The Future of Computing: Harnessing Molecules for Sustainable Data Management

In this contributed article, Erfane Arwani, founder and CEO of Biomemory, discusses how molecular computing (using molecules rather than traditional silicon chips for computational tasks) could be a critical component in revolutionizing data storage, despite the exponential growth of AI.

From complexity to clarity: Harnessing the power of AI/ML and risk-informed strategies to streamline clinical data management

In today’s fast-paced world, driven by demands for speed and efficiency, the field of clinical development has undergone a remarkable transformation. The way trials are being conducted has changed significantly with decentralized clinical trials (DCT) becoming mainstream and the collection of clinical data from wearables and other remote-monitoring devices becoming common practice. While these advances […]

New Study Reveals Data Management Is a Top Challenge in the AI Revolution  

According to a new global study conducted by S&P Global Market Intelligence and commissioned by WEKA, the adoption of artificial intelligence (AI) by enterprises and research organizations seeking to create new value propositions is accelerating, but data infrastructure and AI sustainability challenges present barriers to implementing it successfully at scale. These challenges have been exacerbated by the rapid onset of generative AI that has defined the evolution of the AI market in 2023.

Acceldata and its Data Observability Platform – Solving Big Data Management Challenges

In this video interview with Ashwin Rajeeva, co-founder and CTO of Acceldata, we talk about the company’s data observability platform – what “data observability” is all about and why it’s critically important in big data analytics and machine learning development environments.

Cloudera Continues Rapid Pace of Data Fabric and Data Lakehouse Innovation to Extend Data Management Leadership

Cloudera, the hybrid data company, announced new hybrid data capabilities that enable organizations to more efficiently move data, metadata, data workloads and data applications across clouds and on premises to optimize for performance, cost and security. Cloudera’s portable data services enable simple, low-risk data workload and data application movement for ultimate data lakehouse optionality.

Report: New Data Management Models Are Essential To Operate In The Cloud

As organizations increasingly embrace cloud-first principles and the quantity and variety of their data exponentially increases, Capital One’s new Forrester study finds the vast majority of data management decision-makers are deeply concerned about controlling and forecasting data costs, leveraging data at scale, addressing data quality and consistency, and better protecting data.  

Benefits of Automation for Enterprise Data Management

In this article we’ll take a look at how’s and why’s that organizations from many industries are jumping on the automation for data management movement. It’s important that stakeholders understand how well automation performs repetitive data management responsibilities, what tasks still require a human in the loop, and how to evaluate data management automation capabilities.

Looking Ahead | Observability Data Management Modernization

In this contributed article, Karen Pieper, VP of engineering at Era Software, discusses how organizations today use real-time data streams to keep up with evolving business requirements. Setting up data pipelines is easy. Handling the errors at each stage of the pipeline and not losing data is hard.

Surpassing Decentralized Data Management Woes with Data Virtualization

In this contributed article, editorial consultant Jelani Harper discusses how data virtualization enables organizations to surmount obstacles (i.e. data quality, schema, and data integrations that are foundational to data management) and to focus on benefits (i.e. remote collaborations characteristic of working from home, the takeoff of the cloud as the de facto means of deploying applications, and the shift to external sources of unstructured and semi-structured data). Supplementing it with mutable graph data models boosts its applicability to data of all types.

The State of Data Management – Why Data Warehouse Projects Fail

Based on new research commissioned by SnapLogic and conducted by Vanson Bourne, who surveyed 500 IT Decision Makers (ITDMs) at medium and large enterprises across the US and UK, this whitepaper explores the data management challenges organizations are facing, the vital role data warehouses play, and the road to success.