Your business and your data are both unique. For that reason, your enterprise architecture must also be tailored to fit the exact needs of your business. When data is involved, you want choices, not trade offs. And, more importantly, you want your solutions to build upon and complement one another.
This is the fourth in a series of blogs on analytics and the cloud. Read our introduction to the series. This blog concerns itself with the rise of open source software and how it is used for a whole host of analytical purposes. However, as will be seen in this blog, there are significant gaps in
Although NoSQL database technology has been around for a long time (before SQL actually), not until the advent of Web 2.0, when companies such as Google and Amazon began using the technology, did NoSQL’s popularity really take off. Market Research Media forecasts NoSQL Market to be $3.4 Billion by
Quite often, we see that the need for data security and governance makes some organizations hesitant about migrating to the cloud. This is perfectly understandable given the types of data gathered and used by businesses today, the regulations they must adhere to on both a local and global level,
This is the second in a series of blogs on analytics and the cloud. We will consider the rise of the Internet of Things (IoT), analytics used on that data and how the cloud can be utilized to drive value out of instrumenting a very wide range of ‘things’.
Fundamentally, machine learning is a productivity tool for data scientists. As the heart of systems that can learn from data, machine learning allows data scientists to train a model on an example data set and then leverage algorithms that automatically generalize and learn both from that example
This is the first in a sequence of blogs that takes a peek at what is driving analytics onto the cloud, what are the challenges that will need to be overcome over the next 5 years and how they will be tackled.
If you’re looking to make your business data-driven, then you already know the paramount importance of modernizing your data architecture and its response capabilities. Accordingly, in your search for the ideal approach to delivering analytics for decision making, have you considered data
The choice to flex a data warehouse on a private cloud is a personal one. It offers benefits in three key areas: enhanced control over data and apps, better management and monitoring, and custom tailoring that is built to address specific user requirements and self-service applications.
As many organizations are rapidly learning, predictive analytics delivered on cloud-based platforms has the potential to transform the way organizations conduct business. To take advantage of new possibilities, leveraging enterprise-scale, cloud-based predictive analytics for new opportunities