Profiting from the Data Economy: Beyond Big Data

By David A. Schweidel
Date: Oct 14, 2014
http://www.ftpress.com/articles/article.aspx?p=2257533

David A. Schweidel introduces his book, which considers the role that individual consumers, innovators and government will play in shaping tomorrow's data economy.

While there has been a lot of discussion around the term “Big Data,” much of the discourse treats this as an abstract idea rather than a system in which we are all active participants. While the term has become ubiquitous, interest in the topic has not waned. A Google search for the term turns up approximately 13.7 million search results. A snapshot of Google Trends reveals the meteoric rise of queries for Big Data beginning in 2011 and increasing ever since then.¹ Searching archived Twitter messages using Topsy reveals more than three million tweets referencing Big Data and in excess of two million tweets mentioning #bigdata.
Some contend that this marks a dramatic shift in what businesses and organizations are capable of doing. Others deride or critique it. Author and Duke University professor Dan Ariely likened Big Data to teenage sex: “Everyone talks about it, nobody really knows how to do it, everyone thinks everyone else is doing it, so everyone claims they are doing it.”² Whether you’re an ardent believer that the Big Data revolution is the best thing since sliced bread or you’re skeptical of the buzzword, there is no disputing the fact that more attention is being paid to the topic.
This attention isn’t just coming from corporate broom closets where statisticians are huddled over computers and poring over the output from complex analyses. Instead, data and analytics are garnering attention in the C-suite. In some companies, these topics fall under the purview of the CIO, whereas they are part of the CMO’s responsibilities for other companies. We’ve also seen the emergence of the chief data officer. Although you might expect to see this position at a financial institution or a company based in Silicon Valley, advertising juggernaut Ogilvy and Mather appointed its first global chief data officer in August 2013.³
The interest in capitalizing on the abundance of data extends beyond the boardroom to the public sphere. Microtargeting in political races was used as early as 2004. Local governments are also getting in on the act, with Philadelphia Mayor Michael Nutter naming the city’s first chief data officer in 2012 and New York City Mayor Michael Bloomberg appointing the city’s first chief analytics officer in 2013.

Searching for the Next Generation of Quants

Why are companies, campaigns, and governments focusing on individuals with a knack for data? Probably the same thinking that led Hal Varian, an emeritus economics professor from the University of California at Berkeley and chief economist for Google, to proclaim in 2009 that “the sexy job in the next ten years will be statisticians.”⁴ If you’re able to extract insights and act upon them, they can provide a strategic advantage. Across different types of organizations, statisticians can contribute immensely to improving operations, from increasing efficiency and cutting costs to increasing revenue. However, being well versed in statistics isn’t enough. What many organizations are seeking is a data scientist with the holy trinity of skills: someone with expertise in a particular field, coupled with knowledge of sophisticated statistical tools, and the technical expertise to develop and implement these algorithms on a large scale. This is often depicted as a Venn diagram (see Figure 1.1).

Figure 1.1 Data Science Venn diagram

Are they searching for mythical unicorns? Not necessarily. Both presidential campaigns in 2012 had chief data scientists. According to the vice president of Big Data products for IBM, a data scientist is “part analyst, part artist.”⁵ It’s not enough to crunch numbers in the background. The findings from advanced analytics are only as good as the way in which the insights are communicated to key decision makers. When we talk about using marketing analytics to guide strategy, we’re not just talking about a set of curve-fitting exercises. Instead, we’re talking about storytelling, informed by data, which has the potential to inform decision makers.
Are these three skills all essential, or can we get by with someone who is lacking one of them? With knowledge of statistics and the ability to code, a researcher can make data sing. However, although this may be sufficient from the standpoint of conducting research, what practical decisions can they support? Without a sufficiently deep understanding of the domain in which they’re operating, the impact of the insights on strategy will be limited. Meanwhile, someone who understands statistical models and knows the domain in which they operate is valuable from the standpoint of producing insights, but they are limited in their ability to convert those insights into a scalable solution. We similarly run into problems if individuals are fluent computer scientists but lack knowledge of the statistical models that are often used for evaluating business decisions.
Universities are making an effort to address the significant talent gap. Many have started to offer programs that tap into the interest in Big Data and data science. North Carolina State University, through its collaboration with SAS, launched the Institute for Advanced Analytics and offers an M.S. in analytics. At New York University, you’ll find the Center for Data Science offering an M.S. in data science, as well as an M.S. in business analytics offered by NYU Stern School of Business. The marketing department at the Wharton School of the University of Pennsylvania houses the Wharton Customer Analytics Initiative, while the operations and information management department offers a track in business analytics. Northwestern University offers an M.S. in predictive analytics through the School of Continuing Studies, while Northwestern Engineering houses the M.S. in analytics. You’ll also find an M.S. in analytics offered by the University of San Francisco and an M.S. in marketing analytics at the University of Maryland.
From this small sampling of the programs and the initiatives that have developed in higher education around analytics, you can start to see why it’s difficult to prepare students for the roles that organizations are seeking to fill. There’s a fundamental question about where the appropriate training for dealing with data takes place. The two logical schools in which programs focusing on Big Data would emerge are business schools, where the insights have the potential to guide decisions, and computer science and engineering departments, where the technical tools may take center stage rather than strategic decisions.

From Big Data’s Past to Its Future

The two sides of the Big Data coin may go back as far as the origin of the term. According to The New York Times story by Steve Lohr, author Erik Larson wrote a piece for Harper’s Magazine in 1989 that was subsequently reprinted in The Washington Post.⁶ In it, Larson discusses the direct marketing industry and its practice of merging different facets of consumer data. Decades before data privacy and transparency came into the vernacular, Larson suggests that the “keepers of big data” will “track you for the rest of your consuming life—pitch you baby toys when you’re pregnant, condos when you’re fifty.”⁷
Interestingly enough, Lohr doesn’t ascribe credit for the term “big data” to Larson. Instead, he argues that credit should go to John Mashey, chief scientist for Silicon Graphics in the 1990s. When queried about the use of the term, Mashey told Lohr that “I was using one label for a range of issues, and I wanted the simplest, shortest phrase to convey that the boundaries of computing keep advancing.” Lohr’s logic in giving Mashey credit for coining the term was that it “should go to someone who was aware of the computing context.”
Regardless of to whom you feel credit is due, this exercise into the ancestry of one of today’s most common phrases within technology and business circles offers a curious perspective. Larson exposes the potential benefits and risks associated with compiling disparate pieces of data. Like a jigsaw puzzle, each individual piece of data does not reveal much about the person who produced it. But assemble enough of these pieces, and the full picture begins to emerge. The image that Larson describes is one based on mortgage and tax records, consumer surveys, and records from the Census Bureau. Compared to what we’re capable of seeing today, what Larson envisioned may appear to look like a five-year-old’s finger painting tacked onto a refrigerator, hanging next to a digital photograph taken with the latest digital SLR camera on the market today. The possibilities that he described pointed in the direction of the path that we eventually traveled. However, we have gone leaps and bounds beyond what many would have imagined in the past.
For that, credit is certainly due to technology. Dramatic increases in processing power have paved the way for sophisticated marketing analysis to be conducted on desktop computers instead of mainframes. Rather than speaking in terms of census tracts, we can now talk in terms of individual households or, better yet, members of the household. Retail purchase records from scanners in grocery stores, once heralded as a major advance for the marketing profession, pale in comparison to the amount of data produced by consumers visiting websites and making purchases online. None of what we are talking about today would be possible were it not for technological advances. At the same time, credit should be given to those who had the foresight and saw potential opportunities to put such granular data to use. These two forces, working in parallel, heralded the age of Big Data.

Characterizing Big Data

One of the most frequent ways in which Big Data is defined is in terms of the 3 V’s: volume, variety, and velocity. As the term implies, anything falling under the umbrella of Big Data is large in size. Some have taken this to mean anything that is too voluminous to be stored on a desktop computer. Multiple students have expressed interest in studying Big Data, which I’ve generally taken to mean that they are interested in using software that can handle more rows than a single spreadsheet in Microsoft Excel can accommodate. (Excel has a current limit of just more than one million rows—a vast improvement to the 65,536 rows of the previous version of the software.) In addition to the size of the data being stored, Big Data often comprises a variety of formats. On top of the quantifiable data (or “structured data”), Big Data encompasses “unstructured data” such as text comments, images, and multimedia file types.
Perhaps the most important of the 3 V’s is velocity. It is the velocity with which the data are collected and must be processed that can separate Big Data problems from those that simply involve large quantities of data. One illustration of problems that require data to be collected and processed rapidly is real-time marketing. Whether it is an auction to determine the order in which advertisements will appear alongside search results or targeted messages based on an individual’s mobile browsing history and current location, such problems require that data be processed rapidly.
Walt Disney World maintains an underground bunker called the Disney Operational Command Center to ensure that the theme park operates smoothly. Its staff may attempt to increase the speed with which visitors are moving through the queue of a ride if they find that it is too long, or dispatch costumed employees to entertain guests while they wait.⁸ If visitors are moving through rides more efficiently and having a more pleasant experience at the park, they’re apt to have more time during which they can visit stores and restaurants in the amusement parks, which provides Disney with additional revenue from its visitors. Phil Holmes, vice president of the Magic Kingdom, noted that “if we can increase the average number of shop or restaurant visits, that’s a huge win for us.”
While the majority of cases discussed in this book focus on marketing applications, perhaps one of the most important applications of Big Data today is security. Although Disney’s objective may be to minimize wait times or maximize visitors’ expenditures, imagine if we could apply similar approaches to law enforcement? Consider the FBI’s development of its Next Generation Identification (NGI) system, which will explore the use of facial recognition tools.⁹ Such a system could be used after crimes have been committed, comparing the footage from security cameras to databases that have been compiled previously. If such technology could be deployed more rapidly, it has the potential to reduce the resources that need to be committed to pursuing offenders. For such tasks, time is of the essence. Faster processing of the available data can contribute not only to cost reductions but also to improvements in public safety.
Although the 3 V’s are common to problems that fall under the auspices of Big Data, they ignore at least two other critical factors. First, there are issues as to the veracity of the data. No one questions that there is a lot of data available, but organizations trying to cut through the noise and identify the signal must ask themselves if they can trust the different data streams available. When decisions are being based on the results of data analysis, the findings are meaningless if the data on which they are based is biased in some way.
This has been one of the concerns raised by marketers about the potential use of social media data. Although such data are generally available, do the comments scraped off the Web reflect the thoughts of a brand’s entire customer base? Neglecting to account for known biases in social media data could contribute to problems such as overestimating the importance of an issue to consumers or failing to capture shifts in brand sentiment.¹⁰
Second, and more important, is the value of the data. Many organizations talk about having a Big Data strategy. If they’re referring to a plan to warehouse and access data relevant to their organization, there’s nothing wrong with this statement. In fact, more organizations would probably benefit from having a well-thought-out strategy that integrates the IT function with the appropriate business processes. The problem is, though, that they’re often not referring to how data will be stored and made available to users. Instead, they’re using the term “Big Data” as a crutch. Rather than thinking through what they are trying to achieve and gathering data that are appropriate to addressing those goals, they believe that they have a foolproof strategy: track “everything.”

Is Big Data a Strategy?

Here’s the problem with this so-called strategy. Suppose that reams of data are captured. All that we’ve done is kick the can down the road. Deep in the recesses of the warehouse we’ve built, in the multitude of haystacks we’ve collected, is the golden nugget that we’ve been looking for. The problem is that we now have to go through each of those haystacks. And, more likely than not, we’ll find something. Whether we can do anything with what we find is another story. The focus on collecting more and more data has obscured what we should have been asking ourselves from the start: What actions are we going to take?
It’s true that data are necessary to derive insights, and those insights inform the actions we take. However, thinking strategically requires that we work backward. Asking first what it is that we’re trying to do, we can then identify the insights needed to inform such actions. Based on the insights we need, we can back into the data that are needed to yield such insights. If we start blindly by compiling data without considering where we’re trying to end up, we run the risk that we’ve created more work for ourselves because we now have to sift through mountains of irrelevant data that have been captured in our dragnet.
The challenges faced by many organizations, from city governments to publically traded corporations, don’t require Big Data. Rather, these organizations should be taking a look at the key issues they’re facing and considering how those issues can be investigated. It’s not that more data are necessarily better. Sometimes more data are just more.
What the conversation should be focused on are the data that will lead to bigger insights. Sometimes this does in fact require more data or different types of data. In other cases, it requires rethinking the assumptions we currently hold and applying a different type of analysis. In specific circumstances, Big Data may be the raw fuel powering these insights. However, on its own, Big Data doesn’t tell us what we should be doing. It doesn’t tell political campaigns in which media markets to place advertisements. It doesn’t tell retail stores to whom to send coupons. It doesn’t tell city agencies how best to use their limited resources. Making such recommendations is where the artistry in data science comes into play. If Big Data is a natural resource, then the advanced statistical tools employed by analytic professionals and data scientists serve as the means of extracting and refining the raw material into something of value.
A colleague has suggested that Big Data is like “rocket science.” There are actually people who are doing rocket science, but the common use of the phrase extends beyond the well-credentialed few. There are indeed organizations that are knee deep in Big Data, but the phrase has become a catchall for most things involving data. Like it or not, we are living in the age of Big Data.

Data Versus Insights

For all the interest in Big Data, this is not a book about Big Data. This book is about what can be done with Big Data. More accurately, it’s about what can be done with data, both the good and the bad. When Larson first mentioned the “keepers of big data” back in 1989, companies such as Google, Facebook, and Amazon.com didn’t exist. And yet, marketing analytics and targeting were alive and well—perhaps not as efficiently as they are implemented today, but the ability to draw a portrait of a person based on multiple data sources was practiced by Claritas, Inc., which was eventually acquired by marketing research behemoth Nielsen.
The insights produced to serve marketers didn’t hinge on the amount of data available to them. Instead, it was based on understanding how the available data could be put to use. Larson tees up ideas that are relevant even today, if not ahead of their time, such as permission-based marketing and consumers receiving compensation in exchange for their data. And yet, he determines that he isn’t concerned because the organizations involved in direct marketing and the entities that provide organizations with access to consumer data “don’t really know what they’re doing—at least not yet.”
What makes the present day different from 1989? For one thing, organizations are much more versed in what can be accomplished with the right data. In addition to knowing what can be done with data, there’s also more data available. The data exhaust—the digital trail produced by consumers through their everyday activities—is a potential goldmine. Although there are ample opportunities for organizations to leverage available sources of data, let’s not assume that consumers are victims. Consumers have options at their disposal, and their choices have the potential to separate the winners from the losers.
Organizations of all stripes can be developed or refined based on the insights afforded by consumer data. As organizations have come to realize the tremendous potential that can be extracted from consumer data, the winners will be determined by those who have access to the data they need—that is, those who provide a compelling reason for consumers to share this data with them. Focusing on businesses, this is predicated on an exchange. Neither party has a gun to the head of the other. Consumers and organizations are both willing participants in this data economy.

Data and Value

As with other economic systems, at the heart of the data economy is value. As we will discuss, organizations have made it clear that they see tremendous value in consumer data. Some have explicitly said as much. Others have revealed it through their actions. Depending on the organization we’re discussing, there are a number of ways in which detailed data have been turned into value.
Collecting and storing data should be viewed as an investment. As with other investments, the question that organizations should be able to answer is, what is the payoff associated with compiling databases? Police forces, for example, have turned to predictive analytics. In addition to the hardware and software investment, there’s a cost associated with hiring the analytic talent to conduct the necessary analyses. Here, the payoff can be viewed as increased public safety, as manifested through a reduction in crime.
Google demonstrated that certain search terms are correlated with flu activity.¹¹ From a public health perspective, such information could be useful in determining when it is most essential to ramp up efforts to encourage individuals to get vaccinated. Health insurance providers may take such efforts upon themselves, looking at the potential savings associated with reducing the number of hospitalizations. Employers may also promote vaccinations, hoping to curb the amount of worker productivity that is lost due to workers taking time off to recuperate. Viewing this problem from another perspective, pharmaceutical companies could identify the value of such data if it would enable them to make more efficient use of their marketing budgets. As you can see from this one example, the potential value associated with a particular piece of data depends on the organization’s goals.
Although these two illustrations demonstrate what can be gained by organizations turning to data that is generally available, consider briefly data that may not be available for public consumption: a consumer’s purchasing habits. Consider simply the question of how strongly you prefer Coca-Cola to Pepsi. If Coca-Cola knew which consumers were only interested in its products, which consumers were only interested in Pepsi’s product, and which consumers did not have a strong preference, it might change the way it approached marketing to each of these different consumers.
It might decide, for example, to spend just enough on marketing to loyal Coca-Cola consumers to encourage them to purchase more frequently. For these consumers, though, the company is not worried about them switching over to their competitor. For those consumers loyal to Pepsi, it may not make sense for Coca-Cola to exert any effort marketing toward these individuals. If their brand preferences are so strongly in favor of Pepsi over Coca-Cola, there’s little that Coca-Cola would be able to do to sway them. For the consumers in the middle, perhaps that’s where Coca-Cola’s (and Pepsi’s) marketing efforts have the potential to have the biggest impact.
Coca-Cola and Pepsi, as well as all other publically traded companies, are in the businesses of what’s best for their shareholders, but we could also apply the same thinking to the presidential election. Across the country, there are states that are deep blue and there are states that are deep red. Although a candidate could pour money into advertising in those states where his party has not fared well historically, barring a huge shift in the demographics of the state, such advertising expenditures are not expected to yield much of a payoff. Instead, what we are left with is a deluge of advertising concentrated in battleground states, specifically in counties where the advertising is expected to yield the biggest impact.
As we’ll discuss in more depth, not all data are equally valuable. Detailed data about the television programs viewed by a voter who lives in Wisconsin’s Dane county are likely to be of less value compared to the same data about a voter who lives in Ohio’s Hamilton county. Some pundits considered both Ohio and Wisconsin to be battleground states, so why the difference in the likely value of data from voters in these two counties? Dane county leans heavily to the left, as reflected by the 71.1% of the vote received by President Obama. In contrast, President Obama received only 51.8% of the votes coming out of Hamilton county. Political advertising can exert some sway on voters, but there’s a limit to its effectiveness. Given the strong leaning of Dane county, not much could have been done there by either party to sway voters. Hamilton County, in contrast, was identified as one of the seven most important counties in the 2012 election by The Washington Post.¹² If campaigns knew the programs that different types of voters in Hamilton county were watching, such data could be used to ensure that advertising occurred in the programs viewed by the voters they were most interested in reaching. Regardless of context, the determining factor in how much data are worth to an organization is based on what the organization can do with the data and whether having the data can potentially further the organization’s goals.
Although the ability to take actions that affect consumer behavior is necessary for data to be of value, a few other conditions will affect just how valuable an individual’s particular data are to an organization. First, there needs to be a sufficient number of consumers who are “like you” with regard to your preferences and attitudes. If your outlook is so idiosyncratic that an organization can’t identify other consumers who are similar to you, it’s simply too inefficient for that organization to acquire your data and take actions tailored to you. There just isn’t the scale for this to be viable. Fortunately, it turns out that consumers are not as different from one another as they might think. Or, more precisely, they are similar enough that they can be grouped together into consumer segments, enabling organizations to pick and choose the segments on which they want to focus their efforts.
In addition to there being enough consumers like you, how much an organization is willing to spend on your data hinges on how valuable you (or the segment to which you belong) are to the organization. Frequent travelers are of interest to the airline and hotel industries because of the volume of business they generate, so they may provide these individuals with a separate telephone number for customer service, express check-in, or other perks. Casinos pay particular attention to their high rollers and provide them with a number of complimentary offerings to attract and retain their business because the amount that the casino stands to gain from the individual’s gambling activities can be quite substantial. Undecided voters in swing states are targeted because they can decide which presidential candidate receives the state’s electoral votes. In short, the segment has to matter to the organization.
With all these factors in place, ultimately the value of your data to organizations depends on how readily available such data are from other consumers. Although all consumers may be distinct from each other, the intent in forming a small number of market segments is to identify groups of consumers who are similar enough to each other and sufficiently different from other consumers. Claritas put this into practice with its Potential Rating Index for Zip Markets (more commonly referred to as PRIZM) segmentation scheme.
Members of the Executive Suites segment, for example, tend to place orders at barnesandnoble.com, play golf, and watch Saturday Night Live. This segment consists of upper-middle class singles and couples who typically work white collar jobs. In contrast, although the Bohemian Mix segment falls into the same age range, they are more inclined than the Executive Suites to live in cities and more likely to have children at home. Their media and lifestyle habits also differ—they express an interest in foreign films, are more likely to rent rather than own their home, and are more likely to read GQ.
Organizations must determine which of the segments are of interest to them so that they may focus their efforts on those segments. Once that has been determined, though, it doesn’t matter which particular individuals from the segment provide data to the organization. If other consumers like you are willing to share their data with organizations at no cost, then that’s how much the organization should be willing to spend on data acquisition. However, if each individual in a segment has determined that there is a minimum the organization must offer for someone to be willing to share the data, then it may be in the organization’s interests to invest in acquiring this data.

Value for Value

The ideas being put forth here are not new. The core ideas that we’ll discuss are at least 20 years old. However, since these ideas were first introduced into the public sphere, the landscape has irrevocably changed. Disturbingly, some of the ways in which these changes have occurred may have gone unnoticed.
First, consider the digital marketing platforms available to organizations today. Although consumers may be most familiar with Google as a search engine or a “free” email provider, at the end of the day, Google is an advertising platform. Likewise, although we may think of Facebook and Twitter as social networking products, these too are advertising platforms. We don’t pay a monthly fee or a one-time charge for the rights to use these tools. Rather, we pay every time we use the tools—each query entered into Google, each time we send an email message, each time we view a video on YouTube. With every Facebook post and each tweet we send, we’re paying these companies.
Online platforms will give way to the Internet of Things and connected devices. The notion of the “quantified self” is only beginning to become concrete, with fitness products and other forms of wearable technology. In exchange for what we learn about ourselves based on the data we generate, perhaps in pursuit of our personal goals, we adopt technology and share our data with the developers of the technology. In doing so, we’re paying with our actions, with the data that we produce. And there’s absolutely nothing wrong with that, so long as we’re aware of what we’re doing.
Consumer awareness took on broader significance with the revelations offered by Edward Snowden’s disclosure of documents that detailed the surveillance activities undertaken by the National Security Administration (NSA). Many had talked in vague terms about “big brother” watching us. Set in the aftermath of 9/11, the CBS drama Person of Interest presents a machine that can identify those involved in crimes before the events take place, gathering data through traffic cameras and other devices. In 2008’s The Dark Knight, Batman builds a machine that turns every cell phone into a microphone, allowing him to build a citywide surveillance system to aid him in finding where the Joker is located. Like these fictitious examples, the NSA’s surveillance activities may have been well intentioned. However, in the wake of the information that has come to light, there is increased interest in organizations being transparent about the data they are collecting and how such data are being put to use.
The interest in data transparency permeates the marketing field, as well as the regulatory environment in which companies must operate. In 2012, the Federal Trade Commission (FTC) issued orders to a number of companies to disclose information about data collection and usage practices.¹³ In a March 2012 report, the FTC urged companies to improve transparency in their data collection efforts. In the same report, there is a call to increase consumers’ knowledge about the data practices in which companies engage.
Despite the growing interest and potentially increased scrutiny, how exactly will organizations’ data collection and usage practices change from what they have been historically? And, more important, do consumers care? Whereas the answer to the first of these questions is something of a moving target, the second question is more important. After all, to what extent do consumers need protection against large organizations’ data-hording activities if they are informed of such activities and are not fazed by it?
Forrester examined the second of these questions in a 2012 study.¹⁴ Distinguishing between behavioral data (such as your online browsing history or your purchase history at a retailer) and individual data (for example, your address or Social Security number), it found that all age groups were more concerned about how companies used individual data, with older consumers more wary than younger consumers. The study also found that exclusive deals attract a majority of young consumers to share their data with companies, but the appeal of such incentives do not attract older consumers nearly as much. Mirroring this, the study found that a larger share of older consumers do not complete an online transaction because of something they read in the company’s privacy policy.
So what can be gleaned from these findings? It turns out that consumers are actually concerned with how companies are making use of their data. Although that might seem like a dark cloud hanging over marketing, it’s important to note that consumers’ concerns vary with the type of data being collected and across different consumer groups—and these data collection practices do appear to impact consumer expenditures.
As we’ll discuss, one option for organizations is to collect the data about which consumers are less sensitive and hence more willing to share. However, the key is for the organization to engage consumers like they would approach another collaborator. The consumers have something that is of value to the organization. With access to such information, organizations may change their practices. Marketing expenditures may be made with increased precision. Product lines may expand or contract. Consumers may see a more targeted message. New businesses may develop to meet the consumers’ needs. All these practices may rely heavily on access to the right consumer data.
If we’re willing to share data with companies, consumers have much to gain, as do the businesses that develop if they are successful. It is simply a matter of an exchange—consumers are more willing to share their data if it is clear what they are getting in return. That payment may be in the form of financial compensation or access to a product or service. The idea of an exchange, where value is traded for value, is straightforward enough. However, as recent events have revealed, a host of issues are related to conducting such a transaction.
At the same time, there are also issues focused on the protections that must be in place for consumers. Questions about the information that organizations should present to consumers and the measures that should be taken to ensure that consumers’ data are secure need to be addressed. Just as there is the potential for businesses to grow larger than we would have imagined, fueled primarily by innovative thinking about how consumer data can be used, there is also potential for such data to be exploited and considerable harm levied against consumers. And because of this, there is a legitimate question as to what the role of the government should be. Is a watchdog agency akin to the Consumer Financial Protection Bureau needed to oversee such exchanges? Alternatively, should market forces dictate who will and will not have access to consumer data and how much it is worth?
The next few chapters look at success stories that have emerged in business, where a good part of the companies’ successes have been thanks in no small part to the data provided by their consumers. We’ll also see how existing businesses have refined their practices by leveraging more detailed information about consumers. In addition, we’ll see the benefits that the public has accrued through the innovative work of government agencies using many of the same tools employed by businesses.
With these exemplars of what is possible as a backdrop, we’ll cast a critical eye on the current exchange in which consumers and organizations participate. Although this model functions well in some regards, we put forth reasons that suggest that it may be in need of updating. There are some signs that these revisions are already underway. Based on these harbingers, we’ll discuss the structure that may be needed to support the burgeoning data economy.
Taking into account the events that have unfolded in recent years—from Internet giants and social networking sites coming on the scene, to interest in data, infographics, and statistics going mainstream—this book is not an attempt to predict what course we’ll chart in the near future. However, given how we have seen organizations use data and current activities, there are indicators that suggest a potential direction—one in which consumers, businesses, and the public all stand to benefit.