Oliver Halter is a principal in PwC’s Advisory practice. Oliver focuses on helping clients develop information strategies that leverage technology to create innovation and value. He brings over 20 years’ experience in information technology, master data management, systems development and business consulting. Oliver’s experience includes developing systems architectures for business-to business, business-to-consumer and e-commerce applications, as well as helping clients to improve community sites, content aggregation, billing systems and enterprise applications and integration.
Oliver currently leads PwC’s US firm’s Big Data initiative. He has worked with many clients to help them develop and implement Big Data and analytics strategies to result in increased innovation, improved data-driven decision making, increased customer centricity, competitive advantage and lower costs. Oliver frequently speaks on emerging technology issues and has authored various articles on technology, Big Data and analytics.
Prior to joining PwC in 2010, Oliver was a Partner with Diamond Management and Technology Consulting and a Senior Technology Director with Oracle.
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.
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 …