There are plenty of factors involved in finding the right big analytics tool(s) to meet the unique needs of your particular organization. Not only do your specific needs need to be identified, but you then must evaluate those needs properly against the features of each potential solution. With so many choices and so many factors to be considered, the whole process can be overwhelming. Let’s first explore a few general features and attributes that should be considered when assessing how well a big data analytics tool can meet your organization’s needs. Then we’ll explore 22 tools.
To help you avoid as many roadblocks as possible and to help you overcome the various challenges that exist at the beginning of a big data project, here are ten critical questions you should answer before you start.
Ensuring big data readiness can seem complex and overwhelming, especially if you’re not fully aware of your internal big data capabilities. Understanding where you need to invest, where your data asset value gaps are and how to measure your current big data assets, are just a few formidable tasks that may be standing in the way of maximizing your data’s business value. Here are three ways that can help you fast-track your big data strategy and help you gain an understanding of what it takes to make your strategy successful.
Data is gold in today’s business arena. It’s the currency of the digital age, and while it’s clearly an asset, it can also become a liability when there are obstacles standing in the way of your ability to properly leverage it. The road to extracting maximum value from your information resources is often peppered with problems, hitches and hurdles.
Customers live in a world of instantaneous expectation, where everything moves at breakneck speed. With digital sales, consumer feedback and devices producing data at an equally rapid pace and at larger and larger volumes, putting this massive amount of information to use in real time is the difference between capitalizing on the advantages of a 360-degree view of a target audience, and losing customers to competitors that do. This information, that has become too large and complex for traditional data processing and data management applications, is known as big data.