Data and its applications in Machine Learning and Artificial Intelligence are touted as ‘the fourth industrial revolution. ’ 1 If you’re familiar with the original trilogy, that is no small claim.
CIO’s everywhere have embraced the values of a data-driven company. Silicon Valley led the charge and data science exploded into the mainstream around 2010. Since then data science jobs have risen from less than 100,0002 in 2011 to an IBM-projected 2.72M in 20203. Many if not all major companies have jumped on the data science bandwagon. The Googles and the Facebooks of the world tend to have it very easy. For them, data is the product. Everything you do with that data has the potential to impact the bottom line. But what do you do when your product is very different? How do you use data to profit more from tractors, potato chips, or drywall? How does data help your organization boost revenue or cut costs, how does it help you address your fundamental business problems, how do your data scientists earn their keep?
For the 20 biggest US manufacturing and retail companies, we collected the top Google-ranked articles about their use of big data. From that, we identified a total of 34 data applications. Here’s a breakdown of what they’re using data for:
What is striking to me is that companies have been working on almost all of these for decades. Supply-chain optimization and manufacturing efficiencies have been around since the Model-T. Digital marketing has been around almost as long as digital has. By my count, everything outside of AI assistants has been around before the data revolution – that means 30 out of 34 applications of data (as seen above) are on solving classical business problems.
Many of these companies are doing it in incredibly cool and innovative ways. Frito-Lay used computer vision to make an automated potato inspector that saves the company $10.5M in the US alone 4. Caterpillar is helping its customers save $688K per machine by reducing downtime from 900hrs to 24hrs with a connected fleet of IoT devices 5. Lowe’s is using weather and customer data to understand how to stock their stores after a hurricane – to make sure they have available what people need to rebuild 6.
The fact is though, while these methods are incredibly innovative, the business problems they address are ages old. The applications that move the needle are usually the fundamental ones. When you think about how to use data to drive change, the surest demonstrable return will always be in helping people overcome challenges that they already face.
In a way it should come as no surprise, that same data revolution touches our everyday lives all the time. Yelp in 2012 condensed data on restaurants into a simple metric that helped people decide not whether to eat but where to eat. When you check the temperature to decide whether or not to wear a coat, you’re making a data-driven decision about how you’re going to leave the house. When you check Google Maps traffic it’s not to know if you will leave for work that day, but to know when to leave so your time is optimized. The data subtlety impacts how you get from A to B – it’s not the new, it’s better executing on the old.
In a nutshell, that’s the brilliance of the data revolution, it’s not about blazing new trails or paving new roads – it’s about making all the ones you have run a little bit more smoothly.