Basic reporting and analytics are now competitive table stakes across industries. As 2020 approaches, more companies are using sophisticated algorithms to drive higher levels of efficiency, reduce costs and risks, drive additional revenue, improve customer experience and more. If organizations want to become truly agile in today's dynamic business environment, they have to continually improve their operations and evolve the ways they're using analytics.
"If you're not using advanced analytics yet, you're in trouble," said Bill Franks, chief analytics officer at the International Institute for Analytics (IIA). "Twenty years ago, if you were doing some type of analytics you had competitive advantage. Now if you're not doing analytics, you're falling behind. If companies don't push to adopt the new stuff, it's going to become a problem over time."
Advanced analytics, like data science, lacks a standard description, although characteristically, it involves prediction. Deep learning, neural networks, cognitive computing, and AI come to mind because the algorithms have capabilities traditional input/output systems just can't provide.
"What's commercially possible to do has expanded significantly," said Chris Mazzei, chief analytics officer at professional services company EY. "Decreasing technology costs and the explosion of data changes what's possible to do with analytics, and [the possibilities] are growing every year. That, combined with competitive pressures means if you're not looking for ways to reduce costs, enhance customer experience, create new products and services, if you don't want to manage risks radically different and better, you're in trouble."
Most companies start with basic analytics and then increase the level of sophistication as they begin to realize the limitations of their existing systems. Disruptors are an exception because they use advanced analytics early on in an attempt to outthink and outmaneuver the existing players.
Whether your company is trying to compete more effectively or just stay relevant, advanced analytics is in your future, sooner or later. The question is whether your company will lead or follow. Either way, now is the time to learn all you can about advanced analytics so you understand what benefits it can drive for your company.
Not so long ago, only large companies could afford the tools and specialists necessary to take advantage of advanced analytics. However, as more capabilities are made available through cloud-based services and as more of the complexity is abstracted, more businesses are able to advantage of advanced analytics without spending millions of dollars and hiring data scientists.
For example, lawn care aggregator site LawnStarter started using prescriptive analytics about two years after the founders defined the business concept. The initial goal was to decrease customer churn.
"We have a customer risk model and a provider risk," said Ryan Farley, co-founder or LawnStarter. "We have thousands of lawn care providers in our system and the number of jobs they have ranges from tens to hundreds. Sometimes they take on too much. Before we had predictive analytics, we had to wait for the problem to become obvious." Now LawnStarter is able to operate in a proactive way rather than a reactive way.
In all fairness, Farley wasn't a typical entrepreneur. Previously, he worked for Capital One, which has been using predictive analytics since the 1990s to improve the ROI of its direct mail campaigns. When LawnStarter was founded, the founders wanted to do "cool stuff" rather than follow the traditional method of starting a company, building a product, and writing code. Fortunately, LawnStarter and machine learning platform provider DataRobot were part of the same Techstars accelerator program, so LawnStarter became one of DataRobot's beta customers.
"We were like, 'This is so cool! There's predictive capabilities in our data sets!" said Farley. "We started out doing it for fun, but then we realized there was actually business to be had there. Shortly thereafter, we started investing in the data infrastructure to where we can compile our different data together and make sure everything we're collecting is consistent and accurate."
If you are, what has your journey been like? What advice might you have for others? We'd love to talk with you about it in the comments section below.