and the largest in IT history might have been mitigated by unifying the data formats of the main and backup systems to allow for seamless switching between them. Additionally, during ransomware attacks, storing mission and business-critical data in a cyber vault offers an extra layer of protection, ensuring rapid recovery and minimizing disruption.

It’s clear that management and IT managers must be prepared for unforeseen risks, and data management, though fundamental, must become unified to unlock its value. Data must move from just storage to become a value creator and that’s only possible when data is accessible, secure, ready to be analyzed and ingested by AI and the daily operations of enterprises. Without effectively utilizing scattered operational data, discussing how to convert data into business value becomes challenging, and falling behind in the AI era is a risk no business or organization should take.

The post From Silos to Strategy: Why Holistic Data Management Drives GenAI Success appeared first on Best Data Management Software, Vendors and Data Science Platforms.

Altair’s Christian Buckner offers insights on how the universe is a graph and why it’s time our data started acting like it. This article originally appeared on Solutions Review’s Insight Jam, an enterprise IT community enabling the human conversation on AI.

Reality is not a collection of isolated entities, neatly boxed and categorized. It is a web—vast, intricate, and endlessly interconnected. From the movements of galaxies to the subtleties of human relationships, the universe reveals itself as a tapestry of connections, each thread influencing and entwining with countless others. Yet, in day to day business, when we attempt to model the complexity of our business universe, we usually rely on rigid relational data structures—rows and columns that impose artificial order on an inherently fluid world. And to accommodate this unnatural model, for decades we have built increasingly convoluted and expensive systems that shoehorn real world intricacies into the rows and columns today’s analytics tools understand.

But the world is changing, and so are the tools we use to understand it. Generative AI, with its astonishing ability to create, predict, and understand, demands a different perspective. Generative AI models generate meaningful outputs by capturing patterns, learning from rich contextual data, and leveraging complex interdependencies. Unlike traditional analytics and machine learning, generative AI doesn’t merely process datasets in isolation—it thrives on the relationships between ideas, concepts, and patterns. It begs for data that exposes the complexity of the real world, allowing it to traverse idea to idea and piece together meaning from the natural interconnectedness of reality.

In other words, generative AI needs a graph. With nodes representing entities and edges capturing the relationships between them, a graph can represent massive networks of information and provide a data structure that mirrors the interconnectedness of the real world. Unlike rigid relational models, graphs allow for dynamic connections, enabling generative AI to explore context, infer meaning, and uncover hidden patterns. Graphs can represent hierarchies, peer-to-peer relationships, temporal changes, and multi-dimensional dependencies, all in a single framework. This flexibility makes them uniquely suited to capture the complexity of reality—whether it’s mapping customer interactions, modeling supply chains, connecting field data to manufacturing data and designs, or understanding the intricate cause and effect links in life sciences research. For generative AI, a graph is not just a database; it is a living model of knowledge that it can navigate, learn from, and act upon, unlocking insights and automation that rigid data structures could never achieve.

At the same time, pragmatism dictates that we must meet reality where it stands. Today, the data ecosystems of most organizations are deeply entrenched in relational paradigms. Relational databases, document stores, enterprise applications, and other business systems form the backbone of how businesses store and manage information. Overhauling these systems entirely in favor of a graph-based architecture is not a realistic or immediate solution.

Instead, the path forward lies in building a bridge between what exists and what is needed. By overlaying a representative, knowledge graph-backed data fabric—an abstraction layer that unifies all of an organization’s data, regardless of its source—we can create a cohesive, graph-driven model without dismantling existing infrastructure. When anchored by a knowledge graph, data fabrics serve as the connective tissue in businesses, transforming disparate silos into a seamless web of relationships. They enable organizations to work with their relational systems while simultaneously presenting data in a way that aligns with the interconnected nature of reality, unlocking the potential for generative AI and other advanced analytics to thrive. It harnesses past investments while setting a foundation for the future.

Graph-based models become even more essential when we consider the role of generative AI agents in the evolving landscape of organizations. These agents are not just tools for static analysis or passive insights—they are active participants in the systems they inhabit. To operate effectively, generative AI agents will need the full context of an organization’s collective knowledge: every piece of data, every relationship, every dependency. They must have visibility into the full scope of tools and processes they can interact with to enact operational changes, automate workflows, and inform decision-making. In essence, these agents must not simply read from the graph—they must become a part of it, acting as dynamic nodes that continuously read context, update their understanding, and take actions. A graph-powered data fabric allows these agents to seamlessly navigate the complexities of organizational ecosystems, enabling them to not only analyze and predict but also orchestrate processes and collaborate with humans in a way that mirrors the interconnected nature of the organization itself.

The introduction of generative AI agents into a knowledge graph-powered data fabric transforms the very nature of what a data fabric can be. No longer just an abstraction layer to unify and present data, it evolves into something far more powerful: an AI Fabric. This AI Fabric is not simply a repository or a conduit for information—it becomes an active, intelligent network where AI agents, humans, and systems work in concert. Within this paradigm, AI agents are not just observers or analysts; they are collaborators, orchestrators, and drivers of automation. They leverage the full depth of the knowledge graph to understand context, anticipate needs, and act with precision. The AI fabric enables a new level of operational intelligence, where processes are dynamically optimized, decisions are continuously informed by real-time insights, and automation becomes not just reactive but proactive. It represents a fundamental shift in how organizations operate, bridging the divide between static data infrastructure and living, adaptive business intelligence.

Importantly, this AI Fabric not only enables extraordinary new possibilities but also provides a critical framework for managing risk. When all data, actions, and actors are part of the graph, the single governance model that controls the graph also controls all the entities. Every actor—whether a human user or a generative AI agent—is tightly regulated by one universal governance model, ensuring that only authorized entities can access or act upon specific data or processes. Moreover, the AI Fabric builds traceability and observability into its very foundation. Every action taken within the AI Fabric—whether it’s a decision by an agent, a process execution, or a human intervention—is stored as an entity within the graph itself. These action entities not only provide additional context to inform future decisions by AI agents but also give organizations complete, real-time oversight of what the system is doing at any given moment. This level of transparency ensures accountability, fosters trust, and allows organizations to harness the transformative power of the AI Fabric without losing control over the systems and processes that drive their operations.

The AI fabric represents a profound leap forward—a shift from static, siloed systems to a living, dynamic network that mirrors the complexity of modern organizations. By unifying data, actions, and actors within a single, graph-powered ecosystem, it enables a new era of operational intelligence. Generative AI agents and human users alike can seamlessly collaborate, informed by the full context of an organization’s knowledge and empowered to make real-time decisions that drive meaningful change. At the same time, the AI Fabric ensures control and accountability through universal governance, precise access controls, and comprehensive traceability. Every action is logged, every decision observable, creating an environment where innovation thrives without compromising oversight. The AI fabric isn’t just the next step in data evolution—it’s the operating system for the future.

The alternative – retrofitting generative AI capabilities onto relational data lakehouses and warehouses – unfortunately introduces inefficiencies and artificial complexity, forcing cutting-edge models to work against their nature. Instead of embracing data representations that reflect reality’s interconnectedness, these solutions continue to build on foundations that fracture it, creating brittle systems that fall short of unlocking AI’s true potential. And by doing so, they not only limit the capabilities of generative AI but also constrain the transformative possibilities it holds for businesses and society alike.

The message is clear: the future won’t wait. Graph-powered AI Fabric is the bridge to that future—dynamic, intelligent, and transformative. It’s time to stop forcing innovation into rigid boxes and start embracing the interconnected reality that defines our world. The universe is a graph. Your data should be too.

The post The Universe is a Graph: It’s Time for our Data to Start Acting Like it appeared first on Best Data Management Software, Vendors and Data Science Platforms.

Redpanda Data’s Tristan Stevens offers insights on real-time or left behind and the new reality of AI in the enterprise. This article originally appeared on Solutions Review’s Insight Jam, an enterprise IT community enabling the human conversation on AI.

When a cargo ship struck Baltimore’s Francis Scott Key Bridge in the spring of 2024, the impact rippled far beyond the physical destruction. Within minutes, logistics companies needed to reroute shipments, insurers had to assess risks, and traders had to reevaluate positions in affected companies. For businesses relying on AI systems trained on historical data, this sudden change, in reality, created a dangerous disconnect – their algorithms were still operating in a world where the bridge existed.

This scenario illustrates a critical challenge facing organizations today: building AI systems that don’t just learn from the past, but also understand and adapt to the present. While many companies have successfully experimented with AI in controlled environments, they now face the complex challenge of deploying these systems in production environments where decisions must be made on constantly changing, real-world data.

The Data Currency Challenge

Keeping an AI model current and productive is challenging in the face of a crucial reality: data becomes stale really fast, and the businesses that thrive are going to be the ones with the most up-to-date information. According to Gartner’s “Top Strategic Technology Trends for 2024” report, in 2025, over 75 percent of enterprise-generated data will be created and processed outside traditional centralized data centers or clouds, highlighting the growing importance of edge computing and real-time data processing.

We’ve seen how a major disaster like a bridge collapse can impact real-world scenarios, but these incidents and extreme outliers are becoming more common, showing how data currency is becoming ever more important when dealing with:

• Contextual information about current geopolitical events
• Weather events affecting business operations
• Infrastructure changes impacting supply chains
• Market-moving announcements affecting trading decisions

Training models on yesterday’s data are no longer sufficient. By the time one region wakes up, the business landscape may have already shifted dramatically on the other side of the world.

The Convergence of Traditional and Real-Time Systems

Historically, organizations operated in two distinct worlds: batch processing for historical analysis and real-time streaming for immediate insights. Users had to navigate different tools and technologies depending on their data needs – data warehouses for historical analysis, specialized query languages like Apache Kafka’s KSQL for stream processing, or solutions like ClickHouse for real-time analytics.

Today’s users don’t think in these bifurcated terms. They expect data to be current and accessible through natural language queries, regardless of its source or processing method. This shift has driven a massive convergence of previously separate IT infrastructure components.

• A modern data architecture requires several key components working in harmony:
• Legacy information and batch data that forms the business’s historical foundation
• Contextual and reference data specific to the industry
• Real-time data providing current situational awareness
• AI and language processing capabilities that can integrate all these sources

Data Sovereignty and Security Considerations

As organizations integrate AI into their core processes, data sovereignty is emerging as a strategic choice that balances the opportunities to utilize AI while minimizing the risks of external AI vendors. While large language models from providers like OpenAI offer powerful capabilities, they also raise serious questions about data security and intellectual property protection.

“You only need to leak once,” as the saying goes. We know that whatever gets on to the internet is probably out there forever. Recent incidents highlight these risks. Beyond the widely reported cases of engineers accidentally exposing code through ChatGPT, organizations have faced challenges with:

• Employees uploading sensitive financial data to public AI tools
• Competitors gaining insights through public AI model training data
• Regulatory violations from cross-border data transfers through AI services

The concept of sovereign AI is one solution to this security challenge. Rather than sending sensitive data across the internet to third-party services, organizations can bring AI capabilities within their security boundaries. This approach allows companies to:

• Run AI models within their own infrastructure
• Combine proprietary data with general language models
• Maintain complete control over data lineage and access
• Ensure compliance with regulatory requirements

Best Practices and Implementation Strategies

Prompt Engineering and Quality Control

Asking the right questions of AI systems is crucial for success. Organizations should develop systematic approaches to creating prompts that include sufficient context, direction, and examples. Teams should maintain documentation of successful prompt patterns and implement iterative feedback processes to improve results.

Democratized Access and Integration

Modern AI implementations must balance accessibility with control. Business users should be able to embed AI into their processes with minimal technical overhead while maintaining appropriate safety guardrails. Systems should integrate seamlessly with existing workflows rather than requiring users to switch between multiple applications.

Real-Time Data Quality Management

As data volumes and velocities increase, organizations must implement automated validation checks and monitor data freshness in real-time. Clear data ownership and governance structures ensure accountability throughout the system.

Security and Compliance Integration

Security cannot be an afterthought in real-time AI systems. Organizations should implement comprehensive monitoring of AI interactions and establish clear policies for data usage and retention. Regular security assessments protect sensitive information and maintain compliance.

Continuous Monitoring and Optimization

Systems must adapt as business needs evolve. Organizations should track both technical performance metrics and business impact, using these insights to guide optimization efforts and ensure systems meet business objectives effectively.

Looking Ahead

The Baltimore bridge collapse highlighted a critical truth about modern AI systems: processing historical data alone isn’t enough. Organizations that thrived during the incident were those whose systems could rapidly incorporate and act on real-time information, from rerouting shipments to reassessing market positions. As AI becomes more deeply embedded in critical business processes, this ability to adapt to sudden changes while maintaining security and reliability at scale isn’t just advantageous – it’s essential for business survival and success.

The post Real-Time or Left Behind: The New Reality of AI in Enterprise appeared first on Best Data Management Software, Vendors and Data Science Platforms.

Datadobi’s Carl D’Halluin offers insights on how unstructured data fuels GenAI, but organizations don’t know how to manage it. This article originally appeared on Solutions Review’s Insight Jam, an enterprise IT community enabling the human conversation on AI.

Arguably the most important tech trend in decades, AI has soared in popularity and adoption over the past 12 months. Having hovered around the 50 percent mark between 2018-2023, implementation rates increased dramatically to 72 percent last year, according to a study by McKinsey. Expectations are also high, with three-quarters of organizations believing GenAI will lead to significant or disruptive change in their industries.

This is being translated into tangible performance improvement, and as McKinsey points out, organisations are “already seeing material benefits from gen AI use, reporting both cost decreases and revenue jumps in the business units deploying the technology.”

Despite this positive backdrop, however, there are also a range of significant risks potentially standing in the way of successful implementation. For instance, nearly three-quarters of participants in the McKinsey study have experienced data management challenges, “including defining processes for data governance, developing the ability to quickly integrate data into AI models, and an insufficient amount of training data, highlighting the essential role that data play in capturing value.”

Garbage In, Garbage Out

Given the fact that the vast majority of enterprise data is unstructured, including everything from videos and images to emails and social media content, the successful implementation of GenAI is heavily dependent on the way organizations manage these vast datasets.

The challenge is analogous to the classic computing principle of “garbage in, garbage out”, particularly because GenAI models don’t always work well with unstructured data, particularly if it has been poorly managed. As a result, organizations can easily find AI outputs skewed by poorly managed data, producing unreliable performance.

So, where does that leave businesses who see massive potential in AI, have access to unstructured data but struggle to turn objectives into tangible outcomes? The first step is to establish an enterprise-wide view of all unstructured datasets so leaders can make informed decisions about what data has potential value and, crucially, where it currently resides.

These files, which can exist in their billions, need to be identified, organized and visualized in a manner that can keep pace with rapid developments in AI systems. This can potentially be a highly complex and resource-intensive task, but choosing the correct data is critical for producing accurate, actionable and unbiased outputs.

Armed with these capabilities, organizations can empower their data scientists and accelerate the identification of the correct data to train GenAI models that address their performance improvement priorities.

This all has to take place in the context of effective data governance and a set of policies and processes that control how data is stored, documented and maintained in line with internal and regulatory requirements. Good governance also requires an ongoing commitment to data audits and continual improvement, particularly as additional datasets are added to AI systems. Done well, organizations can go a long way to minimizing the risks associated with unstructured data, from security problems and compliance breaches to poor operational efficiency.

A Change of Mindset

What’s been lacking until relatively recently are the tools to help manage unstructured data. Instead, organizations have found it much easier just to add extra storage. Given the sharp rise in data accumulation rates – in part due to the demands of GenAI systems – this approach is no longer viable. Instead, businesses need data management technology to help them stay ahead of demand. In other words, instead of focusing on the device where unstructured data is stored, IT leaders should turn their attention to how it is managed.

Effective data management technology can bridge the capability gap between raw unstructured data that contains latent value to a situation where teams can train GenAI models with high-quality data. Ideally, the data management solution used with GenAI systems will deliver seamless, reliable and efficient data migration, management and protection across heterogeneous storage environments. For organizations looking to a future where everything from strategic planning to tactical decision-making is augmented and improved by GenAI, getting the data management foundations right is crucial for success.

The post Unstructured Data Fuels GenAI, But Firms Need Help Managing appeared first on Best Data Management Software, Vendors and Data Science Platforms.

BMC’s Ram Chakravarti offers insights on why DataOps will set enterprises up for success. This article originally appeared on Solutions Review’s Insight Jam, an enterprise IT community enabling the human conversation on AI.

In recent years, companies have increased their investments in data quality, management, compliance, and regulatory adherence. This has set up organizations to take off with DataOps—the practice of applying agile methods to data in motion.

This investment has paid off. About 38 percent of North American companies employ a chief data officer (CDO), according to research from PwC, and those companies have grown faster than peers that do not employ a CDO. Similarly, companies with higher data management maturity tend to be linked to higher reported rates of success in other data-driven activities. In these organizations, gone are the days when executives would outline their informational needs, only for data analysts to retreat and reemerge 18 months later with insights.

But those successes aren’t the end of the story. To continue to thrive, organizations on the DataOps journey need to scale the results of their initial data-driven processes across the organization. To do that, they’ll need to maintain consistency, accuracy, and reliability in data handling. And deploying generative artificial intelligence (AI) can help.

Better Governance: Accuracy, Consistency, and Reliability

In practice, data orchestration boils down to something like this: Information is gathered, organized, and flows to the people who need it when they need it, whether that’s part of a self-service effort, or a data pipeline for new workflows and processes. However, operationalizing at scale, at the speed and level of sophistication that stakeholders are increasingly expecting, is an ongoing obstacle.

Organizations face several challenges. Data privacy and security requirements often limit access to data because breaches can have severe repercussions. Inaccurate, incomplete, or inconsistent data can lead to misguided insights and decisions, undermining the trust in data-driven strategies. Teams that produce and manage data may be siloed from the teams that analyze it.

Many challenges can be overcome by automating data pipelines, which makes it easier to collect, apply, and secure data for effective use. By reducing manual interventions and enhancing data processing speed and accuracy, automation significantly improves efficiency, especially in environments with increasing data volume, variety, and velocity. Automated data validation checks, detection and correction of errors in real time, minimizing the risk of inaccurate data entering the analysis pipeline. This is essential for maintaining trust in data-driven decision-making.

Implementing automated compliance checks and access controls helps organizations safeguard sensitive data and adhere to regulations, reducing the risk of breaches and ensuring consistent application of data governance policies.

In addition to technical advantages, automation fosters closer collaboration between IT and data teams. Automating routine tasks allows IT professionals to focus on strategic initiatives, while data analysts can

access high-quality data more efficiently. This alignment is crucial for operationalizing data pipelines and ensuring that both teams work towards common goals with a shared understanding of the data landscape. It also leads to faster insights and improved experiences for the organization.

Harnessing the Power of Generative AI in DataOps

Generative AI is transforming how organizations manage and use data and realize increased value from their technology investments. While many organizations are frustrated by the very real limitations of currently available public models, which are prone to errors and hallucinations, they have the power to improve it.

Most experts believe that the leading models were trained on data sets comprised of the entire internet. Because of that, there’s no easy source of written material for next-generation models. For AI to have value, it needs to be trained on high-quality data sets. The logical next step is to continue AI training with proprietary data in the enterprise, and this requires more reliance on enterprise systems.

The integration of generative AI tools into automated data workflows represents the next frontier in DataOps. These tools can augment human capabilities, providing deeper insights and identifying patterns that might be missed by traditional analysis methods. Generative AI enhances DataOps by providing expert system insights, real-time issue summaries, natural language event correlation, issue clustering, improved chatbot interactions, streamlined knowledge sharing, accelerated development and DevOps processes, and predictive insights for proactive management.

The Path Forward

Data and analytics have become key players in the enterprise, but there is more room to grow, and the changing technology landscape offers even more opportunity. Despite a significant increase in data maturity at many organizations, vast amounts of data are underutilized for the purposes of insights because companies do not have a way to operationalize it. Simultaneously, few organizations have done more than scratch the surface of generative AI.

For technology executives, the message is clear: organizations can’t unlock value from their data assets without AI. To stay ahead in the competitive landscape, it’s essential to invest in automation, AI, and specifically, generative AI, and to integrate them seamlessly into a DataOps strategy. This approach will not only help organizations overcome current challenges but also position them for long-term success.

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