Modernize Your Data Landscape
Healthcare and Commercial Real Estate have tremendous opportunities to leverage data to drive operational excellence. These industries are "data rich" but "insight poor". For over 15 years we have been famous for bringing cutting edge data management approaches from other industries and applying these best practices to Healthcare and Real Estate
Data Strategy
Waterloo Data provides enterprise data solutions for clients in Healthcare, Commercial Real Estate, Financial Services, Software, and Media.
All of our Data Management projects have a Data Strategy component. In most cases this is as simple as confirming business goals, defining project scope, planned milestones, and success criteria. More complex projects require a formal Strategy phase where we perform a structured Gap Analysis; looking at People, Processes, Technology, Data, and Change Management considerations. With these projects we deliver fully documented technology recommendations, roadmaps, solution architecture and POCs to de-risk implementations and ensure stakeholder alignment.
Data Architecture
Data architecture is a framework for managing an organization's data assets. It outlines the principles, policies, standards, models, and technologies used to collect, store, process, and distribute data. The goal of data architecture is to align data processes with business strategy, ensuring that data is handled in a way that supports business objectives, enhances decision-making, and provides competitive advantage. We have reference data architectures for many industries including Healthcare and Commercial Real Estate. Our clients are awestruck when they see how fast we can deliver using these tried and true data architectures.
Data Engineering
Data engineering is a branch of engineering that focuses on the practical application of data collection, storage, management, and analysis. It lays the foundation for making data accessible, reliable, and efficient for use in analytics and decision-making. Data engineers design, construct, and maintain the infrastructure and tools that allow for the handling of large volumes of data. We have reusable data integration frameworks and data source connectors that allow us to shave months off of a typical data engineering engagement.
Data Visualization
Data visualization plays a crucial role in data analysis and business intelligence. Tools and technologies ranging from simple charting libraries to complex data visualization software enable analysts, data scientists, and decision-makers to explore, understand, and communicate data more effectively. We are technology agnostic and work with all the major vendors in this space and also have a strong track record with embedded analytics which is the practice of embedding a BI tool in an existing application.
Gartner defines data management as the
“…practices, architectural techniques, and tools for achieving consistent access to and delivery of data across the spectrum of data subject areas and data structure types in the enterprise, to meet the data consumption requirements of all applications and business processes.”
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Data Strategy
Data Strategy
Data Strategy and Roadmap Definition
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.
Data Architecture
Data Architecture
Data Architecture and Data Engineering
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.
Custom Reporting
Custom Reporting
Custom Reporting and Analytics
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.
Data Warehousing
Data Warehousing
Data Warehousing
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.
