As an open, Kubernetes-based, data and AI platform, IBM Cloud Pak for Data integrates with an array of technology solutions that enhance organizations’ ability to make their data ready for AI. Among those core to the platform are DataOps capabilities that help operationalize data protection, data
Data has grown exponentially over the last decade, to the extent where it is often referred to as a “natural resource,” and its rapid and uncontrolled growth has resulted in its poor management. This exploitation is a result of the theft or breach of data, as well as the limited controls and rights
Data breaches have far reaching consequences. They pose a significant financial cost in lost business, fines, and remediation, often averaging 3.92 million USD according to a study by the Ponemon Institute. Their impact on an organization's reputation spans many years. An organization's first step
Imagine a day in the life of Sarah, a hypothetical Chief Data Officer at a major bank in South Africa. There are many expectations on her shoulders. She struggles to deliver business-ready data to fuel her organization and support the decision makers within the bank. It is her job to put in place a
DataOps is the orchestration of people, process, and technology to accelerate the quick delivery of high-quality data to data citizens. When done right, DataOps creates business value because users know what data they have, can trust the quality and its meaning, and use it without violating
High-quality data is the core requirement for any successful, business-critical analytics project. It is the key to unlock and generate business value and deliver insights in a timely fashion. However, stakeholders across the board are responsible for data delivery, quickly evolving requirements,
The expectation to achieve faster results continues to rise. Businesses everywhere are looking for ways to improve their operational efficiency and effectiveness to enable the best decision-making. The need to optimize typically comes to a head with the reality that there are many silos within any
Most businesses collect data but are unable to use it to generate business value or deliver insights in a timely fashion. Data volume and data types continue to grow, as do the different types of data citizens—ranging from business users to data scientists. As a result, data management and delivery
The number of business segments requiring data to drive contextual insights is increasing. Leaders are seeking new ways to manage the pressures of delivering high-quality data faster across their businesses. To date, many of these projects have focused solely on ingesting data into a data lake
Nearly every business is under competitive, disruptive, and regulatory pressures. As companies face digital transformation and modernization to meet their customers’ expectations, leveraging data and AI at the speed of business can be the biggest differentiator.
However, according to MIT Sloan, 81
Data science is extremely important in today’s data-driven world, but is only effective if it can be efficiently executed in a production environment. Find out about an essential best practice to make your data science effective.