Why breaking down legacy data silos is business-critical
The widespread adoption of powerful analytical tools and AI-based systems that can offer insights into everything from operational efficiency to consumer behaviour has had a
The widespread adoption of powerful analytical tools and AI-based systems that can offer insights into everything from operational efficiency to consumer behaviour has had a
The widespread adoption of powerful analytical tools and AI-based systems that can offer insights into everything from operational efficiency to consumer behaviour has had a
Earlier this year, I sharedthe top trendswe are seeing when it comes to solving enterprise data management problems. We continue to deploy and put these
Enabling real-time data democratization is no easy task philosophically, organizationally, or technologically. At the beginning of data democratization, the biggest barrier was philosophical and bureaucratic.
To succeed in the digital services economy – and the era of data-intensive applications – you need to leverage fresh data to deliver engaging real-time
The digital skills shortage has hit a crisis point and the effects are being felt firmly across the UK. Millions of workers lack the vital
Data brought a slowly moving revolution to the business world. While it has completely changed, and may still change, the way we approach processes, many
Recently, Andreas von der Heydt, Merchandising VP at Chewy, shared an image on LinkedIn that generated a lot of buzz about data storytelling. The image
New options are available so IT leaders can break mainframe data silos. For too long, the difficulties of mainframe data management and transformation have resulted
The key goal of a data-centered architecture is data accessibility. Accessibility can impact future business innovation, improve the ability to generate metadata and new data
Over the last decade, organizations have focused on becoming more “data-driven” as a core tenet of their business strategy. Being data-driven refers to the use
Enterprise machine learning deployments are limited by two consequences of outdated data management practices widely used today. The first is the protracted time-to-insight that stems