Machine Learning Taking Over Data Analytics
Today’s data landscape presents businesses with a unique problem. Where 15 years ago data would have been a limited resource from which organisations strived to milk all the analytics they could, today it has become an overwhelming challenge to transform it into constructed, sensible, and actionable intelligence.
There are three main reasons for this: firstly, the volume of the data is higher than ever before as the sources of it have multiplied; secondly, due to the variety and complexity of these sources, the data is more diverse which makes processing more difficult; and finally, with how fast technology is progressing, stakeholders expect to receive insight quicker.
To help businesses cope with these demands that have the potential of becoming restrictive rather than useful, different platforms and solutions have been created to view this data in an orderly way. These are, for example, cloud analytics and storage as well as automated sorting programmes. Whilst these are an absolute necessity and seem to be a fresh direction in data technology, they are slowly becoming the standard. For businesses to get the absolute maximum out of their collected information, it is important to keep their techniques modern.
The next step after cloud based solutions is exploring AI and machine learning as a facilitator for data analytics. It is often highlighted that AI is “a general-purpose framework to understand complex dynamics”, which would absolutely lend itself for analytics purposes. Stemming from AI, machine learning uses smart algorithms to teach itself and improve based on the experiences it has and any information it takes in. Some examples of AI in real life are: cookies and their effect on online shopping suggestions, predictions, and site customisation; and Nest, the Google-acquired thermostat that learns its users’ temperature preferences depending on time of day, season, etc. Its importance is seen in a survey conducted by MIT Sloan Management on behalf of Google Cloud, where 60% of 500 interviewed global IT leaders believe their organisation’s future success depends on the successful implementation of machine learning.
Applying this to data, centralising and storing all the information in a cloud system creates the perfect basis for machine learning to take place. From this the machine learning system can then ingest the information and feed it into one of its five training models – image analysis, translation, video analysis, text analysis, and speech recognition – to generate unique insights.
One of the benefits of using machine learning in this capability is its ability to analyse the data accumulated over the years and not only show the direction of its development but show predictions of its future. Supported by the cloud, it is also able to store models, which means they are readily available to be used in analysis at any point; speeding up the process and further reducing the time between the point of data collection and the conclusion of deep analysis.
From Dashboard’s point of view, machine learning is the definitive future of data analytics and we believe it is closer than previously thought. With the vigour that other sectors are demonstrating in adopting it into their everyday routine, it would be natural for the trend to take over IT and analytics. The added value would benefit all stakeholders and improve business conduct significantly, which is why we are such fans of this technology.