Data Integration

Most organizations struggle to support knowledge work workflows due to fragmented data systems, poor integration between internal and external platforms, and the absence of a unified information layer.

These challenges result in manual, resource-intensive processes that scale inefficiently with data volume, leading to rising labor costs, delayed decision-making, and operational bottlenecks.

Without a cohesive data strategy and robust orchestration, teams are forced to divert effort from high-value tasks to data extraction and reconciliation. The lack of a centralized, logically complete information source hampers productivity across divisions and makes it difficult to scale operations while maintaining efficiency, agility, and cost control.

A great example of this effect is how as property insurance companies scale they add MGA partners, resulting in more incoming bordereaux data to integrate. As the number of MGAs rise (to help scale the business), the manual data integration work the line of business has to perform rises super-linearly.

To better perform knowledge work on complete information, companies build out robust data integration systems to supply quality information to their knowledge workers.

Defining Data Integration

Data integration is the process of bringing together data from different sources into a unified view, so business users and systems can access consistent, high-quality information. It's essential for analytics, reporting, and enabling coordinated decision-making across departments. Assembling the incoming data into a logical information architecture is the goal of data integration.

In both internal and external data integration systems, we have 3 stages of acquiring and processing data into information:

1. Data Ingestion – Collecting data from multiple sources such as APIs, databases, files, or third-party platforms
2. Data Mapping & Transformation – Standardizing data formats, cleaning inconsistencies, and aligning schemas so the data can work together
3. Data Storage & Access – Consolidating integrated data into a warehouse, data lake, or lakehouse for efficient querying.

Most organizations deal with having to integrate data that is both external as well as internal to their company, too. Internal data integration is for systems that are inside the company data center (on-premise or in the cloud) and are the direct operational control of their IT team. External data integration is for systems, typically cloud SaaS applications, that the company uses for key operations (e.g., external partner's IT systems, billing systems, accounting systems, ERP, CRM).

Examples of systems that connected with external data integration include:

• TPAs and MGAs in Property Insurance
• Accounting companies
• Salesforce
• SAP

In the diagram below we can see an example of external data integration where a research team is bringing in FEMA data, GIS data, and hurricane geospatial data into a centralized cloud data platform.

In the example diagram above, you can see the beginngings of the medallion data pattern where the incoming external data is landed in the "bronze"-layer of the information architecture. Several tools are commonly used for data integration, including Fivetran, Informatica, Talend, Boomi, and SnapLogic.

Orchestration and data governance are essential enablers of effective data integration, ensuring that data flows reliably, securely, and in the correct sequence across systems. Orchestration automates and coordinates complex data pipelines, reducing manual intervention and improving processing efficiency.

Data governance establishes clear ownership, quality standards, and access controls, ensuring consistency and trust in the data being integrated. Together, they create a scalable foundation that aligns data operations with business needs, enabling faster, more accurate decision-making and reducing operational risk.

Consolidating Data with Information Architecture

Once we have the data in our data platform we can use a logical information architecture to arrange the data into useful information. This is critical because knowledge work operates on information, not data, and a quality information architecture is key to enabling the business to transform information at its potential.

A common data platform information architecture today is the medallion data pattern. This design pattern starts with raw data, enriching the data as it moves through the "layers". The final layer is the gold layer and this is the centralized, logically complete information source the knowledge workers will access.

In the knowledge work architecture model, the knowledge work layer exposes already-cleaned, aggregated data through standard APIs (SQL, REST), allowing business users to act in Tableau, Power BI, Looker, and automated report generators without worrying about data silos or plumbing.

Now that we've put together a general knowledge work architecture and laid out the concepts of data integration, let's take a look at the full definition of a data platform to more towards a physical implementation of these concepts.