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    Cracking the Code... Implementing SCRUM at Scale within Enterprise Data at Fannie Mae

    Atif Salam
    Atif Salam
    PHILLIP MANKETO
    PHILLIP MANKETO
    Dave McMunn
    Dave McMunn
    schedule 9 months ago
    Sold Out!
    45 mins
    Case Study
    Intermediate

    Fannie Mae, a leading source of residential mortgage credit in the U.S. secondary market, provides reliable, large­scale access to affordable mortgage credit across the country so people can buy, refinance, or rent homes. In November 2014, Fannie Mae’s Enterprise Data Warehouse completed a multi­year project to implement several thousand data attributes across numerous interfaces which were then integrated with various development organizations working in parallel; the results were sub­optimal. In January 2015, Fannie Mae undertook a brownfield initiative to transform Fannie Mae’s Enterprise Data Warehouse, responsible for sourcing, vending and provisioning data attributes, which informed numerous interfaces, as part of a larger Federal compliance mandate, to SCRUM. Initially, starting with two teams using SCRUM, the initiative progressed to six-teams implementing SCRUM­OF­SCRUMS, within 6 months. Thereafter, the SAFe framework was adopted encompassing twelve­teams (130+ team members) on a single Agile Release Train. As a result of transforming from Waterfall to Agile, Fannie Mae experienced a significant boost in productivity and reduction in delivery risks through the relentless focus on innovation and automation to ship "production ready" code with high and higher frequency. The transformation to Agile has revolutionized how the firm plans for the delivery of large scale ($100 million plus investment/year) programs, has significantly mitigated risk inherent in complex in integration between legacy and new architectures/applications, as well as between internal and external systems. Within the larger context of the transformation to Enterprise Agility, this Experience Report will focus on the changes to the organization, architecture, and technical practices required to implement data attributes every two­-weeks and the corresponding benefits realized.

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