Everyone is talking about how AI and predictive analytics are changing the world. But what does this change actually look like, and how does it work?

Predictive analytics goes something like this:

Data is collected. This could comprise of all sorts of things: web clicks, demographic information, email campaigns or sales, to name a few. To this data, statistical or machine techniques are applied to make some sort of prediction about the future. For instance, by collecting data on smokers and non-smokers and applying these techniques, scientists are able to predict the likelihood of a person who smokes developing a cardiovascular disease. In fact, we can even predict their life expectancy from the number of packs they smoke in a day. Predictive analytics allows us to extrapolate (in this case, about the future of the general population) based on current data.

At Ikiru, we partnered with the UN Refugee Agency on a project called Compass, created to simplify the application process for people seeking asylum in new countries. We can use predictive analytics to forecast migration trends across the globe, giving the UN the potential to anticipate the movement of people years ahead of time, which means that governments can be better prepared for any influx of asylum-seeking people at borders.

These same techniques can be applied to our organizations. Depending on the content and quality of your data, you can use AI and predictive analytics to forecast revenue or conference attendance, and to determine what kind of content to distribute, what products or services to focus on, and even which members might leave your association in the next year – and why.

But what does it entail?

Here’s the thing. Predictive analytics and AI are, in fact, at the very top of the data pyramid. This tip of the triangle is propped up by a number of cultural and structural layers, all of which contribute to successful analytics initiatives, and all of which need to be taken seriously in an organization committed to data. These layers relate to driving, managing, and interpreting your data.

The foundation to any strong predictive analytics initiative is having the driving force of high-level strategic priorities, and the vision and investment of leadership, supporting your efforts and making them meaningful. What is the point of collecting and analysing data if your analyses are never translated into actionable strategies? And how can an analysis project truly take root without the understanding and buy-in of all departments?

Building upon this, the role of people in designing and developing data-driven solutions cannot be underestimated. The people who make up your organization are the ones who will be asking questions of your data, interpreting the results, and offering problem-solving insights. This means that beyond processes and protocols being in place, individuals need to be well trained to listen to and speak the language of data.

With the culture, vision, and people primed for and invested in data analysis, the issue becomes data management. This includes the who, what, where, when and why of data assets, as well as mapping and disseminating data points. Early investment in an efficient data management plan will dramatically streamline your process, and given that the majority of time and money spent in developing data analysis and products goes towards data management, this streamlining could be essential for securing a sustainable analytical strategy.

Ultimately, predictive analytics provides a response to a pre-specified question. It isn’t a magic soothsaying tool, but a supplement to existing practices and decision-making. To unlock its full power requires a strong vision with clear objectives, the appropriate infrastructure for understanding and accessing data assets, and suitably trained and tailored people and processes. This will mean that you can ask the right questions, and get the answers that you need.

Associations are in such a good position to develop strong predictive analytics initiatives – they are so often sitting on piles of significant data, and just need to be equipped to navigate and leverage it. Even more importantly, associations are focused on making positive change and solving problems in their respective industries. The key to these solutions, for the continued betterment of the communities our organizations serve, might well lie in all that data.

Paolo spoke in the “Data Strategy For Dummies” session during SURGE Optimism 2018, an interactive virtual conference hosted by AssociationSuccess.org on November 7th-9th. Click here to watch the sessions on demand.

Paolo is a consummate designer of things that solve real world problems. His background is in software development, data analysis and UX research. His experience has always been grounded in the philosophy of designing solutions around real problems that people face. He’s developed dozens of data products, some of which have been adopted by tech leaders such as Google, HP and Konica Minolta.

His experience has been shaped by being the tech lead and product designer for many innovation centers whose mandate was to develop AI or data products, including the Stanford Research Institute. He eventually took the role as the CEO of Aria, a mental health company that applied predictive AI to patient diagnostics and treatment planning. This spun off into an AI consultancy which developed products for Fortune 1000 companies and non-profits.

He is now the director of Ikiru, an institution that combines AI, design thinking and engineering to co-create data products that solve an organization’s top problems. Nowadays he can be found huddling around the warm glow of his laptop screen as he binge watches Black Mirror, Mad Men and other Netflix staples; a common survival strategy for Canadians during winter season.

Paolo is a consummate designer of things that solve real world problems. His background is in software development, data analysis and UX research. His experience has always been grounded in the philosophy of designing solutions around real problems that people face. He’s developed dozens of data products, some of which have been adopted by tech leaders such as Google, HP and Konica Minolta. His experience has been shaped by being the tech lead and product designer for many innovation centers whose mandate was to develop AI or data products, including the Stanford Research Institute. He eventually took the role as the CEO of Aria, a mental health company that applied predictive AI to patient diagnostics and treatment planning. This spun off into an AI consultancy which developed products for Fortune 1000 companies and non-profits. He is now the director of Ikiru, an institution that combines AI, design thinking and engineering to co-create data products that solve an organization’s top problems. Nowadays he can be found huddling around the warm glow of his laptop screen as he binge watches Black Mirror, Mad Men and other Netflix staples; a common survival strategy for Canadians during winter season.

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