Building A Topic-Centric Experience: Using Business Vocabulary And...

Building A Topic-Centric Experience: Using Business Vocabulary And Semantics To Drive Data Visibility

Rodney Kagarise, Data Modeling Senior Manager with Fannie Mae

Building A Topic-Centric Experience: Using Business Vocabulary And Semantics To Drive Data VisibilityRodney Kagarise, Data Modeling Senior Manager with Fannie Mae

A frequent subject of discussion within the IT community today is how to leverage enormous amounts of data to identify business opportunities, manage risk, and reduce costs. Cloud architectures enable access to an abundance of data at warp speed, while lowering storage and processing costs. Yet, how do corporations make the most of such a huge data opportunity? Capitalizing on the exponential amount of data available, Fannie Mae is applying the latest technology and digital solutions to solve the housing industry’s biggest challenges, today and in the future. Empowering data scientists to accelerate insights and knowledge to facilitate new business capabilities helps deliver on our mission for homeowners and renters. Provide business context for user groups to drive data visibility A topic-centric approach uses an enterprise semantic model to overlay business context to the data. Data glossaries, data lakes, and data stores often lack the context required to make data easy to locate by the people who need to use it. Tagging data in user-friendly terms enables a data consumer to link data quickly and efficiently across technology layers and cross-reference terminology from diverse internal and external sources of data. Associating the data to quality metrics, data profiles, and lineage helps the data consumer verify that the data is fit for purpose. Using topics to drive context, we are answering questions such as: What data is available? And where is the data stored? Additional benefits include a common view of data across an enterprise, simplified data mapping and translation between applications, and a common reference model for technologies such as relational database management systems, NoSQL databases, graph databases, and application programming interfaces.

Explore more data while gaining efficiencies with self service capability The topic-centric experience allows business users and data scientists to shop for data. Our data customers can discover available data without relying on (and waiting for) subject matter experts. Customers have the power to find and compare available data by domain, definition, and data type using their local vocabulary. For example, in mortgage finance, searches on terms such as loan, mortgage, and note map back to the business concept of mortgage loan. Using connections defined in the model, users can also explore related data such as mortgage loans, borrowers, and properties through the association to the original search term. Equipped with search results, users can choose to utilize data from data sources based on availability, quality, provenance, timing, or technology. Advanced analytic functions can also be quickly deployed, like a recommendation engine that uses the model, search, and selection histories to suggest other data of interest. New data inventory becomes instantly visible through automatic mapping from internal and external sources to the model, completing the full-service data experience. Create more business value with data driven decision making Imagine the possibilities that a topic centric approach can provide to your business, including:

• Accelerated insights.

• Improved decision making.

• User-friendly, self-service data exploration.

• Connected data across domains.

• Continuous curation of new data sources.

• Simplified communication between applications.

By enabling a model-driven, topic-centric approach, Fannie Mae creates visibility and access to our voluminous data. Data scientists can extract the most value from our data delivering insights and capabilities faster – enabling the business to deliver on our mission to help people buy, rent, and stay in their homes.

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