Could your data become a money-making product?

Autonomous smart truck. Unmanned vehicles. artificial intelligence controls the Autonomous truck. Hologram car style in HUD/UI/GUI. Hardware

It’s amazing what even small companies can do when they understand the power of data.

What if customers could order a delivery of fresh concrete along with some software to optimise their entire supply chain – both from the same company? Or perhaps a single company builds point-of-sale machines while also offering packages that allow advertisers to digitally model their ideal customers?

Every business can gather huge amounts of highly valuable data if they just know where to look. But having that data is only the first step. The real trick is using it to create new revenue sources or figuring out how data can help reduce costs, increase efficiencies or support new processes (which are all money in the bank as well).

Data is simply ones and zeros. It’s not ethereal or magic. The way those ones and zeros are aligned might be unique to your company and context, but the data itself is entirely neutral. What makes data a valuable intangible asset is how it can inform your decision-making processes and if it can generate extra income for a company – either today or sometime in the future.

When you understand the value of data, a common reflex is to immediately think of ways it can be used to improve operations. That makes perfect sense. After all, the data is being pulled from your operations, so it’s natural that it generates fresh insights about how those operations might be done more efficiently and the impacts they may have across your value chain.

But data can be a product, too. Even the most unique datasets can be packaged up and sold to customers without compromising the integrity or value of that data to your own company. In other words, you might be sitting on a million-dollar revenue stream and not know it.

As the good people at McKinsey wrote, when companies instead manage data like a consumer product, they can realise near-term value from their data investments and prepare for squeezing more value tomorrow.

Consider the example of the concrete delivery company mentioned in the opening section.

The company has been relentlessly growing its reach and range for more than two decades. Its long-term strategy is to be a major player in the construction sector in multiple cities throughout the nation. But to grow larger, it needs a special sauce because, well, concrete is concrete.

Sure, there might be nuances between aggregates and limestone, but for 90% of customers, concrete is a fairly generic product to buy. What separates one concrete company from another is price, and price is usually decided by supply chain efficiencies. Costs are always passed on to the customer, so if costs can be reduced the price of a product falls in concert.

To find that secret sauce, the concrete company looked to its data for help. After outlining a plan for improving efficiency, it discovered that each of its drivers carried cell phones that tracked the location of the fleet in real time.

By gathering that data and linking it to customer orders, a few entrepreneurial employees created a way to isolate and eliminate bottlenecks in the delivery process – from the first customer phone call to the mixing of the concrete, all the way to picking the best route through city traffic.

In just a week, the small team had created the outline of a data-driven solution that reduced delivery costs by 22%. Two months later, the company had hired contractors and enhanced its own data team to pull together a plug-in prototype for a truck-routing decision support tool that both sped up queuing time and lowered carbon emissions.

Before the end of the same year, the concrete delivery company had packaged up the tool and sold it as a solution to a few dozen other companies ranging from an FMCG business to a hairdressing franchise. That one tool added an extra $6.8 million in licensing revenue for the company, $5.4 million of which was essentially free cashflow.

The breakthrough came when the concrete company realised its data was similar to data being generated by other firms with delivery needs. They judged correctly that those other companies might appreciate creating insights based on loading their specific data into the system. The concrete company had built a lucrative product out of its data.

The second example is equally informative.

With its point-of-sale machines in thousands of supermarkets, the company had an incredible insight into the purchasing habits of consumers. The data collected had no personal identifiers or other private details. It was all metadata – things like time of purchase, location, type of product, quantity of items, etc. Just ones and zeros, really.

The data was directly useful because it allowed the company to see where its machines were encountering a fault. They could then send technicians to fix problems quickly. The data also provided feedback for the company’s engineers to issue updates in later point-of-sale models.

One day, an engineer realised all this data coming from thousands of machines could create a “digital twin” of those shopping centres. Coupled with other forms of data, the digital twin could be asked “what if” questions to observe how and when people bought the items on shelves.

Because the CEO had experience in the marketing world, he understood that this digital twin software would be immensely helpful to marketers looking for cheap ways to test products before they were released. It could run simulations of ideal positions for stock, the best time for releases and other key dynamics – and it could do millions of simulations in a few hours.

The financial result of the company turning the data from its point-of-sale machines into a separate product was an extra $59 million per year from licenses. The package performed so well that it eventually became a sister company that was sold for hundreds of millions of dollars.

Data is more than just a boost to your company. It can easily become a stand-alone product that can add serious revenue to the bottom line. This mindset reflects the idea that the value of data is effectively inexhaustible since it can be digested and used over and over, by different players, with no (or nearly zero) loss in quality.

In other words, data might just be the only sustainable resource humans have ever discovered.

Gathering the right data and using it wisely can be a competitive advantage. But leaders can only ensure that the hard work they do today is reusable tomorrow by managing their data like a product.

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