Although machine learning has been around for decades – and is becoming more widespread – its popularity is exploding in the age of big data, with the market expected to grow at a 42% compound annual growth rate by 2024. As the world becomes more and more digitally connected, with unprecedented amounts of data being generated every day, organisations need tools to help them sift through and process this extraordinary amount of data. Through machine learning, companies are building models that can quickly process the vast amounts of data and harness it in countless ways. If used effectively, machine learning can have a huge organisational impact in five key ways: decision making, forecasting, personalisation, efficiency improvement and asset management.
Decision Making
Machine learning has transformed the way businesses process and analyse data, and is delivering insights faster than ever before. The faster decision makers get insights, the faster they can make critical decisions. Often, competitive advantage is found not in minutes or hours, but in milliseconds.
For example, machine-learning-based software trained to identify anomalies in the company’s security environment can detect a data breach immediately and notify the appropriate teams within the organisation. The intelligence derived from such machine learning models enables these teams to make quick decisions on effective remediation, protect customer data, preserve business reputation and avoid costly corrective actions. To optimise these decision-making benefits of machine learning, organisations need to collect and present the right data to the data modelling environment. They then need to build useful predictive models and make forecasts from the data. Ideally, they can even fully automate the decision-making process through so-called “reverse ETL”.
Forecasting
Particularly in the face of supply chain disruptions and delays, organisations are now under immense pressure to anticipate market trends and customer behaviour. Machine learning models built into data analytics enable more accurate and robust capabilities to forecast demand, enabling more efficient inventory management and cost reduction. One application of this could be to address the often chaotic nature of the supply chain. This can seem very unpredictable, but if the data is broken down into an overall average, plus a trend component and a seasonality component, an autoregressive forecasting model can work very well. This helps minimize wasting Inventory while quantifying the risk associated with total depletion. By quantifying the probability of an adverse event, such as running out of stock, it becomes manageable.
Personalization
Today’s end users and consumers are used to getting what they want, exactly when they want it. Creating a personalised, customised experience is a key strategy for competing in today’s market. Machine learning platforms can be used to analyse user behaviour and provide personalised recommendations, such as additional products, based on purchase history.
Efficiency Improvement
Machine learning and artificial intelligence skills are key to unlocking not only productivity, but also efficiency and innovation within an organisation. Because machine learning allows computers to perform repetitive tasks – and to do them much faster than humans – organisations can reallocate human resources to higher-value activities.
Machine learning and artificial intelligence skills are key to unlocking not only productivity, but also efficiency and innovation within an organisation. Because machine learning allows computers to perform repetitive tasks – and to do them much faster than humans – organisations can reallocate human resources to higher-value activities. A great example of this is machine learning models that perform extensive document searches in a fraction of the time it takes for humans to scan and cross-reference documents. This leads to a decrease in the cost of information retrieval activities related to regulatory compliance and legal research, freeing up staff to creatively engage in other efforts across the enterprise to create value.
Asset Management
It is sometimes difficult for businesses to know exactly when maintenance or upgrades are needed. Moreover, the costs of these efforts can be high. Predictive machine learning models can help by gathering performance data on equipment and components to monitor their condition and calculate the remaining lifetime of assets.
Working with large amounts of data is always challenging, but to drive business and stay ahead of the competition, decision makers need to unlock the full potential of machine learning. Of course, to achieve the best results in these machine learning applications and many others, these machines need to be trained correctly, not just overloaded with any and all data. It is crucial that the machine learning model consumes clean data sets – the quality of the organization’s data has a direct correlation with the quality of the insights the organization gains.
Source: Datanami