Machine learning is providing the needed algorithms, applications, and frameworks to bring greater predictive accuracy and value to enterprises’ data, leading to diverse company-wide strategies succeeding faster and more profitably than before. The good news for businesses is that all the data they have been saving for years can now be turned into a competitive advantage and lead to strategic goals being accomplished. Revenue teams are using machine learning to optimise promotions, compensation and rebates to drive the desired behaviour across selling channels. Predicting propensity to buy across all channels, making personalised recommendations to customers, forecasting long-term customer loyalty and anticipating potential credit risks of suppliers and buyers are also key.

Unlike advanced analytics techniques that seek out causality first, machine learning techniques are designed to seek out opportunities to optimise decisions based on the predictive value of large-scale data sets. And increasingly, data sets are comprised of structured and unstructured data, with the global proliferation of social networks fueling the growth of the latter type of data.

Machine learning is proving to be efficient at handling predictive tasks including defining which behaviours have the highest propensity to drive desired sales and marketing outcomes. Businesses eager to compete and win more customers are applying machine learning to sales and marketing challenges first.

Machine learning’s ability to scale across the broad spectrum of contract management, customer service, finance, legal, sales, quote-to-cash, quality, pricing and production challenges enterprises face is attributable to its ability to continually learn and improve. Machine learning algorithms are iterative in nature, continually learning and seeking to optimise outcomes. Every time a miscalculation is made, machine learning algorithms correct the error and begin another iteration of the data analysis. These calculations happen in milliseconds which makes machine learning exceptionally efficient at optimising decisions and predicting outcomes.

The economics of cloud computing, cloud storage, the proliferation of sensors driving Internet of Things (IoT) connected devices growth, pervasive use of mobile devices that consume gigabytes of data in minutes are a few of the several factors accelerating machine learning adoption. Add to these the many challenges of creating context in search engines and the complicated problems companies face in optimizing operations while predicting most likely outcomes, and the perfect conditions exist for machine learning to proliferate.

The following are the key factors enabling machine learning growth today:

1. Exponential data growth with unstructured data being over 80% of the data an enterprise relies on to make decisions daily.

2. The Internet of Things (IoT) networks, embedded systems and devices are generating real-time data that is ideal for further optimising supply chain networks and increasing demand forecast predictive

3. Generating massive data sets through synthetic means including extrapolation and projection of existing historical data to create realistic simulated data.

4. The economics of digital storage and cloud computing are combining to put infrastructure costs into freefall, making machine learning more affordable for all businesses.