8 Ways To Monetize Data
Data may be a company's most valuable asset, but few are maximizing its economic benefit. Here eight ways that organizations are deriving real, bottom-line value from their data.
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Data is the new currency. Armed with it, new companies are disrupting established industries, and traditional businesses are transforming the way they operate. Not all organizations are equally adept at translating data into dollars, but their ability to do so is impacting their ability to compete.
"Where knowledge is power, data is wealth. It's not intrinsic in the data, it's what you do with it," said Bruce Daley, an analyst at market intelligence firm Tractica and author of Where Data Is Wealth: Profiting From Data Storage in a Digital Society. "The companies that are most progressive in thinking about data differently are the companies that are changing the economy, like Google and Uber. Most businesses lag way behind in terms of the idea that data could be their primary reason for being."
Some businesses, such as information service providers, have always been about deriving value from data. However, the ability to use and monetize data is now impacting almost every type of business. As a result, driving value from data must now be contemplated as part an overall business strategy.
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"The organizations that are doing this well are trying to address a business problem at hand, not a data problem at hand," said Dan DiFilippo, global & US data and analytics leader at PricewaterhouseCoopers (PwC), in an interview. "You have to look at the problem you're trying to solve, whether that's expanding into a new market, trying to leverage more from existing customers, or acquiring new customers or employees. Then you can start looking at how data plays into to that."
Most organizations realize they have a wealth of data -- but not all of them are able to realize its potential value because technological and cultural challenges often stand in the way. Even though more lines of business are better at leveraging their data for their own purposes than they once were, the value of the data from an enterprise perspective may not yet be fully realized. Data quality issues are common. In addition, compliance, privacy, and security issues may limit the ways in which the data can be used.
"I think the piece a lot of folks miss is: You need to understand the business so you can understand the value of the data and then [you can] monetize it," said Young Bang, VP of the civil health business at Booz Allen Hamilton, in an interview.
Here are some ways to positively impact the bottom line using data.
Busy hospitals, clinics, and healthcare providers can easily lose track of the services they've rendered. Each procedure has a description and an assigned code, both of which may include errors. Using analytics, those organizations can identify patterns associated with the codes and the procedures, so that patient invoices can be flagged for potential errors or missing charges. Intelligent data use also helps those same organizations improve the ROI of collections. Specifically, they are able to identify the right person to contact, the channel that is most likely to elicit a response, and the time of contact that is most likely to yield a positive result.
"Who is likely to pay and how much they will actually pay are common predictive models," said Alex Guazzelli, chief scientist at advanced analytics software company Opera Solutions, in an interview. "Companies are segmenting their collections to see who's likely to pay, and who's likely to pay more so they can maximize their collections. A lot of people don't pick up their phones anymore, so it's important to understand which channels you should use to contact a person and the right time to make that contact."
Organizations commonly use surveys and social media sentiment analysis to better understand customer satisfaction levels. By combining data from a number of sources, airlines are now able to infer how satisfied a customer likely is based on factors such as where in a row of seats he or she sits.
"If 10 out of 10 times you're in a middle seat, you're probably not very satisfied. You have to aggregate data from lots of sources, including customer information and flight information, to come up with a signal that tells you what happened in the past," said Alex Guazzelli, chief scientist at Opera Solutions. "Now you have information that can be used to tell you that the customer may not still fly with you, so maybe you should offer a free upgrade to a coach-plus seat."
Data is changing the relationship companies have with their customers. Tangible goods manufacturers are supplementing their products with flexible software options and services to create new revenue streams and offer customers more choices. At the same time these companies are providing higher levels of personalization. Across industries, new economic models are being explored, such as replacing automobile ownership with fleets of self-driving cars and supplementing traditional insurance with micro-insurance options.
Booz Allen Hamilton has a wealth of data that's generating revenue, but the company isn't selling it. Instead, its clients are paying for answers to questions and for data scientists who can help them understand the data.
"Instead of selling the data, the model is [to] pay for solving a problem or providing answers, which is a different revenue model we're using on the government and commercial sides," said Young Bang, VP of the civil health business at Booz Allen Hamilton. "There's so much open source and social data that, if you're just selling data, a lot of it is available [elsewhere] for free. The value comes from marrying that data, understanding the mission of a business, and understanding what problem that business is trying to solve."
Online retailers typically sell their products on a number of different sites. Supplemental sales channels often include Amazon.com, eBay, and online marketplaces maintained by very large retailers such as Walmart and Best Buy. Selling through all of those channels is very data-intensive, because the pricing, products, and customer types often vary across channels. Sometimes the price discrepancies are so significant they signal potential fraud or piracy.
"One of our partners, which sells on dozens of e-commerce websites, builds datasets of their own products and pricing. When they compare that with their existing expected pricing data, they are able to detect stolen goods and suppliers who are mispricing their items," said Gabriel Pulatti, business development at Web-crawling platform and services company Scrapinghub. "Now the retailer can go to the marketplace and say, 'Hey, I think this guy is selling stolen goods.'"
Companies have been using data to understand customer churn for years, but their approach to customer retention is shifting from reactive to proactive. Rather than using a narrow set of data points to determine how best to serve customers next time, organizations are now using richer combinations of data, more intelligent tools, and data science to determine when customers will likely churn, why they are likely to churn, and what the company should do to preempt it.
"It's not just about CRM or ERP data anymore. If they can use third-party data, they will," said Alex Guazzelli, chief scientist at Opera Solutions. "Social media data has an impact on churn, but I think companies are still not leveraging it yet because of the legal issues. I think it's just a matter of time."
Marketers have been using data for the last couple of decades to more accurately target customers and improve the ROI of their campaigns. However, simple website clickstream analysis, though still important, is just one source of data. In today's environment, the same organizations need to understand customer behavior across channels using more data from within the enterprise and from third parties.
"Companies are tracking their customers on the Web and in their stores to get a holistic view of the customers. They're also using third-party data to [understand customers' buying habits] outside the store," said Alex Guazzelli, chief scientist at Opera Solutions. "For example, using the credit card information, you can tell a lot about a person, such as what their spending patterns are, generally, and how much they can spend."
Data ownership remains a challenge in some organizations because it may not be clear whether marketing or IT owns the data, according to Jay Marwaha, CEO of digital marketing analytics platform provider Syntasa. "It's a huge barrier for making leaps in properly monetizing the data, and it becomes a barrier from an implementation standpoint," he said, in an interview.
Industries tend to evolve along a path. Then an eBay, an Amazon, a Google, or an Uber may come along and change the rules of the game. Especially as the Internet of Things finds its way into businesses of all kinds, more companies will be revisiting their business models to discover new revenue sources and to stay relevant.
"The question is how you can create value in the datasets and bundle them in a way that's useful. We see more companies struggling with this," said Joseph Zaloker, director of technical marketing at Arrow Electronics. "When we look at where people are going to make money, it's the service revenue, it's the data revenue, it's ecosystem building."
Mobile phone and cable TV industries provide some clues about how business models and revenue models may shape up for other types of businesses in the future. Already, device and industrial equipment manufacturers are using data analytics for predictive maintenance purposes. In the future, some of those tangible products may be supplemented with their own app ecosystems or value-added data services that drive nontraditional forms of revenue. Zaloker said that device manufacturers are actively looking for ways to turn customers into subscribers.
Each organization has a unique selling proposition that will likely change over time, if it hasn't already. As more businesses run on software, and as the value of their data assets continues to grow, they have to reimagine the value they're providing both to customers and as part of a value chain. In the interest of driving the maximum value from data, some companies are creating new operating entities, restructuring, or investing in new generation companies that will allow them to remain relevant.
"Every enterprise has more data then they know what to do with. Why shouldn't they be monetizing it?" asked Manish Gupta, CMO of cloud integration and data management platform provider Liaison Technologies. "I think every business today is thinking of becoming a platform player. Instead of saying they're going to build Service A, they're saying I can build a model for Service A with a platform [that will allow me] to sell different things."
Each organization has a unique selling proposition that will likely change over time, if it hasn't already. As more businesses run on software, and as the value of their data assets continues to grow, they have to reimagine the value they're providing both to customers and as part of a value chain. In the interest of driving the maximum value from data, some companies are creating new operating entities, restructuring, or investing in new generation companies that will allow them to remain relevant.
"Every enterprise has more data then they know what to do with. Why shouldn't they be monetizing it?" asked Manish Gupta, CMO of cloud integration and data management platform provider Liaison Technologies. "I think every business today is thinking of becoming a platform player. Instead of saying they're going to build Service A, they're saying I can build a model for Service A with a platform [that will allow me] to sell different things."
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