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1. Lowering the cost of innovation
2. Procuring large scale resources quickly
3. Handling Batch Workloads Efficiently
4. Handling Variable Resource Requirements
5. Running Closer to the Data
6. Simplifying Hadoop Operations
The biggest reason that investments in big data fail to pay off, though, is that most companies don’t do a good job with the information they already have. They don’t know how to manage it, analyze it in ways that enhance their understanding, and then make changes in response to new insights. Companies don’t magically develop those competencies just because they’ve invested in high-end analytics tools. They first need to learn how to use the data already embedded in their core operating systems, much the way people must master arithmetic before they tackle algebra. Until a company learns how to use data and analysis to support its operating decisions, it will not be in a position to benefit from big data.
If you want to know more on this topics, have a look at one of my new projects: www.betterdecisionsforum.com, register and keep in touch.
What’s driving the internet of things?
We have entered a new age of embedded, intuitive computing in which our homes, cars, stores, farms, and factories have the ability to think, sense, understand, and respond to our needs. It’s not science fiction, but the dawn of a new era.
It’s been said that agile BI is simple but not easy. That is, the concept of working in short iterations, delivering your BI system in small increments, and evolving the solution based on feedback is easy enough for most people to understand. However, as with many things, the devil is in the details.
Sure it is in the details and as far as I see, agile BI is not that easy 🙂
Events cause people to synchronize and act together. When something dramatic happens on TV, with a favorite sports team, or outside our own windows, people come together on Twitter