How Machine Learning revolutionises your business insights 

21 June 2021

How Machine Learning revolutionises your business insights 

Machine Learning (ML) capabilities within Data Analytics continues to develop and advance at a rapid rate. While the ability to run Machine Learning algorithms has existed in many forms, recent developments to support rapid modelling on vast amounts of data showcases the expanding potential of ML.  

Businesses wishing to integrate ML into their data strategy can expect a wide range of advantages and real-world practical applications, from rapidly accelerating the path to insights, to confidently processing billions of rows of data to model potential outcomes. As capacity and potential develops, Machine Learning continues to revolutionise the Business Intelligence and advanced analytics landscape.  

What is Machine Learning and Artificial InteIligence?

Artificial Intelligence (AI) and Machine Learning have both evolved greatly from their origins. While closely connected, there are key differences between the two concepts that separate them in how they interact with data sets.  

Artificial Intelligence is a term coined in 1956 and refers specifically to the concept of replicating human cognitive ability within technology. AI can be categorised into two forms, applied AI, or general AI. Applied Artificial Intelligence is more common, however, and incorporates the application of Artificial Intelligence to specific purposes such as a self-driving car, while general AI concerns the overall practice of building machines capable of thought.  

Machine Learning can be thought of as a sub-category of Artificial Intelligence and involves the construction of models that are capable of learning and improving through experience. In doing so, ML can achieve Predictive Analytics – forecasting and modelling potential future outcomes to inform business decisions.  

Variations of Machine Learning  

Machine Learning models are created and achieved in four distinct variations: semi-supervised, supervised, unsupervised, and reinforced. 

Supervised Machine Learning  

Supervised learning involves achieving Machine Learning capabilities through using labelled data. This gives the system some predefined knowledge that it can use as a baseline to compare against other data points. As the model continues to correctly reach results, it can more accurately react to unlabelled data. 

Unsupervised Learning  

Unsupervised learning purely relies on training data that isn’t labelled, meaning that it can avoid much of the negative aspects and difficulty of labelled data, such as precision, time-consumption, and quality.  

As the model has no point of reference, it instead uses neural networks to inspect and recognise patterns and trends to form conclusions about relationships between separate points.   

Semi-supervised Learning 

Semi-supervised learning uses a combination of a small amount of labelled data and a large amount of unlabelled data. This means that, although there is an initial point of reference, systems learn from a more independent stance unhindered by preventable challenges as outlined above.   

Reinforcement Learning 

Reinforcement learning prioritises evolution through rewards given when a correct path or string is accomplished, through environmental feedback. It was Reinforcement Learning combined with deep neural networks that Google’s AlphaZero used to defeat the world’s strongest chess engine, Stockfish, in 2017. 

How does Machine Learning advance Data Analytics and Business Intelligence? 

Incorporating Machine Learning into an overarching Business Intelligence strategy provides numerous benefits throughout an entire business, such as informing market-leading strategies and enhancing insights.  

Some advantages of incorporating ML efforts include: 

Automation of manual processes 

A core benefit of introducing ML functionality is the ability to seamlessly automate complex and often time-consuming manual processes, giving data analysts more time to explore data further, draw greater insights, and perform other value-adding activities. 

Increased sales 

Machine Learning can be used to suggest corresponding products to pre-existing customers, engaging with the audience while increasing sales. This is the same concept that services like Netflix, Spotify, and YouTube use to maximise your use and enjoyment of their platform for maximum retainment.  

Insights gained from unlimited datasets 

ML models are capable of analysing incredibly vast datasets in a short timeframe, potentially even real-time and on-demand. This equips businesses with the ability to react to datasets with precision and flexibility, and to model the impact of strategic decisions. 

Recognition of market patterns and trends 

Machine Learning enables users to recognise, interpret, and react to industry trends and patterns. This is invaluable when making market-leading decisions and maintaining the competitive edge in increasingly saturated markets. 

The Future of Machine Learning  

Although Machine Learning functions have existed for decades, their capabilities have continued to develop to become more efficient, rapid, and accessible than ever before. As technology continues to evolve in future, we expect to see the following advancements:

  • Increased speed  

Recent developments have focused on integrating Machine Learning with quantum computing abilities. This leads to faster data processing as ML proceeds to take advantage of the acceleration on offer, yet currently holds a high error rate. In the years to come, we expect Quantum Computing integration to become more prevalent and mainstream. 


  • Increased cognitive capabilities 

As ML processes develop, the ability to learn through experience and draw conclusions from data is expected to become more intelligent, more advanced, and more streamlined than ever – leading to smarter, faster insights on-demand.  


  • Improvements in the unsupervised learning processes 

Currently, unsupervised learning processes are made more complex due to large amounts of training data, involve lengthy training, and possess a large risk of errors and inaccuracy. As learning processes evolve, this strand of learning will continue to improve.  


Delivering trusted intelligence 

At DataShapa, we are passionate and committed to delivering trusted intelligence that empowers data and allows for data-driven insights on-demand. We are able to seek and implement a wide range of ML capabilities to improve your business insights and inform strategies with flexible, rapid automation sequences.  

To learn more about the Data Analytics and Business Intelligence landscape, read our insights here. For any queries and questions you may have, or to learn more about how ML can improve your current BI strategy, contact us here – one of our market-leading experts will respond as soon as possible. 

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