Big Science for Big Data
Professor Gary King from Harvard famously said that “Big Data is not about the Data”. In other words, the data itself has no immediate value. It is like crude oil that needs to be refined before it can be put to good use. This refinement of “crude” data is achieved with analytics.
This is the building block for the growth of ‘big data: we are seeing amazing advancements in the way we can analyse the data. We now have algorithms that can understand spoken words, translate them into written text and analyse this text for content, meaning and sentiment. Algorithms can now look at photos, identify who is in them and then search the Internet for other pictures of that person. More advanced algorithms emerge every day to help us understand the world of travel and predict future trends. This type of algorithm is known as “cognitive” as it emulates many of the learning algorithms the natural world uses to build biological neural networks to solve pattern recognition.
Open Source and Big Data
One of the interesting features of analytics in the last few years is that instead of secretly guarding the incredibly sophisticated algorithms and software used to implement these machine learning algorithms, organisations and individuals creating the algorithms are generally making them available as open source for everybody to use. Google is an activate participant in this trend and recently made its TensorFlow algorithm for Deep Learning available as open source. There are some good commercial reasons why they do this, but regardless, it does have the effect of accelerating progress in the area quite dramatically.
Putting it all together
The current scenario we find ourselves in is a fascinating one. The connections that the Internet enabled is creating vast quantities of high-quality data. The Cloud is providing all of the computing resources we will ever need. And cognitive algorithms can analyse data in ways that we thought would never be possible by a machine.
The convergence of three important, inter-dependent technology enablers: connections, cloud and cognitive, has the potential to usher in a new industrial revolution. What steam power did to physical labour 150 years ago, this convergence will do to cognitive labour … and our working lives and societies will change forever.
But what does all this mean for travel?
The vast majority of successful commercial applications of big data today are in the field of e-commerce and online advertising. Amazon and Google are probably the best examples of this in practice, because a very large proportion of the data collected by the apps and devices we use today reflect our preferences and life-style choices as human beings … and a very good way to monetise this data is through e-commerce and digital advertising.
Three Types of Analytics
Not all Big Data analysis involves predictive algorithms and cognitive computing. In fact, most computing analysis doesn’t. There are essentially 3 primary types of analytics applied to big data today:
- Descriptive Analytics: This is the most widespread today and typically used to segment customers. It is based on past behaviours and usually only simple metrics such as averages, totals etc. are used to provide insights.
- Predictive Analytics. Fewer organisations use this approach, mainly because it is more difficult, or large histories of good quality data are not available. With predictive analytics, the goal is usually to target individual customers based on predictions of their future behaviour.
- Prescriptive Analytics: Only a small number of very sophisticated organisations have deployed these solutions. In this scenario, very large quantities of high-quality data are required, along with powerful computing resources and cognitive / machine learning expertise. The power of prescriptive analytics is that the ideal action for each customer can be automatically taken. In this way, prescriptive analytics can either replace or augment a task that previously required a human user to manage. A great use case here is Alexa or Siri: imagine AI assistants like Alexa or Siri pro-actively engage with a customer to manage a lost baggage situation, using predictions of customer behaviour to determine the next best action.
Descriptive Analytics enjoys widespread use today and is well understood. What is more interesting is the recent trend we are seeing to use Predictive Analytics to measure the propensity consumers have to buy a basket of travel products.
This propensity model is the key to unlocking the value of Big Data in travel. Propensity predictions can support very sophisticated digital marketing campaigns that target a “segment of one” with highly customised offers and super-personalised travel experiences, to optimise conversion rates, online media spend and revenue per visit to your e-commerce site.
Next time: What do predictive propensity models look like and how do you use propensity models?