Predictive science is already changing our behaviour. We now measure and quantify our lives daily through a myriad of apps and business processes. Its impact on areas including political campaigns, professional sports, consumer credit, healthcare and fraud detection is palpable. It is also fundamentally changing business growth.
Organisations are harnessing the power of technology to enable and support greater efficiency and effectiveness. This movement is part of a computing evolution – from the early days of the centralised mainframe to the explosion of data in the cloud, there is a proliferation of devices feeding new information into the connected mesh.
The rate of data creation is rapidly accelerating. We are faced with inordinate amounts of information, often with no structured plan for what to do with it. Data science has become the force with which we can put this information pile to work, applying predictive analysis to very big data.
Prediction without prescription is an empty gesture
Most companies rely primarily on predictive models for using data. This is the examination of past behaviours and historic data to give insights into the future. The new science demands that this should be aligned with the prescription of data – the ability to ensure that predictions are realised through defined actions.
While there is a plethora of tools and technologies to offer prediction, they lack prescription. Just as you wouldn’t expect a doctor to diagnose your illness without then prescribing your treatment, the same should be said of how we manage data.
For years we have been using various simplistic forms of statistic modelling that have been hamstrung by focussing on a single variable. Rory McIlroy is one of the most recognised golfers in the world – his every swing is scrutinised and analysed. But if we only look at his average driving distance (total number of drives/total distance) we fail to account for other factors, such as the state of the course, how the weather behaved that particular day, or even how the competition performed.
The application of science
If you apply those contextual attributes to the descriptive statistics of McIlroy’s play, you can predict the ultimate outcomes based on the factoring of multiple variables. This is the realm of big data and huge computing power.
As the variables expand and the data grows, so does our predictive ability. It is enhanced by machine learning and the development of a heuristic approach. It is the application of science which allows us to prescribe actions that complement the predictive outcomes.
The algorithms needed to supply such outcomes are based on heuristics which support the machine learning environment:
- Psychographic – personality-based traits
- Demographic – individual traits, such as your sex or job title
- Geographic – your location or the weather
- Firmographic – information about the company you work for
- Histographic – background, transactional data and publicly available information
Data is the key, but science is what works out its relevance and how we use it. Data science and predictive analytics give us insights and behavioural visibility. Once these insights are within our control, we can use them to prescribe sales persons’ activities and ultimately drive productivity and effectiveness.
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