Welcome to the new Age of Data
I’m sure you must have heard about all the buzz on Data science and how Machine Learning and Deep Learning is enhancing the way we use #data to make machines smarter. There has been a sudden surge in demand for Data Scientists that got me interested. I hadn’t really looked at Machine Learning until I attended the AWS DevDay conference here in Melbourne and was awestruck at their capabilities and emphasis on Data science. I did have a brief understanding earlier but by the end of the day I realised that we are beyond the Digital Age; we can now fairly say we are headed towards the “Data Age”.
In the 21st century, data has emerged as the lifeblood of our digital society. It permeates every aspect of our lives, influencing our decisions, shaping our experiences, and driving innovation across various sectors. From businesses and governments to healthcare and entertainment, the importance of data cannot be overstated. Let us delve into the significance of data in today’s world, exploring how it impacts our lives and why it has become the foundation of progress and development.
With electronic hardware evolution and drop in costs; especially sensors that have become cheaper by every passing moment and brilliant data capturing capabilities and endless available virtual storage, it was time someone started making some sense of this data and started to use it for the greater good. From image recognition, speech analysis, chatbots to smart cars and home robots; the applications are endless. But this use of data isn’t new. Several organisations have been doing this for a long time.
An example is the airline industry where the auto-pilot feature that controls the aircraft from source to destination has been based on using historical data to make adjustments and manurers however this is just the tip of the iceberg.
In the software world, the evolution of search engines has proved just that; example: search recommendations based on personal historical searches and context based recommendations, search engines were able to use historical data to provide more accurate recommendations. This was nothing but simple data science and inherently had large data learning models running in the background.
But with the advent of cloud computing and as expensive (in terms of time complexity) computation becomes cheaper and faster, we have been able to develop complex learning models that would have otherwise not been possible with traditional machines.
In addition the emphasis on math and statistics has never been greater. We have now come beyond computer science and business knowledge being the core requirements of technology. The applications at this point are endless and several tech giants are developing tools that will help us develop applications faster.
But the main question is – are we prepared? Are we as technologists doing enough to learn and understand this and are our organisations prepared to adopt these in the businesses that they operate in?
Well time will only tell.