Creating a body language of online learning with graph databases
Sean York of Pearson discusses how graph technology becomes a medium for enriching online environments.
Intelligent, context-aware data and analytics technologies widen the decision-making aperture
These four innovations can help companies achieve an optimal mind-machine balance when it business decision making.
Demystifying machine learning part 4: Image and video applications
Some firms are using machine learning to process large amounts of unstructured data, but it’s not widespread—yet.
Digital haystacks: Extracting insight from enterprise data
How big data innovation helped PwC transform enterprise search to deliver key data and increase employee effectiveness.
The end of data standardization
We can no longer deny the drive to diversify data management technology that began in the mid-90s. The aspiration to achieve one single and simple database management system has died. I grew up with the advent of commercial relational databases in the late 80s and early 90s. At the time, the promise was clear: you could store everything in a relational database that was carefully modeled and expandable. And in doing so, you acquired the ability to access, govern and securely manage every bit of data in a single technology environment. Most companies decided on a relational database standard and ported some or all of their applications towards that single database backend. All the principles of good architecture – including cost and skill optimization played out – until they didn’t. All seemed swimming until one of my clients – a major European railway operator – wanted to geo code every bit of equipment and every centimeter of their railway network. As hard as we tried, we couldn’t meet the client’s demands well with a relational database. The advent of spatial data management systems came to the rescue. Questions like ‘What is the total book value of all assets deployed within …