Welcome to Part IV of this article series on data monetization. (Here are Part IPart II and Part III for easy reference.)

Data monetization opportunities are determined by defining a problem to be solved in terms of focus and state. If the focus of the problem to be solved is internal and the state is new, then the defined monetization opportunity is increasing market share or entering new markets. Businesses that use data to reach new customers or enter new markets question the non-consumption of their products rather than trying to increase consumption with their existing customers. This naturally means that the questions these businesses ask are new and will generally require new data and methods to answer. Monetizing data to increase market share is not an easy approach, as it usually means “business as usual” will no longer be sufficient, taking the organization well out of their comfort zone.


The important aspect of this monetization opportunity is that it is about the business solving the internal question of how to reach more customers with its current products and services. It is not about developing new products to solve the problems of new customers. The focus is on what the business can do differently to have further reach in the market.

Like data collection for business optimization (data monetization opportunity detailed in part two of this series), collection for increasing market share is primarily about improving data integration techniques and capabilities across the enterprise. A wider business view of data will shed light on where key information may be missing to gain other customers. This may include customer intelligence data, general market intelligence or additional data sources that provide further insight in to why customers do not user the business’s products or services. In order to add new data sources quickly, businesses must have extensible architectures that do not require extensive modeling or transformation to onboard data. Big data technologies such as Hadoop are valuable considerations even if the business is not ready to include big data itself. The nature of big data technologies allows for integration of data of all types and models without the heavy lifting of traditional ETL.

Packaging data for those charged with changing business processes to increase market share, such as analysts, data science teams or executives, means making sure the right data is in the right hands at the right time. Not all data will be made available to all users for a variety of reasons. Data governance programs with enterprise scope provide the definition of access rights, security classifications and guiding principles for data use including decision rights. Data governance initiatives that originate within departments and maintain focused scope will not have the breadth of authority to make this successful. When provisioning data, associated reports and analytics capabilities, it is necessary to provide accurate, consistent environments to ensure all those involved are making decisions and taking action on the same information. Nothing is worse than when an executive realizes they are making decisions off inaccurate data or inconsistent reporting. (Trust me on this one if you haven’t already experienced this.)

The delivery of data to inform new business processes and new decisions should be intuitive, easy to use and foster further exploration. Visualization tools are excellent resources for achieving this. With the ability to incorporate established security requirements and governed business rules, data visualization tools ensure business professionals are looking at the same data across the enterprise, yet have the flexibility to change how they view it and to drill down as necessary. Good design will provide aggregate data closest to “answer form” ̶ providing the key, common nuggets of information business leaders need at the ready and easy methods for getting to deeper data with immediate response.

Increasing market share, reaching new customers and entering new markets requires new business processes, new decisions and often new data. In order to use data to inform how the business should change, the data architecture must be extensible so that new data can be added as needed without extensive coding  ̶ and the enterprise data environment must be governed to ensure data consistency and security. Data should be packaged and delivered to decision makers in ways they can consume without requiring technical intermediaries. Data can provide many valuable insights for increasing market share. The key to monetizing these insights is having the organizational fortitude to act on the changes required to make it happen.

Read Part I
Read Part II
Read Part III
Part IV: Above
Part V: Coming Soon

Anne Buff is a thought leader on SAS's best practices team.

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