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.

 

DataSoup

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.

However, like stone soup, it is possible to create exponential value from your data by augmenting and blending it with other sources.

For starters, think of the data your business collects or generates as a Factor Endowment. In other words, your internal data is akin to natural resources and will be leveraged to increase firm profits and create new revenue.

When making data soup, these endowments should comprise the core of your assets. Whether it’s your customer data, application data, or even sensor data being generated from a “smart device” – this is your starting point. Its the stock ingredient that should have some intrinsic value; but will be worth a lot more when properly garnished by a talented chef and served in a high-end restaurant (and perhaps paired with a fine wine or dessert).

Once you have your core ingredients, you’re going to mix in other datasets to enhance its flavor and increase the value of your soup. External data sets, such as: demographic, social media, weather and market information are supplemental ingredients that should be sourced and measured with intention and care.

The complexity of each emerging data domain requires specialization. No one company should attempt to tackle all of these on their own because the opportunity cost against their own comparative advantage far outweighs any perceived cost savings.

Unlike Alphabet Soup, where you want everything from A to Z in the mix, Data Soup is about creating your own proprietary recipe. And, this is not just about “big data”. In some cases, data is like salt- you just need a pinch to enhance the overall flavor.

The story of Data Soup boils down to this:

  • As a data provider: Competitive markets drive profit margins towards zero in the long run; therefore, focus on specialization and cooperation with other firms to create optimal trade agreements and value creation.
  • As a consumer (or buyer) of data, there are no one-sized fits all strategies when it comes to data intelligence. Seek specialized providers that when brought together create a whole that is greater than the sum of its parts.

In the end, we will all be better off by focusing on what we do well and contributing back to the global data community.

Lastly, consider the following: are you growing and selling individual ingredients, or are you creating awesome sauce? What is the difference to your company in terms of creating and capturing the most value and profits?

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