The predictive capability that Big Data offers businesses and society is quickly generating excitement in the wider business arena.  Data is bigger, and access is faster and cheaper than ever, and everyone is keen to capitalise on this.

However, this excitement hasn’t always been accompanied by an understanding of the platform infrastructure and analytics needed to make sense of Big Data – sifting through the noise (and there’s a lot of it) to draw-out powerful insights.

Below is a series of four stages that businesses should consider to get the most out of Big Data.

 

Infrastructure

The first stage is, creating an ecosystem that provides the right architecture and infrastructure to gather, load,  sort, clean,  improve and enrich your data at speed with minimal difficulties. Data is increasingly unstructured and so you need a flexible infrastructure to take it in, manipulate, analyse, define business rules  and then output it to an automated back-end that closes the loop.

 

Layering

Secondly, make sure you are including the right data. This will be a mix of Declared (gathered directly from the customer through a ‘value exchange’) and Inferred (through their behaviours or through other sources of information) data. This information can be pulled from a wide variety of sources, some open and free, others licensed. They add context to events and individuals, and by layering on information such as home moving, social listening and weather data from any one of many sources,  an adaptive data engine can help build predictive models that show a greater likelihood to drive a desired response.

 

Insight

When deriving insight from big data, businesses need to look beyond data correlations to causality. What’s interesting is not the arising trend itself, but the underlying drivers behind emerging behavioural patterns. You need to match the findings of what is happening from the data to why by blending this with insights from research and an understanding of human psychology. Together this enables you to identify these drivers – motivators, barriers and triggers – on an individual level, and use them to contextualise broader patterns and trends.

 

Action

Finally, and most importantly, is how this insight and understanding is applied. If enhanced data is not actioned  little value can be drawn from it. In regards to promoting behaviour change the biggest challenge is to think data and talk human – use your insights to inform on-going timely relevant communications that add value to an individual, and become trusted, appreciated and welcomed. This is the key to Big Data success because, at the end of the day, insights are nothing without subsequent action.  Actions speak louder than insight.