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It’s the Age of Data, Time to Incorporate a Data Monetization Strategy into Digital Transformation

We are already living in the Age of Data. In fact, every organization operating today is essentially a data organization. However, only 1 in 12 are actually monetizing data to its full extent.

Many organizations are already leveraging Data and Analytics to generate growth. Data Monetization is the means to achieve that growth.

To achieve Data Monetization, there are 2 pathways an organization can take–one with an internal focus and the other with an external focus.  Forward-thinking organizations have already embedded Data Monetization Strategies into their Digital Transformation and Strategy Development efforts.

The internal focus pathway centers around gaining Operational and Organizational efficiencies by leveraging data to improve our operations, productivity, and offerings.  It also enables ongoing, tailored dialogue with customer by leveraging customer data.

The external pathway centers around making our data available to Partners and Customers. Through this, the goal is to leverage our data to create new revenue streams.

These pathways are not mutually exclusive. We can pursue both pathways for both Operational and Revenue improvements.

A successful Data Monetization Strategy requires us to focus on the highest-value opportunities that align with our Corporate Strategy. To better understand how to evaluate and prioritize these opportunities, let’s dig a little deeper.

Path to Data Monetization: Internal Focus

As mentioned, the internal focus looks at improving Operations and Organization. We can categorize internal initiatives into 2 areas:

  1. Cost Reduction – Data Monetization efforts leverage data to increase productivity or reduce consumption and waste (either of raw materials or low-value activities).
  1. Revenue Growth – For top-line growth, we leverage data to improve sales performance or reduce customer churn.

Pathway to Data Monetization: External Focus

The external focus explores new business models enabled by sharing data with Partners and Customers. Specifically, there are 3 types of new business models, progressing in levels of sophistication, value impact to customers, and, likewise, revenue potential:

  1. Data-as-a-Service (DaaS) – Data is sold to end customers or intermediaries who analyze the data for insights.
  1. Insight-as-a-Service (IaaS) – This business model takes to the next level by applying advanced analytics to the data to generate actionable insights.
  1. Analytics-enabled-Platform-as-a-Service (APaaS) – We apply proprietary algorithms to generate enriched data for customers via a self-service platform.

The external pathway to Data Monetization offers much greater opportunities than the internally focused alternatives. Although Applied Data shows the greatest revenue potential and likewise value to customers, it also has the longest time to value. Therefore, we should carefully analyze each option and build a robust business case that balances our strategic vision, capabilities, and competition.

Interested in learning more about Data Monetization? Check out our framework presentation, Pathways to Data Monetization. It discusses the 3 business models in further depth, as well as how to implement your Data Monetization Strategy via Data Platform and Data Factory.

You may also be interested in this other framework, the Building Blocks of Data Monetization.

About David Tang

David Tang is an entrepreneur and management consultant. His current focus is Flevy, the marketplace for premium business documents (e.g. business frameworks, presentation templates, financial models). Prior to Flevy, David worked as a management consultant for 8 years. His consulting experience spans corporate strategy, marketing, operations, change management, and IT; both domestic and international (EMEA + APAC). Industries served include Media & Entertainment, Telecommunications, Consumer Products/Retail, High-Tech, Life Sciences, and Business Services. You can connect with David here on LinkedIn.

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