By Russ Banham, Contributor
Most companies have an enormous amount of data at their disposal. If this data is digitized, it can be collected, aggregated and morphed into predictive analytics, drawing all sorts of useful insights. But, what if these insights might also be of value to other entities—customers, suppliers, and even outside businesses?
“Because of digitization, companies have a rare opportunity to use their data to make their products and services better for their customers,” Mario Spanicciati, chief marketing officer at BlackLine, a publicly traded financial and accounting automation software provider, said. “They also have the opportunity to develop new products, helping customers improve their business prospects.”
This digital transformation opens companies to the possibility of monetizing information. By packaging up data as a product for sale, companies can develop a new revenue stream.
“Your data can be valuable in surprising ways,” Timo Pervane, a partner at management consultancy firm Oliver Wyman, explained. “All companies have valuable data for their own purposes, but their data also may have value to other companies, too.”
“All companies have valuable data for their own purposes, but their data also may have value to other companies, too.”
— Timo Pervane, Partner at Oliver Wyman
Several companies have emerged as leaders in data monetization. Payroll provider ADP provides predictive HR workforce analytics through its ADP DataCloud product. The cloud software packages data from over 30 million employees across 95,000 ADP clients to give companies a better understanding of how their human capital management metrics measure up.
Retailer Tesco has since 1996 collected insights on customer behavior by analyzing shopper Clubcards. Its subsidiary, dunnhumby, collects and assesses this data from over 38 million Clubcard users to produce predictive analytics and personalize the shopping experience through various digital marketing campaigns.
And agriculture giant Monsanto provides analytics on crop performance through its Climate FieldView product.
Yet, despite growing adoption of big data monetization, many business leaders are still unsure if their company could do the same. The consensus from Pervane, a champion of data repackaging, is resoundingly: Yes. It’s just up to the company to get creative about what type of value it offers.
“[The] possibilities abound,” Pervane said. “Data collected and aggregated from credit- and debit-card transactions, for instance, may suggest trends in consumer buying that could influence stock prices. Thermostat settings for different demographics and regions might be of interest to an energy trader. And the number of trucks going by tollbooths can be a proxy for GDP growth.”
Similarly, some businesses present data in the form of data-as-a-service products. The use of benchmarking data is a case in point.
“In a very ethical and compliant way, you can anonymize [customer] data to tease out interesting insights across industry segment, geography, size of business, number of employees, and so on,” Spanicciati said. “You can provide these insights to all your customers for free, or you can sell it to them and possibly third parties on a subscription basis.”
BlackLine, a cloud-based accounting platform with a long list of clients (and their data footprints), has opted to offer collated insights as a complementary product on its platform. The company provides real-time, actionable benchmarking and intelligence to its Fortune 1000 and midsize clients, empowering them to continually improve the efficiency of their period-end accounting activities and other key processes.
“We have the world’s largest set of accountancy process execution data—information that has never really been gathered or available before,” Spanicciati said. “Consequently, we’re able to help clients analyze how their accounting teams perform compared to our benchmarks. Armed with this knowledge, they can continuously improve their accounting operations.”
While the company doesn’t charge its customers for this value, BlackLine Insights adds to its customer retention and gives it more marketing clout.
Reforming Sluggish Industries
Insurance has long been a data intensive industry, but at the same time it has been criticized as a technology laggard. XL Catlin, a large property and casualty insurer and reinsurer, is one of only a few major insurers to capture its own data and align it with third-party data to refine its insurance products and customize them to meet clients’ needs.
“We’ve spent quite a bit of time and rigor this past year understanding our current data enterprise-wide—underwriting, claims, loss reserving, and so on—with a goal of connecting this data and converting it into insights,” data scientist Henna Karna, the Bermuda-based company’s chief data officer, said.
As part of the insurer’s digital transformation, these insights will be selectively provided to key customers and partners, like insurance brokers. “Our stakeholders can use insights from the data to better align their management of risks with our insurance products,” Karna said. “In some cases, these close collaborations also may result in the creation of entirely new insurance products based on a client company’s evolving risk profile.”
By sharing real-time data insights, the customer is better able to manage its retained risks. At the same time, this data exchange helps XL Catlin refine its insurance coverage to more closely align with the risks the client needs to transfer.
But will the insurer eventually sell this information to customers and brokers?
“Our goal is be able to provide value to our customers and partners,” she replied, explaining that XL Catlin sees its data-as-a-service offering as a differentiator from other insurance companies. “Value creation done in a thoughtful and purposeful approach can lead to value capture for all parties.”
Pervane agreed that data-as-a-service need not be purely transactional. “Certainly companies should sell their data if they can make money from it and manage the risks,” he said. “But really, the most valuable data monetization strategy is to capture and analyze data to drive your own business—that takes precedence.”
“The most valuable data monetization strategy is to capture and analyze data to drive your own business—that takes precedence.”
— Timo Pervane
Where to Start
When thinking about turning data into a separate line of business, Pervane advised companies first determine if the data they have is a “must-have” for other businesses or just a “nice-to-have.”
“You need a highly important use case that nobody else has,” he explained. “In figuring this out, it helps to identify who will find value in your data and how.”
Once this is determined, a company will need to implement cloud-based data integration technology tools (like Dell Boomi), as well as technologies like predictive data analytics and machine learning to tease out insights.
According to Spanicciati, customers whose data serves as the feedstock of the data monetization strategy should be well informed throughout the process. “Transparency is critical,” he said. “You want customers to understand you would never expose their data. Instead, you need to get across that you’re aggregating and analyzing it for their benefit—so they can improve their products and services.”
He further recommended that companies offer their customers the opportunity to opt out. (In such cases, they would not share in the benefit of the data-as-a-service product.)
“You want customers to understand you would never expose their data. Instead, you need to get across that you’re aggregating and analyzing it for their benefit—so they can improve their products and services.”
— Mario Spanicciati, chief marketing officer at BlackLine
Yet for Pervane, it’s important to keep in mind that selling data-as-a-service is a separate business with its own P&L. “You’re going to need a sales team that understands the value of your data and can articulate it to sell it, and a customer success function to ensure the people you’ve sold the data to are getting the most value from it,” he said.
Nevertheless, data-as-a-service can turn into a new and highly profitable revenue stream. “If you get it right, this is high-margin, high recurring revenue, and highly sticky business, with low attrition rates,” he said. “We’re still in the very early stages. What will drive this off the baseline is the availability of more successful use cases.”
Russ Banham is a Pulitzer-nominated business journalist and author who writes frequently about the intersection of business and technology.