How your company can be ‘Google Smart’ with machine learning

Google has been using machine learning to improve its business analytics for years and judging from all the recent excitement about the technology, you’d assume enterprises everywhere would be following suit. Yet, according to some studies, only 22 percent of companies are already using machine learning’s analytical power by implementing algorithms in their data management platforms.

So why aren’t more organisations taking advantage of the science that can make them “Google smart”? Probably because they believe it’s too complicated. They might think their data is too voluminous or unreliable,  might consider the required data preparation too time intensive or might assume only data scientists have the skill set needed to leverage machine learning. While those concerns may have held true a few years ago, today, all those objections can be overcome.

In the past, using machine learning algorithms was complex, and the outcomes could be perplexing and unpredictable – it was difficult to understand how the technology classified data, so you were never sure what type of results you might get. However, the rapid adoption of the cloud and new technology tools have combined to help simplify machine learning and make it more accessible to a broader base of IT professionals.

Machine learning is data-driven, which means you need a lot of data to make it work. Furthermore, machine learning requires a lot of computational power, particularly to learn models. Fortunately, the cloud is ideally suited to deliver on these requirements and can also play an integral role in simplifying the use of machine learning and making the technology more affordable and manageable.

Additionally, there are also now a variety of commercially available tools that lower the barrier of entry and the complexity of machine learning while still working natively with the languages and frameworks used by the technology in the cloud.

For example, recently introduced drag and drop components give line-of-business (LOB) developers and power users the tools to complete many of the tasks needed to leverage machine learning’s power without the complex coding including:

Of course, while machine learning has a considerable amount of raw power that can now be used more easily for data insights, we are not yet at a point where we can simply plug in data and get instant results.