Moving from data to insights with a data pipeline

The need for data-driven insights is nothing new. Organizations are still challenged, though, to derive meaningful insights from data across multiple systems, such as systems of record, managerial systems and publicly available data. 

One key way to transform a mass of data into a trove of useful insights is to implement a data pipeline. This is a method wherein we ingest raw data from various data sources, then port it into a data store (like a data lake or data warehouse) for analysis. Before data flows into the data store, though, we perform processing such as filtering or masking for data standardization and integration. With these actions, we provide the "piping" for advanced analytics like dashboards--the magic visualizations that help transform “just data” to “real insights.” 

A few traits of a data pipeline that make this approach ideal for harnessing an organization’s data for meaningful insights: 

• Optimized for structured and unstructured data—Information is found in both structured and unstructured data. Unified analytics engines now exist to support ingestion and transformation while optimizing performance. Our teams at CGI have used several of these products to implement custom subroutines that prepare data to be ingested into the destination, assure data quality and enrich data. These pipelines access reference data stores and utilize APIs to cloud-native artificial intelligence features. As a result, organizations better leverage both structured and unstructured data for more informed decision-making. 
• Can help fill data gaps—Organizations across the public sector face a similar challenge: large volumes of program data but limited availability of data experts to steward the data ecosystem. We recognize that much of an organization’s data is not perfect for analysis at its origin—it may be incomplete or less timely than we’d like. Perhaps data flows are inconsistent or data coverage is incomplete. A data pipeline assures continuous and accurate transfer of data in sync from multiple organizational data silos into centralized, organized and secure data stores. Moreover, the pipeline embeds additional information obtained from utilizing organizational or external data sources, further enhancing the quality and value of data. 
• Tailorable to your ecosystem and data needs—When architecting solutions that employ a data pipeline, we must design systems that can operate effectively with the organization’s existing systems—both modern and legacy. We tailor the pipeline to integrate with our customers’ existing systems in a cloud-agnostic hybrid model, leveraging reusable components to reduce effort.  We make tailoring efficient by creating highly configurable, metadata-driven reusable components. For a recent effort, we tailored our data pipeline’s built-in data copy subroutine to efficiently copy data housed on-premises into the data warehouse, enhancing the data on-the-fly using publicly available geospatial data for map-based data mashups. 
• Supports a strong cyber posture—Fundamentally, we must evolve beyond risky activities like downloading program data (often containing sensitive data such as personally identifiable information), then manipulating this data in spreadsheets that are emailed or stored on shared drives. A data pipeline keeps data more secure and automates tasks such as masking to enhance privacy. By employing cloud-based technologies to support our data pipeline solutions, CGI helps our clients leverage the security controls of those cloud solutions. 

The ability to move from volumes of data to truly actionable insights exists. Agencies just need support to figure out how to harness that data and move it through a pipeline where it can emerge more usable.