Data = Money, Let us see how?

Companies today are swimming in a rising tide of digital data. That data resides in their information systems and in the ecosystem that surrounds them.


Tealfeed Guest Blog

3 years ago | 2 min read

Companies today are swimming in a rising tide of digital data. That data resides in their information systems and in the ecosystem that surrounds them. Companies that are fastest and best at turning that data into insights, and using those insights to improve their businesses, will be rewarded in the marketplace.

The stakes are high. Those who advance furthest fastest will have a significant competitive advantage. Those who fall behind will be out of the race.


With a solid operating model in place, organizations can begin the process of turning data into value. Two fundamental steps during the insights journey are collecting the relevant data and refining it appropriately. 


Defining certain requirements based on particular use cases will help ensure that only relevant data is captured. First, identify business use cases (internal & external) you believe in, and then think about the models and data you need to operationalize them, not vice versa. 

Some use cases will require significant time series of data. Others depend on the timeliness and “freshness” of data. Carefully organizing data into several logical layers and then employing logic by which to stack these layers can help generate more meaningful data.


Once the organization has successfully captured all relevant raw data, it must begin the process of making sense of it all. Enrich the data with the knowledge of domain experts, never losing sight of the fact that human expertise is as important in making data useful as is the power of analytics and algorithms.

Post data collection and refinement, comes Data Monetization.


There are two primary paths to data monetization. The first is internal and focuses on leveraging data to improve a company’s operations, productivity, and products and services, and also enable ongoing, personalized dialogues with customers.

For Example: Digital disruptors such as Amazon, Netflix, and Airbnb monetize data internally by gaining an intimate understanding of their customers. They look at things such as demographics, special needs, historical purchases and interactions, shopping behaviours, and pivotal events, offering highly personalized products and services within an end-to-end experience, delighting customers at every touchpoint — from discovery and purchase to post-purchase. This customer-centricity allows internal data monetization, creating competitive advantage.

Pic credits: Modern Dealership
Pic credits: Modern Dealership

The second path is external and involves creating new revenue streams by making data available to customers and partners. 

For Example: Verizon, Deutsche Telekom, and Telefonica have leveraged the data, anonymized and aggregated, across various use cases for their B2B clients and partners by offering:

  • Geotargeting and geofencing for retailers and tourism.
  • Traffic flow and density planning for ad agencies, government agencies, public transportation companies, city planners, and health care organizations.
  • Fraud detection for financial institutions and credit card companies.
  • Smart targeting and click-stream insights for brands and digital advertisers.
  • Location, layout, and staff planning for retail stores.


To maximize the potential for internal and external monetization, companies should set up a “Data Factory” that automates the process of collecting, enriching, transforming, and deriving insights from data. It’s a complex undertaking requiring a set of design principles that touch on design thinking, lean startup, and agile methodologies for success.

New capabilities based on this new architecture, such as data-driven tailoring of products and services, will yield not only radical improvements in operational effectiveness, but also new sources of competitive advantage.

“Uber, Amazon, eBay—these companies really understand the value of their data.” But many other organizations have yet to focus on data as an asset.


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