Data Architecture - Data architecture spans the entire enterprise, from the cloud, to 3rd party vendor packages, to in-house applications, to analytics in all its forms. A comprehensive data architecture enables you to accommodate mergers and acquisitions, manage multiple un-integrated legacy data layers, and flexibly migrate systems to meet changing business requirements.
Data Warehouse Architecture - Very few organisations can deliver a data warehouse architecture that allows for incremental growth, quick time-to-market, and the flexibility to accommodate changing business needs. Our staff has been helping clients develop these capabilities for nearly 20 years. Our staff has been integral to the development of data warehouse technologies for a wide range of clients (see our client page.)
Agile Data Warehousing - Delivering solutions quickly is key. Traditional "waterfall" methods have long delivery times and can be delayed drastically when errors or omissions are discovered. Agile techniques, adapted for the data warehouse and BI delivery space, reduce the impacts of these problems and greatly improves the ability to predict delivery schedules.
Exploration Data Warehouse - In-memory data warehouses yield extremely fast response times for complex queries ranging over many domains of corporate data. This brings huge productivity gains to skilled statistical analysts, or "explorers", which enables them to deliver valueable insights and forecasts for an organization.
Metadata Management - Meaningful and consistent definitions of terms, and the consistent application of business rules across an enterprise is an important step to building a cohereng information delivery practice. We have skilled practitioners who have spent many years helping organizations define, catalog, and reconcile ambiguous and inconsistent terminologies that lead to errors, inaccurate reporting, and poor decision-making.
Master Data Management - Many organizations dedicate extensive processing power to keeping data syncronized across multiple platforms. Discrepancies in these data stores are a large source of errors and problems in operational systems. A strong master data management practice achieves a "single version of the truth" across the enterprise, simplifies systems, and makes core data more reliable and accurate.
Correlative / Predictive Data Analytics - Most data analysis approaches search for "tall poles" which indicate problems or success areas. A more comprehensive approach can reveal "correlative data" and "predictive data" to predict customer success and forecast customer behavior. Our experts will mentor your staff to routinely achieve and deliver insightful analysis.
Data Warehouse in the Cloud - Organizations constrained for resources can use a pay-as-you-go model, with data secured safely in the cloud. We design your data and connect you to incredibly performant cloud-based environments, from reports to OLAP analytics to complete statistical analysis engines.
Data Quality - The credibility of data ecosystem is critical. When errors are detected, reputations of the data team and the delivery systems can be badly damaged. Routine monitoring of data quality can detect problems before they become visible, and bugs in applications systems and ETL processes can be repaired before any harm is done. We can help you establish data quality monitoring and data warehouse metrics to maintain and reinforce quality in every aspect of the of the information life cycle.
