Valuing Data as an Asset

Valuing data is a rather abstract process and standardized methods have yet to emerge. While many types of data already have a market price (such as information that can be purchased about an individual) or a zero price-point, data that is created as a by-product of a company’s normal operations is far more intangible.


There are clear benefits for understanding and valuing data, including the ability for a company to more accurately value itself in a merger or acquisition. This is particularly relevant for companies in highly acquisitive application markets.

Companies should also measure the change in the value of their data over time. The question every CEO should be asking his or her team is: are we constantly increasing the value of our data? And the follow-up question is: how do we measure that?

Data Soup: Specialization, Cooperation and Seasoning in your Data Intelligence Strategy

Stone Soup is a folk story in which hungry strangers persuade local people of a town to give them food. While the story is usually told as a lesson in cooperation, especially amid scarcity, it also serves as a tutorial in specialization and the importance of a properly crafted data intelligence strategy.

At the beginning of the story, each village family relies solely on the yield of their own crops for sustenance, and therefore they practically starve. One by one, the families are convinced to contribute a portion of their harvest to the communal soup. As each new ingredient is added, the soup becomes heartier and more flavorful. In the end, everyone enjoys an awesome meal.



As with the villagers’ crops, your company’s internal data silos are of limited value in the insight they can produce on their own. It doesn’t matter how many ways you try to slice-and-dice it, you will hit a point of diminishing returns.

The Moral Limits of Data: What Won’t You Do?

[vc_row][vc_column][vc_column_text]Map courtesy of Sickweather

Map courtesy of Sickweather

Ten years ago, I was part of a team that deployed some cutting edge call center routing technology (Cisco ICM) that could use the phone number a customer was calling from, match it with data about them and then route the caller to a prioritized queue based on what we knew.

This was a hot trend because a company could then provide enhanced support for their preferred customers. For example, a “Gold Card” member would wait less time for an agent than a lower value customer would.

Fast forward a few years, and you can now take the data you collect on a customer customer, append it with other data sources, and then hire some quants to create psychological and personality profiles of each person. Now when a customer calls in, you can route them to a specific agent that is trained on what language patterns to use that will most effectively influence that caller’s behavior to your desired outcome.

This begs the moral question: are you providing better customer service or rather deploying thinly masked manipulation technics?

The Destruction of Private Data Markets

[vc_row type=”in_container” bg_position=”left top” bg_repeat=”no-repeat” scene_position=”center” text_color=”dark” text_align=”left”][vc_column][vc_column_text]Data markets have traditionally relied on privileged access to information and a monopoly over distribution. Such scarcity of data ensures that it remains both expensive and highly restrictive.

Fortunately, with the emergence of: open data, crowdsourcing and machine-to-machine communications, previously privileged information is quickly becoming commoditized. In many cases, the quality and quantity of the data available is outstripping that of commercial sources.

As example, data aggregators such as: Infogroup, Acxiom and Neustar Localeze have held the business listings market captive. If you own a business with a physical location and you want it to show-up in: navigation devices, online maps and business directories (such as then you need to pay each of the aforementioned companies an annual fee to “verify and distribute” information about your business to their list of paid subscribers.

These data aggregators enjoy the benefits of a double-sided network. They charge a business for distributing their information while simultaneously charging the data subscribers and directories for “verified” listings.