Ryan Maguire is CTO & VP of emerging capabilities and partnerships at Kin + Carta. Prior to navigating our way through social distancing, Ryan and I connected about his work at Kin + Carta, a business “built for the 2020’s” – with a collaborative ecosystem of 1,600 strategists, engineers, and creatives across four continents.
When we last checked in, like so many of us, Ryan and his teams are spending their time closely monitoring and manoeuvring the impacts COVID-19 on their business, clients and investors. However, here is the conversation we had in early March about big data and the need for businesses to more effectively address the issue of reaching a tipping point.
M.R. Rangaswami: In all the conversation about Big Data and business, what misconceptions do you believe leaders have about data?
Ryan Maguire: Advancements in “Big Data” are just about us being able to measure, organize and interpret information at a much greater scale than ever before. So the biggest shift becomes what data do we pay attention to and what are we ignoring. Additionally, the sources (IoT, user devices, cloud solutions, etc) of data creation is growing at an exponential rate. So this shift is growing in the complexity of where to place your focus!
Data should inform every decision about how business shapes the experience of their customers. At Kin + Carta, we’re talking a lot about building Data-Driven Experiences via our Data Modernization practice. The business misconceptions are they need high power data lakes and complexity in their data platform to consume “ALL” data. While challenging, we believe that the modern data approach doesn’t require this complexity and organizations can take data in place to drive better experiences and decisions. We call this our “Storage to Story” approach.
M.R: What do you think are the biggest challenges for leaders trying to make smarter, more data-driven decisions?
Ryan: Because of the sheer abundance of data most organizations are wrestling with, making adjustments to our mindsets and processes is as important as anything. First, creating better access to data is everything. The days of anyone “owning” data need to be replaced with cross-functional, organizational-wide mechanisms for sharing data sources.
In a similar sense, demystifying artificial intelligence will help make solutions stick. Particularly in artificial intelligence and machine learning, we have to hold ourselves accountable to building AI that’s explainable. For example, if an algorithm recommends a financial product to a customer that customer, the regulators and more should be able to know why this product was suggested to them.
M.R.: For leaders trying to use Artificial Intelligence to better leverage data, where should they begin?
Ryan: First and foremost, the best place to start with AI is not to start with AI. Too many organizations jump to what they believe the solution is, but haven’t properly defined the business problem they’re actually trying to solve. It sounds so simple, but the projects that fail or misuse data often boil down to not properly identifying the problem we’re solving, and therefore WHAT data we should be looking at.
Too many measures of success can prevent you from properly measuring the effectiveness of an investment. Moving to a ‘North Star’ metric is usually a smart way to create clear KPIs without so many constraints that progress is slowed by too many potentially contradictory perspectives on what success looks like.