Next week I will be presenting at the Cornell Hospitality Research Summit on big data and revenue management. In preparation for the Summit, I have recorded a short presentation that outlines how big data can help augment revenue management. Alex Dietz talks about how big data can help revenue management.
At most banks, data is stored in separate databases and data warehouses. Customer data is stored in marketing databases, fraud analyses are done on transactional data, and risk data is stored in risk data warehouses. Oftentimes even liquidity, credit, market, and operational risk data is stored separately as well. Bringing
In the first installment of this series on Hadoop, I shared a little of Hadoop's genesis, framing it within four phases of connectivity that we are moving through. I also stated my belief that Hadoop has already arrived in the mainstream, and we are currently moving from phases three of connecting people to phase four
So, you've heard the Hadoop hype and you are looking – or have already invested – into Hadoop. Maybe you have also realized some benefits from the Hadoop ecosystem. But now you want to maximize those benefits by using advanced analytics, or you might have heard about algorithms or machine learning libraries available
Would you build a house without a proper foundation? Most of us wouldn’t dare, but that’s exactly what many retail businesses are doing today. When building a house, if you don’t get the foundation right, paint, wallpaper and fixtures won’t matter much. It’s no different in the retail industry. Success
In the world of IT, very few new technologies emerge that are not built on what came before, combined with a new, emerging need or idea. The history of Hadoop is no exception. To understand how Hadoop came to be, we therefore need to understand what went before Hadoop that led to its creation. To understand
The energy & utilities industry as a whole has experienced a seismic shift over the past five years due to rising costs and price pressures, and has become a priority discussion on the political and media agenda. Falling demand overall combined with “peakier” peaks is making supply, forecasting and public
For many years companies have been working to increase their use of predictive analytics and to execute analytic models faster on increasingly granular and growing volumes of data. Recently, there has been a great focus on "faster" from a technology standpoint, as modelers seek to iterate quickly and fail fast on
Recently, I was reading an online article about predictive modeling and "big data." Its premise was to determine whether the use of big data actually led to more accurate and meaningful predictive models and forecasts. After citing numerous external examples and internal tests that the authors had compiled, it stated
I’m sure, like me, you've been annoyed at being stuck in a traffic jam in a city centre somewhere, or been frustrated at your kids leaving lights on, or annoyed with the heating coming on when the weather’s warmed up and you've not got round to adjusting the thermostat. Now,
