eBook publisher data transformation: A case study

5 min read

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The client, a respected international publisher of scholarly books, was in the midst of a shift from primarily selling print books to instead delivering ebooks to university libraries worldwide.

The client was utilizing a third-party Software as a Service (SaaS) vendor for ebook hosting, a costly service that offered little flexibility for integrations or expansion. Moreover, a separate, Azure-based analytics platform managed by another vendor suffered from a flawed implementation and poor data integrity.

The client partnered with Tricon Infotech to build a new, customized ebook hosting platform that would support emerging license models like collection subscriptions and perpetual ownership of individual titles. The new platform would include integrated data collection and analytics so both the client and its library customers could see how users were interacting with the new platform, and to better understand demand for specific ebooks and collections.

Tricon Infotech launched the new hosting platform in under 12 months, including a new, AWS-based data analytics system that was both GDPR compliant and Counter-compliant (COP4, later COP5) in accordance with industry standards.
Tricon's successful data transformation implementation

The new customizable dashboards offered benefits to library customers and sales teams alike: Librarians could see accurate usage of purchased ebooks, as well as demand for titles that students and scholars wanted to use but the library didn’t own, which offered upsell opportunities and usage insights to the client.

Additionally, the new data system pushed metadata and content to Google for indexing in the Google Scholar program, so users who were browsing books could see the client’s other relevant titles. This also demonstrated demand for specific titles, which helped the client to better focus its content acquisition and development efforts.

The result was robust, reliable data and reporting dashboards that helped the client optimize its products and increase its sales, as well as improved, targeted purchases by libraries (with often-limited acquisition budgets).

Key takeaways

  • Data management and analytics needs to be considered from the inception of any new program.
  • Future reporting needs may change, so it’s best to collect the maximum amount of raw data, including historical data. Then filter out noise into successive, manageable data warehouses.
  • Business Intelligence may be equally useful to both content/solutions providers and their customers and help grow a mutually profitable relationship.
  • Data management and analytics needs to be considered from the inception of any new program.
  • Future reporting needs may change, so it’s best to collect the maximum amount of raw data, including historical data. Then filter out noise into successive, manageable data warehouses.
  • Business Intelligence may be equally useful to both content/solutions providers and their customers and help grow a mutually profitable relationship.
  • Data management and analytics needs to be considered from the inception of any new program.
  • Future reporting needs may change, so it’s best to collect the maximum amount of raw data, including historical data. Then filter out noise into successive, manageable data warehouses.
  • Business Intelligence may be equally useful to both content/solutions providers and their customers and help grow a mutually profitable relationship.
  • Data management and analytics needs to be considered from the inception of any new program.
  • Future reporting needs may change, so it’s best to collect the maximum amount of raw data, including historical data. Then filter out noise into successive, manageable data warehouses.
  • Business Intelligence may be equally useful to both content/solutions providers and their customers and help grow a mutually profitable relationship.
  • Data management and analytics needs to be considered from the inception of any new program.
  • Future reporting needs may change, so it’s best to collect the maximum amount of raw data, including historical data. Then filter out noise into successive, manageable data warehouses.
  • Business Intelligence may be equally useful to both content/solutions providers and their customers and help grow a mutually profitable relationship.
  • Data management and analytics needs to be considered from the inception of any new program.
  • Future reporting needs may change, so it’s best to collect the maximum amount of raw data, including historical data. Then filter out noise into successive, manageable data warehouses.
  • Business Intelligence may be equally useful to both content/solutions providers and their customers and help grow a mutually profitable relationship.
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