Understanding the Value of Data Throughout the Entire
Software Development Lifecycle

Our forward-thinking approach is to combine development teams with analytics experts at the beginning of the software development lifecycle to make data analyzable from the start.

Our Approach

Download: Analytics in the Modern Org

Bridging the Gap Between Development & Analytics

Decision makers in modern organizations need the ability to combine and analyze data from many systems in order to gain insights and drive business direction. Historically, this has been achieved by forming analytics teams responsible for transforming data from source systems into a central data store, on top of which they create reports and dashboards.

The problem is, with each additional data source coming online, and with each new question asked by the business, an additional burden is added to the analytics team. How does an organization embrace this growth? We propose a shared ownership strategy that allows analytics to scale with the organization. The idea is to combine development teams with analytics experts early on in the application development/configuration process and make the data analyzable from the get-go. This realignment makes increased data attributes, sources, and granularity an asset rather than a burden on the business, and positions an organization to use data to be competitive in their market.

Our teams are purposefully brought together with embedded analytics skillsets as well as event-driven architecture understanding. With a sense of responsibility for making raw data analyzable, we can answer tomorrow's questions without redesign.

Check Out Our Latest Blog Post On Data Analytics

  • A pair of glasses sitting on an open book showing a decision tree

    Becoming Certified in AWS Machine Learning

    By Shalyn Nystrom | Data Analytics
    In March of 2019, AWS introduced their newest certification, the AWS Certified Machine Learning - Specialty certification. The certification is “intended for individuals who perform a development or data science role. It validates a candidate’s ability to design, implement, deploy, and maintain machine learning (ML) solutions for given business problems.”...