It's not the big that eat the small. It's the fast that eat the slow. In [...]
Balancing security and speed at AWS Summit Auckland 2017 More than 2400 people attended the AWS [...]
Bulletproof is pleased to announce the Data Lake solution delivered for Department of Environment, Land, Water and Planning (DELWP) has been nominated as a finalist in the ‘Digital Transformation’ category for the CRN Impact Awards.
To wrap up our Azure blog series, let's examine in a bit more detail why organisations are increasingly making Azure a focus of their business and technology strategies.
In this instalment in our Microsoft Azure series, let’s take it a step further and dig deeper into Microsoft Azure specifically - looking at what it is and some of the reasons it could be of interest for organisations.
A look at Cloud adoption and the roadblocks stopping key decision makers from that first step.
Research has shown that the longer you use AWS, the greater the potential benefits. For instance, after three years using AWS, customers can expect to realise $3.50 in benefits for every $1 spent. This figure rises to $8.40 for every $1 after five years using AWS. What these cost savings tell us is that the longer your business and your teams are using AWS, the savvier they get at cost optimisation.
There are a host of strategic benefits to engaging in Cloud cost optimisation conversations. While the primary goal will be to eliminate unnecessary Cloud expenditure and optimise performance, there are strategic wins that can come out of the exercise too.
Lowering costs is one the major reason businesses migrate to the Cloud. While it’s true that Cloud can be cheaper than on-premises data centres, it's not always a given. Simply picking up your workloads and shifting them to AWS might generate initial cost savings. However without ongoing cost optimisation tweaks, the long-term Cloud vs On-Premises cost savings can end up much of a muchness. There's further long-term cost savings that Cloud can provide, but it requires further analysis and reporting.
The next piece of the data science puzzle is looking at taking those first steps. Continuing our data science series, let’s look at how you get started, why the CIO is best-placed to take ownership of data science and then we'll look at the 5 things you need to consider to make data science work.