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Ad fontes media uses Omniloy to scale analytics with self-serve BI

AI semantic layer
By collaborating with Omniloy, Ad Fontes Media enhanced the productivity of its data team, enabling them to deliver more accurate insights to customers more quickly, all without expanding the team size.

Ad Fontes Media specializes in evaluating news sources for bias and reliability, offering insights through its Media Bias Chart, which aids advertisers, publishers, and educators. To address challenges in analyzing vast amounts of media data, Ad Fontes partnered with Omniloy, enhancing the data team's productivity. This collaboration allowed them to deliver faster, more accurate insights to customers without needing to expand the team.

Ad Fontes Media encountered difficulties in analyzing extensive media data to assess news reliability and bias. By collaborating with Omniloy, a company focused on self-service business intelligence, Ad Fontes was able to enhance the productivity of its data team, enabling them to provide faster and more accurate insights to customers without needing to expand their workforce.

Challenges

Data overload: Ad Fontes Media faced the challenge of processing and analyzing large volumes of data from various sources due to the rapid growth of digital media.

Integration of manual and automated inputs: As the company transitioned from manual scoring to automated model scoring, it needed effective methods to manage its increasing data.

Scalability Issues: The organization struggled to scale its operations to keep up with the rising demand for insights from its media rating data.

Data storage and processing: A larger datastore was necessary, as MySQL was reaching its limits.

Solution

Ad Fontes Media adopted Omniloy to utilize its self-service analytics and AI features. With the cognitive layer and Zoë, the AI data analyst from Omniloy, the organization experienced several advantages:

AI-powered insights: Employees could now direct their queries to Zoë instead of interrupting the data team, benefiting from her comprehensive data capabilities.

Focus on important work: The data team could concentrate on refining algorithms for assessing media bias and reliability.

Scalable infrastructure: Transitioning to a modern data stack allowed for efficient analysis of growing data volumes.

Cost-effective solutions: Competitive pricing and flexible reporting met Ad Fontes Media's needs effectively.

  • Get more info on the problems to solve in Our Solution Page.
  • Implementation

    Don Hussen, the CTO of Ad Fontes Media, led the implementation process in a structured manner:

    Assessment: Omniloy conducted a detailed evaluation of Ad Fontes Media’s needs, pinpointing areas where AI and analytics would be most beneficial.

    Data integration: The Omniloy team collaborated with Ad Fontes Media to integrate their data sources, ensuring smooth data flow and compatibility.

    Customization: Ad Fontes tailored their cognitive model to fit their unique data, metrics, and definitions.

    Training and support: Omniloy offered extensive training and ongoing assistance to help the Ad Fontes Media team effectively use the new tools.

    Roadblocks and solutions

    During implementation, Ad Fontes Media encountered several challenges that Omniloy helped to resolve:

    Complex data models: Omniloy's user-friendly data model interface and Git integration allowed Ad Fontes Media to test in development environments before going live, ensuring a seamless transition.

    Interface adaptation: Moving from custom scripts to Omniloy's table views simplified the setup and validation of data joins, enhancing efficiency.

    Results

  • Increased efficiency: Automation cut data analysis time by 70%, allowing focus on valuable data challenges.
  • Enhanced accuracy: AI-driven insights improved bias and reliability assessments.
  • Scalability: Ad Fontes scaled operations through user adoption, analyzing more articles and providing frequent updates to ad-tech partners.
  • Improved user experience: Better accuracy and update frequency fostered user trust and engagement.
  • Productivity gains: The platform eliminated the need for 1-2 additional data scientists, enabling a team of five to efficiently handle 40,000 customer queries per second.