Latest Big Data Whitepapers

Introduction By collecting customer data from many different channels, and unifying it into one location, CDPs offer all teams within the business—from marketing to analytics—a single, accessible, and real-time view of their customer. In doing so, CDPs have real business impact. A study by Oracle reports that companies using a CDP earn 2.5x more in... Read More

As we look towards a remote working future, one of the biggest challenges currently faced by HR Leaders is how to ensure organisational performance is not only maintained but improved. Organisation Network Analytics (ONA) has the potential to transform our understanding of the organisation, enabling us to understand the real network of relationships, communication and... Read More

Using Cyber AI to Protect Against Advanced Email Attacks Antigena Email is the world’s first Cyber AI solution for the inbox. By learning the normal ‘pattern of life’ for every user and correspondent, the technology builds an evolving understanding of the ‘human’ within email communications. In the case studies that follow, Darktrace’s evolving sense of... Read More

Data lakes can store masses of structured or unstructured data in raw format until your enterprise needs that data for analytics. That’s why data lakes are now seen as an attractive alternative to traditional data warehouses. However, enterprises like yours struggle to realize the expected return on data lake investments because of unexpected data quality, data governance, and data immediacy challenges. This paper describes how to address these issues and prevent your pristine data lake from devolving into a useless data swamp.

Data lakes can store masses of structured or unstructured data in raw format until your enterprise needs that data for analytics. That’s why data lakes are now seen as an attractive alternative to traditional data warehouses. However, enterprises like yours struggle to realize the expected return on data lake investments because of unexpected data quality, data governance, and data immediacy challenges. This paper describes how to address these issues and prevent your pristine data lake from devolving into a useless data swamp.

Data lakes can store masses of structured or unstructured data in raw format until your enterprise needs that data for analytics. That’s why data lakes are now seen as an attractive alternative to traditional data warehouses. However, enterprises like yours struggle to realize the expected return on data lake investments because of unexpected data quality, data governance, and data immediacy challenges. This paper describes how to address these issues and prevent your pristine data lake from devolving into a useless data swamp.

Data lakes can store masses of structured or unstructured data in raw format until your enterprise needs that data for analytics. That’s why data lakes are now seen as an attractive alternative to traditional data warehouses. However, enterprises like yours struggle to realize the expected return on data lake investments because of unexpected data quality, data governance, and data immediacy challenges. This paper describes how to address these issues and prevent your pristine data lake from devolving into a useless data swamp.

By automating every phase of your software delivery lifecycle—build, test, and run—you can deliver the efficiency, agility, and quality your business needs now. Download our e-book to learn about: Eliminating gaps and blind spots between Dev and Ops teams Integrating and orchestrating data flows from disparate sources Auto-scaling resources for optimal reliability, quality, and cost... Read More

Data and analytics projects play a vital role in digital transformation. This white paper explores the essential role of workflow orchestration across the data lifecycle, from ingestion and integration to workflow creation. Learn how you can: Orchestrate disparate data sources across diverse infrastructure Maintain consistency and quality at enterprise scale Deliver insights to users automatically,... Read More

Data lakes can store masses of structured or unstructured data in raw format until your enterprise needs that data for analytics. That’s why data lakes are now seen as an attractive alternative to traditional data warehouses. However, enterprises like yours struggle to realize the expected return on data lake investments because of unexpected data quality, data governance, and data immediacy challenges. This paper describes how to address these issues and prevent your pristine data lake from devolving into a useless data swamp.

One of the obstacles for large on-premise clusters are the requirements for high-end data centers in addition to the challenge of ever-increasing energy costs. Large cloud providers have increased their focus on HPC and added more specialized HPC services to their offerings. They leverage the success of their cloud computing ecosystem and are perceived by some to have the most agile and flexible solution available; allowing you to pay on a per-use basis and to adjust your capacity on the fly to meet your needs. But is this really a cost effective long-term strategy for running AI workloads or simulations that can benefit from capacity at scale?

Many analytics and business intelligence tools today reduce analytics to a visualisation exercise. Today’s challenges require more than self-service applications that only produce descriptive visualizations based on limited data. Yet when self-service tools focus on data visualisation, they minimize the importance of data governance and data integrity in enterprise settings. This limits our ability to gain true insights from our data and make meaningful business decisions.