In an increasingly uncertain world, we look at how predictive analytics is helping organisations to forecast the future with accuracy and confidence.
Predictive analytics: a definition
Predictive analytics is a category of data analytics aimed at making predictions about future outcomes, based on historical data and analytics techniques.
Such techniques span multiple data sources, and typically encompass both statistical modelling and machine learning. These statistical models, which translate data sets into insights, represent the fabric of predictive analytics.
Through leveraging sophisticated tools entrenched in data science, any organization can now use past and current data to reliably forecast trends and behaviours, milliseconds, months, or years into the future.
Typically created to support marketing agendas through enhancing customer lifetime value and customer segmentation; or manufacturing via predictive maintenance and quality assurance, the models leverage deep-learning at scale and in real-time, to add value to big data and build advantage.
A brief history
There’s no question that the rise in big data has driven a need for analytics to cut through, understand and derive value from the swathes of data sets engulfing organisations.
According to Gartner, by 2025, 70% of organisations will shift their focus from big, to small and wide data, providing more context for analytics, and making AI less data hungry. This is largely in response to the fact that data volumes in isolation are largely irrelevant.
Meaningful data, however, has become one of the most valuable commodities within a business and the practice of harnessing, understanding and deriving meaning from it, has resulted in the rise of the Chief Data Officer, alongside dedicated departments to manage, monetise and make sense of it beyond pure data collection.
Data analytics is broken down into five core areas. Descriptive, which provides a summary of historical performance; Real-time, which provides insights into current data; Diagnostic, which is focused on the “why” surrounding events; Predictive, which applies statistical analysis techniques to ascertain the precise likelihood of an action, event, or behaviour occurring; and Prescriptive, which is concerned with converging all of the aforementioned areas to advise on what to do next.
To BI or not to BI?
Predictive analytics is essentially, an advanced form of business intelligence (BI), which uses analysis to predict future events. Whereas traditional BI typically uses data from a finite source such as finance and accounting for example, predictive analytics looks at multi-dimensional new and historical data to identify patterns, behaviour and trends.
Leveraging techniques such as data mining, statistical algorithms, machine learning and artificial intelligence, the practice creates dynamic insights from to detect risks and unearth opportunities. Interdependencies and relationships between the various behaviour factors, known as regression modelling, can be analysed in a way which would be impossible for the human brain to achieve.
In fact, neural networks, or algorithms designed to identify relationships within a data set, mimic the way in which the human brain functions to supercharge the analysis and break new ground in what’s achievable. This in-depth, precise level of insight allows users to make the very best decisions and steer a business in the right direction.
It’s important to note that many BI platforms have evolved to encompass big data; cloud; IoT and AI, and as such, some industry experts consider predictive analytics to be a branch of BI. The terms are arguably intertwined, and to add to the perceived overlap, as machine learning has become pivotal to predictive analytics, predictive analytics projects are sometimes referred to as machine learning.
On this latter point, it’s important to distinguish between the two. While machine learning is a fundamental enabler of predictive analytics, in isolation, it cannot deliver the insights the practice is synonymous with.
Predictability in unpredictable times
When you consider that some of the most high profile uses of predictive analytics include weather forecasting; political campaign performance; climate change; and the spread of diseases, it’s easy to get a sense of its importance. These are all highly complex, and in a world which has become increasingly unpredictable in the face of Brexit; Covid; and political tensions, predictive analytics makes looking into the future more accurate and reliable than with previous tools.
As well as having access to a level of visibility that can help offset external challenges and mitigate uncertainty, it seeks out routes to circumnavigate bottlenecks to reduce costs and increase profitability.
A good example of this is in the sourcing, retention and nurturing of profitable customers. While it’s impossible to influence issues such as rising fuel and labour costs, and address driver shortages which impact supply chains, funnelling resources into the right customers, and communicating in an open, meaningful and informative way, can drive up the necessary profitability to mitigate some of these challenges.
Cases in point
Predictive analytics brings deep, real-time understanding across multiple business activities, across numerous departments. From allocation of the right resources at certain times, in say, a hospitality company looking to mitigate rising labour costs and Covid absences, to stock replenishment and marketing campaign timing, the opportunities to bring immense value are endless.
In manufacturing in particular, firms are already reaping dividends through enhanced performance and productivity on the shop floor.
As machinery continues to become increasingly sophisticated, and excessive levels of downtime, untenable, manufacturers are adopting predictive manufacturing analytics to predict the location, nature and frequency of equipment failure.
Through analysing data from a range of sources such as sensors; manual visual inspections, vibrations, electricity consumption, and temperature, and mapping these against both historical patterns and wider use in industry, it’s clear to see how the clarity of the insights gathered are far superior to those which traditional BI can produce.
Foresight in the face of adversity
Against this backdrop, it’s no wonder that the global predictive analytics market is set to grow from USD 10.5 billion in 2021 to USD 28.1 billion by 2026
But challenges around skills shortages particularly data scientists, prevail. In parallel, implementation methodologies require dedicated experience and expertise, which in any new, fast growing discipline, isn’t easy.
Thankfully, a new breed of solutions has emerged which bridge the gap between business need, and the potential lack of skills available to deliver the capabilities. Senseye PdM for example, is a cloud-based platform which is machine learning-infused, and developed for scale, capable of processing vast volumes of data.
This combination of technology and innovation will continue to bring data science to the forefront of industry, allowing more and more organisations to fulfil their potential and turn insights into foresight.
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