Organisations are amassing data in rising quantities from a growing number of sources. Some of this data will relate directly to core activities, for example, current transactional data from sales systems. Other data may be less directly connected to the business, for example, historical data on former customers. And yet more data may have unknown relevance, for example, data on how competitor businesses are perceived on social media.
One significant challenge for businesses is that the potential value and relevance of each data set is not always obvious, particularly for newer types of unstructured data, and there may be limited appetite and resources to investigate what could turn out to be blind alleys. The other major challenge is that manual approaches to querying and reporting on data are slow, have limited scalability, and lack the sophistication to find deep insight.
Traditionally, businesses have used data on their performance as a kind of rear-view mirror. On a monthly, quarterly and annual basis, teams will review the numbers, try to identify the reasons for success or failure, and then update the corporate strategy accordingly. Faced with a growing number and variety of data sources, just understanding which data is worth analysing is becoming more difficult. And periodic, backward-facing analysis is no longer fit for the challenges of a fast moving digital world.
By taking advantage of machine learning and data science, organisations can bring both scale and speed to the analytics challenge, helping the business predict future outcomes from current data of all kinds, and take a proactive approach.
New machine-learning techniques enable systems to sense, process and act on information in a way that optimises business outcomes. By feeding data into a model and steering it towards desired goals, business can train the model to respond in the desired way to new data that it has never seen before. The real value of machine learning comes from iteration: by continually re-training the model with new data, you can increase its sophistication and enable it to react appropriately even as external conditions change.
Cloud-based machine-learning tools make it easy to create intelligent systems that seek known patterns in new data. These systems can then augment human capabilities, boosting efficiency and speed by transforming huge volumes of data into insight. A common first use-case is predictive analytics: using known outcomes from previous conditions to predict future outcomes based on current conditions.
Recent years have seen the democratisation of analytics, bringing powerful statistical tools within reach of non-specialist users. The latest cloud-based machine-learning solutions guide business users to the most appropriate types of analysis based on the data they upload.
For example, businesses can use regression-analysis tools to predict future outcomes based on the past relationship between two variables – such as predicted demand and actual sales. Anomaly-detection tools use unusual patterns in data to flag up risks and potentially fraudulent behaviour. To segment customers or determine the optimal price for a new product, businesses can make use of clustering tools to split out collections of data points into groups.