Solving Your Most
Complex Data Needs

Every one of our projects uses our proven I.D.E.A. implementation process where we Identify, Design, Execute and Assess. During the first two phases, we meet with our client stakeholders to understand their analytical objectives, profile their existing data sources, understand their current technology, and then develop a roadmap that addresses the organizational changes necessary to move from their current state to their desired state. Typical activities include creating a gap analysis of current vs. future capabilities, providing technology roadmaps and team development recommendations, and developing estimated timelines and budgets.

Quite a bit of the work that we provide for our customers consists of Data Architecture and Data Engineering services. Data Architecture typically involves designing database tables, designing data file systems for storage, and designing data marts for ad-hoc reporting and data exploration. Data Engineering involves the creation of data integration pipelines for combining data across internal and external data sources, incorporating business logic to clean and normalize the data, and automation of these pipelines so that data is automatically refreshed according to the needs of the business. The incorporation of automation to “stage and prep” the data for analysis delivers tremendous benefits to the organization and is one of the low hanging opportunities we attempt to solve early.

Our roots in Data Management go back to the mid 90s when the world was in the midst of the first wave of enterprise data warehousing. Over the last 25 years we have seen a renaissance in the tools and technologies that are used for data warehousing. In fact, there have never been so many vendor options to consider when building a data warehouse. We are vendor agnostic when it comes to data warehouse technologies and have deep expertise with all the leading solutions including Snowflake, Microsoft, Amazon, Google, and others. Let us help you make informed decisions on which tools and technologies are the best fit for your requirements and help you avoid costly mistakes and risks surrounding your data warehouse implementation.

Some of our clients already have mature and robust data management systems in place and are in need of customized analytics to derive actionable insights. In some cases, this means using existing business intelligence tools like Tableau, PowerBI, Looker and others to create reports and dashboards. In other cases, it means using tools like Python and other platforms to build advanced analytics to support machine learning, predictive analytics, and natural language processing. These solutions cover the full range of analytical capabilities from operational reporting to exploratory data analysis to simulation and optimization.