Our data scientists prefer to work in Python and R, but we are a tool-agnostic organization.
Mosaic is a top artificial intelligence consulting firm, and uniquely qualified to support firms in the recommendation of a data architecture with our extensive knowledge of end-to-end analytics solutions. Our machine learning consultants understand that analytics tools needs to support the use case and organization. We use insights from past projects, your current environment and use case requirements to facilitate ease of use and increases time to insight. We can configure infrastructure for both BI and advanced analytics and provide self-service tools for business users.
A successful data analytics infrastructure must take a holistic approach for all stakeholders, and this process requires collaboration between IT and business units, while requiring governance to ensure success. Timing and scheduling of operational workflows need to be designed with the end user in mind.
Here are some analytics tools we frequently work with.
The analytics tool platform must match and support the entire data-to-analysis lifecycle. Analytics should be integrated into business process and workflows, with a documented use case identification process gated by a data custodian and steward team. A center of excellence (CoE) should standardize on a small set of analytics tools until use cases demand alternatives. Technologies should be selected with a growing user community. Best practices include starting small, staying the course with selected tools and continuing to grow. A successful data analytics platform should be able to support open source languages R and Python, and in some cases, a CoE can build momentum using various libraries in these tools. The CoE could, for example, adopt an open-source (R & Python) analytics engine based approach, plugging model outputs into a self-service dashboarding platform, allowing business users to dig into the data. A fully functioning data analytics CoE will need to manage repository operations such as backups, replication and recovery while planning and managing dynamic repository lifecycle, including scaling and performance tuning.
Our data scientists have worked in a diverse set of customer environments using a number of different analytics tools. If you need assistance, please let us know!