Remember paper maps? Me neither. Our maps apps are amazing at getting us from Point A to Point B as efficiently as possible.
Thanks to those apps, drivers everywhere have unknowingly absorbed the basics and the benefits of Machine Learning (ML). Machine Learning is a machine that continuously learns to suggest optimal, actionable decisions at any specific moment in time. Most of us don’t have the time or energy for route analysis and optimization; we’re more than are happy to hand off that cognitive load of deciding a route to a machine learning tool.
Unlike the ubiquitous maps apps on our phones, Machine Learning is hardly mainstream in the business world, which is a problem. Only 10% of businesses in the United States have established ongoing machine learning capabilities and those skew to the bigger tech conglomerates. Product teams and developers everywhere else are using a standard technology toolkit to make their products and processes more efficient: A/B testing, decision trees, conditional logic.
It would seem as though ML is the obvious go-to tool to automate informed decision-making for known everyday business events (things like personalizing content on a website, determining a delivery logistics path, upselling a gamer on a power pack). So why isn’t it being used in more places?
Part of the lack of ML utilization in the workplace has to do with our discomfort with data. We know that oceans (lakes, sets, mountains, pick a term) of data can be useful. But entrenched biases in historical data sets skew results unfairly. Also, it takes time to analyze the vast data sets that encompass all user behaviors. Cool data analytics software (Tableau, Power BI or Qlik) only helps us visualize our data well after the point where any optimization opportunity could be acted upon.
Most everyday decision-making events in a product or process don’t require data scientist-like analysis, or even a data scientist to define.
To be competitive machine learning needs to be mainstreamed. Not just building the model but the whole clunky process from start (data collection), to building models, using models to make decisions, and managing it all (ML Ops). Right now, almost all ML solutions require in-depth expertise, complicated piecemeal set-ups for different parts of a decision, and expensive ongoing management.
Successes or failures in meeting our business goals are now determined in microseconds or minutes, not days or months. Two-week sprints are becoming the tech industry’s version of paper maps. Successful companies are the ones using ML to power the optimal paths for their customers and clients.
It’s time to shift our mindset in the business world, similarly to when we evolved beyond static paper maps to dynamic digital directions. Let’s evolve the value of data from analytic naval gazing (which may or more not have predictive use) to using Machine Learning to process data that reflects real-time information to drive the best decision. Data is not the destination, the decision is the destination.
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