example, the items you’ve bought online in the past give online shopping sites such as Amazon.com enough of your background to be able to make suggestions for additional purchases. Using past data to recommend future purchase or actions is a common way to derive value from data. In this section, I introduce three ways organizations can identify data with the greatest potential value.
Monetizing data
Over the past two decades, many organizations have come to view data as the primary fuel of the information age. Since the dawn of the twenty-first century, many organizations with data as their central business driver either started or expanded rapidly. Amazon relies on customer data to make additional purchase suggestions, while companies such as Facebook and Google rely on data as their primary product to drive advertising revenue. All these organizations found ways to turn data into revenue.
As data becomes more directly associated with revenue, data giants Google, Facebook, and Amazon control a growing demand for access to that data. Users have long been encouraged to share their personal data and activities, with little or no compensation. In the beginning, the perception was that sharing personal data was harmless and had little value.
However, a growing number of consumers and business partners realize that their data has value. Legislative bodies have recognized the importance of personal data and are passing new levels of privacy protection legislation each year. Data not only has value in and of itself but, when linked to other related personal data, can also provide valuable insight into personal behavior.
The realization that personal data has value has resulted in a game of sorts. Organizations that value consumer data attempt to acquire as much data as possible, while consumers are becoming more willing to deny free access to their personal data or demand compensation. Compensation often takes the form not of a direct monetary payment but of other perks or discounts.
Exchanging data
As organizations realize the increasing value of consumer and partner data, the more they explore ways to leverage that value. When consumers interact with any organization, or organizations interact with partners, a trail of data artifacts is left behind. Artifacts that document transaction timing and contents, as well as any changes to data, describe how entities interact with organizations. As more interactions with all types of organizations become more automated, the quantity and frequency of data artifacts increases.
Organizations that collect data artifacts find that not all are useful — at least not to that organization. However, as data becomes more and more valuable, many organizations have expanded the scope of data they collect with the intention of selling that data to other organizations. As data becomes a source of both direct and indirect revenue, data collection and management moves from a supporting role to a strategic planning concern.
For example, political campaigns routinely spend large sums of money to purchase demographic information on customers who have purchased specific types of products. Political candidates who strongly support environmental issues find value in identifying people who purchase green products because these customers are likely potential supporters. The identities can then be used to solicit campaign donations.
Sharing and exchanging data isn’t always bad. In some cases, you want your data to be shared among businesses and organizations. For example, sharing the complete service history for your car could make getting service easier and more reliable. With shared service data, you could take your car to any service provider and not have to remember the last time you had the oil changed or tires rotated. Techniques that support beneficial and responsible data sharing among organizations can be valuable to business and consumers.
Verifying data
One of the obstacles to realizing the full value of data is the dependence on its quality. Quality data is valuable, while incomplete or untrusted data is often worthless. What’s worse, low-quality data may require more budget to clean than it will potentially generate in revenue. The only way to realize data’s true value is to ensure that the data is valid and represents entities in the real world.
Verifying data has long been one of the highest costs associated with collecting and using data. Campaigns that depend on physical or email addresses will have little effect if the target addresses are largely incorrect. Bad data can come from many sources, including mischievous data submission, sloppy data collection, or even malicious data modification. An important aspect of relying on data is putting controls in place that verify the source of any collected data, along with that data’s adherence to collection requirements.
A simple approach to verifying data in a distributed environment is to carry out a simple validation at the source and again at the server as the data is stored in a repository. While validating data at least twice may seem excessive, the practice makes user errors easier to catch and ensures that data received by the server is clean.
One of the reasons data verification is so important is that organizations are relying more and more on their data to direct business efforts. Aligning business activities with expectations based on faulty data leads to undesirable results. In other words, decisions are only as good as the data on which those decisions are based. The “garbage in, garbage out” adage still holds true.
Understanding and Satisfying Regulatory Requirements
The information age offers many new opportunities and just as many (if not more) challenges. The vast amount of data available to organizations of all types empowers advanced decision-making and raises new questions of privacy and ethics. Consumer protection groups have long been voicing concerns about how personal data is being used. In response to discovered abuses and the recognition of potential future abuses, governing bodies around the world have passed regulations and legislation to limit how data is collected and used.
Although collecting a few pieces of information about a customer may seem innocent, it doesn’t take long for accumulated data to paint a picture of an individual’s personal characteristics and behavior. Knowing the past behavior of someone makes it relatively easy to predict the person's future actions and choices. Predicting actions has value for marketing but also poses a danger to an individual’s privacy.
Classifying individuals
The concern is that personal data has been, and will continue to be, used to classify individuals based on their past behavior. Classifying individuals can be great for marketing and sales purposes. For example, any retailer that can identify engaged couples
