Machine Learning (ML) is a powerful tool. In simple terms, ML enables computers to take data and recognise the patterns in that data. Those patterns can then be used to make predictions or classify data in astounding ways to optimise and improve business operations.
Artificial intelligence (AI) and ML have been around since the 1950's. It all started with simply enabling computers to recognise patterns and provide outputs. This is known as symbolic reasoning. From there, researchers began to investigate if we could enable computers to use artificial intelligence to recognise patterns and make predictions of their own.
...wait, that sounds powerful. Too powerful.
Not to worry, this is still dealing with 'Narrow AI'. This means computers are confined to performing a specific task. They can't take over the world, terminator style.
Why are AI and ML getting SO MUCH air time across the globe NOW?
Recent advancements in computing power and the ability to access 'big data' (large volumes of data) is helping ML emerge with strength and capacity. With the rise of more connected devices through IoT (Internet of Things), cloud-based software and edge computing, data-driven analytics is emerging as a powerful tool to provide businesses with better information to make decisions. Data-driven models, for data-driven decisions.
The question is, is there a way to take messy, sometimes incomplete data sets and combine them with the elegance of machine learning algorithms to deliver commercially applicable digital products?
The good news is that there are ML techniques for almost any kind of data quality. Of course, the more (good quality) data the better. If your company operates with data warehouses or data lakes which are accessible and organised, you're already leading the way.
ML should be considered by all businesses, particularly those that are asset-intensive or deal with large amounts of data. Investing in understanding how your company can transform and adapt using digital technologies can lead to safer, lower cost operations, involving fewer human intensive decision-making processes.
Although, advanced data-analytics is not always needed. Sometimes there are mathematical or statistical techniques which are more fit-for-purpose to display and interpret the data. At SIG ML, we're focussed on finding the right solution based on the problems or ideas specific to your business and delivering end-to-end AI-driven software applications to successfully integrate digital tools into different teams and business functions.
A final note:
If you are commencing your digital transformation, or already implementing digital technologies, it is highly recommended to involve subject matter experts (SME's) every step of the way. It is incredibly important to not only consider how to develop the optimal ML algorithm/s for your business, but how to effectively engage the SME's and make sure that they are taken on the journey towards digital excellence.
It's about framing the problem or idea in a clear and concise manner, understanding how to make the most of the data you have and engaging with the relevant stakeholders and co-innovating solutions together.
SIG ML has a simple and scalable workflow that is structured to ensure no matter where you are on your digital transformation journey, we can provide your business with insights that drive innovation and business growth. To find out more about the importance of an integrated, collaborative approach in delivering business value, check out this post.
In addition, if you'd like to learn more about the capabilities of Machine Learning or have a project in mind, please feel free to get in touch.