3 Business Models for the Data Era


A walk through the meatpacking district in New York City is a lively affair. In the early 1900s, this part of town was known for precisely what its name implies: slaughterhouses and packing plants. At its peak, there were nearly 300 such establishments, located somewhat central to the city and not far from shipping ports. Through the years, this area of town has declined at times, but in the early 1990s, a resurgence began that shows no signs of ending.

Located in some proximity to the fashion district of Manhattan, the Meatpacking district stands for what is modern, hip, and trendy; a bastion of culture in the work-like city. Numerous fashionable retailers have popped up, along with trendy restaurants. And, on the fringes, there is evidence of the New York startup culture flowing from the Flatiron District, sometimes known as Silicon Alley. A visit to one of the companies in this area opened my eyes to some of the innovation that is occurring where data is the business, instead of being an enabler or addition to the business.


As I entered the office of this relatively newborn company, I was confident that I understood their business. It was pretty simple: They were a social sharing application on the web that enabled users to easily share links, share content, and direct their networks of friends to any items of interest. The term social bookmarking was one description I had heard. The business seemed straightforward: Attract some power users, enable them to attract their friends and associates, and based on the information shared, the company could be an effective ad-placement platform, since it would know the interests of the networks and users.

But what if social bookmarking and ad placement was not the business model at all? What if all that functionality was simply a means to another end?


Data has the power to create new businesses and even new industries. The challenge is that there are many biases about the use of data in a business. There is a view that data is just about analytics or reporting. In this scenario, it’s relegated to providing insight about the business. There is another view that data is simply an input into existing products. In this case, data would be used to enrich a current business process, but not necessarily change the process. While these cases are both valid, the power of the Data era enables much greater innovation than simply these incremental approaches.

There are three classes of business models for leveraging data:

Data as a competitive advantage: While this is somewhat incremental in its approach, it is evident that data can be utilized and applied to create a competitive advantage in a business. For example, an invest- ment bank, with all the traditional processes of a bank, can gain significant advantage by applying advanced analytics and data science to problems like risk management. While it may not change the core functions or processes of the bank, it enables the bank to perform them better, thereby creating a market advantage.

Data as improvement to existing products or services: This class of business model plugs data into existing offerings, effectively differentiat- ing them in the market. It may not provide competitive advantage (but it could), although it certainly differentiates the capabilities of the company. A simple example could be a real estate firm that utilizes local data to better target potential customers and match them to vacancies. This is a step beyond the data that would come from the Multiple Listing Service (MLS). Hence, it improves the services that the real estate firm can provide.

Data as the product: This class of business is a step beyond utilizing data for competitive advantage or plugging data into existing products. In this case, data is the asset or product to be monetized. An example of this would be Dun & Bradstreet, which has been known as the defini- tive source of business-related data for years.

In these business models, there are best practices that can be applied. These best practices are the patterns that teach us how to innovate in the Data era.


Procter & Gamble is legendary for the discipline of brand management. To be the leading consumer packaged-goods company in the world, brand is everything. Ultimately, consumers will buy brands that they know and understand, and those that fulfill an expectation. This was one of the first lessons that Scott Cook learned when he joined Procter & Gamble in the 1970s. He also observed that the core business processes for managing a brand (accounting, inventory, etc.) would be transformed and subsumed by the emerging personal computer revolution. This insight led him to co- found Intuit in 1983, really at the dawn of the expansion of personal computers. The premise was simple: Everyone, small companies and individuals alike, should have access to the financial tools that previously had been reserved for large enterprise companies.

Now broadly known for tax preparation software (TurboTax), along with software solutions for small businesses (QuickBooks) and individuals (Quicken), Intuit has transformed many lives. At over $4 billion in revenue and nearly 8,000 employees, the company is a success by any barometer. Intuit is one of the few software companies to challenge Microsoft head-on and not only live to tell about it, but to prosper in its wake. Microsoft Money versus Quicken was a battle for many years; Microsoft was trying to win with software, while Intuit was focused on winning with data, utilizing software as the delivery vehicle.
Robbie Cape, who ran Microsoft’s Money business from 1999 to 2001, believes that Intuit’s advantage had very little to do with technology. Instead, he attributes Intuit’s success to its marketing prowess. While there may be some truth to the statement, its very hard to believe that Intuit had deep- enough pockets to out-market Microsoft. Instead, the differentiation seems to come from data.

The NPD Group analyst Stephen Baker said that Intuit won by building out critical mass in financial software and the surrounding ecosystem. Intuit had the insight that adjacency products and services, leveraging data, made the core software much more attractive. This insight led to their early and sustained domination of the retail channel.

Intuit’s ability to collect and analyze a large amount of sensitive and confi- dential data is nearly unsurpassed. Nearly 20 million taxpayers use TurboTax online, sharing their most personal data. Over 10 million customers use QuickBooks, with employee information for over 12 million people flowing through its software. Brad Smith has been cited as declaring that 20 percent of the United States Gross Domestic Product flows through QuickBooks. No other collection of data has this type and extent of financial information on individuals and small businesses.

With these data assets, Intuit began to publish the Intuit Small Business Index. The Index provides summarized insights about sales, profit, and employment data from the small businesses that use QuickBooks. This information can provide headlights to business and salary trends, which ultimately becomes insight that can be fed back into the product. This was the point that Microsoft Money either missed or simply could not achieve: The value was never in the software itself. The value was in the collection, analysis, and repurposing of data to improve the outcomes for the users.

In 2009, Intuit purchased Mint, a free web-based personal-finance application. Mint took the Intuit business model a step further: They provide their software for free, knowing that it’s a means to an end. The social aspects of Mint enable users to do much more than simply track their spending. Instead, it became a vehicle to compare the spending habits of an individual to others of a similar geography or demographic. The user can do these comparisons, or the comparisons can show up as recommendations from Mint. Further, Mint brings an entirely different demographic of data to Intuit. While the Intuit customer base was largely the 40-and-over demographic (people who had grown up with Quicken, QuickBooks, etc.), Mint attracted the Millennial crowd. The opportunity to combine those two entirely different sets of data was too attractive for Intuit to pass up.

To date, Intuit has not had a strategy for monetizing the data itself. Perhaps that may change in the future. However, with data at the core of its strategy, Intuit has used that data to drive competitive advantage, while software was merely the delivery vehicle. The companies that tried the opposite have not fared so well.


Chapters 1 through 9 offer a multitude of examples in which data is being utilized to improve existing products or services. In the pursuit of a business model leveraging data, this category is often the low-hanging fruit; more obvious, although not necessarily easy to do. The examples covered previously are:

Farming and agriculture: Monsanto is using data to augment applica- tions like FieldScripts, which provides seeding prescriptions to farmers based on their local environments. While Monsanto could provide prescriptions through their normal course of operation, data has served to personalize and thereby improve that offering.

Insurance: Dynamic risk management in insurance, such as pay-as-you- drive insurance, leverages data to redefine the core offering of insurance. It changes how insurance is assessed, underwritten, and applied.

Retail and fashion: Stitch Fix is redefining the supply chain in retail and fashion, through the application of data. Data is augmenting the buying process to redefine traditional retail metrics of inventory, days sales outstanding, etc.

Customer service: Zendesk integrates all sources of customer engage- ment in a single place, leveraging that data to improve how an organiza- tion services customers and fosters loyalty over time.

Intelligent machines: Vestas has taken wind turbines — previously regarded as dumb windmills — and turned them into intelligent machines through the application of data. The use of data changes how their customers utilize the turbines and ultimately optimizes the return on their investment.

Most companies that have an impetus to lead in the Data era will start here: leveraging data to augment their current products or services. It’s a natural place to start, and it is relatively easy to explore patterns in this area and apply them to a business. However, it is unlikely that this approach alone is sufficient to compete in the Data era. It’s a great place to start, but not necessarily an endpoint in and of itself.


Previously in this chapter, the examples demonstrated how data is used to augment existing businesses. However, in some cases, data becomes the product; the sole means for the company to deliver value to shareholders. There are a number of examples historically, but this business model is on the cusp of becoming more mainstream.


In 1841, Lewis Tappan first saw the value to be derived from a network of information. At the time, he cultivated a group of individuals, known as the Mercantile Agency, to act “as a source of reliable, consistent, and objective” credit information. This vision, coupled with the progress that the idea made under Tappan and later under Benjamin Douglass, led to the creation of a new profession: the credit reporter. In 1859, Douglass passed the Agency to his brother-in-law, Robert Graham Dun, who continued expansion under the new name of R.G. Dun & Company.

With a growing realization of the value of the information networks being created, the John M. Bradstreet company was founded in 1849, creating an intense rivalry for information and insight. Later, under the strain caused by the Great Depression, the two firms (known at this time as R.G. Dun & Company and Bradstreet & Company) merged, becoming what is now known as Dun & Bradstreet.

Dun & Bradstreet (D&B) continued its expansion and saw more rapid growth in the 1960s, as the company learned how to apply technology to evolve its offerings. With the application of technology, the company intro-duced the Data Universal Numbering System (known as D&B D-U-N-S), which provided a numerical identification for businesses of the time. This was a key enabler of data-processing capabilities for what had previously been difficult-to-manage data.

By 2011, the company had gained insight on over 200 million businesses. Sara Mathew, the Chairman and CEO of D&B, commented, “Providing insight on more than 200 million businesses matters because in today’s world of exploding information, businesses need information they can trust.”

Perhaps the most remarkable thing about D&B is the number of companies that have been born out of that original entity. As the company has restruc- tured over the years, it has spun off entities such as Nielsen Corporation, Cognizant, Moody’s, IMS Health, and many others. These have all become substantial businesses in their own right. They are each unique in the markets served and all generate value directly from offering data as a product:

Nielsen Corporation: Formerly known as AC Nielsen, the Nielsen Corporation is a global marketing research firm. The company was founded in 1923 in Chicago, by Arthur C. Nielsen, Sr., in order to give marketers reliable and objective information on the impact of market- ing and sales programs. One of Nielsen’s best known creations is the Nielsen ratings, an audience measurement system that measures television, radio, and newspaper audiences in their respective media markets. Nielsen now studies consumers in more than 100 countries to provide a view of trends and habits worldwide and offers insights to help drive profitable growth.

Cognizant: Starting as a joint venture between Dun & Bradstreet and Satyam Computers, the entity was originally designed to be the in-house IT operation for D&B. As the entity matured, it began to provide similar services outside of D&B. The entity was renamed Cognizant Technology Solutions to focus on IT services, while the former parent company of Cognizant Corporation was split into two companies: IMS Health and Nielsen Media Research. Cognizant Technology Solutions became a public subsidiary of IMS Health and was later spun off as a separate company. The fascinating aspect of this story is the amount of intellec- tual property, data, and capability that existed in this one relatively small part of Dun & Bradstreet. The interplay of data, along with technology services, formed the value proposition for the company.

IMS Health: IMS became an independent company in 1998. IMS’s competitive advantage comes from the network of drug manufacturers, wholesalers, retailers, pharmacies, hospitals, managed care providers, long-term care facilities and other facilities that it has developed over time. With more than 29,000 sources of data across that network, IMS has amassed tremendous data assets that are valuable to a number of constituents — pharmaceutical companies, researchers, and regulatory agencies, to name a few. Like Lewis Tappan’s original company back in 1841, IMS recognized the value of a network of information that could
be collected and then provided to others. In 2000, with over 10,000 data reports available, IMS introduced an online store, offering market intelligence for small pharmaceutical companies and businesses, enabling anyone with a credit card to access and download data for their productive use. This online store went a long way towards democratizing access to the data that had previously been primarily available to large enterprise buyers.

Moody’s: Moody’s began in 1900, with the publishing of Moody’s Manual of Industrial and Miscellaneous Securities. This manual provided in-depth statistics and information on stocks and bonds, and it quickly sold out. Through the ups and downs of a tumultuous period, Moody’s ultimately decided to provide analysis, as opposed to just data. John Moody, the founder, believed that analysis of security values is what investors really wanted, as opposed to just raw data. This analysis of securities eventually evolved into a variety of services Moody’s provides, including bond ratings, credit risk, research tools, related analysis, and ultimately data.

Dun & Bradstreet is perhaps the original innovator of the data-is-the-product business model. For many years, their reach and access to data was unsur- passed, creating an effective moat for competitive differentiation. However, as is often the case, the focus on narrow industries (like healthcare) and new methods for acquiring data have slowly brought a new class of competitors to the forefront.


Despite its lack of broad awareness, CoStar is a NASDAQ publicly traded company with revenues of $440 million, 2,500 employees, a stock price that has appreciated 204 percent over the last three years, a customer base that is unrivaled, and a treasure trove of data. CoStar’s network and tools have enabled it to amass data on 4.2 million commercial real estate properties around the world. Simon Law, the Director of Research at CoStar, says, “We’re number one for one very simple reason, and it’s our research. No one else can do what we do.” Here are some key metrics:

* 5.1 million data changes per day

* 10,000 calls per day to brokers and developers
* 500,000 properties canvased nationwide annually
* 1 million property photographs taken annually

CoStar has an abundance of riches when it comes to real estate data. Founded in 1987 by Andrew Florance, CoStar invested years becoming the leading provider of data about space available for lease, comparable sales information, tenant information, and many other factors. The data covers all commercial property types, ranging from office to multi-family to industrial to retail properties.

The company offers a set of subscription-based services, including

CoStar Property Professional: The company’s flagship product, which offers data on inventory of office, industrial, retail, and other commer- cial properties. It is used by commercial real estate professionals and others to analyze properties, market trends, and key factors that could impact food service or even construction.

CoStar Comps Professional: Provides comparable sales information for nearly one million sales transactions primarily for the United States and the United Kingdom. This service includes deeds of trust for properties, along with space surveys and demographic information.

CoStar Tenant: A prospecting and analytical tool utilized by profes- sionals. The data profiles tenants, lease expirations, occupancy levels, and related information. It can be an effective business development tool for professionals looking to attract new tenants.

CoStarGo: A mobile (iPad) application, merging the capabilities of Property Professional, Comps Professional, Tenant, and other data sources.

The value of these services are obviously determined by the quantity and quality of data. Accordingly, ensuring that the data remains relevant is a critical part of CoStar’s business development strategy.

Since its inception, CoStar has grown organically, but also has accelerated growth through a series of strategic acquisitions. In 2012, CoStar acquired LoopNet, which is an online marketplace for the rental and sale of properties. CoStar’s interest in the acquisition was less about the business (the marketplace) and much more about the data. Said another way, their acquisition strategy is about acquiring data assets, not people or technology assets (although those are often present). As a result of the acquisition, it is projected that CoStar will double their paid subscriber base to at 160,000 professionals, which represents about 15 percent of the approximately 1 million real estate professionals. Even more recently, in 2014, CoStar acquired, a digital alternative to classified ads. The war chest of data assets continues to grow.

The year 2008 was one of the most significant financial crises the world has seen. Financial institutions collapsed, and the real estate market entered a depression based on the hangover from subprime debt. Certainly, you would expect a company such as CoStar to see a similar collapse, given their dependence on the real estate market. But that’s not exactly what happened.

From 2008 to 2009, CoStar saw an insignificant revenue drop of about
1 percent. This drop was followed by an exponential rebound to growth in 2010 and beyond. Is it possible that data assets create a recession-proof business model?

CoStar Financial Results

While there are other data alternatives in the market (Reis Reports, Xceligent, CompStak, ProspectNow, and others), the largest collection of data is a differentiator. In fact, it is a defensible moat that makes it very hard for any other competitors to enter. For CoStar, data is the product, the business, and a control point in the industry.


In 1959, Richard O’Brien founded IHS, a provider of product catalog databases on microfilm for aerospace engineers. O’Brien, an engineer himself, saw how difficult it was to design products and knew that utilizing common components could dramatically increase the productivity of engineers. However, he took it one step further by applying technology to the problem — using electronic databases and CD-ROMs to deliver the knowledge furthered the productivity gains. And engineers love productivity gains.

This attitude toward data was set in the company’s DNA from the start as the company could see how to improve the lives and jobs of their clients, just through a better application of data. In the 1990s, IHS started distributing their data over the Internet, and with an even more cost-effective way to share data assets, they decided to expand into other industries. Soon enough, IHS had a presence in the automotive industry, construction, and electronics.

As seen with CoStar, IHS quickly realized that business development for a data business could be accelerated through acquisitions. Between 2010 and 2013, they acquired 31 companies. This acquisition tear continued, with the recent high-profile $1.4-billion acquisition of R.L. Polk & Company. As the parent company of CARFAX, R.L. Polk cemented IHS’s relevance in the automotive industry.

IHS’s stated strategy is to leverage their data assets across interconnected supply chains of a variety of industries. For their focus industries, there is $32 trillion of annual spending in those companies. IHS data assets can be utilized to enhance, eliminate, or streamline that spending, which makes them an indispensible part of the supply chain. IHS’s data expertise lies in

*Renewable energy

*Automotive parts
*Aerospace and defense
*Maritime logistics

IHS also has a broad mix of data from different disciplines.

While some view data as a competitive differentiator or something to augment current offerings, CoStar, IHS, and D&B are examples of compa- nies that have a much broader view of data: a highly profitable and defensi- ble business model.


The role of data in enterprises has evolved over time. Initially, data was used for competitive advantage to support a business model. This evolved to data being used to augment or improve existing products and services. Both of these applications of data are relevant historically and in the future. How- ever, companies leading in the data era are quickly shifting to a business model of data as the product. While there have been examples of this in history as discussed in this chapter, we are at the dawn of a new era, the Data era, where this approach will become mainstream.

The company that started as a social bookmarking service quickly realized the value of that data that they were able to collect via the service. This allowed them to build a product strategy around the data they collected, instead of around the service that they offered. This opportunity is available to many businesses, if they choose to act on it.

This post is adapted from the book, Big Data Revolution: What farmers, doctors, and insurance agents teach us about discovering big data patterns, Wiley, 2015. Find more on the web at

1 comment:

  1. It is the very true. The data revolution opens up a huge realm of possibilities for businesses, governments and NGOs to work together to tackle key sustainability issues for both the environment and society.