• Aptitive optimized data pipelines for a mass media company to improve access to data they needed and find better insights
• Empowered by a new data infrastructure, the client now enjoys faster, more accurate reporting, deeper client engagement, and is poised to take advantage of new analytics applications

The client is a mass media company with a forward thinking technical strategy. Despite a robust commitment to technology, they were losing competitive advantage by not taking control of the data these systems produced. Aptitive helped them identify useful data assets and built a cutting-edge analytics platform that solved these problems.

The client’s employees were relying on multiple tools for storing, cleaning, and processing data to generate business reports. Without a centralized location to house the business logic of these systems, they couldn’t process data in a timely way. This was not only impacting the quality of their business decisions but also degrading the quality of reporting information available to clients.

The Aptitive team leveraged several tools, including Microsoft Azure Data Factory and Snowflake, to properly ingest the client’s data into a newly built data warehouse. This new data warehouse featured a repeatable pipeline, which could pull data from each important source and ingest it into the system in a fraction of the time of their previous solution.

Aptitive’s standard and reusable data pipeline processes created a single data source for both their internal efforts and client-facing analytics. The system provided an immediate boost to business efficiency, facilitating greater teamwork and employee engagement while improving on vital reporting processes.

In addition, Aptitive cleaned, normalized, and ingested data from all the client’s third-party vendors. This ensured that their new data modeling capability supported any new data sources they wished to ingest or analyze in the future, laying the foundation for more efficiency gains in the future.

Our efforts also helped the client establish an iterative approach to increasing the business value of their data, positioning them for near real-time analytics and the ability to embrace new data science use cases with confidence and ease.

Scripps