After a day of mountain biking in the Kosiosko Royal National Park in Australia’s Thredbo Valley, I found myself in the depths of a serious conversation around Artificial Intelligence with a fellow cyclist, whilst hurtling down a mountain trying to avoid falling off the side of a cliff.
Laughing as I realised he knew far more about data than I ever will know, one of his first comments was ‘all this talk about Artificial Intelligence is way off – Machine Learning will come first. Those talking about AI are literally doing just that, talking. AI will follow long behind’.
It is not often I find a kindred spirit, especially one who rides a bike like that, but there you go, that’s what they call serendipity.
This conversation got me thinking and doing so lead to this 4 part blog series, where we will explore what successful organisations who work with Tealium in Australia are doing to prepare for Machine Learning and Artificial Intelligence beyond.
We are currently having a lot of conversations with customers and prospects who want to know about Artificial Intelligence and whether Tealium is going to solve the challenges of scale and automation through AI. The answer to this is yes …. and no. Do we believe, Machine Learning and Artificial Intelligence will eventually enable much of the long sought-after Nirvana where our electronic relations with people interested in our brands will be as fluid, relevant, and consistent as those we have created in the face to face world? Yes. Is Tealium embracing that as part of what we offer? Most definitely yes.
However, we would be negligent if we simply told you to go out and buy another tool (what is another one in an average stack of 90) that you can solve the problems of scale and automation with. Unfortunately, it doesn’t work like that.
If you are puzzled over how to bring together the many growing sources of data you have, to unify that data, understand it and act on it, all in the same time it takes your customer to click the purchase button, then read on Macduff.
At Tealium we have spent ten years understanding what makes data fast and useful to all, and whilst we are backing the capabilities of ML and AI to ultimately solve these issues, we don’t foresee it will happen by some magical shake of a wand. Anyone seriously embarking along the path to the world of ML and subsequent AI is preparing now by consciously building a neutral, cross-channel dataset. One that is ‘Machine Learning Ready’ so that when they are ready to start utilising the power of Machine Learning, their data will be ready too.Anyone seriously embarking along the path to the world of ML and subsequent AI is preparing now by consciously building a neutral, cross-channel dataset. Click To Tweet
When developing something truly unique that maps unchartered territory, it helps to have some guiding principles upon which to make decisions.
We find our most successful ML pioneers are building a neutral dataset which is anchored in the following principles:
These principles form the basis upon which brands are consciously designing a unified, customer at the center, first-party dataset. Not just giving lip service to the concept of data as an asset, but actually treating it as such. The principles and their outcomes can be summarised as follows:
Customer at the Center: This concept has many facets, but when it comes to communications, the underlying dataset must be one primed for delivering what the customer needs next, not what an organisation wants them to receive. All decisions on how, when and what to communicate should be based on the state of the customer (or potential customer) with respect to the organisation at hand. That can only be achieved if there is a centralised understanding that can be activated as it is generated. To be effective this must be:
Real time: The moment matters in the electronic world just as it does in face to face relationships. Ask any shop assistant how they make the most sales – it is by watching and listening to their customer’s needs and responding appropriately. There is no campaign, channel or device. Data is the language of relationships so to capture the moments that matter most, data must be activated in real-time by design.Data is the language of relationships so to capture the moments that matter most, data must be activated in real-time by design. Click To Tweet
Access and Ownership: As the volume of data grows, organisations must enable data democracy. For many it is also about ownership of first-party data in a consolidated manner:
Governance By Design: Whether you call it governance, privacy or simply respectful, sustainable relationships and the complexity of communications, combined with the growing understanding of the value of data, means organisations are now architecting this into their datasets and tools.
AI/ML Ready: As the volume of data grows so will the need to automate it using the power of Machine Learning and beyond. Having built a dataset that is customer-centric, real time, accessible, and properly governed means organisations will be ready to utilise the data. The core processes to enabling this mandate are:Having built a dataset that is customer-centric, real time, accessible, and properly governed means organisations will be ready to utilise the data. Click To Tweet
Tealium helps our customers build a strong data foundation first, one firmly anchored in the bedrock of an owned, accessible-to-all, neutral, governed dataset that will be fluid in nature. We believe this will enable capabilities such as ML to flourish, but without the principles as fundamentals, there will be a constant need to rebuild the underlying architecture. A process that is costly both in time and resource.
The first step towards ML readiness is building a strategy and roadmap for uniformly onboarding data from any source in a state that is immediately ready for use by all.The first step towards ML readiness is building a strategy and roadmap for uniformly onboarding data from any source in a state that is immediately ready for use by all. Click To Tweet
This is a concept we will explore in part #2 of the ML/AI Data Readiness blog post series.