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In 2025, data management is no longer a backend operation. It has become a strategic cornerstone for shaping innovation, efficiency and compliance. This article dives into five key data management trends that are set to define 2025. This reduces manual errors and accelerates insights.
Data architecture definition Data architecture describes the structure of an organizations logical and physical data assets, and data management resources, according to The Open Group Architecture Framework (TOGAF). An organizations data architecture is the purview of data architects. Curate the data.
Most AI workloads are deployed in private cloud or on-premises environments, driven by data locality and compliance needs. AI a primary driver in IT modernization and data mobility AI’s demand for data requires businesses to have a secure and accessible data strategy. Cost, by comparison, ranks a distant 10th.
In today’s data-driven world, large enterprises are aware of the immense opportunities that data and analytics present. Yet, the true value of these initiatives is in their potential to revolutionize how data is managed and utilized across the enterprise. Take, for example, a recent case with one of our clients.
A high hurdle many enterprises have yet to overcome is accessing mainframe data via the cloud. Mainframes hold an enormous amount of critical and sensitive business data including transactional information, healthcare records, customer data, and inventory metrics.
A modern data and artificial intelligence (AI) platform running on scalable processors can handle diverse analytics workloads and speed data retrieval, delivering deeper insights to empower strategic decision-making. They are often unable to handle large, diverse data sets from multiple sources.
It demands a robust foundation of consistent, high-quality data across all retail channels and systems. AI has the power to revolutionise retail, but success hinges on the quality of the foundation it is built upon: data. The Data Consistency Challenge However, this AI revolution brings its own set of challenges.
The McKinsey 2023 State of AI Report identifies data management as a major obstacle to AI adoption and scaling. Enterprises generate massive volumes of unstructured data, from legal contracts to customer interactions, yet extracting meaningful insights remains a challenge.
In 2024 alone, the average cost of a data breach rose by 10% 1 , signaling just how expensive an attack could become. The risk of cybersecurity lapses, data breaches, and the resulting penalties for regulatory non-compliance have made it more important than ever for organizations to ensure they have a robust security framework in place.
The data landscape is constantly evolving, making it challenging to stay updated with emerging trends. That’s why we’ve decided to launch a blog that focuses on the data trends we expect to see in 2025. Poor data quality automatically results in poor decisions. That applies not only to GenAI but to all data products.
Enterprise applications have become an integral part of modern businesses, helping them simplify operations, manage data, and streamline communication. However, as more organizations rely on these applications, the need for enterprise application security and compliance measures is becoming increasingly important.
According to a Gartner’s report , about 75% of compliance leaders say they still lack the confidence to effectively run and report on program outcomes despite the added scrutiny on data privacy and protection and newly added regulations over the last several years. Image Credits: anecdotes.
Data sovereignty has emerged as a critical concern for businesses and governments, particularly in Europe and Asia. With increasing data privacy and security regulations, geopolitical factors, and customer demands for transparency, customers are seeking to maintain control over their data and ensure compliance with national or regional laws.
Research from Gartner, for example, shows that approximately 30% of generative AI (GenAI) will not make it past the proof-of-concept phase by the end of 2025, due to factors including poor data quality, inadequate risk controls, and escalating costs. [1] AI in action The benefits of this approach are clear to see.
Data sovereignty and the development of local cloud infrastructure will remain top priorities in the region, driven by national strategies aimed at ensuring data security and compliance. The Internet of Things will also play a transformative role in shaping the regions smart city and infrastructure projects.
Add to this the escalating costs of maintaining legacy systems, which often act as bottlenecks for scalability. The latter option had emerged as a compelling solution, offering the promise of enhanced agility, reduced operational costs, and seamless scalability. For instance: Regulatory compliance, security and data privacy.
As enterprises navigate complex data-driven transformations, hybrid and multi-cloud models offer unmatched flexibility and resilience. Heres a deep dive into why and how enterprises master multi-cloud deployments to enhance their data and AI initiatives. The terms hybrid and multi-cloud are often used interchangeably.
Heres the secret to success in todays competitive business world: using advanced expertise and deep data to solve real challenges, make smarter decisions and create lasting value. And its modular architecture distributes tasks across multiple agents in parallel, increasing the speed and scalability of migrations.
There are two main considerations associated with the fundamentals of sovereign AI: 1) Control of the algorithms and the data on the basis of which the AI is trained and developed; and 2) the sovereignty of the infrastructure on which the AI resides and operates.
For instance, an e-commerce platform leveraging artificial intelligence and data analytics to tailor customer recommendations enhances user experience and revenue generation. These metrics might include operational cost savings, improved system reliability, or enhanced scalability.
“AI deployment will also allow for enhanced productivity and increased span of control by automating and scheduling tasks, reporting and performance monitoring for the remaining workforce which allows remaining managers to focus on more strategic, scalable and value-added activities.”
Our Databricks Practice holds FinOps as a core architectural tenet, but sometimes compliance overrules cost savings. There is a catch once we consider data deletion within the context of regulatory compliance. However; in regulated industries, their default implementation may introduce compliance risks that must be addressed.
Allow me to explain by discussing what legacy platform modernization entails, along with tips on how to streamline the complex process of migrating data from legacy platforms. The first is migrating data and workloads off of legacy platforms entirely and rehosting them in new environments, like the public cloud.
Thats why we view technology through three interconnected lenses: Protect the house Keep our technology and data secure. For example, when we evaluate third-party vendors, we now ask: Does this vendor comply with AI-related data protections? Are they using our proprietary data to train their AI models?
For investors, the opportunity lies in looking beyond buzzwords and focusing on companies that deliver practical, scalable solutions to real-world problems. RAG is reshaping scalability and cost efficiency Daniel Marcous of April RAG, or retrieval-augmented generation, is emerging as a game-changer in AI.
Once an organization sees signs of security vulnerabilities or compliance risks, it’s a clear indicator that they need to consider modernization,” says Vikas Ganoorkar, global cloud migration and modernization leader at IBM Consulting. He advises using dashboards offering real-time data to monitor the transformation.
In todays fast-paced digital landscape, the cloud has emerged as a cornerstone of modern business infrastructure, offering unparalleled scalability, agility, and cost-efficiency. As organizations increasingly migrate to the cloud, however, CIOs face the daunting challenge of navigating a complex and rapidly evolving cloud ecosystem.
According to Kari Briski, VP of AI models, software, and services at Nvidia, successfully implementing gen AI hinges on effective data management and evaluating how different models work together to serve a specific use case. Data management, when done poorly, results in both diminished returns and extra costs.
And third, systems consolidation and modernization focuses on building a cloud-based, scalable infrastructure for integration speed, security, flexibility, and growth. Were piloting Simbe Robotics Tally robots, which improve on-shelf availability, pricing accuracy, promotional compliance, and supply chain operations.
Data governance definition Data governance is a system for defining who within an organization has authority and control over data assets and how those data assets may be used. It encompasses the people, processes, and technologies required to manage and protect data assets.
He says, My role evolved beyond IT when leadership recognized that platform scalability, AI-driven matchmaking, personalized recommendations, and data-driven insights were crucial for business success. In a world of data-sharing, operating in silos is never a smart decision. These are her top tips: 1.
Governance: Maps data flows, dependencies, and transformations across different systems. Testing & Validation: Auto-generates test data when real data is unavailable, ensuring robust testing environments. Data Navigator Agent : Maps data flows, dependencies, and transformations. Optimizes code.
Further, we were able to achieve increased energy savings and a simplified hybrid multicloud environment These improvements speak directly to our commitment to performance, scalability, and reliability. I aim to fortify defenses, ensure compliance, and safeguard our data. So, what do I take from all of this?
However, enterprise cloud computing still faces similar challenges in achieving efficiency and simplicity, particularly in managing diverse cloud resources and optimizing data management. Enterprise IT struggles to keep up with siloed technologies while ensuring security, compliance, and cost management.
But Fernndez projects an increase in the future, comparing it with what has happened with the chief data officer (CDO) role , which is currently a mandatory presence at many large companies despite being barely present just five years ago. One thing is to guarantee the quality and governance of data.
A well-known fact about Data – Data is crucial Asset in an organization when managed in an appropriate way Data Governance helps Organizations to manager data in appropriate way Some Customers Says Data Governance is a Best Practice and Optional but not a Mandatory Strategy to Implement.
As the technology subsists on data, customer trust and their confidential information are at stake—and enterprises cannot afford to overlook its pitfalls. Yet, it is the quality of the data that will determine how efficient and valuable GenAI initiatives will be for organizations.
In the five years since its launch, growth has been impressive: Fourthline’s customers include N26, Qonto, Trade Republic, FlatexDEGIRO, Scalable Capital, NN and Western Union, as well as marketplaces like Wish. The valuation of the company is not being disclosed. And business has grown 80% annually in the last five years.
Preventing data loss, complying with regulations, automating workflows and managing access are four key challenges facing financial institutions. Imagine a bustling bank, made not of bricks and mortar, but of a swirling mass of data in the cloud. Holding all this sensitive data requires tremendous care.
AI agents , powered by large language models (LLMs), can analyze complex customer inquiries, access multiple data sources, and deliver relevant, detailed responses. In this post, we guide you through integrating Amazon Bedrock Agents with enterprise data APIs to create more personalized and effective customer support experiences.
Machine learning analyzes historical data for accurate threat detection, while deep learning builds predictive models that detect security issues in real time. This flexible and scalable suite of NGFWs is designed to effectively secure critical infrastructure and industrial assets.
Azure Key Vault Secrets integration with Azure Synapse Analytics enhances protection by securely storing and dealing with connection strings and credentials, permitting Azure Synapse to enter external data resources without exposing sensitive statistics. Data Lake Storage (Gen2): Select or create a Data Lake Storage Gen2 account.
These stem from the complexity of integrating multiple mini-apps, ensuring a seamless user experience while addressing security and compliance concerns. Enterprises must enact robust security measures to protect user data and maintain regulatory compliance.
We look at data as a valuable commodity. Just like refining materials in the aluminium process, we are refining data to unlock untapped potential,” Carlo explains. Under his leadership, EGA has evolved its digital strategy, aligning data refinement with operational excellence. Everyone is going to say AI. (15:15)
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