Around five years ago, a certain type of story started to pop up. The headline would be some version of “every company is now a data company” and the article went on to explain how business success would increasingly be defined by companies’ ability to collect, sort, and analyze the growing mountain of data related to their operations and customers. Since then, this breed of story has been written again and again, with no end in sight.
This isn’t to say those stories are wrong. On the contrary, the “every company is a data company” mantra is much more than a platitude delivered at conferences and in trade journals, especially during a pandemic that’s forced those same companies to take a much closer look at what is and isn’t working internally.
Even prior to the COVID-19 outbreak, every day brought new evidence that the most successful companies were data-driven and willing to invest a lot in staying on the cutting edge. McDonalds’ recent $300 million acquisition of Dynamic Yield to obtain better insight and intelligence from its data is one sign of the value that leading companies are placing on data. So is the rise of “experience management” firms such as Qualtrics, Podium, and InMoment that help firms gather and make sense of data from their customers.
But the data company hype obscures a much more meaningful and difficult to answer question: What’s actually worth measuring when you can measure almost everything?
At its most fundamental level, data is about the search for leading indicators. The kind of information that has predictive power, enabling you to truly understand your customers and put you a step ahead of the competition. Even five years after those data company stories started appearing, a surprising number of companies are focused too much on lagging indicators like profit margins and sales numbers.
Companies want to get a better view of their customer or operations, but many are handicapped by having their data siloed in different systems. A lot of organizations risk drowning in data that they don’t have the skills or systems to create reliable information out of or to access in a way that isn’t overly time-consuming.
A common misconception that prevents many companies from embracing data is that it’s something for the Googles, Ubers, and Facebooks of the world, but not for the little guys. A few years ago, there was some truth to that: Setting up a data infrastructure could be a seven-figure investment after you factored in hardware, software and developer costs.
These days, it’s not cheap or free but it has become dramatically more accessible for all stripes of companies. With the advent of the pay-as-you-grow cloud services and SaaS-based platforms, companies can start investing in their data for dollars per day and have the flexibility to scale up or down as the value of the solution grows, rather than investing a fortune up front in hopes it might one day pay off.
The sheer variety of companies that are now able to access data and use it to obtain actionable leading indicators also means the data revolution has truly arrived, a mere five years after it was advertised.
One company we’ve worked with in the past that specializes in commercial building installs, for example, was able to identify and track a crucial leading indicator it wasn’t previously aware of. It found that if its product wasn’t ordered 120 days before a job started it led to delays that made the difference between making money or not. Another company in the asphalt business that was once a client has been able to analyze publicly available rail data to identify patterns in shipments from competitors’ plants. That enables it to assess how close its rivals are to capacity and shapes how aggressive it is in bidding on new contracts.
The most successful companies are the ones finding ways to access and interpret key leading indicators. One company in the storage business, for example, has been able to leverage the data from reviews that customers leave about their experience at different sites. Combing through the data using keywords—especially ones having to do with rodents or water damage—to identify positive and negative responses has enabled it to address problems and soothe unhappy customers rapidly. It’s also been able to scrape through call center transcripts to identify patterns in customer comments, using AI transcription tools that were once unavailable or prohibitively costly.
These are prime examples of data that in the past wouldn’t have been seen as useful or accessible but whose value is now being unlocked through technology that’s cheap enough for most to access. And its trickle-down effects are being felt everywhere, from ER waiting rooms to car dealerships.
The good news for companies who haven’t yet jumped on the data train is that much of this gold dust is already in their hands. The cloud-based analytics solutions that are now widely available and affordable offer the key to liberating that trapped data and putting it to work.
The bad news is that it still takes work. Perhaps more than it ever did in the past, since the volume of data that needs sifting has gone up in lockstep with the lowered cost of its collection.
That’s a major detail that’s been left out of the majority of stories written about data’s ability to change every business. Sure, today every company could be a data company. But not without a lot of effort.